case study on deforestation in india

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Home > Books > Forest Degradation Around the World

Deforestation in India: Consequences and Sustainable Solutions

Submitted: 05 October 2018 Reviewed: 12 March 2019 Published: 04 October 2019

DOI: 10.5772/intechopen.85804

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Forest Degradation Around the World

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Deforestation is one of the most pressing environmental issues that the world is facing currently. It is the conversion of forested land to non-forested land by humans. Deforestation occurs when a land dominated by naturally occurring trees is converted to provide certain services in response to the human demand. The indiscriminate felling of trees has resulted in a reduction of 3.16% in the global forest cover from 1990 to 2015. Although India has seen an increment in the total forest cover of ca. 1%, still there are certain regions in the country that have sought a decrease in the forest cover. The main reasons attributed to the reduction in forest cover are shifting cultivation, rotational felling, other biotic pressures, diversion of forest lands for developmental activities, etc. Continuous illicit cutting of trees has impacted the microclimatic conditions, hydrological cycle, soil quality, biodiversity, etc. of the country, thereby making the country more vulnerable for any uneventful happening. Sustainable forest management practices, alternatives for shifting cultivation, promotion of plantation outside the forest and the usage of certified forest products, etc. are some of the measures that can be adopted to curb the rate of deforestation.

• deforestation
• forest cover
• sustainable solutions

Author Information

Rima kumari.

• Department of Environmental Sciences, Central University of Jharkhand, India

Ayan Banerjee

Rahul kumar.

• Department of Land Resource Management, School of Natural Resource Management, Central University of Jharkhand, India

Purabi Saikia *

Mohammed latif khan.

• Department of Botany, Dr. Harisingh Gour Central University, India

*Address all correspondence to: [email protected]

1. Background

Forest is a conditional renewable resource which can be regenerated but needs a certain period of time to maintain its sustainable functioning. In India, the forest resources have been found to be depleting at a pace which is much high [ 1 ]. Rapid industrialization, urbanization and over-exploitation have resulted not only in decline but also in permanent loss of forest cover to an alarming rate [ 2 ]. The major driver behind all these factors is the uncontrolled population growth of humans which leads to the dramatic increase in the demand for wood and forest products. The over-exploitation of forest resources has taken place beyond the sustained yields to fulfil the needs of humans, thus bringing a change in the net forest cover [ 2 , 3 ]. With the current rate of population explosion, the world population could be expected to increase from 7.6 billion to about 10 billion in the next 30 to 35 years [ 4 ]. The growing demand for food can be expected to rise by 50% in the given period, and it is a matter of serious concern. Rational utilization and proper management of the forest resources are the most viable ways to prevent mass destruction of forests and large-scale species extinction. It is necessary to find the links between the growing demands and meeting the demands in a sustainable manner. The scope of future studies must focus on the solution to establish this link by incorporating the principles of forestry, restoration ecology and natural resource economics.

Deforestation occurs when a land initially dominated by naturally occurring tree species is converted to provide certain services in response to the human demand. The UN Food and Agricultural Organization (FAO) has defined deforestation as the conversion of forest to another land use or the long-term reduction of tree canopy cover below the 10% threshold . Forest areas around the world are majorly cleared for agriculture, logging, mining and large-scale developmental projects. The Food and Agricultural Organization [ 5 ] coordinated the Global Forest Resources Assessment (FRA) which reported a 3.16% decline in the global forest cover from 1990 to 2015, and the total forest cover stands at about 30.6% in the present time compared to 31.6% in 1990. The rate at which the forest cover is declining poses a direct threat in the near future if not checked. With an estimated annual loss of 18.7 million acres, it is evident that future demands on forest resources would certainly lead to immense competition among nations [ 6 ]. Recently, in 2016, a study from the Maryland University reported that 73.4 million acres of the global tree cover were lost. Such a destruction of this essential and self-sustaining resource puts the implementation of the principle of sustainable development as mentioned in the Brundtland Report and Sustainable Development Goals of the 1992 of Rio Earth Summit in the state of question. And, it is an urgency to conserve the forests of which a vital part is already lost.

The value of forest is simple to understand but sometimes tough to quantify. The various values of the forest have been shown in Figure 1 . Forest has a major contribution on the global economy and supports livelihood of the majority of rural populations in the world [ 7 ]. The direct uses of forest are most easy to quantify as it is directly related to economic returns. The indirect use and option value although play a major role in defining the valuation of the forest resources are seldom quantified and are being intangible in nature [ 8 ]. There is also a no-use value of the forest resources which considers leaving the current forest intact as a heritage for the future generation and for satisfaction and happiness of the current population. But, these eco-centric views alone cannot suffice the support for a change in policy and land use pattern. There are some other important values of forests that are difficult to quantify. One of the major roles that forests play is that it acts as a major carbon sink [ 9 , 10 , 11 ]. Plants utilize carbon dioxide in the process of photosynthesis and store it in the form of carbohydrates, and these carbohydrates reach the soil as dead organic matter and contribute to soil carbon sink. When forests are cleared, less CO 2 is absorbed by plants, and atmospheric CO 2 concentration increases with the passage of time due to unavailable sink. Also, there is a marked reduction in soil organic carbon with the loss of vegetation cover, thereby affecting the productivity of the ecosystem. Productivity is believed to be an indicator of carbon assimilation capacity, and hence the more productive the forests are, the more carbon it stores [ 12 , 13 , 14 ]. The tropical forests are among the most productive as well as the most threatened ecosystem in present time with the maximum rate of deforestation. Thus, it is imperative to control the rate of deforestation in order to avoid the adversities associated with it.

Different values of forests ecosystems

2. Impacts of deforestation

The value and functions of forests are immense and diverse. Similarly, the impacts of deforestation are global and commune the whole forest community. One of the major constraints in understanding the true impact of desertification on forests is the inadequacy and imprecise form of data.

2.1 Impacts on global climate

The effect of large-scale deforestation is global, but it is necessary to assess its impacts on microclimate, regional climate and global climate to form a holistic understanding of mechanism [ 15 ]. The association of deforestation with the increased CO 2 concentration in the atmosphere and changes in the mass balances and surface energy can result in climate change at the local and global level [ 16 ]. The change in land use pattern especially the clearing of forest cover affects both hydrometeorological and global CO 2 concentrations leading to more warming as CO 2 readily absorbs infrared radiation [ 17 ]. Clearing tree cover and vegetation leads to increase in albedo of the region as bare soil reflects more solar radiation than vegetation, which again is a factor for altering regional radiation flux [ 15 , 18 ]. One of the noticeable changes in regional climates occurs when the cloud formation shifts to higher elevations from lowland plains as a result of deforestation in the later area [ 19 ]. In a global scale, deforestation leads to warmer and drier weather due to the synergistic effect of reduced evapotranspiration, increased albedo and CO 2 concentration that triggers desertification, loss in biodiversity and melting of polar ice caps, ultimately leading to food insecurity. All these effects are successors of extremes in climate variation that are produced by the large-scale reduction in forest cover [ 20 ]. The estimated quantity of CO 2 added to the atmosphere due to deforestation in the tropics is roughly two billion tonnes [ 21 ]. It is interesting to note that the CO 2 emissions due to clearing of forest will almost be equivalent to 25% of what is added to the atmosphere due to anthropogenic emissions [ 22 ]. The shift in climate is somehow correlated to reducing forest cover. Further researches can clearly define the mechanisms and pathways by which these shifts are progressing and how they can be mitigated.

2.2 Impact on hydrology and soil quality

The global water cycle depends on the amount and distribution of precipitation for which one of the influencing factors is evapotranspiration [ 23 ]. There is a direct effect on drinking water on the basis of quality and quantity, fisheries and aquatic habitats, occurrence of flood and drought, life of dams on account of increase siltation and agriculture due to poor quality irrigation and crop yield [ 24 , 25 ]. It must be recognized that the protective role of forests is operative and has a major impact on urban water resources [ 26 ]. Forests play an important role in maintaining the watersheds [ 27 ]. The degraded or degrading watersheds can be recovered by forestation, but once the forest or vegetation cover is lost, the watershed becomes vulnerable to erosion. This erosion leads to siltation in the downstream areas and consequently reduces the depth of river bed increasing the chances of flood. There are two main effects of deforestation that increase the chances of flooding. One is by reducing the tree fountain effect, i.e. interception and moisture uptake by the trees would cease after deforestation reducing the moisture holding capacity of soil that leads to greater runoff and erosion. And other is by the process of soil compaction and poor soil structure that will lead to reduced organic matter content of soil devoid of vegetation cover [ 28 ]. There are severe long-term effects on soil as a cause of deforestation. During slash-and-burn or shifting cultivation, an area of forests clears and exposes the bare soil to weather extremes of high solar insolation and heavy rainfall [ 29 ]. In the absence of the forest cover and organic matter, soil could not accommodate heavy precipitation, and the fertile layers of soil used to be easily washed away ultimately reduces the long-term productivity. The effect on soil is dependent on the interrelation and synergistic effect of evapotranspiration and infiltration that are directly altered due to loss in vegetation cover [ 23 ].

Deforestation directly increases erosion and siltation rates. There is an increased risk of flooding in Yangtze River basin in China and the major river basins in East Asia and the Amazonian basin [ 23 , 30 ]. The slopes and terrains are more vulnerable to such situations. There are formations of shallow gullies which may be accounted for the concentrated flow of runoff that prevails due to long-term erosion. Cultivation and ploughing along the slopes cause rapid erosion in the areas with less vegetation cover [ 31 ]. In the Loess Plateau, the slopes of steepness greater than 15° showed shallow gully erosion as a result of cultivation activities [ 32 ]. The availability of dead vegetation can reduce the surface runoff from the early season rain and check soil erosion [ 33 ]. It is necessary to sustain the forest and vegetation cover to maintain the soil productivity and water quality of both the surface and underground sources.

2.3 Impact on biological diversity

Forests are very rich in biodiversity and store a vast gene pool, and the majority of species occur in the tropical forests. It consists of two-thirds of all known species and 65% of 10,000 species that are recognized as endangered by the International Union for Conservation of Nature (IUCN) [ 34 ]. The biodiversity could be regarded as an important asset that is necessary to conserve for future utilization. The World Health Organization states that the traditional people, almost 80% of them, rely on the local biodiversity for traditional medicines. A loss in the biodiversity may directly affect their health care and well-being [ 35 ]. Another most noticeable impact of deforestation is the increasing human-wildlife conflict. The decreasing forest cover is limiting the habitats of many species due to which is forcing them to intrude with human welfare [ 36 ]. There are increasing instances of invasion and animal killing. The northern margin of West Bengal, India, forms a significant portion of the Himalaya Biodiversity Hotspot. The area has observed heavy fragmentation in the last decade, and as a result, there was a huge loss in the agricultural crop and both human and elephant life due to conflicts. There are reports of mortality of 20 elephants and 50 persons annually from this area [ 37 ]. It is also estimated that if deforestation in the Himalayas continue at the current rate, the dense forest cover (>40% canopy cover) will be restricted to 10% of land area in the Indian Himalayas by 2100. This may lead to a significant loss of 366 endemic plants and 35 endemic vertebrates [ 38 ]. The loss in biodiversity is of global concern irrespective of regional and local importance. Conserving the forest and an increase in forest cover certainly find a positive correlation with the increase and sustenance of biodiversity. Conversion of forest land to agricultural fields and other land use could be a threat to major loss in biological diversity in the near future.

2.4 Impact on economic and social welfare

Forests contribute to the world economy in terms of timber production and other forest produces. There are different contributions of forest as a means of direct employment in forestry services and other value-added contributions as recreation and aesthetics. The loss of tropical forest cover annually may account for about 45 billion US dollars [ 39 ]. The destruction of forest eliminates the sources of economic gain directly obtained and also eliminates the potential gain from the resources that the forest sustains as biodiversity, soil and water. Also, the destruction of forest increases the negative externalities in the form of increasing CO 2 concentration, risk of flood and human-wildlife conflict [ 40 ]. The proximity of settlements to protected areas further intensifies the chances of human-wildlife conflict (HWC). It is reported that settled households face high risk of HWC due to their close proximity of the Kanha National Park in Central India [ 41 ]. Deforestation has its social influence in the form of long-term effect. Development is a serious concern for indigenous community as it certainly leads to a change or shift of their culture and tradition. The cultural and religious aspects of the community are seldom preserved amid infrastructural advancement that leads to land and social conflict [ 42 ]. In a study of household survey from rural areas of Madhya Pradesh and Chhattisgarh state of India, it was found that the poorest of the local community gained about 30% of their living from forest produce which was claimed to be even higher than the returns from agriculture. Also, forest provides an option as safety net during the period of crisis in rural areas [ 43 ]. Various ecological services provided by the forests have been lost due to deforestation which in turn has immediate effects on the local communities dependent on these services for their daily needs. The inclusive approach for the sustainable management of forest resource is a vital consideration that considers economy as a subset of the society which in itself is a subset of the environment. Such considerations can help sustaining the constantly declining forest cover and its long-term impacts.

3. Forest cover status in India

The forest cover was found to be increased by ca. 1% for the year 2017 which is 21.54% of the total geographic area when compared to that of forest cover status in 2015 which is a positive sign towards the constant efforts that are being put in to protect the forest ( Figure 2 ). This positive change in the forest cover is mainly attributed to the conservation and management practices that include afforestation activities, participation of local peoples for better protection measures in plantation areas and traditional forest areas, expansion of trees outside forest, etc. Also, with this increase in the forest cover, the country has procured 8th position among the top 10 countries reporting the greatest annual forest area gain. Although there has been an increase in the total forest cover in India, still there are certain regions within the country that has sought a reduction in the same ( Figure 3 ). The main reasons attributed for this reduction are shifting cultivation, rotational felling, other biotic pressures, diversion of forest lands for developmental activities, etc. [ 44 ]. A transition in the various forest cover classes has also occurred over the past. The present status of forest cover (%) of India belonging to various forest cover classes has been shown in Figure 4 . It has been found that there is a reduction in the moderately dense forest and an increment in the open forest depicting degradation of forest cover to some extent. Changes within the forest cover classes result in decrease in the production capacity, thereby leading to forest degradation [ 45 ]. Control and regular check of these activities can help in strengthening the conservatory efforts of forest protection. This is the necessity of the present time to conserve the forests in order to sustain the vital ecosystems and the major carbon sink to combat the effects of global climate change and ultimately maintain the environmental balance.

Forest cover status in India for the last 30 years (1987 to 2017).

MODIS-based land use/land cover map of India for the year (a) 2001 and (b) 2017.

Forest cover (%) of India for the year 2017 (Source: [ 44 ]).

4. Case studies of forest disturbances from different parts of India

Forests are an invaluable resource that is being subjected to so many threats. In order to protect our forests, it is very much necessary to understand the reasons behind their destruction. Differentiating the agents and causes of deforestation will enable in understanding the same [ 36 ]. Several disturbances within the forest directly or indirectly contribute in destruction of the forest. This can be interpreted from the results obtained while surveying in different forest patches in India.

4.1 Disturbances in the forests of Arunachal Pradesh

Arunachal Pradesh is one of the states that has more than 75% forest cover and has the maximum very dense forest cover type [ 44 ]. The state is highly diverse in terms of climate as well as forest cover with tropical, sub-tropical, temperate and alpine forests having higher NDVI (≥7.0) [ 46 ]. However, the pressure on forest resources is consistently increasing with the rise in population, development activities, large number of wood-based industries and unsustainable land use practices resulting in their degradation [ 47 ]. This has also resulted in decrease in the forest cover of the state [ 44 ]. Several disturbances were being observed during the field survey in the forests of Arunachal Pradesh during 2007 to 2010. The major disturbances that were found include lopping, cut stumps, litter collection, soil removal, grazing, fire, NTFP collection and fuelwood collection ( Figure 5 ). Of these, fuelwood collection was found to be the most recurrent activity followed by grazing. Generally, fuelwood collection has not been considered as the major cause of deforestation but leads to the same in certain regions with reduced forest area such as in the Philippines, Thailand and parts of Central America [ 36 ]. Forest fire has also been observed as an occasional event in certain parts of Arunachal Pradesh. Fires are generally used as a tool in clearing the forest for shifting cultivation which is one of the major agricultural practices performed in the state. Fires when used responsibly act as a valuable tool in managing forest and agriculture, but when abused, it can lead to deforestation [ 48 , 49 ]. Other disturbances that can be an indicator of deforestation include NTFP collection and presence of cut stumps in certain forests. Forests of the state are highly diverse in endemic as well as nonendemic species, which need intensive monitoring and management to conserve the species-rich ecosystems from ever increasing anthropogenic pressure and changing climatic conditions [ 50 ].

Major disturbances in the forests of Arunachal Pradesh.

4.2 Disturbances in the forests of Madhya Pradesh

Madhya Pradesh is among one of the states of India which is endowed with rich and diverse forests and comprises the largest forest cover in the country [ 44 ]. This is mainly because of the efforts that the state has put in to conserve and harness this invaluable resource through innovative measures like community participation and decentralization (MP) [ 51 ]. Even after these continuous efforts, there are certain regions within the state where the occurrence of several types of disturbances in the forest has been found. The common disturbances that were being observed during the field survey (2017–2019) include fire, grazing, fuelwood collection, forage removal, litter collection, NTFP collection, lopping, thatch collection, root collection, soil removal, etc. ( Figure 6 ). Among all these fire has been found as the major recurrent type of disturbance in the forests of Madhya Pradesh. Other major disturbances were grazing followed by fuelwood collection. Since every type of deforestation is not intentional but some which are the results of amalgamation of anthropogenic and natural factors like wildfires and subsequent overgrazing can prevent the growth of young trees [ 52 ] and thus eventually degrade the quality and productivity of the forest.

Major disturbances in the forests of Madhya Pradesh.

4.3 Disturbances in the forests of Jharkhand

The name of the state ‘Jharkhand’ itself connotes ‘area of land covered with forests’ and has been exhibiting a unique relation with forests since time immemorial [ 53 ]. During the forest cover assessment [ 44 ], a net increase of 29 sq. km in the forest cover has been observed in the state which is mainly because of the plantation and conservation efforts within recorded forest areas. Although, there was an increment of 314 sq. km in the forest cover within the recorded forest areas, because of the felling of trees outside the forests area, its effect on forest cover has been offset. Also, several types of disturbances can be seen within the forest areas during the field survey (2016–2018), and the major disturbances were fuelwood collection, grazing, forage removal, lopping, cut stumps, thatch collection, root collection, soil removal, litter collection and NTFPs collection ( Figure 7 ). Most of these disturbances were occasional in nature. Although these disturbances are not that recurrent, a regular check is necessary in order to prevent the forests from degrading and in achieving a sustainable forest cover. The forest management strategies should focus on the increasing demands of different timber and non-timber forest produce to conserve the plant diversity of the natural forests of the state [ 54 ].

Major disturbances in the forests of Jharkhand.

5. Joint Forest Management in India: a case study

Forest management and protection by the local communities is an age-old practice in India which can be traced back to the protective nature of the Bishnoi Community of Rajasthan towards the local forest and animals as the black buck. The idea of community-based forest management emerged in an administrative level in the 1970s and 1980s. The declaration of the Government of India in June 1990 marked the establishment of Joint Forest Communities in different India states as per the National Forest Policy of 1988. The Earth Summit of 1992 provided with a clear objective of Sustainable Forest Management to which India responded in a positive way. From an increase in the forest cover, non-timber forest product (NTFP) to conservation of native flora and fauna, a whole new realm of forest management strategy by the collaboration of forest departments and local communities aided in decelerating the degradation of natural forest in India [ 55 ]. The Participatory Forest Management (PFM) is equivalent to an informal contract in which the local communities are allowed to consume a portion of harvest and NTFP if they protect and conserve it for 5–10 years. In India there is no legal authority of the local community on the forest resource where as in other countries as in Nepal the Community Forest User Groups (CFUG) are registered under their Forest Act, 1993 [ 56 ]. It is the positive effort of the local communities of India that the area under Joint Forest Management increased from 22,017,583 ha to 2,144,000 ha in March 2006 with 106,482 recognized Joint Forest Management committees countrywide [ 57 ].

6. Mitigation measures to curb deforestation

Deforestation is a major environmental challenge which has been persistent from the past, and the situation is more worsened at present. Therefore, there is an urgent need to focus on the mitigative measures in order to prevent the distressing effects of deforestation in the near future. In order to alleviate the problem of deforestation, the strategies should be based on the underlying causes of the same. Also, the strategies for mitigating the problem of deforestation require its effective implementation that needs the recognition of the roles of national, state and municipal governments along with the pro-active role of the civil society and private society [ 36 ]. The continuous increase in the human population especially in the developing countries has resulted in enhanced pressure on the forests for human settlements and other land use practices. A reduction in the growth rate of human population plays a crucial role in reducing the practice of deforestation [ 36 ]. Alternatives to slash-and-burn agriculture can be adopted as a strategy to mitigate the deforestation by boosting the agricultural and forestry productions for the shifting cultivators ([ 58 , 59 ]). The sustainable alternatives for slash-and-burn cultivation will assist the poor farmers in leading a better life without destroying additional forests [ 60 ]. Public policies and laws with greater security, accessibility to the minimal inputs required to maintain or enhance food production and an opportunity to the cultivators to market their products will aid in the sustainability of shifting cultivation systems [ 61 ].

Another strategy that can be applied to control the rate of deforestation is through the adoption of sustainable agroforestry, sustainable logging, agro-pastoral production systems, etc. [ 62 ]. The sustainable forest management practices can be promoted only if it is ecologically, economically and socially sustainable [ 36 ]. Agroforestry has been considered as one of the methods to curb deforestation which in turn aid in reduction of CO 2 emissions and mitigation of climate change effect [ 63 , 64 ]. The adoption of agroforestry practices has resulted in an increase in the income of agroforestry adopters as compared to that of non-agroforestry adopters and has also contributed towards improving soil fertility, reducing deforestation and conserving soil and water [ 65 ]. The provision of protected areas is one of the key steps towards an attempt to reduce deforestation which is generally motivated through biodiversity conservation [ 34 ]. Also, the global endeavours to reduce tropical deforestation are dependent heavily on the establishment of protected areas. It has been found that protection reduced deforestation as approximately 10% of the protected forests would have been deforested if they would have not been protected [ 66 ]. Tropical protected areas reduced deforestation which was liable for around one-tenth of total anthropogenic carbon emissions, thus playing a significant role in mitigating the effects of climate change and protecting biodiversity and ecosystem services [ 67 ]. Similarly, a total loss of 15.4% in the unprotected mangrove cover was compensated by the 15.7% rise in the protected mangrove cover (protected by government as per Ramsar Convention) which resulted in a net increase of 13.3% in mangrove cover across India in the last 25 years [ 68 ]. Implementation of forest certification can be among one of the strategies to control deforestation around the world. Forest certification is a process through which the producers identify their products in the marketplace and receive greater market accessibility and higher prices for their products by fulfilling certain stringent sustainable forestry standards [ 69 ]. Certification has played an important role in protecting Penten forests from 1986 to 2007, and the certified forests experience 20 times less deforestation than non-certified areas [ 70 ]. Thus, certification of forest can play a major role in controlling deforestation since the timber certification was found to be negatively related with deforestation, i.e. the increase in the certification process has resulted in a declination in the deforestation rate [ 71 ].

7. Role of Indian government in forest conservation

The Indian Forest Act, 1927: The act is an amalgamation of laws relating to forests, the transit of forest produce and the duty leviable on timber and other forest produces. It defines the procedures for declaring an area of a reserved forest, a protected forest or a village forest by the state government. With the amendment in the Act in 2012, it also prohibited the fresh clearances in forests and setting fire in a reserved forest.

Forest Conservation Act, 1980 (with an amendment in 1988): The main purpose of the proposition of this act was to conserve the forests and to look into the matters connected therewith or ancillary or incidental thereto. With the implementation of this act, a prior approval of the Central Government is required for any sort of diversion of forest areas for the non-forestry purposes.

[ 73 ]: The establishment of the National Forest Policy was also among one of the steps taken by the Government of India in order to ensure compensatory afforestation, essential environmental safeguards, sustainable utilization, maintenance, restoration and enhancement of forest areas.

Wildlife Protection Act, 1972: The wildlife protection act was enacted basically to protect wild animals, birds and plants and for matters connected therewith or ancillary or incidental thereto with a view to ensure the ecological and environmental security of the country.

The Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006: The act was framed with the purpose to recognize and vest the forest rights and occupation in forest land in forest dwelling scheduled tribes and other traditional forest dwellers who have been residing in such forests for generations but whose rights could not be recorded. These recognized rights of the forest dwelling scheduled tribes and other traditional forest dwellers include the responsibilities and authority for sustainable use, conservation of biodiversity and maintenance of ecological balance which in turn aid in strengthening the conservation regime of the forests while ensuring livelihood and food security of the forest dwellings scheduled tribes and other traditional forest dwellers.

Apart from these laws, the Government of India has also established Forest Survey of India (FSI), an organization under the Ministry of Environment, Forest and Climate Change, whose primary work is to gather and evaluate the country’s forest wealth through a nationwide survey to measure forest areas [ 74 ]. This in turn aids in determining the factors and drivers behind the loss or gain in forest cover of any particular region in India. Another council, i.e. the Compensatory Afforestation Fund Management and Planning Authority (CAMPA), was established by the Government of India in 2009 as a National Advisory Council under the chairmanship of the Union Minister of Environment, Forest and Climate Change for the monitoring, technical assistance and evaluation of compensatory afforestation activities. This was particularly meant to promote afforestation and regeneration activities as a way of compensating for forest land diverted to non-forest uses [ 75 ]. Certain scheme such as Integrated Forest Protection Scheme (IFPS) was being formulated by the government to save the forests from fire. The scheme was designed by combining the forest fire protection and management technique along with forest conservation. Various other missions and programmes such as the National Mission for a Green India (NMGI) and National Afforestation Programme (NAP) were also being launched by the Government of India where the main aim of NMGI was to improve the quality of five million hectares of degraded forests and to bring another five million hectares of non-forest areas under forest cover through social and farm forestry. On the other hand, the NAP was launched with the objective to develop the forest resources with people’s participation, with a focus on improving the livelihood of the forest-fringe communities, especially the poor [ 75 ].

The Ministry of Environment, Forest and Climate Change has been optimistic in strengthening the role of women in conservation of forest at local community levels since long. The National Forest Policy [ 73 ], for the first time, acknowledged the necessity of including woman members in forestry schemes. The Joint Forest Management Policy of 1990 mandated woman representatives not less than 40% in general body and 50% in executive body of the local forestry institutions like the JFM committee. Later in 2002, the Biodiversity Authority of India reframing the local biodiversity management committee structure mandated the reservation of one-third of its members as women. Thus, this understanding of the role of women in the local-level conservation measures and implementation of related rules has aided in improving the management of forest in rural regions of the country [ 76 ].

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Indian forest loss 'worse than feared' due to climate change

Forest loss from temperature and rainfall alterations adding to deforestation issue.

Forest loss in India could become an even bigger problem than anticipated in the coming years, with new research revealing climate change has caused significant recent losses.

The first ever national-scale study of the relationship between forest loss and rainfall and temperature trends in India, led by the University of Reading, revealed they may have contributed to large declines since the turn of the century, exacerbating already worrying deforestation largely driven by agricultural expansion in the country.

The new research is in contrast to official reports that show relatively small decreases in forest coverage in recent years. It warns the rapid changes to the climate observed in some regions will necessitate targeted preservation action and funding to reduce the risk to biodiversity in India.

Alice Haughan, a PhD researcher at the University of Reading and lead author of the study, said: "India has seen dramatic forest loss in recent decades, with land use changes to accommodate crops, livestock and a growing population cited as causes. While the contribution of land use change to forest loss has been studied extensively, little attention has been given to the role of climate change in recent decreases.

"The rapid changes to the climate we identified suggests India's forest loss in the coming decades could be far worse than feared, as deforestation is only one part of the problem. The high levels of reduction seen are also concerning for biodiversity, as India relies on connected forests for wildlife preservation."

The new study, published in Global Change Biology , looked at forest loss between 2001 and 2018 -- a period where little data exists.

The authors calculated the velocity of changes to India's climate for the first time, a relatively new technique used to quantify climate change and reveal the rate at which it is impacting a country.

It also analysed variability in climate change impacts across different regions and seasons, revealing that the impact of climate change on forest loss varied greatly between different locations and seasons.

Far greater forest losses were seen where and when the climate was changing most rapidly. Decreases in rainfall were seen to have the strongest effect on increasing forest loss, with temperature decreases in some regions also having a negative impact.

Haughan said: "Our study of Indian tropical and subtropical regions shows that rainfall rather than temperature comes into play as the biggest factor in forest loss, in contrast to trends found in many temperate studies."

The authors argue that, because research has until now largely focused on annual changes to India's climate, this has masked more dramatic changes to temperature and rainfall within seasons, such as the monsoon seasons.

India is in the top 10 countries in the world for forest coverage, with tropical and subtropical forests covering more than a fifth of the country.

India is also one of the most biodiverse countries, containing 8% of the world's biodiversity and four recognised biodiversity hotspots. An estimated 47,000 plant species and 89,000 animal species can be found in the country, with more than 10% of each thought to be on the list of threatened species. Around 5,500 plant species are thought to be endemic to India.

• Ecology Research
• Rainforests
• Environmental Awareness
• Early Climate
• Origin of Life
• Environmental Policies
• Land Management
• World Development
• Global warming
• Deforestation
• IPCC Report on Climate Change - 2007
• Climate engineering
• Climate model
• Paleoclimatology
• Attribution of recent climate change

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Journal Reference :

• Alice E. Haughan, Nathalie Pettorelli, Simon G. Potts, Deepa Senapathi. Determining the role of climate change in India’s past forest loss . Global Change Biology , 2022; DOI: 10.1111/gcb.16161

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Investigating the impact of tropical deforestation on Indian monsoon hydro-climate: a novel study using a regional climate model

• Original Paper
• Open access
• Published: 18 May 2024

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• Abhishek Lodh   ORCID: orcid.org/0000-0002-4951-0226 1 , 2 , 3 &
• Stuti Haldar 4 , 5  

This study uses a state-of-the-art regional climate model (RCM) to examine how tropical deforestation affects the meteorology of the Indian Summer Monsoon (ISM). Incorporating insights from existing research on deforestation by climate scientists, alongside evidence of environmental deterioration in semi-arid, hilly and tropical regions of Southeast Asia, this research seeks to elucidate the critical influence of anthropogenic reasons of climate change on the hydroclimate of ISM. Employing “tropical deforestation” design experiments with the ICTP-RegCMv4.4.5.10 RCM the study evaluates the effects on meteorological parameters including precipitation, circulation patterns and surface parameters. This experimental design entails substituting vegetation type in the land use map of RegCMv4.4.5.10 model, such as deciduous and evergreen trees in Southeast Asia with “short grass” to mimic tropical deforestation. Findings reveal that deforestation induces abnormal anti-cyclonic circulation over eastern India curtails moisture advection, diminishing latent heat flux and moisture transport, leads to a decrease in precipitation compared to control experiment scenario. Alterations in albedo and vegetation roughness length attributable to deforestation impact temperature, humidity, precipitation, consequently exacerbating drought and heatwave occurrences. Additionally, the study also explores deforestation-induced feedback on ISM precipitation variability. The study concludes that deforestation substantially alters land-surface characteristics, water and energy cycle, and atmospheric circulation, thereby influencing regional climate dynamics. These findings offer foundational insights into comprehending land-use and land-cover changes and their implications for climate change adaptation strategies.

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1 Introduction

Anthropogenic activities such as tropical deforestation are eroding carbon sinks and driving the release of greenhouse gasses (GHG) to the atmosphere (Watson et al. 1997 ; Stocker et al. 2013 ). These emissions alter the regional and global climate patterns and can impact the ability to predict and react to important weather events. Indian summer monsoon (ISM) is the seasonal migration of winds from equatorial region towards the Indian monsoon zone with changes in the atmospheric circulation features in lower and upper atmosphere. In the twenty-first century climate change scenario, the ISM activity over the Indian subcontinent is known to be linked to both atmospheric GHG content and the alterations of vegetative cover associated with intensive forest management. The ability to predict the conditions of monsoon events is important because in addition to climate impacts, it also has significant socio-economic implications. Studies using remote sensing across the tropics have revealed that during the 1990s and the 2000s, net deforestation increased by 62 percent (Kim et al. 2015 ). Due to its connection to major weather patterns, deforestation poses acute threats to human health, ecological systems and other socio-economic sectors (Revi et al. 2022 ). Thus, it is crucial to understand the impact of increasing deforestation rates on monsoon variability to estimate, adapt and mitigate its wide-ranging socio-economic ramifications. This is especially true in resource-constrained underdeveloped and developing economies such as in India. Agriculture is a key sector susceptible to the repercussions of tropical deforestation and other land degradation activities. Recently, leaders from across the globe met at the 27th annual United Nations Climate Change Conference (COP27) held in Sharm El-Sheikh, Egypt, resulting in the commitment to halt and reverse deforestation and land degradation by 2030. India in particular risks falling short of its commitments to combat climate change if urgent action is not taken. Deforestation extends beyond a simple, local ecological concern by exerting controls on large-scale monsoon patterns that are detrimental to human health and agricultural supply chains (Yasuoka and Levins 2007 ; Lawrence and Vendecar 2015 ). These manifestations of climate variability either due to natural or man-made reasons, presents substantial financial implications, as they mandate considerable expenditure for adaptation and policy adjustments regarding resource utilization, diverting public investments away from key sectors such as education, health and other basic services (Chambwera et al. 2014 ; Farid et al. 2016 ; Srinivasan et al. 2023 ). Thus, the forested areas of the Indian subcontinent hold economically important natural resources such as coal and iron-ore. Extraction of these essential minerals not only leads to deforestation, but also reduces the capacity of the region to act as carbon sinks. Therefore, the magnitude of tropical deforestation has significant environmental and sociological implications (Strandberg et al. 2023 ). Notably, deforestation leads to desertification which further compounds environmental challenges by reducing the recovery capacity of formerly forested land area. Desertification refers to the deterioration of land in regions with arid, semi-arid, and dry sub-humid climates, driven either by natural climate change and/or human action (Lodh 2021 ). For example, deforestation can result from urban expansion and industrial growth such as agricultural land conversion. In the tropics, both desertification and deforestation have been responsible for shifts in local and regional weather and climate (e.g. in the Thar desert, India and deforestation in north-east India). As per the Food and Agriculture Organization of the United Nations (FAO), deforestation entails changing forest areas to other types of land uses such as farming lands, urbanized zones, barren lands, or any other usage that leads to a significant decrease in tree canopy, dropping below a 10% threshold (Lodh 2021 ). Hence, halting deforestation is crucial for efforts to limit global warming to less than 1.5° Celsius and reduce the rate of biodiversity loss and protect jobs and livelihoods. Forests can act as both source and sinks of carbon dioxide. Forests worldwide release approximately 8.1 billion metric tons of carbon dioxide annually as a result of deforestation and other land-related disruptions, whereas they intake about 16 billion metric tons of carbon dioxide each year (Harris et al. 2021 ).

Studying the land surface mechanisms in the dry and semi-dry areas of north-west India, reveals their impact on mesoscale atmospheric circulations at a regional scale (Bollasina and Nigam 2011 ; Lodh 2020 ; 2021 ). This can occur through the transfer of heat, moisture and momentum between the Earth’s surface and the atmosphere (BaidyaRoy and Avissar 2002 ). Previous studies using general circulation models have indicated that global climate change resulting from land-use and land-cover changes, particularly deforestation and land degradation, has severe consequences on weather and climate (Henderson-Sellers et. al. 1993 ; Xue and Shukla 1993 ; Polcher and Laval 1994a , b ; Gupta et al. 2005 ; Hasler et al. 2009 ). The land surface and its vegetation interact directly with the atmosphere to exchange heat and moisture. Consequently, they play a pivotal role in moderating responses within the climate system (Polcher and Laval 1994b ; Pielke et al. 1998 ). Significant reduction in precipitation over Indian land region (~ 18%) has been reported due to impacts of deforestation across monsoon impacted regions, due to shift in Inter-Tropical convergence zone (Devaraju et al. 2015 ). Similar studies project that, rapid deforestation over India would result in a decrease in rainfall over north India by 2 mm/day (Gupta et al. 2005 ). Polcher and Laval 1994b modeled the impacts of deforestation to be statistically significant and independent of ENSO effects. Bathiany et al. ( 2010 ) reported that complete deforestation of the tropics would exert a global warming of 0.4 degree Celsius. Spracklen and Garcia-Carreras ( 2015 ) asserted that by the 2050s deforestation will reduce regional rainfall over the Amazon basin by 12% (21%) in the wet (dry) season. Under global climate warming, there are increasing uncertainties on the projections of compounded extreme weather events, leading to low confidence of how their impacts will feedback into future climate change. Thus, improved sensitivity and accuracy of numerical models tuned to currently observed land use change (like tropical deforestation scenarios) are important for better predicting the impacts of future global change.

Pioneering climate modeling work on deforestation induced shifts in land-surface albedo (Charney 1975 ; Charney et al. 1975 , 1977 ; Dickinson et al. 1983 ) forms the motivation for the tropical deforestation design simulations conducted in this study. Although there have been prior attempts to understand the biogeophysical aspects of tropical deforestation, the central aim of this research is to investigate tropical deforestation within the scope of the Biosphere–Atmosphere Transfer Scheme (BATS) vegetation module coupled with RegCMv4.4.5.10 model, over southeast Asia. This is achieved by conducting climate model experiments involving changes in vegetation i.e. a proxy for land use land cover (LULC) change design experiments. The objective is to derive findings that not only support, but also enhance the insights obtained from earlier research endeavors (Xue and Shukla 1993 ; Polcher and Laval 1994a , b ; Werth and Avissar 2002 , 2005a , b ; Gupta et al. 2005 ; Bollasina and Nigam 2011 ; Devaraju et al. 2015 ; Spracklen and Garcia-Carreras 2015 ) while delving into the question of whether the degradation of land and the deforestation of tropical regions exert significant influences on the meteorological patterns of the Indian monsoon.

The idea is to identify potential alterations in regional precipitation and circulation trends within the Indian monsoon system by means of carefully devised sensitivity experiments. These experiments seek to elucidate the underlying mechanisms that govern the interplay between land surface and vegetation, with a specific focus on the impacts of land degradation and tropical deforestation. Hence, to investigate feedback due to anthropogenically changing biogeophysical scenarios to the atmospheric circulations over the Indian subcontinent, the study conducted in this paper is important for identifying and bridging the gap in extant literature. In the wake of the recent COP 27 summit in Egypt, there is global consensus recognizing the urgent need for effective adaptation and mitigation strategies against climate change. Hence, the study conducted in this paper aligns with this broader discourse by estimating the risks of climate variability on land and ecosystems and their ripple effects in economic and social systems. In countries such as India, where there’s a limited capacity for adaptation and mitigation, the consequences of climate variability are particularly evident (Rasul and Sharma 2016 ). Tropical monsoons are vital for irrigation in these countries, and any disruption can threaten the agriculture that millions depend on for their livelihoods and food security (Marambe et al. 2015 ). Unfavorable changes in the monsoon patterns, thus, bear heavily on the poor and the vulnerable communities in the rural areas which further triggers forced migration (Clement et al.  2021 ). Moreover, deforestation disrupts local and regional climate patterns that results in unpredictable temperature variability, humidity fluctuations, and heat stress that potentially leads to fatalities (Kovats and Hajat 2008 ). In economic terms, the crowding out of investments from critical sectors like education, health care, infrastructure, and energy are some of the indirect risks associated with the financial burden of climate change adaptation and mitigation (Chambwera et al. 2014 ). Whereas, at the macro level, reduced agricultural productivity directly translates to falling export revenues and increased reliance on food imports, further constricting the state’s fiscal capacity.

Thus, understanding the intricate relationships between deforestation, monsoon variability, and their socioeconomic implications is crucial for devising effective adaptation strategies. This calls for an evidence-based policy making and governance that prioritizes sustainable land use planning, afforestation, and reforestation efforts to curtail the climate induced risks (Pörtner et al. 2022 ). The findings of this paper are relevant not only for India but also other Low- and Middle-Income Countries (LMICs) with populations substantially dependent on land and ecosystem services for their livelihoods. The framework of this study is underpinned by the IPCC AR6’s emphasis on socially just and equitable climate resilient development pathways (New et al. 2022 ). Therefore, the study aims to serve as a catalyst for informed, impactful policymaking, thereby fostering a sustainable future.

2 Materials and methods

2.1 brief model description and region of study.

The model used in this study is RegCM (version 4.4.5.10) which is one of the first limited area community model from ICTP (Dickinson et al. 1989 ). Operating on an Arakawa B-grid, RegCM is a hydrostatic, compressible model that utilizes a sigma-p vertical coordinate system. In this model, thermodynamic and wind variables are staggered horizontally, employing a time-splitting explicit integration approach. The model dynamics are resolved using horizontal momentum, continuity and thermodynamic equations. The calibrated version 4.4.5.10 of the ICTP regional climate model (RCM), RegCMv4.4.5.10, has the capability to simulate a range of atmospheric and land surface processes, including radiation, precipitation, soil moisture and vegetation dynamics, at high spatial and temporal resolutions. The RegCMv4.4.5.10 was installed in “parallel” mode at the central high-performance computing facility of the Indian Institute of Technology, Delhi. The model domain is over South Asia, spanning 17°E—123°E and 16°S—40°N, with a horizontal resolution of 60 km and 18 vertical levels in the atmosphere (using sigma coordinate) as shown in Fig.  1 a. Different RCM studies have meritoriously used the ICTP-RegCM model for its research purpose (Giorgi et al. 2012 , 2015 ; Dash et al. 2015 ; Lodh 2017 ; 2021 ; Camara et al. 2022 ).

a Control landuse map in the baseline experiment ( b ) modified land use map for the deforestation experiment using BATS coupled RegCMv4.4.510 model (*the legend colors represent the land cover/ vegetation classes in the BATS land surface model)

2.2 Details of the control experiment

The RegCMv4.4.5.10 model is a regional climate model that accurately simulates atmospheric, cloud microphysics, land surface, planetary boundary layer and radiation processes. In this study, the BATS coupled RegCMv4.4.5.10 model (Dickinson et al. 1993 ; Elguindi et al. 2014 ) is invoked for numerical simulations (for both Control and tropical deforestation design experiments). The BATS1E vegetation scheme incorporates twenty vegetation types, soil textures ranging from fine (clay) to intermediate (loam), to coarse (sand) and different soil colors (light to dark) for the soil albedo calculations (Dickinson et al. 1986 ). The BATS scheme comprises of a vegetation layer, a layer for snow, a surface soil layer with a thickness of 10 cm, a root zone layer spanning 1–2 m and a third layer of deep soil measuring 3 m in thickness. The latent heat (LHF) and sensible heat (SHF) formulations are calculated from the bulk aerodynamic formulations. For the control run, the Emanuel convection scheme over land and the Grell convection scheme over the ocean, is combined with the Arakawa Schubert 1974 closure, along with the University of Washington planetary boundary layer (PBL) and Holtslag scheme, for the two sets of control experiments. They are abbreviated as RCM-CONTROL-UW and RCM-CONTROL-Holtslag, respectively. In RCMs, PBL parameterization is employed to depict the impacts of subgrid-scale turbulence that cannot be explicitly resolved due to the model grid’s limited resolution. Parametrizing the boundary layer in climate models is essential for replicating processes within the boundary layer, such as vertical mixing and other turbulence-related phenomena. The UW turbulence closure scheme (Bretherton et al. 2004 ) is a 1.5-order local, down-gradient diffusion parametrization scheme possessing the capability to compute vertical fluxes within and outside of the PBL. The UW scheme also integrates directly the emission and deposition flux terms as part of the calculation of turbulent tracer tendency. The Holtslag PBL scheme (Holtslag et al. 1990 ; Holtslag and Boville 1993 ) adopts a non-local diffusion concept, considering counter-gradient fluxes arising from large-scale eddies in an unstable mixed atmosphere. For more detailed information, please refer to Elguindi et al. ( 2014 ). The details and results of the second control simulation, RCM-CONTROL-HOLTSLAG (Emanuel over land and Grell over ocean with Arakawa Schubert 1974 closure and Holtslag PBL scheme) are described in detail in Lodh ( 2021 ). The lateral boundary condition scheme employed is relaxation, exponential technique. The Subgrid Explicit Moisture Scheme (SUBEX) resolves the non-convective clouds and precipitation by linking the average grid cell humidity to the cloud fraction and cloud water (Elguindi et al. 2014 ; Sundqvist et al. 1989 ). The auto-conversion rate over land and ocean is 0.250E-03. The details of the control experiments (RCM-CONTROL-UW and RCM-CONTROL-HOLTSLAG) are provided in Table  1 .

2.3 LULC map

The contemporary state of land cover mapped data over India as represented by the Global Land Cover Characterization (GLCC) datasets, used in the control simulations are shown in Fig.  1 a. Figure  1 b shows the LULC change map as used in the deforestation design experiment. “Tropical deforestation” is mimicked in the RCM by replacing vegetation classes along the principal axis of Asian monsoon i.e. over the tropical rain-belt regions over Myanmar, Indonesia, Sumatra, Thailand and Cambodia (Indo-Chinese peninsula, and Maritime subcontinent) by short grass. Terrestrial variables like elevation, sea surface temperature, and three-dimensional isobaric meteorological data are horizontally interpolated from a latitude–longitude mesh to a high-resolution (166 × 108 × 18) domain on either a Rotated Mercator (ROTMER) projection.

2.4 Details of the deforestation design experiment

In the context of the deforestation design experiment, specific regions within north-east India, Eastern Ghats, Western Ghats, Indo-Gangetic plains, peninsular India and areas along the principal axis of the monsoon (encompassing Myanmar, Indonesia, Sumatra, Thailand, and Cambodia) are designated for alteration in LULC to mimic tropical deforestation in the model. These regions, originally in the LULC map is classified as having “forest” vegetation, irrigated crops and mixed farming are replaced with “short grass” in the RCM. This modification is applied to the original ASCII test land use map utilized for the RCM’s control simulation. The primary objective here is to isolate and analyze the climate change signal arising from tropical deforestation. The conversion process involves changing the land cover to “short grass” in all regions along the path of the monsoon’s principal axis where tropical deforestation is implemented. This shift to “short grass cover” aligns with findings from various studies (Scharn et al. 2021 ) indicating an increase in the prevalence of evergreen shrubs. This phenomenon is driven by soil moisture conditions, which are in turn influenced by precipitation patterns.

Similar to the control experiments, the convection scheme is Emanuel over land and the Grell scheme over the ocean, combined with the Arakawa Schubert 1974 closure. For the tropical deforestation design experiments titled RegCMv4.4.5.10—DEFORESTATION, both the University of Washington and Holtslag PBL schemes are utilized. They are abbreviated as RCM-DEF-UW and RCM-DEF-Holtslag, respectively. Since the sensitivity to LULC change is dependent on the choice of parametrization and RCM used, hence we use two different PBL schemes. In these experiments, the original ASCII text land use map from the control simulation is modified to incorporate the land use change for the tropical deforestation design experiments. Important BATS vegetation parameters relevant to the tropical deforestation design experiments, such as roughness length, vegetation albedo, maximum fractional vegetation cover, and leaf area index, for the land use type “short grass” are provided in Table  2 . In the Holtslag PBL scheme, the value of critical Richardson number for land and ocean is 0.25. In the UW PBL scheme, standard T-Q V -Q C advection is employed with 15 as the efficiency value of enhancement of entrainment by cloud evaporation. These adjustments are consistent across both RCM-DEF-UW and RCM-DEF-Holtslag experiments. Its significant to highlight that the implications from the tropical deforestation simulations, especially the insights from the second design experiment (RCM-DEF-UW), represent novel contributions of the ICTP-RegCM model, that haven’t been addressed in prior research, as communicated in Lodh ( 2021 ). The simulation length and initial condition for both the sets of control (RCM-CONTROL-UW, RCM-CONTROL-HOLTSLAG) and design (RCM-DEF-UW, RCM-DEF-HOLTSLAG) experiments is 00 UTC of 1st October 1999 to 00 UTC of 1st January 2011, where a spin-up time of 1 year and 3 months is neglected to mitigate errors due to initial values. While performing the design experiments (using the RegCMv4.4.5.10 simulations) the combination of physical parameterization schemes employed are the same as in control simulations (for details, refer to Table  1 ). The experiments are run continuously around the year from 00 UTC of 1st October 1999 to 00 UTC of 1st January 2011 using six hourly NCEP/NCAR-2 reanalysis data as boundary forcing (Kanamitsu et al. 2002 ) and Reynolds weekly SST (Reynolds et al. 2002 ). In both the control and deforestation design experiments, the RegCMv4.4.5.10 model employs a 30-s time step. The time step for the BATS land surface model is 600 s. Every 30 min, the radiation model is invoked, and emission computations are carried out every 18 h. This aligns with the model configuration described in Lodh ( 2021 ) where impact of extended desertification was studied.

This research study offers new perspectives on the impacts of tropical deforestation, leveraging the RegCMv4.4.5.10 model in conjunction with two PBL schemes. To assess the significance of the changes in precipitation (and other meteorological variables) resulting from the deforestation of tropical rainforests, the Wilcoxon’s nonparametric signed rank test is performed. The Wilcoxon Signed Rank Test computes the sum of ranks for positive and negative differences after ranking the differences between matched observations in absolute values. The lesser of these two sums is subsequently taken into consideration to establish the test statistic. The null hypothesis assumes that the median of the differences is zero, indicating no systematic shift between the paired samples. If the calculated test statistic falls within a critical region, determined by the chosen significance level, the null hypothesis is rejected, suggesting a significant difference between the paired samples (Lodh 2021 ). Hence, the objective, of the design experiment is to enumerate the effects of “tropical deforestation” on the ISM precipitation, circulation, surface fluxes, and other meteorological variables.

3 Results and discussion

The rainfall climatology for the JJAS (June, July, August, September) season across India, based on 10 years of data (2001–2010) sourced from IMD, TRMM observations, and the RegCMv4.4.5.10 model, are depicted in Figures S1 and S2 (from here on, figures labeled with ‘S’ can be found in the supplementary material). Figure S1 displays the spatial distribution of rainfall during the JJAS period, with the highest rainfall concentrations in the Western Ghats and north-east India. In contrast, the least rainfall is seen over north-west India. The RCM-CONTROL-UW model proficiently reproduced actual rainfall distribution across all studied regions, except northeastern India and the southern peninsula, where the rainfall level is higher than the IMD climatology.

The accuracy and verification of rainfall, temperature, and soil moisture measurements produced by the RegCMv4.4.5.10 model was previously established in Lodh ( 2021 ). Figure S2 also depicts that the rainfall bias with respect to TRMM observations falls within the range of − 1 to − 2 mm/day over the monsoon core region of India (MCRI), which is located in central India. Concurrently, the JJAS mean surface temperature bias (Figure S3 ) over MCRI ranges from + 0.5 to 2 °C (Lodh 2021 ). The sensitivity of the atmospheric circulation and precipitation in the lower and middle troposphere over the Indian monsoon domain region to the tropical deforestation has been examined through anomalies of wind at 850 hPa and 500 hPa, moisture transport (MT) fluxes, precipitation, 500-hPa vertical velocity, surface fluxes, near surface air temperature, soil wetness, albedo etc. for monsoon JJAS season.

The control experiments are classified into two distinct types: RCM-CONTROL-UW and RCM-CONTROL-Holtslag. Analogously, the design experiments have their respective categorizations as RCM-DEF-UW and RCM-DEF-Holtslag for each simulation set. Here and in the subsequent section describing tropical deforestation results, anomaly is defined as the difference fields (design-control) between the corresponding fields. In the three panel figure the top panel is “Control” experiment run, middle panel is “design” experiment run and bottom panel is “anomaly (design-control)” with positive anomaly in “blue” and negative anomaly in “red” shades.

3.1 Effect of tropical deforestation on mean ISM meteorology

The tropical deforestation design experiment is to test the role and importance of vegetation along principal axis of monsoon travel (Krishnamurti and Bhalme 1976 ). Figure  2 shows the JJAS (2001–2010) mean precipitation (mm/day) and anomaly (design-control) for deforestation (a) RCM-DEF-UW (b) RCM-DEF-Holtslag, design experiments, respectively. Seasonal mean JJAS precipitation is decreasing by approximately − 2 mm/day (i.e. ~ 30% less w.r.t control run, p  < 0.01), over north-east India, western Himalayan region of north India, Chennai and its nearby locations of peninsular India and Indonesia, Thailand and Myanmar regions of the South-east Asia dur to tropical deforestation. It is important to mention here that both the RCM-DEF-UW and RCM-DEF-Holtslag design experiments are for capturing the impact of tropical deforestation within the scope of the RegCMv4.4.5.10 model. All the parameterization schemes to run the RCM-DEF-UW and RCM-DEF-Holtslag model are the same except, UW PBL scheme is used to represent boundary layer processes in first design experiment whereas Holtslag PBL scheme is used in the second design experiment.

The JJAS (2001–2010) composite precipitation (mm/day) and anomaly (design-control) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively. ( The regions where there is decrease in precipitation is significant at 99% confidence level) [*(top) CONTROL exp, (middle) DESIGN exp; and (bottom) anomaly (design-control)]

Figure S4 and S5 shows the probability distribution functions of annual precipitation (mm/day) over central India from the Control (RCM-CONTROL-UW) and deforestation (RCM-DEF-UW) design experiments. The mean precipitation over central India and north-west India is decreasing by − 6.2 mm/day and − 3.4 mm/day, respectively over the period 2001–2010 due to deforestation. Also, the precipitation variability is decreasing due to tropical deforestation implemented in the RCM. Figures  3 , 4 show the anomalous wind at 850 hPa and 500 hPa level during JJAS season and it can be concluded that magnitude of wind has decreased over South-east Asia, Bay of Bengal (BOB) and central India region with a tendency to form anomalous anticyclonic circulation at 850 hPa extending up to 500 hPa, in both the RCM-DEF-UW and RCM-DEF-Holtslag simulations. The subsidence during JJAS season has increased over east India, Bangladesh and the maritime subcontinent wherever deforestation is implemented in the model, as seen in Fig.  5 . The anomalous positive values of 500-hPa vertical velocity, represents subsidence. From Fig.  6 , it is observed that MT at 850 hPa, has decreased over South-east Asia, BOB, Eastern Ghats, Bengal and western Himalayan region of north India. It is important to note here that due to tropical deforestation, the direction of the anomalous flow in MT is opposite to the direction of mean JJAS monsoon flow. The decline in monsoon precipitation over India due to tropical deforestation can be linked to reduction in MT at 850 hPa, vertically integrated moi  sture transport flux upto 300 hPa (Figure S6 ) over north India, Bay of Bengal (BOB) and its neighboring land region over south-east Asia (approx. by − 15 kg/m/sec). Due to deforestation the meridional component of vertically integrated moisture transport flux is reduced over South-east Asia, BOB and north India, whereas the zonal component of the vertically integrated moisture transport flux is reduced along the principal axis of monsoon, including the BOB region (Figure S6 ). Accompanying this reduction in MT, due to deforestation there is an increase in the height of the lifting condensation level (Figure not shown) over India by 15%, in the deforestation experiment compared to the control. Thus, tropical deforestation inhibits formation of clouds over the Indian sub-continent. Figure  7 explicitly showcases a divergence in the water vapor flux spanning the entire column up to 300 hPa, covering India and the maritime subcontinental region (with a significance level of p  < 0.01). This divergence further contributes to the observed decrease in JJAS precipitation. It is worth noting that in this context, negative values linked with the convergence of vertically integrated water vapor flux indicate convergence, while positive values denote the divergence of water vapor flux. Therefore, tropical deforestation induces a displacement in water vapor over the primary monsoon axis. This leads to a shift in both the amount of precipitable water (Figure S7 ) and the moisture flux on a regional scale over the region under the principal axis of Asian monsoon.

The JJAS (2001–2010) composite mean and anomaly (design-control) of wind (m/sec) and change in direction at 850 hPa for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite wind (m/sec) anomaly (design-control) and change in direction at 500 hPa for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS 850-hPa wind (msec −1 ) and 500-hPa vertical velocity (hPa/sec; shaded, positive values representing subsidence) anomaly (design-control) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) of moisture transport at 850 hPa (kg/m/sec) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) of convergence of vertically integrated water vapor flux (mm/day) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The Monsoon Hadley Index has shown a noticeable decline of approximately − 1.2% per decade from 2001 to 2010, in tropical deforestation experiment in comparison to the control experiment (Fig.  8 ). This is evident for both RCM-DEF-UW and RCM-DEF-Holtslag design experiments. Consequently, the deforestation along the primary axis of monsoon destabilizes the wind patterns of the Indian monsoon up to 500 hPa. Other metrics such as near surface air temperature, SHF, soil wetness, LHF and evaporative fraction for the Control (RCM-CONTROL-UW) and deforestation (RCM-DEF-UW) design experiments are represented in Figs.  9 , 10 , 11 , 12 , 13 . As a consequence of deforestation there is increase in near surface (2 m) air temperature over Indo-Gangetic plains, north, east and north-east India and some regions in south-east Asia, as depicted in Fig.  9 . Over the maritime sub-continent, near surface air temperature increases only in the RCM-DEF-UW design experiment. The SHF has increased by + 40 Wm −2 ( p  < 0.01) over the whole of the tropical rain-belt of south and south-east Asia, i.e., from India to Thailand and Cambodia (Fig.  10 ). There is increase in Bowen’s ratio over north-west India due to tropical deforestation (Figure S8 ). This temperature shift can be attributed to the escalation in SHF, which is prominently observed across the tropical rain-belt, extending from India through to the regions of Thailand and Cambodia in Southeast Asia, as shown in Figs.  9 , 10 (Boysen et al. 2020 ). There is decrease in surface soil moisture and LHF over south-east Asia, Eastern Ghats, Western Ghats, peninsular India, Indo-Gangetic plain and north India, is highlighted in Figs.  11 , 12 . Due to deforestation the evaporative fraction (Fig.  13 ) and recycling ratio (Figure not shown) has also decreased over the Indian subcontinent and nearby areas. Tropical deforestation leads to a decline in precipitation recycling due to reduction in soil moisture and evapotranspiration, especially over the primary monsoon regions of India and Southeast Asia during the summer monsoon months (JJAS). Thus, the region of changes in the near surface parameters in the RCM-DEF-UW and RCM-DEF-Holtslag, design experiments, is synonymous with the regions where tropical deforestation is done in the model. Thus, an impact of tropical deforestation on near surface temperature over Indian sub-continent is clearly derived from this study.

Sensitivity of Monsoon Hadley (MH) index as calculated for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) ( p  < 0.01) of near surface air temperature at 2 m ( ° C) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) of sensible heat flux (Wm −2 ) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments respectively

The JJAS (2001–2010) composite mean and anomaly (design—control) ( p  < 0.01) of soil wetness (mm) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) of latent heat flux (Wm −2 ) ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly (design-control) of evaporative fraction for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

In the deforested zones, there’s a noticeable increase in upward longwave energy flux, ranging between + 20 to + 40 Wm −2 , as depicted in Fig.  14 . The albedo also increases by 0.05 units (Fig.  15 ), which is also reported in similar studies (Mabuchi et al. 2005 , Mabuchi 2011 ; Strandberg et al. 2023 ) on vegetation change. Conversely, there’s a reduction in net radiation at the surface by approximately − 5 to − 40 Wm −2 . Net radiation, in this context, is the sum of net upward longwave energy flux and net downward shortwave energy flux, which is calculated as (SWin − SWout + LWin − LWout), as illustrated in Fig.  16 . The pattern of albedo alteration corresponds with the changes in net radiation, though with an inversion in polarity. Moreover, it has been observed that the regions subjected to deforestation within the model see an increase in surface upward longwave-radiation flux (as corroborated by Sud et al. 1988 , 1996 ; Eltahir 1996 ; Zheng and Eltahir 1997 ; Durieux et al. 2003 ; Sen et al. 2004 ; Li et al. 2016 ). Generally, an increase in OLR indicates cooling of the atmosphere or the atmosphere becoming drier or less conducive to cloud formation resulting in decrease in precipitation. Its important to note the sign convention used in this study: the direction of surface downward shortwave energy flux moving from the atmosphere to the land surface is taken as positive. On the other hand, the direction of surface upward longwave energy flux transferring from the land surface to the atmosphere is considered negative. This is attributed to lower surface roughness, as surface warming and anomalous increase in sensible heating. Hence, decreased evapotranspiration, increase in net upward longwave flux, Bowen’s ratio, SHF lead to a warmer, higher and drier planetary boundary layer ( p  < 0.01; Figure S9 . Thus, due to tropical deforestation over a dry surface the rate of ascent of the boundary-layer top and deepening of the convective boundary layer tends to be faster, with negative feedback of rainfall with soil moisture. Bhowmick and Parker 2018 also predicts the same using theoretical framework just that the negative or positive feedback depends upon the atmospheric profile, Bowen’s ratio (inversion) and convective instability parameter of the region. It is important to mention here that both the simulations, RCM-DEF-UW and RCM-DEF-Holtslag are unanimous in the deriving the conclusions from the deforestation experiments.

The JJAS (2001–2010) composite mean and anomaly (design-control) of net upward longwave energy flux (Wm −2 ) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly of albedo (design-control) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The JJAS (2001–2010) composite mean and anomaly ( p  < 0.01) of net radiation (W/m −2 ) anomaly (design-control) (positive is downward) for deforestation ( a ) RCM-DEF-UW ( b ) RCM-DEF-Holtslag, design experiments, respectively

The ISM precipitation appears to decrease due to tropical deforestation. One significant reason is the albedo increase, which exceeds a critical point of 0.03 (Dirmeyer and Shukla 1994 ). This deforestation leads to a notable decrease (increase) in net radiation and LHF (SHF). Such variations disrupt the radiative equilibrium over the Indian region, which, in turn, disrupts the ISM circulation, resulting in reduced ISM rainfall. This observation aligns with the conclusions drawn by studies by other researchers although their focus was on different global locations (Charney 1975 ; Charney et al. 1975 , 1977 ; Ripley 1976 ; Shukla and Mintz 1982 ; Zeng and Neelin 1999 ; Zickfeld et al. 2005 ).

3.2 Statistical analysis of tropical deforestation design experiment

This section covers the statistical analysis of tropical deforestation using the RegCMv4.4.5.10 model in conjunction with the BATS vegetation module. The influence of the deforestation design experiment is examined through the substitution of forested areas with the “short grass” type of vegetation, characterizing deteriorated pastures, within the context of the “Control” vegetation map. The tropical deforestation design experiment entails methodical adjustment of the albedo, roughness length, and hydrological characteristics of the surface. This is achieved by modifying the model’s namelist file, utilizing a “fudge” parameter set to “true,” and introducing the updated land-use map that accounts for tropical deforestation. The experimentation spans a duration of 11 years, wherein the analysis primarily focuses on the decade following a spin-up phase of 1 year and 3 months. Throughout the experimental phase, actual sea surface temperature (SST) data is incorporated, including significant El Nino and La Nina events. In order to assess the significance of the outcomes, the Wilcoxon rank sum statistical test (Lodh 2021 ; Lorenz et al. 2016 ) is applied to the anomalies of the 10 JJAS (June to September) months for both the control and deforested design experiment runs (as depicted in Figure S10 ). The anomalies for the JJAS months are calculated by taking the difference between the JJAS monthly figures from the control run and those from the experimental design run. We assume that these monthly anomalies are statistically independent, based on the justification that the auto-correlation time frames within the samples don’t exceed one month. The observations in this segment echo the earlier results from Sect.  3.1 . It is evident that variations in precipitation, SHF, LHF, and the convergence of the integrated water vapor flux are not only consistent with the previous findings but also statistically noteworthy with a significance level of p  < 0.01.

3.3 Effect on variability of mean precipitation and surface fluxes

This section addresses the land–atmosphere feedback arising from tropical deforestation through fluctuations (measured as standard deviation) of the ISM rainfall, latent and sensible heat transfers (Ferranti et al. 1999 ). For this purpose, data is collected from each grid point, spanning both the control study and the design experiments that simulate tropical deforestation. Figures  17 (a–f) map out the standard deviations pertaining to precipitation, LHF and SHF, as observed in both control and deforestation scenarios. A consistent pattern of precipitation variability is evident from Fig.  17 a and b for both setups. Intriguingly, in months of May and June, in the control simulations there’s a pronounced fluctuation in rainfall (i.e. variability in rainfall) especially over regions like the Western Ghats, north-east India and Himalayan foothills extending upto north-west India. In the experiments simulating tropical deforestation, there’s a noticeable decrease in rainfall variability (both strength and spread) over northwest India during May and July. This suggests that tropical deforestation adversely impacts the variability of the Indian monsoon, particularly its movement towards the northwest. Also, the consistency of surface energy fluxes, notably LHF, is diminished over areas like the Indo-Gangetic plains and central India due to such deforestation. The SHF variability also experiences a drop, especially in the monsoon trough area that stretches from central India towards the north and north-west. As a result, reductions in surface fluxes, integral to the energy budget, simultaneously influence terrestrial parameters (Yuan et al. 2021 ). These parameters include soil moisture, evapotranspiration processes, types of vegetation, and overall ecosystem dynamics. Consequently, this plays a significant role in shaping the precipitation variability, as indicated by studies from Pielke et al. 1998 and Ferranti et al. 1999 . Terrestrial coupling index of 2 m-temperature and atmospheric coupling index of PBL (Lodh 2020 ) is also calculated for the two control experiments (RCM-CONTROL-UW and RCM-CONTROL-Holtslag) for the time period May to September 2001–2010 (Figures S11 and S12 ). Over central India and north-west India, the value of terrestrial coupling index of 2 m-temperature and SHF ranges between 30–45 W m −2 in the RCM-CONTROL-UW and RCM-CONTROL-Holtslag simulations. However, the strength and domain of extent of coupling is larger in the RCM-CONTROL-Holtslag simulations. This is because the standard deviation (or variability) in SHF is more in the RCM-CONTROL-Holtslag simulations than RCM-CONTROL-UW. The atmospheric coupling index of SHF and PBL across central and CI and north-west Indian sub-continent with values ranging between 150 and 250 m (200 and > 250 m) in the RCM-CONTROL-UW (RCM-CONTROL-Holtslag) simulations (Figures not shown). Furthermore, the deforestation design experiments reveal heightened shifts in land–atmosphere interactions over northwestern India in terms of coupling between 2 m-temperature to SHF to PBL, abating (strengthening) the latent (sensible) heat feedbacks of land–atmosphere and convective activities in the region.

Standard deviation of monthly rainfall in ( a ) baseline/control experiment ( b ) deforestation design RCM-DEF-UW experiment, c and d same as in ( a and b ) but for latent heat flux (LHF), e and f same as in ( a and b ) but for sensible heat flux (SHF)

3.4 Effect on dominant modes of variability of precipitation, soil moisture and surface fluxes

This section explores into the impact of feedback, triggered by tropical deforestation, on the primary three modes of variability observed in precipitation, soil moisture, LHF and SHF. The variance percentage linked to each empirical orthogonal function (EOF) is marked at the top right of each corresponding graph, as seen in Figures S13 ,  S14 ,  S15 , S16 . The figures from the control, RCM-CONTROL-UW and tropical deforestation, RCM-DEF-UW design experiment is reported here. In the design experiment simulating tropical deforestation (see Figure S13 ), the spatial layout of EOF1 for precipitation largely mirrors its counterpart in the control, accounting for roughly the same variance (~ 67.8%). EOF1’s peak variability can be pinpointed over areas like the Western Ghats and north-east India, which are regions of substantial rainfall during the JJAS season over India. Turning to the control setup, as depicted in Figure S13 (a and b), the spatial layout for EOF2 highlights positive rainfall anomalies over regions like Western Ghats and north-east India, while negative anomalies are evident over the foothills of Himalaya. This distribution signifies the typical positioning of the tropical convergence zone in line with both the active and break cycles of the monsoon. Active monsoon phases align with positive time coefficients of EOF2 precipitation, whereas negative values point to subdued monsoon phases, as detailed by Ferranti et al. 1999 . For the tropical deforestation design experiment, the spatial outline of EOF2 prominently features peaks where the control experiment’s EOF2 has troughs. This suggests that alterations in land use due to tropical deforestation reshape the second dominant mode of precipitation variability. Similarly, the spatial representation of EOF2 for soil moisture (Figure S14 ) within the tropical deforestation design experiment underscores the repercussions of deforestation-driven changes in land use and cover on the variability of soil moisture, starting from the second mode. The positive time coefficients for both EOF2 and EOF3 in soil moisture, which account for variability percentages of roughly 6% and 4.5% respectively, distinctly demonstrate the influence of tropical deforestation on soil hydration in areas that lie along the monsoon’s main trajectory. A consistent pattern emerges when evaluating the impact of deforestation on the second and third dominant variability modes for LHF (with roughly 7% variability) is depicted in Figure S15 . Whereas from Figure S16 , the second mode of variability in SHF (with roughly 9% variability), depicts the impact of deforestation on SHF, implying impact of LULC change on land surface fluxes.

4 Conclusion

Numerical simulations assessing changes in LULC (tropical deforestation) were conducted utilizing the ICTP RCM version RegCMv4.4.5.10. The RegCMv4.4.5.10 simulations effectively determine the model’s capability to replicate the climate conditions in India. Also, the technique to implement “tropical deforestation” in the RegCMv4.4.5.10 model was able to isolate the mechanisms that drive the development of meteorological events in event of tropical deforestation. This study quantitatively demonstrates the impact of tropical deforestation on the alteration of rainfall, temperature, and atmospheric circulation over Indian subcontinent and its nearby regions (regions lying over the principal axis of Asian monsoon). These anomalies impact the local hydro-climate, leading to drought-like conditions, further decreasing the intensity and duration of monsoon rain, forming an irreversible hysteresis loop. This confirms the existence of teleconnection effects due to tropical deforestation. To investigate the influence of tropical deforestation design experiments, two distinct simulation sets were executed:

Control simulation using the USGS natural land use map as its foundation.

A design simulation that employs a modified land use map, specifically tailored to replicate the effects of deforestation.

The results of the tropical deforestation design experiments were assessed for versatility by employing two unique combinations of PBL parameterization schemes: the Holtslag PBL and the University of Washington Turbulence closure PBL. Both of these were integrated with the RCM. For convection processes, the Emanuel convection scheme was applied over land, while the Grell scheme was used over the ocean, incorporating the Arakawa Schubert 1974 closure. The key findings of these experiments are outlined as follows:

Due to tropical deforestation, the JJAS precipitation over India, Indo-Chinese peninsula and the maritime sub-continent, along the principal axis of the Asian monsoon, experience a statistically significant decrease. The observed reduction is ascribed to the emergence of an anomalous anti-cyclonic circulation over eastern India, a consequence of tropical deforestation. This results in diminished convective heating, leading to a notable drop in precipitation when contrasted with the control experiment. Moreover, the anomalous anti-cyclonic flow that forms over the northern sector of the Bay of Bengal due to tropical deforestation curtails moisture advection and the vertically integrated moisture flux upto 300hPa, effectively diverting moisture away from adjacent areas, inhibiting the travel of atmospheric rivers. On land, the wind’s intensity diminishes, and its direction reverses due to a decrease in surface roughness. The findings from this current research using the RegCMv4.4.5.10 model corroborates these observations, highlighting both local impacts and a decrease in the ISM rainfall. Furthermore, the recycling ratio decreases, resulting in a negative soil moisture feedback due to deforestation (Leite-Filho et al. 2021 ). As mentioned earlier in the tropical deforestation experiment, the decrease in surface pressure combined with decreasing wind magnitude leads to a calm but dry northwesterly wind over the Indian (land) region. This prevents moisture-laden southerly winds from moving towards land. The experiments focused on deforestation underscore that an increase in vegetation albedo and a decline in surface roughness (refer to Table  2 ) lead to reduced solar radiation absorption on the surface. This results in a cooling effect, especially pronounced over northwestern India. This cooling narrows the temperature differential between land and sea, consequently impacting the north–south pressure gradient and the Indian monsoon’s Hadley index. This shift means the easterly winds originating from the Bay of Bengal don’t reach the mainland, which subsequently results in diminished precipitation inland. Hence, the elevation in albedo, attributed to the loss of vegetation, negatively and significantly impacts the evolution of the monsoon (ISM). Deforestation further influences the variability and the primary three Empirical Orthogonal Functions (EOFs) linked to the oscillation of the ISM. This influence manifests through alterations in surface energy fluxes, soil moisture content, evapotranspiration, and, consequently, rainfall across the Indian region (See Fig.  18 ). Its pivotal to recognize, however, that the effects of deforestation on precipitation patterns possess inherent uncertainties too.

The experiments focusing on deforestation highlight that increases in albedo, owing to diminished plant cover, contributes to a decrease in JJAS soil moisture, evapotranspiration, and rainfall. This increase in albedo results in more radiation being reflected. Within the framework of the tropical deforestation design experiments, there is a noted average decline in precipitation by approximately − 2 mm/day. Concurrently, albedo surpassed the pivotal threshold value of 0.03. Albedo is an important factor, acting as a thermostat in regulating the climate's response to tropical deforestation. As deforestation affects tropical regions, there’s a marked elevation in albedo, leading to the cooling of the surface. This cooling prompts a greater atmospheric subsidence, essential for sustaining long-wave radiation emission into the atmosphere. The decline in rainfall over a broader geographical area, beyond the region where deforestation occurs (i.e., teleconnection effects), is influenced by the higher albedo of deforested surfaces and the reduced evapotranspiration of crops and pastures compared to natural vegetation. Both from this study and previous literature it is found out that LHF, a significant factor in maintaining the recycling ratio, is considerably reduced by 25% due to deforestation. Any alteration in the lower boundary condition in a regional climate model (RCM) when paired with a decrease in vegetation (such as deforestation or desertification) is further amplified by a reduction in rainfall, perpetuating heat wave and drought-like conditions.

Precipitation plays a crucial role in the terrestrial and atmospheric water cycle, and repercussions of human-induced climatic shifts (anthropogenic climate change) such as tropical deforestation, on rainfall patterns hold profound consequences for farming practices, especially in the prime grain-producing regions of India. The increase in tropical deforestation over the hot spot regions of land–atmosphere like South-east Asia leads to alterations in precipitation over India through various mechanisms like alterations in soil moisture, evapotranspiration (ET), and energy fluxes over India. This transformation is facilitated by multiple pathways, including shifts in soil moisture, evapotranspiration (ET), and energy flux dynamics within India. A noticeable consequence is the diminished MT across the Bay of Bengal (BOB). Tropical deforestation leads to a decline in the total water content available in the atmosphere (precipitable water), the proportion of rainfall that returns to the atmosphere through evaporation (recycling ratio), and the efficiency with which rainfall is produced from available moisture (precipitation efficiency). In tandem, there’s an increase in surface reflectivity (albedo) and a decrease in the net radiation received at the surface. This ultimately hampers the upward movement and convergence of water vapor, diminishing the propensity for low-level cloud formation. A secondary consequence is the weakening of low-level winds, promoting the development of an anti-cyclonic circulation pattern. In general, the results from the deforestation experiment reports that large-scale deforestation reduces net radiation at surface and the total heat flux by ~ 20Wm −2 .

The alterations in LHF and MT combined with changes in the Monsoon Hadley index, have profound consequences on the water and energy cycles of the region. This results in a net reduction in summer monsoon (JJAS) rainfall over India. Furthermore, the increase in sensible heat flux (SHF), temperatures near the surface, and the increase in outgoing longwave radiation accentuate these shifts. Numerical analyses conducted using a regional climate model (RCM) reveal that tropical deforestation can amplify the effects of surface reflectivity (albedo) and evapotranspiration, leading to noticeable shifts in-ground and air temperature during the Indian monsoon by + 1 to + 1.5 °C. Consequently, higher land surface temperatures and increased SHF, coupled with a reduction in soil moisture and LHF, yield arid conditions. Such conditions can jeopardize the sustainability of the residual forested regions as well as agricultural activities in previously deforested areas (Dickinson and Henderson-Sellers 1988 ). Furthermore, tropical deforestation sets in motion a feedback loop: it fosters the propagation of drought conditions and, when combined with elevated temperatures and heightened water stress, further exacerbates the mortality rates of trees. This cycle of deterioration ultimately perpetuates the adverse effects of deforestation.

On a short-term (biophysical) timescale deforestation type LULC change influences both the weather and climate by altering the land-surface energy fluxes. While the conclusions drawn from proxy tropical deforestation design experiments using RCM (RegCMv4.4.5.10) are open for further exploration, possibly using a non-hydrostatic version of the ICTP-RegCM coupled with an earth system model, the research conducted in this study provides simulations of the impact of various deforestation scenarios on the Indian monsoon hydro-climate. These insights can be used to inform policy and management decisions related to land use, conservation, and climate change adaptation in the region.

The findings of the study add significant value to the existing knowledge on the impact of anthropogenic activities, particularly deforestation on climate change and monsoon variability. It specifically highlights how the deforestation induced feedback influences the interannual variability of ISM precipitation and surface fluxes. It has profound implications for diverse socioeconomic sectors from agriculture to human health in India and other LMICs where adaptation and mitigation capacities are constrained due to inequity, high rates of informality, high debt service ratios, and high costs of capital among others. The paper’s experimental design describes how deforestation contributes to alterations in albedo, roughness length, wind patterns, water evaporation and surface hydrological properties. It also details how these changes can impact regional climate patterns, leading to unpredictable temperature variability and humidity fluctuations. Such climate disruptions also bear financial implications for countries like India already dealing with many economic and infrastructure challenges.

From an agro-economic perspective, the evidence provided by the study underscores the direct ramifications of variable monsoon patterns on food security and agricultural sustainability. For instance, the standard deviation of rainfall as seen in Fig.  17 (a and b) shows relatively small variability over north-western India during the months of May to July in the scenario of deforestation potentially disrupting agricultural productivity (also monsoon precipitation decreases). This threatens the livelihoods of a large proportion of the population reliant on agriculture, potentially leading to forced migration and other socio-economic disruptions. From a policy perspective, these findings highlight the urgent need for effective adaptation and mitigation strategies as recognized in the recent COP 27 summit. Sustainable land-use planning, afforestation, and reforestation efforts should be a priority to alleviate the impacts of deforestation on monsoon patterns and related socioeconomic consequences. In this regards, land–atmosphere interactions using Dirmeyer’s indices for various landuse—land cover change like afforestation, desertification, irrigation intensification is also planned as a future work.

The study also underscores the differential impacts of these environmental changes on various social groups. The vulnerable and marginalized resource-dependent communities, particularly those with limited access to information are at high risk from climate change. It aligns with the IPCC Sixth Assessment Report, that emphasizes on socially just and equitable climate resilient development pathways, indicating a need for more inclusive, meaningful, and comprehensive communication and action strategies. The study thus provides a holistic perspective of tropical deforestation on climate of India, connecting climate research and need for climate adaptation.

Flowchart of the consequences of tropical deforestation experiment

Data availability

The datasets generated from RegCMv4.4.5.10 model run and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

International Centre for Theoretical Physics, Trieste, Italy is acknowledged for making the RegCMv4.4.5.10 model codes available for this study. The RegCMv4.4.5.10 executables were built on central HPC computing facilities available at Computer Services Centre and Centre for Atmospheric Sciences, IIT Delhi. National Center for Environmental Prediction/National Center for Atmospheric Research acknowledged for providing high-resolution meteorological datasets for setting the initial and boundary conditions to run the model. NCAR and UCAR are acknowledged for NCL and NETCDF4, analysis and software packages, respectively. The Grid Analysis and Display System (GrADS) version 2.0 software, NCAR Command Language (Version 6.2.0), Ultra scale Visualization Climate Data Analysis Tools (UVCDAT) package built with Python 2.7.4 and SciPy package ( http://www.scipy.org/ ), are used for scientific computation and plotting. Wealth of online resource available at scholar.google.com was also helpful. The first author is grateful towards MHRD, Govt. of India, Institute student fellowship supporting his Ph.D. research work. The authors also acknowledge Lund University, Sweden for providing research support. With deepest respect the first author offers gratitude towards faculty members at CAS, IIT Delhi: Prof. H. C. Upadhyaya, Prof. A. D. Rao and Prof. Somnath B. Roy for providing time to time advice. Finally, Johan Eckdahl (Ph.D.) and Gautam Sharma (Ph.D.) are thanked for helping in the final editing of the manuscript.

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The author Dr. Abhishek Lodh conceptualized and designed the study, developed the methodology, installed and run the regional climate model, curated the data, developed the verification and other scripts along with software, visualized the results, wrote the first draft of the paper, reviewed and edited the final document. The second author, Dr. Stuti Haldar, reviewed and edited the manuscript in current context of regional climate change and provided insights into the socioeconomic implications of the study. Both the authors discussed the results and commented on the manuscript’s findings.

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Lodh, A., Haldar, S. Investigating the impact of tropical deforestation on Indian monsoon hydro-climate: a novel study using a regional climate model. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06615-z

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• Published: 15 May 2024

Severe decline in large farmland trees in India over the past decade

• Martin Brandt   ORCID: orcid.org/0000-0001-9531-1239 1   na1 ,
• Dimitri Gominski   ORCID: orcid.org/0000-0002-8135-1341 1   na1 ,
• Florian Reiner   ORCID: orcid.org/0000-0003-1299-1983 1   na1 ,
• Ankit Kariryaa   ORCID: orcid.org/0000-0001-9284-7847 1 , 2 ,
• Venkanna Babu Guthula 2 ,
• Philippe Ciais   ORCID: orcid.org/0000-0001-8560-4943 3 ,
• Xiaoye Tong   ORCID: orcid.org/0000-0002-9709-0633 1 ,
• Wenmin Zhang   ORCID: orcid.org/0000-0001-6520-9559 1 ,
• Dhanapal Govindarajulu   ORCID: orcid.org/0000-0003-3951-1281 4 ,
• Daniel Ortiz-Gonzalo 1 &
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Agroforestry practices that include the integration of multifunctional trees within agricultural lands can generate multiple socioecological benefits, in addition to being a natural climate solution due to the associated carbon sequestration potential. Such agroforestry trees represent a vital part of India’s landscapes. However, despite their importance, a current lack of robust monitoring mechanisms has contributed to an insufficient grasp of their distribution in relation to management practices, as well as their vulnerability to climate change and diseases. Here we map 0.6 billion farmland trees, excluding block plantations, in India and track them over the past decade. We show that around 11 ± 2% of the large trees (about 96 m 2 crown size) mapped in 2010/2011 had disappeared by 2018. Moreover, during the period 2018–2022, more than 5 million large farmland trees (about 67 m 2 crown size) have vanished, due partly to altered cultivation practices, where trees within fields are perceived as detrimental to crop yields. These observations are particularly unsettling given the current emphasis on agroforestry as a pivotal natural climate solution, playing a crucial role in both climate change adaptation and mitigation strategies, in addition to being important for supporting agricultural livelihoods and improving biodiversity.

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Wildfires, fungi, insects and droughts cause considerable tree mortality in forests and other forested ecosystems 1 . Anthropogenic climate change, human appropriation and mismanagement are further aggravating tree die-offs, which have reached alarming levels in recent years 2 , 3 . These events are well documented, and the public awareness is high because operational monitoring systems based on satellites are able to locate and quantify forest diebacks and deforestation 1 . However, only little is known on potential widespread diebacks and human appropriation of trees outside forests, such as in drylands or trees on farms. Agroforestry is a land-use practice that integrates multifunctional trees in agricultural land to maximize socioecological benefits from trees 4 . Agroforestry trees have drawn a lot of interest in relation to tree planting and restoration as an underexplored pathway towards climate mitigation, but little attention is paid to the protection of existing on-farm trees in mature agroforestry systems and how they are being affected by the mentioned threats.

According to a recent state-of-the-art land-cover map 5 , 56% of India is covered by farmland, and only 20% is covered by forest. While the separation of forests and tree plantations is not always clear, it is unequivocal that these classifications exclude a large part of India’s trees, which are scattered within farmlands, in urban areas or grown as hedgerows 6 , 7 . These multipurpose trees, such as Prosopis cineraria, Azadirachta indica, Madhuca longifolia, Acacia nilotica, Dalbergia sissoo, Syzygium cumini, Sesbania grandiflora, Albizia procera, Artocarpus heterophyllus and Cocos nucifera , provide a variety of ecosystem services related to environmental conservation (for example, soil fertilization, shade) and products used for consumption (for example, fruits, fuelwood, fibre, mulch, medicine, fodder) or the generation of income (for example, wood, sap, medicine, fibre) 7 , 8 , 9 , 10 . Agroforestry trees in India consist of remnant trees from forests (for example, Madhuca longifolia ) that were left when land was cleared for agriculture but also include cultivated species (for example, Cocos nucifera ). India is well known as a globally leading nation regarding the large-scale implementation of agroforestry and even urban systems 7 . However, the systematic quantification of tree resources within these systems is challenging at a national scale 11 , and current mapping approaches capture mainly block plantations but fewer individual trees on farms 11 . India has the largest agricultural area cover in the world, which makes tree cover and count in India uncertain and possibly one of the most overlooked woody elements globally.

Agroforestry trees can experience substantial temporal dynamics in their populations as they grow in highly managed ecosystems where some trees may be planted and others removed. The monitoring of these trees has become fundamental over the past decade, not only to quantify available resources but also to identify possible climate-related diebacks, as observed in forests 1 . A prominent example of a tree grown on farms is the Neem tree ( Azadirachta indica ), which can grow tall, with canopies of up to 20 m in diameter, and live up to several hundred years 12 . Recent newspaper reports indicate that thousands of Neem trees in India have been infected by fungi over the past years, possibly leading to diebacks, which had already been identified as a potential problem several decades ago 13 , 14 . Fungi, such as Phomopsis azadirachtae , attack old trees in particular while younger trees are stronger and survive the fungal infections. Pathogens/diseases are among the risks to trees intensified by global change, and fuelled by the synergistic effects of climate change and shifts in agricultural practices, among other social–environmental factors 15 . Due to the large number of trees at the national scale, it is a challenge to inventory them for a single year or repeated on an annual basis, and the extent of mortality from diseases, climate or human disturbances remains largely unknown.

Previously it has not been possible to study changes in scattered non-forest trees over time at a large scale. Individual trees that grow scattered in farmlands can be mapped from sub-metre-resolution satellite imagery, but these are not available as time series over larger areas, such as the country of India, and thus cannot be used for quantifying dynamics at the level of individual trees in agricultural fields 16 . Satellite systems with a spatial resolution coarser than 10 m (for example, Sentinel 2) can detect groups of trees but not support detection of individual trees. The RapidEye archive is an often overlooked resource of satellite imagery, providing global coverage from micro-satellites back to August 2008. Together with PlanetScope (starting 2017), these satellite constellations provide a unique opportunity of repeated global coverage with images at a spatial resolution of 3–5 m, which allows the identification of individual trees 17 , 18 . We have developed a heatmap-based tree-detection approach 19 , 20 and deployed the RapidEye and PlanetScope satellite constellations to study changes in farmland trees at the individual tree level in India between 2010 and 2022.

For this study, we map adult trees that can be identified as individuals within farmlands at a spatial resolution of 3–5 m. The spatial resolution of the imagery applied impedes the detection of small trees and limits the study to larger trees, typically with a crown size >10 m 2 . Farmlands were delineated by a previously published state-of-the-art land-cover map (10 m resolution) 5 . This includes trees in fallow fields, hedgerows and trees along rural roads and rivers but excludes recently planted trees, which are not detectable in our images ( Methods ). Groups of trees and perennial plantations that form larger closed canopies, such as palm and rubber trees, are typically classified as ‘tree cover’ in the baseline land-cover map and are masked out in our study. Dense tree plantations can be part of agroforestry systems but are highly managed and include a high temporal dynamic from harvest and replanting, which makes this type of land use less relevant for this study, which is aiming at detecting longer-term changes in old farmland trees related to disturbances rather than recurrent management practices.

We formed country-wide custom generated mosaics of high-quality satellite scenes for each year 2010–2011 (RapidEye) and 2018–2022 (PlanetScope) and trained two deep-learning tree-detection models: one for RapidEye and one for PlanetScope, respectively. We merged the results from 2010 and 2011, and from 2018–2022, to ensure robustness in particular towards variations in image quality ( Methods ). The tree crown centres of individual trees mapped in 2010 or 2011 were tracked, and equivalents were identified during 2018–2022 (Fig. 1a,b ). We used a buffer area of 15 m around the tree centre to account for potential spatial shifts in satellite image pairs. If a tree centre mapped in 2010 or 2011 was not found in one of the annual images between 2018 and 2022, it was counted as disappeared (Fig. 1b ). When developing the models, we aimed for a very low false positive rate from the 2010/2011 detections (3%), meaning that only very few misclassifications exist. This method is tailored towards accurately detecting the disappearance of large trees and consequently is not designed for a wall-to-wall mapping of all individual trees in 2010/2011 as it may miss small trees. Therefore, we did not attempt to quantify the gain of small trees and net changes over the full period of analysis. We use the crown size to determine the size of a tree, which includes a considerable uncertainty at tree level 21 .

India has experienced massive losses of large farmland trees, illustrated here at the tree level tracked with RapidEye (2010/2011) and PlanetScope (2018–2022). a , Trees that have disappeared are shown here as a percentage in relation to the total number of mapped trees in 2010 aggregated to 5 × 5 km grids. b , A tree detected in either 2010 or 2011 but not during 2018–2022 was classified as disappeared. If a tree mapped in 2010/2011 was observed in any of the years between 2018 and 2022, it was classified as remained. c , Trees mapped with RapidEye have an average crown area of 96 m 2 (confidence >0.8; Methods ). Many regions have lost up to half of these large trees within farmlands during 2010–2018 (note that only trees in farmlands are considered, using WorldCover as a mask layer). The rectangle marks the area of the close-up, and the arrow shows the location within India. d , Trees mapped with RapidEye in 2010, shown here as a false colour composite. e , The same area in 2021 is illustrated here with Google Earth imagery, showing that only three of the large trees have remained. Remaining farmland trees: 22 million; disappeared: 2.5 million (confidence >0.8). Basemap in a from Natural Earth ( https://www.naturalearthdata.com/ ). Credit: c , e , Google Earth; d , Planet Labs PBC.

For the 2010–2018 change, we focused on trees that were detected with a high confidence (>0.8) in the RapidEye period ( Methods ) as these are typically large trees that are clearly identifiable, such as the Neem tree. This means that the instances of detection and changes reported are reliable but conservative and implies that the real numbers are probably higher. As a result, we refrain here from reporting absolute numerical values of change in number of trees. Results show that 11 ± 2% of the high-confidence trees mapped in 2010 or 2011 were not detected in the 2018–2022 images (Fig. 1 ). The 2% uncertainty was quantified by a manual checking of 1,000 random samples. We found that trees detected by RapidEye with a confidence >0.8 have an average crown size of 96 m 2 ( Methods ), which implies that the trees covered by this analysis were generally mature trees that have reached a later stage of development, and such a high loss rate of mature trees over less than a decade is unexpected. The disappearance of mature farmland trees was observed in many areas, but numbers rarely exceed 5–10%, except for areas in central India, in particular in the states Telangana and Maharashtra, where we document massive losses of large trees (Fig. 1 ). Here several hotspot areas have lost up to 50% of their large farmland trees, with up to 22 trees per square kilometre disappearing (Fig. 1c–e ). Smaller hotspot areas of loss are also observed, such as in eastern Madhya Pradesh around Indore (Extended Data Fig. 1 ).

We then focused on the period 2018–2022, where higher-quality PlanetScope imagery was further used for annual wall-to-wall tree detections (Fig. 2 ), although the detection reliability for small trees with a crown area below 10–20 m 2 is relatively low (Extended Data Table 1 and Methods ). We mapped about 0.6 billion trees within the land-cover classes of cropland, urban and bare. The average number of trees per hectare is 0.6 (s.d. 1.6), and the highest densities are found in northwest (Rajasthan) and south-central India (Chhattisgarh), with up to 22 trees per hectare. We then tracked individual trees during 2018–2022. The condition was that a tree that was detected in either 2018 or 2019 with a high confidence and was not detected in the three consecutive years 2020–2022 was classified as disappeared, which ensures that the tree had actually disappeared and did not simply fail to be detected. We developed a metric termed ‘change confidence’, which quantifies the certainty to which a given tree actually disappeared ( Methods ). We focus exclusively on reporting tree losses accompanied by a high change confidence (>0.7). This approach acknowledges that the real figures may surpass those presented here, while intentionally omitting fluctuations of smaller trees and losses of trees characterized by considerable uncertainty in their reported numbers. We find that 5.3 million trees (2.7 trees per square kilometre) observed in 2018/2019 were not detected in 2020–2022. Trees with a change confidence above this threshold have an average crown size of 67 m 2 . The uncertainty in the number of disappeared trees was 21%, quantified by manually checking a random sample of 1,000 trees classified as disappeared. This number of disappeared trees, considered a conservative estimate due to the method applied, was still high considering that a majority of the losses must have occurred between 2018 and 2020. Several hotspot areas stand out. The example shown in Fig. 3b–d is not a local exception from the rule, as we observed similar situations all over Indian croplands, reflecting a considerable national-scale thinning of India’s large farmland trees over such a short period. Some regions have lost more than 50 trees per square kilometre.

a , Every tree within farmlands, urban areas and bare areas was mapped, and their density is shown here summed at the hectare scale. b , A demonstration of the framework to map the dynamics of individual trees in different agroforestry systems. Disappearance of trees can be caused by natural factors, part of management practices or related to disturbances, depending on the agroforestry type. The basemaps are from Google Earth, 2021. c , A tree that was detected in either 2018 or 2019 (or both) with a high confidence but was not found in three consecutive years (2020–2022) was mapped as disappeared, shown here as red points. Number of mapped trees: 597,638,431. See Extended Data Fig. 2 for an illustration of the workflow. Basemap in a from Natural Earth ( https://www.naturalearthdata.com/ ). Credit: b , Google Earth.

a , We estimate that 5.6 million trees have disappeared between 2018/2019 and 2020–2022, here shown per square kilometre. b , Zoom to a hotspot area in northwestern India where a substantial part of large farmland trees have disappeared over recent years. The arrow shows the location of the area within India. c , A PlanetScope scene captured in 2019, where trees classified as disappeared are still alive. The scene is shown as a false colour composite with near infrared as red colour, causing tree crowns to appear in reddish colours. The rectangle in b marks the location. d , A considerable number of the trees detected in 2019 are not present anymore in 2022, visualized here with a Google Earth basemap. The crown sizes of the disappeared trees here are >150 m 2 (manually measured on the screen), which means trees were mature. Basemap in a from Natural Earth ( https://www.naturalearthdata.com/ ). Credit: b , d , Google Earth; c , Planet Labs PBC.

A potential driver of tree losses is climate change, with rapid increases in temperature in central and southern India 22 and unfavourable trends in rainfall and drought conditions over the past decade (Extended Data Fig. 3a–c ). However, compared with the long-term mean precipitation, the past decade was above average for most areas 23 (Extended Data Fig. 2d ), and there is little evidence supporting climatic changes as the direct and main reason for the observed losses. We thus conducted qualitative interviews with villagers from the Telangana, Haryana, Kerala, Maharastra, Andrah Pradesh, Uttar Pradesh, Kashmir and Jammu provinces, all affected by the disappearance of trees in our maps (Fig. 1 ). All participants in the interviews verified the considerable reduction in the populations of mature trees within and along fields. These trees were removed owing predominantly to alterations in the cultivation practices. The establishment of new boreholes, which resulted in an augmented water supply, facilitated the expansion of paddy rice fields with the objective of boosting crop yields. The decision to remove trees within fields is often driven by the perception that their benefits are relatively low and concerns that their shading effect, in particular for trees with a very large crown such as Neem trees, may adversely affect crop yields. While all interviewees reported that native trees within fields have become rare, several report that block plantations have increased. Fungi and climate were not perceived to have had an impact on tree populations by the interviewees.

The outcome of this study is twofold. First, we provide a tool that enables the monitoring of every individual large tree every year at a sub-continental scale (Extended Data Figs. 2 and 4 – 6 ). The imagery employed is not free of charge, but the cost is orders of magnitude lower than conventional sub-metre imagery 17 . We developed a deep-learning-based method that can be trained with simple point labels, which can be rapidly optimized for regional use 19 , 20 , 24 , 25 , 26 . The database not only provides a complete inventory of mature farmland trees, but also offers the opportunity to locate and quantify trees that have disappeared. The database includes detection confidence values for each year 2018–2022. If a tree that has been detected with a high confidence (above a given threshold) is not detected anymore over several consecutive years, there is a high probability that the tree has disappeared. By combining the confidence values of each year, a change confidence can be derived, quantifying the uncertainty of whether a tree has disappeared or not. In this study, we refrained from making statements on low or medium confidence changes to take a conservative approach to the reporting of losses, but the dataset will be open for various applications. The database can be updated on an annual basis and scaled to global scale, potentially being a tool to support scientists, agroforestry practitioners, land managers and policymakers. Potential applications beyond inventorying are the monitoring of illegal logging of trees and the monitoring of survival rates of restoration areas. As a potential for future research, the spectral information could be used to study changes in the health of trees.

Second, our study reveals a concerning trajectory, documenting a considerable depletion of large farmland trees over the past decade in parts of India. This finding is particularly unsettling given the current emphasis on agroforestry as an essential natural climate solution, playing a crucial role in both climate change adaptation and mitigation strategies, as well as for livelihoods and biodiversity 27 , 28 , 29 , 30 , 31 . A certain loss rate is natural, and the cutting of trees is also part of agroforestry management systems, and not every lost tree is related to climatic disturbances or human appropriation, just as not every mapped tree contributes to livelihoods of local communities. There are also large areas in India where observed losses do not exceed expected dynamics. Nonetheless, an observable trend is emerging in several areas where established agroforestry systems are replaced with paddy rice fields 32 , which are being expanded and intensified 33 , a development facilitated by the availability of newly established water supplies. Large and mature trees within these fields are removed, and trees are now being cultivated within separate block plantations typically with lower ecological value.

At a first glance, this may contradict official reports and recent studies stating that tree cover in India has increased considerably in recent years 34 , 35 . It is, however, important to note that we report only gross losses and did not consider tree gains as a separate class. We also masked out block plantations. We thus cannot provide information on net tree changes, and our results do not contradict reports concluding that there has been a net increase in planted trees outside forests as a result of tree planting being encouraged and actively carried out in India. However, newly planted trees do not always contribute to biodiversity 36 , and it can take a long time until they efficiently contribute to livelihoods. It is also unclear how newly planted trees survive in times of climate change. In light of the vast scale of global forest mortality caused by recent wildfires and droughts, the figures presented here may appear relatively modest, yet the substantial loss of particularly large farmland trees is disconcerting as virtually every large tree that has been lost may impact the ecosystem and, in the long term, the well-being and sustenance of communities.

The expansion and intensification of agriculture can lead to the simplification of natural and cultural landscapes 37 , resulting in the loss of biodiversity 38 , depletion of carbon reservoirs 39 and disappearance of indigenous knowledge on the ecological functioning of agroforestry systems. However, and despite receiving limited attention, diverse and complex multifunctional landscapes are still maintained by indigenous peoples and smallholders 40 . According to the last census, more than 86% of the farmers in India are smallholder farmers with less than 2 hectares of land (and more than 67% of farmers less than 1 ha) 41 , in which trees may play an important role in their livelihoods 42 . Our results will be freely available, thereby inviting the research community to seize this opportunity for fieldwork and in-depth investigation to address existing uncertainties in agroforestry dynamics.

This study generates country-wide custom mosaics for RapidEye for 2010 and 2011 (5 m resolution) and for PlanetScope (3 m resolution) for 2018, 2019, 2020, 2021 and 2022. We trained deep-learning models to detect individual non-forest trees for each year and studied changes between years by tracking the tree crown centre over the years (Extended Data Figs. 2 and 4 – 6 ). We calculated a change confidence for each tree, which is composed of a multi-year detection confidence to be able to quantify the uncertainty of the detection and of the change. The 2010/2011 maps detected mostly larger trees, but differences in image quality impeded exhaustive wall-to-wall mapping. The newer maps available after 2018 provide a more spatially consistent mapping of all farmland trees; however, the mapping was designed to exclude dense plantations and small/young trees. We did not set a strict height or crown-size threshold for the Planet data analysis, but a previous study has shown that trees below 4 m in height and with a crown size below 10 m 2 are likely not to be detected 17 and that trees with crown sizes above 20 m 2 can be mapped with a relatively high confidence. Hence, comparing the 2010 maps with the 2018–2022 maps gives an overview of the disappearance of large trees, while the tracking of trees during 2018–2022 includes all trees that have reached a certain crown size, excluding newly planted trees.

Monitoring trees at the level of individuals over time requires consistency in high-quality images and reliable tree-detection methods. While this would be easier to address by using sub-metre imagery, these data are not available as time series over large areas. Imagery from nano-satellites is available at 3–5 m resolution at a high temporal frequency, but the quality of the images captured from several hundred different satellites varies substantially. This concerns in particular the image sharpness, and smaller trees will not be visible in images that are blurred: a type of information that is not obvious from the scene metadata. Imagery at this spatial resolution is not suitable for pixel-wise stacking as image composites due to variations in the viewing angle. What is most important for the detection of trees is sharpness, and most other aspects, such as the number of spectral bands, can be neglected. Our solution to this issue was to filter each downloaded scene for sharpness using a blur kernel 26 , and if a certain threshold was not reached, the image was blacklisted and the area was filled with different images until the threshold was passed.

The method used for the tree detection in this study diverges from previous deep-learning-based approaches 16 , 17 . Studying changes at tree level at a continental scale requires a highly accurate and robust detection method that works over a variety of satellite images and landscapes. Here we developed a heatmap-based detection method 19 , 20 , 43 that can be quickly trained with a large amount of point labels and is able to separate adjacent tree crowns reliably. The applied data are globally available, which makes our method widely applicable beyond the scope of farmland trees in India.

Image preparation

The RapidEye constellation was operational from 2009 to 2019 and consisted of five nano-satellites. The spatial resolution is 5 m, and the spectral bands are red, green, blue, red edge and near infrared. The PlanetScope constellation consists of 130+ nano-satellites with a resolution of 3–4 m and red, green, blue and near-infrared bands. We formed custom 1° x 1° mosaic tiles, each consisting of about 30 scenes for RapidEye and about 120 scenes (Dove Classic) or 60 scenes (Super Dove) for PlanetScope 17 . A histogram-matching algorithm with Landsat and Sentinel 2 scenes from the same period was applied for each tile to adjust differences in colours and form a homogeneous mosaic. The images were acquired following a phenological window derived from the Moderate Resolution Imaging Spectroradiometer phenology product (MOD09): For areas with deciduous trees, determined by the GlobCover map, images were acquired from the period between ‘senescence’ and ‘mid-greendown’. This particular period was selected as it represents the period after herbaceous vegetation has passed its productivity peak, whereas trees still have green leaves, facilitating the detection of tree crowns. For areas with evergreen trees, images were acquired between the mid-greendown and the ‘dormancy’, which is a period where herbaceous vegetation is not productive, but trees have full green leaves. If too few images were available meeting this criteria, we progressively extended the length of the time window until a maximum of 60 days was reached. If too few images were still to be found, we loosened the filtering criteria, allowing progressively lower ‘visible-confidence-percent’ values down to 60 to be included. We applied strict filtering criteria to use only images that are entirely free of clouds and have a low sun elevation (below 50), which facilitates the identification of trees by their shadow. The ground sample distance (GSD) of PlanetScope images varies between 3.1 and 4.7 m; we retained only images that have a GSD close to 3.1 m and never used imagery with a GSD above 4 m.

The identification of small trees is possible only in sharp images, and since there is a large variation in image sharpness between scenes that cannot be seen in the metadata, we applied a blur kernel to estimate the sharpness of the scene directly after download. The blur kernel is described in ref. 26 , and we followed their recommendation to set a threshold of 0.23. We disregarded all scenes below this threshold and redownloaded the areas until the scene was classified as sharp. We applied the blur kernel only in images where the forest, shrubland, water, bare soil and wetland classes accounted for less than 50% of the scene coverage as the calculation of the blurriness score was found to be unreliable over these land-cover classes.

Since there is no reliable metadata on cloud cover in RapidEye images, we calculated the standard deviation for each downloaded scene and disregarded all scenes where the blue band had a very high standard deviation, which is typically the case in cloudy images. This did not guarantee fully cloud-free images but, together with the blur kernel, removed most of the contaminated scenes.

The entire framework was automated and the filling of a 1° x 1° tile required about 2–3 hours using a university connection, but it can be performed in a parallelized way so the downloading, filtering and processing of one year requires less than one week for all India. All processing was done locally, and the raw data for one year is about 2.5 terabytes. As a final step, we applied a contrast limited adaptive histogram equalization to each mosaic and normalized the bands to values between 0 and 255 (ref. 44 ).

Training data

We manually labelled trees with point labels at the tree crown centre, using separate models for PlanetScope (about 130,000 labels) and RapidEye (about 100,000 labels), including labels from all years. For each labelled tree, we used high-resolution images in Google Earth and Bing to verify whether the trees existed. A large number of sub-metre images around 2010 were available from Bing maps and were used to verify labels from RapidEye, while Google Earth images were from recent years and used to verify labels from PlanetScope images. The labels were generated as an iterative process. In the first round, labels were generated over a variety of landscapes and image conditions (different viewing angles, sharpness levels and so on), being spatially well distributed across India. Then a first model was trained and trees were predicted over the study area. Subsequently, labels were added in areas where the performance was not satisfying, according to a visual inspection, and this process was repeated until the result was visually satisfying.

Trees are more challenging to map in RapidEye images at 5 m (6.5 m ground sample distance) as compared with PlanetScope data. They become clearly visible if they cover four pixels, which would translate to a crown size of >100 m 2 . To reduce the number of false positives in our classification, we did not aim at a wall-to-wall mapping of trees in RapidEye images in 2010, but instead opted for a very low misclassification rate. Consequently, we labelled only clear examples, resulting in some heterogeneity in the tree maps (related to image quality, remaining cloud cover, sun elevation and visibility of the trees), generating a sample set of trees from 2010 with a certain randomness. By adding a second year, 2011, the combined results became more spatially consistent and homogeneous.

Tree detection

To produce the most reliable assessment of tree densities, we adapted a detection approach previously used to count dense objects, such as persons in a crowd. Our method is inspired by previous work 19 , 20 , 43 and uses convolutional neural networks (CNNs) to produce a confidence map indicating the location of individual trees (Extended Data Fig. 6 ). The peak of the confidence map (‘heatmap’) is assumed to be the centre of a tree crown (Extended Data Figs. 2 and 5 ). The advantage of this method over previous methods 16 , 17 is that it can be trained by point labels, which enables a rapid generalization and adjustment over large areas and different scene quality levels.

Heatmap-based detections typically transform discrete point labels into continuous Gaussian kernels, more suited to smooth optimization. It requires deciding on a fixed kernel scale, which is not straightforward here given the high level of variability in tree sizes, image quality and potential offsets between labelled and actual tree centres. A previous work 24 proposed a solution with adaptive kernels, where the model is given a degree of freedom through a scale map that resizes the Gaussian kernels on the fly during training. They also weigh the regression loss to focus on misestimated pixels, similarly to the Focal loss 25 . Here we applied the same method, with two important modifications: we removed the linear approximation used originally and instead drew exact Gaussian kernels on the fly, and we allowed only scale factors >1, with a minimum standard deviation of 4.5 m for PlanetScope and 5 m for RapidEye. We found that these adjustments led to smoother heatmaps with fewer artefacts, which ultimately made it easier to tune the important hyperparameters of scale regularization and base Gaussian standard deviation (6 m for PlanetScope, 6.7 m for RapidEye).

We trained the models over 2,500 epochs, with a learning rate of 1 × 10 –5 until epoch 2000, after which we used a linearly decreasing learning rate. The training process was monitored using 20% of the training data as validation data. We trained five models with different semi-random splits of the training data. After creating a list of possible random training and validation splits with an 80/20 proportion of the number of labelled areas, we selected splits that had a low Wasserstein distance between the distribution of trees per area in the training and validation sets. With a relatively high number of labelled areas (918 for PlanetScope, 791 for RapidEye), it ensures a diversity of training areas in the different models, while avoiding performance issues from unbalanced training and validation sets. The F1 scores on the heatmaps for all models ranged between 0.63 and 0.65 for a close radius of 20 m, and between 0.67 and 0.69 for a wider radius of 50 m (precision 0.83, recall 0.61 for 50 m), which is close to the values from ref. 19 , although they used aerial images of higher spatial resolution. We then predicted on the mosaics using an ensemble over the five models and averaged the results of the heatmaps. The local maxima of the ensemble heatmap were converted to a point file, reflecting the centres of tree crowns. The peak confidence at the crown centre was saved as an attribute value associated with each centroid, reflecting the detection confidence. All peaks with a confidence equal to or above 0.35 were included in the following analyses. If the confidence was below 0.35, we considered this as no tree being detected.

A previously published tree crown segmentation model 17 was updated with 52,000 manually drawn crown labels from India, and a tree crown segmentation was conducted for the year 2021 (Extended Data Fig. 4 ). Although this method is less reliable than the heatmap-based detection, it provides an overview of the crown-size distribution of the trees. Results showed that trees with a crown size below 20 m 2 were under-detected (Extended Data Table 1 ). We further located 22.7 million large trees with a crown size >100 m 2 , which matches with the number of high-confidence trees detected by RapidEye in 2010/2011 (22 million; confidence >0.8). Note that trees with a crown size >50 m 2 or >100 m 2 represent only a small fraction of the woody populations, with proportions of about 20% and 4%, respectively (Extended Data Table 1 ).

We sampled trees from the heatmap predictions for the same area from both PlanetScope ( n  = 2.1 million) and RapidEye ( n  = 1.2 million) for 2019 (where RapidEye was still operational). We used the tree crown segmentation to determine the tree crown sizes of the point predictions from both PlanetScope and RapidEye by overlaying the tree crown segments with the point predictions and associating the crown area as an attribute to the points. We found that the average tree crown size of the samples was 55 m 2 from PlanetScope and 62 m 2 from RapidEye. Trees detected with a confidence >0.8 had an average crown size of 96 m 2 for RapidEye. For PlanetScope, trees with a confidence >0.7 had a crown size of 67 m 2 ; for <0.7, the crown size was on average 59 m 2 . This gives evidence that a higher confidence value also indicates a larger crown size.

Mapping tree changes

This study focuses on tree losses rather than gains as new trees are typically not growing large over a few years, and consequently they would not be detected in a reliable way. Moreover, the 2010 classification did not allow for wall-to-wall mapping.

We developed a framework that via crown centre detection can track individual trees over an arbitrary number of temporal steps. We defined a circular buffer area of 15 m around the centre of a detected tree and searched for centroid equivalents over the following images/years. If no tree was detected within the buffer area in the later years, the tree was counted a loss. If several trees were detected within the buffer, the closest tree was chosen.

For the long-term comparison between the early and late epoch, we combined the results from RapidEye (2010 and 2011) to reflect the early period and those of PlanetScope (2018–2022) to reflect the later period. Tree crown centres found in the early period were then compared with the later period, and a tree was classified either as disappeared, which means it was observed only in 2010/2011 but not during the later period, or as remaining, which means the tree was observed in both periods.

For the tracking of trees over the period 2018–2022, we developed a metric we named change confidence, which quantifies the confidence of our tree detection into the following three classes: disappeared, tree, and low-confidence tree/misclassification. A tree was marked as a low-confidence tree if it was detected in only one year with a low or medium confidence. The detection may be due to a misclassification or to the tree being too small to be reliably monitored over several years and was thus only detected in one year, so it was not included in the reported statistics. A centroid was marked as ‘tree’ if a tree was detected in one year with a very high confidence or in several years with a low or medium confidence. We marked a tree as disappeared if it was detected in either 2018 or 2019 with a high confidence, or in both years with a medium confidence, and then was not detected in three consecutive years (2020, 2021 and 2022). Mapping of disappearing trees and remaining trees with a low change confidence (<0.7) should be treated with caution. For example, if a tree mapped during 2018–2022 was mapped only in 2018 with a confidence of 0.5, it is likely that the tree is small or a false detection. If the tree is mapped only in 2018 with a confidence of 1.0, it is likely that the tree was then lost, as it was not mapped during 2019–2022. This assumes that a tree with a high confidence is a large tree that should be detected at least twice over 5 years. By contrast, if a tree was mapped with a lower confidence of 0.5 in 2018, but also in 2021 and 2022, the class is tree and the overall change confidence is higher as the tree was detected in 3 years, which reduces the risk of a misclassification.

Sources of uncertainties

Our change confidence metric quantifies uncertainty at tree level. Focusing only on trees with a high change confidence reduces the uncertainty of the change instances reported, but misses a number of cases of change that are real—but where the confidence is low due to image quality issues or the tree having a small crown. There are, however, a number of remaining sources of uncertainty that are challenging to quantify.

A tree was counted as disappeared during 2018–2022 only if the combined confidence of 2018 and 2019 was above 0.7 and the tree was not detected between 2020 and 2022. While this gives a certain robustness, we observed cases where the image quality was excellent in both 2018 and 2019 resulting in very high-confidence predictions, but the image quality was less ideal in all subsequent years 2020–2022, so some trees were missed in these three years, causing trees to be falsely classified as being disappeared. To account for this, we assumed that high-quality images in general generate higher confidence predictions compared with lower-quality images, so we calculated the average detection confidence of all trees within 1 × 1 km cells as a measure of image quality for each particular cell, which also accounts for quality variations within images. We then calculated a linear slope through the 1 × 1 km confidence grids, with time as the independent variable. A strong negative slope (<−0.05) implies that the average confidence in 2020–2022 was considerably lower than in 2018–2019, so we flagged this area as uncertain, and losses were not included in our reported numbers. The confidence slope map represents an additional layer (to the change confidence) that quantifies uncertainty that we provide with the database.

Some RapidEye scenes are spatially shifted, which leads to erroneous classifications of tree losses. When comparing the 2010/2011 results with the 2018–2022 results, we calculated the proportion of trees disappearing for each RapidEye scene footprint. If the proportion was above 40%, scenes were probably shifted and we masked them. We also manually masked scenes that were clearly shifted, visible by sharp edges along footprints.

A final source of uncertainty that cannot be quantified is the land-cover map included in our analysis. We used the WorldCover map from 2020 to retain only croplands for the 2010–2018 comparison, and cropland, urban and bare for the 2018–2022 comparison. The quality of the land-cover map impacts the results, and it happens that large farmland trees or groups of trees are masked out as forest or that the underlying classification is not correct. Future versions may include custom land-cover maps, which may lead to improved results. Using a land-cover map from 2020 to study changes over 10 years can include areas that have been cleared for cropping and have not previously been farmland, or were under fallow, or plantation forest. However, we observed that these areas rarely included large trees but rather shrubs and small trees, and by reporting mainly the loss of high-confidence trees, these are automatically excluded.

Evaluation of the tree detections and changes

We randomly selected 1,000 points mapped as disappeared trees between 2010/2011 and 2018–2022 to evaluate the uncertainty on the reported long-term losses. The same 1,000 points were used to evaluate whether trees were correctly mapped in 2010/2011 or whether it was a false detection. We further selected 1,000 random trees that had been mapped as disappeared trees over 2018–2022 to evaluate the uncertainty related to disappeared trees over the PlanetScope period. Each point was manually and visually checked on the images and in Google Earth, also using the historic images where available. For the losses, we considered only high-confidence changes, with values above 0.8 for RapidEye and 0.7 for PlanetScope. Uncertainties would be higher if different confidence thresholds were used. False detections of trees for 2010/2011 were found to be 3%. False losses for 2010–2018 were 2%. False losses for 2018–2022 were 21%.

We further conducted 12 qualitative interviews with villagers in the Telangana, Haryana, Kerala, Maharastra, Andrah Pradesh, Uttar Pradesh, Kashmir and Jammu provinces during August 2023. The interviews were about 20–60 minutes each, and we asked about soils, management systems, water resources, changes in the number of trees and possible reasons for changes. Participants were on average 59 years old. We explained our research briefly before conducting the interviews. We informed participants that we would use the information to understand the dynamics of large agroforestry trees. All interviewees accepted and provided answers to all questions.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data can be viewed online at https://rs-cph.projects.earthengine.app/view/tree and are available via Zenodo at https://zenodo.org/records/10978154 (ref. 45 ).

Code availability

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Acknowledgements

M.B., F.R., W.Z. and X.T. are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY). M.B. also acknowledges funding from a DFF Sapere Aude grant (no. 9064–00049B). A.K. and R.F. acknowledge support by the Villum Fonden through the project Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (DeReEco).

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These authors contributed equally: Martin Brandt, Dimitri Gominski, Florian Reiner.

Authors and Affiliations

Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

Martin Brandt, Dimitri Gominski, Florian Reiner, Ankit Kariryaa, Xiaoye Tong, Wenmin Zhang, Daniel Ortiz-Gonzalo & Rasmus Fensholt

Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

Ankit Kariryaa & Venkanna Babu Guthula

Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France

Philippe Ciais

Global Development Institute, University of Manchester, Manchester, UK

Dhanapal Govindarajulu

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Contributions

M.B. designed the study, prepared the data, sampled the training data, trained the models, conducted the analyses, designed the figures and wrote the manuscript. F.R. wrote the codes for the data preparation and data analyses, supported by X.T. D. Gominski wrote the codes for the tree-detection framework. The importance of the contributions of M.B., F.R. and D. Gominski can be considered as equal. A.K. and V.B.G. conducted the interviews. W.Z. analysed the climate data. P.C., R.F., D.O.-G. and D. Govindarajulu reviewed the manuscript.

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Extended data

Extended data fig. 1 examples of tree losses 2010–2018..

a , The lefthand side shows a RapidEye false color composite from 2010, the righthand side shows the area in 2021 illustrated in Google Earth. The red points are trees mapped in 2010, that are not present anymore during 2018–2022. Green points are trees mapped both in 2010 and 2018–2022. b, Close-up showing the disappearance of several very large trees. Credit: a , Planet Labs PBC; b , Google Earth.

Extended Data Fig. 2 Detecting change at tree level.

a, A PlanetScope scene from 2018 shown as a false color composite with NIR shown as red color, causing tree canopies and active fields to appear in reddish colors. We trained a deep learning model with point labels and predicted a heatmap, reflecting the confidence values of the predictions. The local maxima of the heatmap were extracted as points, reflecting the location of tree crown centers. The detection confidence was saved as an attribute value associated with each point. b, The same as (a) but for 2021. c, The above-mentioned workflow was applied for 2018, 2019, 2020, 2021 and 2022. A tree that was detected with a high confidence in 2018 and/or 2019 but not in 2020, 2021 and 2022, was classified as a loss, otherwise as a tree. If the overall confidence over 5 years was in the range between 0.4–0.7, the tree was marked as a low confidence tree. Credit: a , b , Planet Labs PBC; c , Google Earth.

Extended Data Fig. 3 Climate change in India.

a, Trend in air temperature during 2010–2020. b, Trend in precipitation during 2010–2020. c, Trend in SPEI during 2010–2020. SPEI is a measure of drought, including both rainfall and air temperature. Negative values reflect conditions that are unfavorable for vegetation growth. d, SPEI anomaly of the period 2010–2020 as compared to the long term mean 1960–2010.

Extended Data Fig. 4 Tree crown segmentation.

We segmented tree crowns over all images, using a previously published model15 plus about 50,000 new labels. The results were not used for the temporal change analysis because the heatmap-method has shown to be more robust. The segmentation gives however a good overview on the general distribution of tree crown sizes (Extended Data Table 1 ). Credit: Google Earth.

Extended Data Fig. 5 Gaussian heatmaps.

We predict heatmaps where the local maxima represent the tree crown centers. The heatmap itself can serve as a tree cover map with a precision down to each individual tree, and the size of the gaussian can be used to estimate the crown size, which was however not utilized in this study. This visualization uses different zoom levels. Credit: Google Earth.

Extended Data Fig. 6 Model architecture.

We use the UNet architecture with a ResNet-50 encoder and two custom heads, one predicting a heatmap indicating tree center confidence and the other predicting a scale map. The scale map is used during training to let the model resize the individual Gaussian kernels and make them fit visual features. Credit: of the greyscale image on the left: Google Earth.

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Brandt, M., Gominski, D., Reiner, F. et al. Severe decline in large farmland trees in India over the past decade. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01356-0

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DOI : https://doi.org/10.1038/s41893-024-01356-0

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Wildlife & Biodiversity

India lost 668,400 ha forests in 5 years, 2nd highest globally: report.

India was ranked between Brazil and Indonesia in UK-based firm’s report

By Shuchita Jha

Published: monday 20 march 2023.

India has seen the highest rise in deforestation in the last 30 years, with a stark surge recorded between 2015 and 2020, according to a new report . During these years , the country was ranked second only after Brazil, with average deforestation of 668,400 hectares (ha), noted the report by Utility Bidder, a United Kingdom-based comparison site for energy and utility costs.

Also read:  Halt deforestation by 2030: Are we on track to meet global pledge?

The report,  released in March 2023 , analysed deforestation trends of 98 countries in the last 30 years with the help of data aggregator Our World In Data’ s figures from 1990 to 2000 and 2015 to 2020. 

While India lost 384,000 ha of forests between 1990 and 2000, the figure rose to 668,400 ha between 2015 and 2020. Zambia recorded the second biggest deforestation increase for the same period, with a rise to 189,710 ha between 2015 and 2020, compared to 36,250 ha from 1990-2020. 

With a difference of 284,400 ha in forestry loss between 1990 and 2020, India has seen the biggest increase in deforestation, the document read. 

“As the country with the second largest population in the world, India has had to compensate for the increase in residents — this has come at a cost in the way of deforestation,” it added.

Brazil, which ranked first with 1,695,700 ha of deforestation between 2015 and 2020, mostly lost the forests due to climate change.  However, this is much lower than the 4,254,800 ha it lost between 1990 and 2000. 

Palm oil cultivation in Indonesia led to the destruction of 650,000 ha of forests, making it the third-highest loss in the world, right behind India.

Also read:  Warming beyond borders: Amazon deforestation heats up Tibet, says new study

The study further revealed that cattle rearing was the leading cause of global deforestation, leading to a loss of 2,105,753 ha annually.  This was followed by the cultivation of oil seeds that caused 950,609 ha of forestry loss.

As mentioned earlier in this report, palm oil has been a big driver of deforestation for many years, but that isn’t the only oil-based product responsible for forestry loss, the document read.

“Soyabeans provide us with lots of nutrients and health benefits, but many ha of grassland and forests have been destroyed to make room for the yielding of this crop,” it added.

After cattle rearing for meat and oil seed cultivation, logging is the third highest factor responsible for deforestation, causing around 678,744 ha of annual deforestation globally.

The report further revealed that while Brazil has reduced its deforestation by 2,559,100 ha from 2015 to 2020 and Indonesia by 1,876,000 ha for the same period, India’s figures have only increased significantly. 

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Deforestation and Forest Loss

Explore long-term changes in deforestation and deforestation rates across the world today., which countries are gaining, and which are losing forests.

Before we look specifically at trends in deforestation across the world, it's useful to understand the net change in forest cover. The net change in forest cover measures any gains in forest cover — either through natural forest expansion or afforestation through tree planting — minus deforestation.

This map shows the net change in forest cover across the world. Countries with a positive change (shown in green) are gaining forests faster than they're losing them. Countries with a negative change (shown in red) are losing more than they're able to restore.

A note on UN FAO forestry data

Data on net forest change, afforestation, and deforestation is sourced from the UN Food and Agriculture Organization's Forest Resources Assessment . Since year-to-year changes in forest cover can be volatile, the UN FAO provides this annual data averaged over five-year periods.

How much deforestation occurs each year?

Net forest loss is not the same as deforestation — it measures deforestation plus any gains in forest over a given period.

Between 2010 and 2020, the net loss in forests globally was 4.7 million hectares per year. 1 However, deforestation rates were much higher.

The UN FAO estimates that 10 million hectares of forest are cut down each year.

This interactive map shows deforestation rates across the world.

Read more about historical deforestation here:

The world has lost one-third of its forest, but an end of deforestation is possible

Over the last 10,000 years the world has lost one-third of its forests. An area twice the size of the United States. Half occurred in the last century.

Global deforestation peaked in the 1980s. Can we bring it to an end?

Since the end of the last ice age — 10,000 years ago — the world has lost one-third of its forests. 2 Two billion hectares of forest — an area twice the size of the United States — has been cleared to grow crops, raise livestock, and for use as fuelwood.

Previously, we looked at this change in global forests over the long run. What this showed was that although humans have been deforesting the planet for millennia, the rate of forest loss accelerated rapidly in the last few centuries. Half of the global forest loss occurred between 8,000 BCE and 1900; the other half was lost in the last century alone.

To understand this more recent loss of forest, let’s zoom in on the last 300 years. The world lost 1.5 billion hectares of forest over that period. That’s an area 1.5 times the size of the United States.

In the chart, we see the decadal losses and gains in global forest cover. On the horizontal axis, we have time, spanning from 1700 to 2020; on the vertical axis, we have the decadal change in forest cover. The taller the bar, the larger the change in forest area. This is measured in hectares; one hectare is equivalent to 10,000 m².

Forest loss measures the net change in forest cover: the loss in forests due to deforestation plus any increase in forest through afforestation or natural expansion. 3

Unfortunately, there is no single source that provides consistent and transparent data on deforestation rates over this period of time. Methodologies change over time, and estimates — especially in earlier periods — are highly uncertain. This means I’ve had to use two separate datasets to show this change over time. As we’ll see, they produce different estimates of deforestation for an overlapping decade — the 1980s — which suggests that these are not directly comparable. I do not recommend combining them into a single series, but the overall trends are still applicable and tell us an important story about deforestation over the last three centuries.

The first series of data comes from Williams (2006), who estimates deforestation rates from 1700 to 1995. 4 Due to poor data resolution, these are often given as average rates over longer periods — for example, annual average rates are given over the period from 1700 to 1849 and 1920 to 1949. That’s why these rates look strangely consistent over a long period of time.

The second series comes from the UN Food and Agriculture Organization (FAO). It produces a new assessment of global forests every five years. 5

The rate and location of forest loss changed a lot. From 1700 to 1850, 19 million hectares were being cleared every decade. That’s around half the size of Germany.

Most temperate forests across Europe and North America were being lost at this time. Population growth meant that today’s rich countries needed more and more resources such as land for agriculture, wood for energy, and construction. 6

Moving into the 20th century, there was a stepwise change in demand for agricultural land and energy from wood. Deforestation rates accelerated. This increase was mostly driven by tropical deforestation in countries across Asia and Latin America.

Global forest loss appears to have reached its peak in the 1980s. The two sources do not agree on the magnitude of this loss: Williams (2006) estimates a loss of 150 million hectares — an area half the size of India — during that decade.

Interestingly, the UN FAO 1990 report also estimated that deforestation in tropical ‘developing’ countries was 154 million hectares. However, it was estimated that the regrowth of forests offset some of these losses, leading to a net loss of 102 million hectares. 7

The latest UN Forest Resources Assessment estimates that the net loss in forests has declined in the last three decades, from 78 million hectares in the 1990s to 47 million hectares in the 2010s.

This data maps an expected pathway based on what we know from how human-forest interactions evolve.

As we explore in more detail later on , countries tend to follow a predictable development in forest cover, a U-shaped curve. 8 They lose forests as populations grow and demand for agricultural land and fuel increases, but eventually, they reach the so-called ‘forest transition point’ where they begin to regrow more forests than they lose.

That is what has happened in temperate regions: they have gone through a period of high deforestation rates before slowing and reversing this trend.

However, many countries — particularly in the tropics and sub-tropics — are still moving through this transition. Deforestation rates are still very high.

Deforestation rates are still high across the tropics

Large areas of forest are still being lost in the tropics today. This is particularly tragic because these are regions with the highest levels of biodiversity.

Let’s look at estimates of deforestation from the latest UN Forest report. This shows us raw deforestation rates without any adjustment for the regrowth or plantation of forests, which is arguably not as good for ecosystems or carbon storage.

This is shown in the chart below.

We can see that the UN does estimate that deforestation rates have fallen since the 1990s. However, there was very little progress from the 1990s to the 2000s and an estimated 26% drop in rates in the 2010s. In 2022, the FAO published a separate assessment based on remote sensing methods; it did not report data for the 1990s, but it also estimated a 29% reduction in deforestation rates from the early 2000s to the 2010s.

This is progress, but it needs to happen much faster. The world is still losing large amounts of primary forests every year. To put these numbers in context, during the 1990s and first decade of the 2000s, an area almost the size of India was deforested. 9 Even with the ‘improved’ rates in the 2010s, this still amounted to an area around twice the size of Spain. 10

The regrowth of forests is a positive development. In the chart below, we see how this affects the net change in global forests. Forest recovery and plantation ‘offsets’ a lot of deforestation such that the net losses are around half the rates of deforestation alone.

But we should be cautious here: it’s often not the case that the ‘positives’ of regrowing on planting one hectare of forest offset the ‘losses’ of one hectare of deforestation. Cutting down one hectare of rich tropical rainforest cannot be completely offset by the creation of on hectare of plantation forest in a temperate country.

Forest expansion is positive but does not negate the need to end deforestation.

The history of deforestation is a tragic one, in which we have lost not only wild and beautiful landscapes but also the wildlife within them. But, the fact that forest transitions are possible should give us confidence that a positive future is possible. Many countries have not only ended deforestation but have actually achieved substantial reforestation. It will be possible for our generation to achieve the same on a global scale and bring the 10,000-year history of forest loss to an end.

If we want to end deforestation, we need to understand where and why it’s happening, where countries are within their transition, and what can be done to accelerate their progress through it. We need to pass the transition point as soon as possible while minimizing the amount of forest we lose along the way.

In this article , I look at what drives deforestation, which helps us understand what we need to do to solve it.

Forest definitions and comparisons to other datasets

There is no universal definition of what a ‘forest’ is. That means there are a range of estimates of forest area and how this has changed over time.

In this article, in the recent period, I have used data from the UN’s Global Forest Resources Assessment (2020). The UN carries out these global forest stocktakes every five years. These forest figures are widely used in research, policy, and international targets, such as the Sustainable Development Goals .

The UN FAO has a very specific definition of a forest. It’s “land spanning more than 0.5 hectares with trees higher than 0.5 meters and a canopy cover of more than 10%, or trees able to reach these thresholds in situ.”

In other words, it has criteria for the area that must be covered (0.5 hectares), the minimum height of trees (0.5 meters), and a density of at least 10%.

Compare this to the UN Framework Convention on Climate Change (UNFCCC), which uses forest estimates to calculate land use carbon emissions, and its REDD+ Programme, where low-to-middle-income countries can receive finance for verified projects that prevent or reduce deforestation. It defines a forest as having a density of more than 10%, a minimum tree height of 2-5 meters, and a smaller area of at least 0.05 hectares.

It’s not just forest definitions that vary between sources. What is measured (and not measured) differs, too. Global Forest Watch is an interactive online dashboard that tracks ‘tree loss’ and ‘forest loss’ across the world. It measures this in real time and can provide better estimates of year-to-year variations in rates of tree loss.

However, the UN FAO and Global Forest Watch do not measure the same thing.

The UN FAO measures deforestation based on how land is used. It measures the permanent conversion of forested land to another use, such as pasture, croplands, or urbanization. Temporary changes in forest cover, such as losses through wildfire or small-scale shifting agriculture, are not included in deforestation figures because it is assumed that they will regrow. If the use of land has not changed, it is not considered deforestation.

Global Forest Watch (GFW) measures temporary changes in forests. It can detect changes in land cover but does not differentiate the underlying land use. All deforestation would be considered tree loss, but a lot of tree loss would not be considered as deforestation.

As GFW defines ‘forest loss’, “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.”

Therefore, we cannot directly compare these sources. This article from Global Forest Watch gives a good overview of the differences between the UN FAO's and GFW's methods.

Since GFW uses satellite imagery, its methods continually improve. This makes its ability to detect changes in forest cover even stronger. But it also means that comparisons over time are more difficult. It currently warns against comparing pre-2015 and post-2015 data since there was a significant methodological change at that time. Note that this is also a problem in UN FAO reports, as I’ll soon explain.

What data from GFW makes clear is that forest loss across the tropics is still very high, and in the last few years, little progress has been made. Since UN FAO reports are only published in 5-year intervals, they miss these shorter-term fluctuations in forest loss. The GFW’s shorter-interval stocktakes of how countries are doing will become increasingly valuable.

One final point to note is that UN FAO estimates have also changed over time, with improved methods and better access to data.

I looked at how net forest losses in the 1990s were reported across five UN reports: 2000, 2005, 2010, 2015, and 2020.

Estimated losses changed in each successive report:

• 2000 report : Net losses of 92 million hectares
• 2005 report : 89 million hectares
• 2010 report : 83 million hectares
• 2015 report : 72 million hectares
• 2020 report : 78 million hectares

This should not affect the overall trends reported in the latest report: the UN FAO should — as far as is possible — apply the same methodology to its 1990s, 2000s, and 2010s estimates. However, it does mean we should be cautious about comparing absolute magnitudes across different reports.

This is one challenge in presenting 1980 figures in the main visualization in this article. Later reports have not updated 1980 figures, so we have to rely on estimates from earlier reports. We don’t know whether 1980s losses would also be lower with the UN FAO’s most recent adjustments. If so, this would mean the reductions in net forest loss from the 1980s to 1990s were lower than is shown from available data.

Forest transitions: why do we lose then regain forests?

Globally, we deforest around ten million hectares of forest every year. 11 That’s an area the size of Portugal every year. Around half of this deforestation is offset by regrowing forests, so overall, we lose around five million hectares each year.

Nearly all — 95% — of this deforestation occurs in the tropics . But not all of it is to produce products for local markets. 14% of deforestation is driven by consumers in the world’s richest countries — we import beef, vegetable oils, cocoa, coffee, and paper that has been produced on deforested land. 12

The scale of deforestation today might give us little hope for protecting our diverse forests. But by studying how forests have changed over time, there’s good reason to think that a way forward is possible.

Many countries have lost and then regained forests over millennia.

Time and time again, we see examples of countries that have lost massive amounts of forests before reaching a turning point where deforestation not only slows but forests return. In the chart, we see historical reconstructions of country-level data on the share of land covered by forest (over decades, centuries, or even millennia, depending on the country). I have reconstructed long-term data using various studies, which I’ve documented here .

Many countries have much less forest today than they did in the past. Nearly half (47%) of France was forested 1000 years ago; today that’s just under one-third (31.4%). The same is true of the United States; back in 1630, 46% of the area of today’s USA was covered by forest. Today, that’s just 34%.

One thousand years ago, 20% of Scotland’s land was covered by forest. By the mid-18th century, only 4% of the country was forested. But then the trend turned, and it moved from deforestation to reforestation. For the last two centuries, forests have been growing and are almost back to where they were 1000 years ago. 13

Forest Transitions: the U-shaped curve of forest change

What’s surprising is how consistent the pattern of change is across so many countries; as we’ve seen, they all seem to follow a ‘U-shaped curve.’ They first lose lots of forest but reach a turning point and begin to regain it again.

We can illustrate this through the so-called ‘Forest Transition Model.’ 14 This is shown in the chart. It breaks the change in forests into four stages, explained by two variables: the amount of forest cover a region has and the annual change in cover (how quickly it is losing or gaining forest). 15

Stage 1 – The Pre-Transition phase is defined as having high levels of forest cover and no or only very slow losses over time. Countries may lose some forest each year, but this is at a very slow rate. Mather refers to an annual loss of less than 0.25% as a small loss.

Stage 2 – The Early Transition phase is when countries start to lose forests very rapidly. Forest cover falls quickly, and the annual loss of forest is high.

Stage 3 – The Late Transition phase is when deforestation rates start to slow down again. At this stage, countries are still losing forest each year, but at a lower rate than before. At the end of this stage, countries are approaching the ‘transition point.’

Stage 4 – The Post-Transition phase is when countries have passed the ‘transition point’ and are now gaining forest again. At the beginning of this phase, the forest area is at its lowest point. But forest cover increases through reforestation. The annual change is now positive.

Why do countries lose and then regain forests?

Many countries have followed this classic U-shaped pattern. What explains this?

There are two reasons that we cut down forests:

• Forest resources: we want the resources that they provide — the wood for fuel, building materials, or paper;
• Land: We want to use the land they occupy for something else, such as farmland to grow crops, pasture to raise livestock or land to build roads and cities.

Our demand for both of these initially increases as populations grow and poor people get richer . We need more fuelwood to cook, more houses to live in, and, importantly, more food to eat.

But, as countries continue to get richer, this demand slows. The rate of population growth tends to slow down. Instead of using wood for fuel, we switch to fossil fuels , or hopefully, more renewables and nuclear energy . Our crop yields improve, so we need less land for agriculture.

This demand for resources and land is not always driven by domestic markets. As I mentioned earlier, 14% of deforestation today is driven by consumers in rich countries.

The Forest Transition, therefore, tends to follow a ‘development’ pathway. 16 As a country achieves economic growth, it moves through each of the four stages. This explains the historical trends we see in countries across the world today. Rich countries — such as the USA, France, and the United Kingdom — have had a long history of deforestation but have now passed the transition point. Most deforestation today occurs in low-to-middle-income countries.

Where are countries in the transition today?

If we look at where countries are in their transition today, we can understand where we expect to lose and gain forest in the coming decades. Most of our future deforestation is going to come from countries in the pre-or early-transition phase.

Several studies have assessed the stage of countries across the world. 17 The most recent analysis to date was published by Florence Pendrill and colleagues (2019), which looked at each country’s stage in the transition, the drivers of deforestation, and the role of international trade. 18 To do this, they used the standard metrics discussed in our theory of forest transitions earlier: the share of land that is forested and the annual change in forest cover.

In the map, we see their assessment of each country’s stage in the transition. Most of today’s richest countries — all of Europe, North America, Japan, and South Korea — have passed the turning point and are now regaining forests. This is also true for major economies such as China and India. The fact that these countries have recently regained forests is also visible in the long-term forest trends above.

Across tropical and sub-tropical countries, we have a mix: many upper-middle-income countries are now in the late transition phase. Brazil, for example, went through a period of very rapid deforestation in the 1980s and 90s (its ‘early transition’ phase), but its losses have slowed, meaning it is now in the late transition. Countries such as Indonesia, Myanmar, and the Democratic Republic of Congo are in the early transition phase and are losing forests quickly. Some of the world’s poorest countries are still in the pre-transition phase. In the coming decades, we might expect to see the most rapid loss of forests unless these countries take action to prevent it and the world supports them in their goal.

Not all forest loss is equal: what is the difference between deforestation and forest degradation?

Fifteen billion trees are cut down every year. 19 The Global Forest Watch project — using satellite imagery — estimates that global tree loss in 2019 was 24 million hectares. That’s an area the size of the United Kingdom.

These are big numbers and important ones to track: forest loss creates a number of negative impacts, ranging from carbon emissions to species extinctions and biodiversity loss. But distilling changes to this single metric — tree or forest loss — comes with its own issues.

The problem is that it treats all forest loss as equal. It assumes the impact of clearing primary rainforest in the Amazon to produce soybeans is the same as logging plantation forests in the UK. The latter will experience short-term environmental impacts but will ultimately regrow. When we cut down primary rainforest, we transform this ecosystem forever.

When we treat these impacts equally, we make it difficult to prioritize our efforts in the fight against deforestation. Decision makers could give as much of our attention to European logging as to the destruction of the Amazon. As we will see later, this would be a distraction from our primary concern: ending tropical deforestation. The other issue that arises is that ‘tree loss’ or ‘forest loss’ data collected by satellite imagery often doesn’t match the official statistics reported by governments in their land use inventories. This is because the latter only captures deforestation — the replacement of forest with another land use (such as cropland). It doesn’t capture trees that are cut down in planted forests; the land is still forested; it’s now just regrowing forests.

In the article, we will look at the reasons we lose forests, how these can be differentiated in a useful way, and what this means for understanding our priorities in tackling forest loss.

Understanding and seeing the drivers of forest loss

‘Forest loss’ or ‘tree loss’ captures two fundamental impacts on forest cover: deforestation and forest degradation .

Deforestation is the complete removal of trees for the conversion of forest to another land use such as agriculture, mining, or towns and cities. It results in a permanent conversion of forest into an alternative land use. The trees are not expected to regrow . Forest degradation measures a thinning of the canopy — a reduction in the density of trees in the area — but without a change in land use. The changes to the forest are often temporary, and it’s expected that they will regrow.

From this understanding, we can define five reasons why we lose forests:

• Commodity-driven deforestation is the long-term, permanent conversion of forests to other land uses such as agriculture (including oil palm and cattle ranching), mining, or energy infrastructure.
• Urbanization is the long-term, permanent conversion of forests to towns, cities, and urban infrastructure such as roads.
• Shifting agriculture is the small- to medium-scale conversion of forest for farming, which is later abandoned so that forests regrow. This is common in local subsistence farming systems where populations will clear forest, use it to grow crops, and then move on to another plot of land.
• Forestry production is the logging of managed, planted forests for products such as timber, paper, and pulp. These forests are logged periodically and allowed to regrow.
• Wildfires destroy forests temporarily. When the land is not converted to a new use, forests can regrow in the following years.

Thanks to satellite imagery, we can get a birds-eye view of what these drivers look like from above. In the figure, we see visual examples from the study of forest loss classification by Philip Curtis et al. (2018), published in Science . 20

Commodity-driven deforestation and urbanization are deforestation : the forested land is completely cleared and converted into another land use — a farm, mining site, or city. The change is permanent. There is little forest left. Forestry production and wildfires usually result in forest degradation — the forest experiences short-term disturbance but, if left alone, is likely to regrow. The change is temporary. This is nearly always true of planted forests in temperate regions — there, planted forests are long-established and do not replace primary existing forests. In the tropics, some forestry production can be classified as deforestation when primary rainforests are cut down to make room for managed tree plantations. 18

'Shifting agriculture’ is usually classified as degradation because the land is often abandoned, and the forests regrow naturally. But it can bridge between deforestation and degradation depending on the timeframe and permanence of these agricultural practices.

One-quarter of forest loss comes from tropical deforestation

We’ve seen the five key drivers of forest loss. Let’s put some numbers on them.

In their analysis of global forest loss, Philip Curtis and colleagues used satellite images to assess where and why the world lost forests between 2001 and 2015. The breakdown of forest loss globally and by region is shown in the chart. 20

Just over one-quarter of global forest loss is driven by deforestation. The remaining 73% came from the three drivers of forest degradation: logging of forestry products from plantations (26%), shifting, local agriculture (24%), and wildfires (23%).

We see massive differences in how important each driver is across the world. 95% of the world’s deforestation occurs in the tropics [we look at this breakdown again later]. In Latin America and Southeast Asia, in particular, commodity-driven deforestation — mainly the clearance of forests to grow crops such as palm oil and soy and pasture for beef production — accounts for almost two-thirds of forest loss.

In contrast, most forest degradation — two-thirds of it — occurs in temperate countries. Centuries ago, it was mainly temperate regions that were driving global deforestation [we take a look at this longer history of deforestation in a related article ] . They cut down their forests and replaced them with agricultural land long ago. But this is no longer the case: forest loss across North America and Europe is now the result of harvesting forestry products from tree plantations or tree loss in wildfires.

Africa is also different here. Forests are mainly cut and burned to make space for local subsistence agriculture or fuelwood for energy. This ‘shifting agriculture’ category can be difficult to allocate between deforestation and degradation: it often requires close monitoring over time to understand how permanent these agricultural practices are.

Africa is also an outlier as a result of how many people still rely on wood as their primary energy source. Noriko Hosonuma et al. (2010) looked at the primary drivers of deforestation and degradation across tropical and subtropical countries specifically. 21  The breakdown of forest degradation drivers is shown in the following chart. Note that in this study, the category of subsistence agriculture was classified as a deforestation driver, so it is not included. In Latin America and Asia, the dominant driver of degradation was logging for products such as timber, paper, and pulp — this accounted for more than 70%. Across Africa, fuelwood and charcoal played a much larger role — it accounted for more than half (52%).

This highlights an important point: around one in five people in sub-Saharan Africa have access to clean fuels for cooking, meaning they still rely on wood and charcoal. With increasing development, urbanization, and access to other energy resources, Africa will shift from local subsistence activities into commercial commodity production — both in agricultural products and timber extraction. This follows the classic ‘forest transition’ model with development, which we look at in more detail in a related article .

Tropical deforestation should be our primary concern

The world loses almost six million hectares of forest each year to deforestation. That’s like losing an area the size of Portugal every two years. 95% of this occurs in the tropics. The breakdown of deforestation by region is shown in the chart. 59% occurs in Latin America, with a further 28% from Southeast Asia. In a related article , we look in much more detail at which agricultural products and which countries are driving this.

As we saw previously, this deforestation accounts for around one-quarter of global forest loss. 27% of forest loss results from ‘commodity-driven deforestation’ — cutting down forests to grow crops such as soy, palm oil, and cocoa, raising livestock on pasture, and mining operations. Urbanization, the other driver of deforestation, accounts for just 0.6%. It’s the foods and products we buy, not where we live, that have the biggest impact on global land use.

It might seem odd to argue that we should focus our efforts on tackling this quarter of forest loss (rather than the other 73%). But there is good reason to make this our primary concern.

Philipp Curtis and colleagues make this point clear. On their Global Forest Watch platform, they were already presenting maps of forest loss across the world. However, they wanted to contribute to a more informed discussion about where to focus forest conservation efforts by understanding why forests were being lost. To quote them, they wanted to prevent “a common misperception that any tree cover loss shown on the map represents deforestation.” And to “identify where deforestation is occurring; perhaps as important, show where forest loss is not deforestation.”

Why should we care most about tropical deforestation? There is a geographical argument (why the tropics?) and an argument for why deforestation is worse than degradation.

Tropical forests are home to some of the richest and most diverse ecosystems on the planet. Over half of the world’s species reside in tropical forests. 22 Endemic species are those which only naturally occur in a single country. Whether we look at the distribution of endemic mammal species , bird species , or amphibian species , the map is the same: tropical and subtropical countries are packed with unique wildlife. Habitat loss is the leading driver of global biodiversity loss. 23 When we cut down rainforests, we are destroying the habitats of many unique species and reshaping these ecosystems permanently. Tropical forests are also large carbon sinks and can store a lot of carbon per unit area. 24

Deforestation also results in larger losses of biodiversity and carbon relative to degradation. Degradation drivers, including logging and especially wildfires, can definitely have major impacts on forest health: animal populations decline, trees can die, and CO 2 is emitted. However, the magnitude of these impacts is often less than the complete conversion of forests. They are smaller and more temporary. When deforestation happens, almost all of the carbon stored in the trees and vegetation — called the ‘aboveground carbon loss’ —  is lost. Estimates vary, but on average, only 10-20% of carbon is lost during logging and 10-30% from fires. 25 In a study of logging practices in the Amazon and Congo, forests retained 76% of their carbon stocks shortly after logging. 26 Logged forests recover their carbon over time, as long as the land is not converted to other uses (which is what happens in the case of deforestation).

Deforestation tends to occur in forests that have been around for centuries if not millennia. Cutting them down disrupts or destroys established, species-rich ecosystems. The biodiversity of managed tree plantations, which are periodically cut, regrown, cut again, and then regrown, is not the same.

That is why we should be focusing on tropical deforestation. Since agriculture is responsible for 60 to 80% of it, what we eat, where it’s sourced from, and how it is produced are our strongest levers to bring deforestation to an end.

Carbon emissions from deforestation: are they driven by domestic demand or international trade?

95% of global deforestation occurs in the tropics. Brazil and Indonesia alone account for almost half. After long periods of forest clearance in the past, most of today’s richest countries are increasing tree cover through afforestation.

This might put the responsibility for ending deforestation solely on tropical countries. But, supply chains are international. What if this deforestation is being driven by consumers elsewhere?

Many consumers are concerned that their food choices are linked to deforestation in some of these hotspots. Since three-quarters of tropical deforestation is driven by agriculture, that’s a valid concern. It feeds into the popular idea that ‘eating local’ is one of the best ways to reduce your carbon footprint. In a previous article , I showed that the types of food you eat matter much more for your carbon footprint than where it comes from — this is because transport usually makes up a small percentage of your food’s emissions, even if it comes from the other side of the world. If you want to reduce your carbon footprint, reducing meat and dairy intake — particularly beef and lamb — has the largest impact.

But understanding the role of deforestation in the products we buy is important. If we can identify the producing and importing countries and the specific products responsible, we can direct our efforts towards interventions that will really make a difference.

Read more about the imported deforestation here:

Do rich countries import deforestation from overseas?

Rich countries import foods produced on deforested land in the tropics. How much deforestation do they import?

One-third of CO 2 emissions from deforestation are embedded in international trade

In a study published in Global Environmental Change , Florence Pendrill and colleagues investigated where tropical deforestation was occurring and what products were driving this. Using global trade models, they traced where these products were going in international supply chains. 27

They found that tropical deforestation — given as the annual average between 2010 and 2014 — was responsible for 2.6 billion tonnes of CO 2 per year. That was 6.5% of global CO 2 emissions. 28

International trade was responsible for around one-third (29%) of these emissions. This is probably less than many people would expect. Most emissions — 71% — came from foods consumed in the country where they were produced. It’s domestic demand, not international trade, that is the main driver of deforestation.

In the chart, we see how emissions from tropical deforestation are distributed through international supply chains. On the left-hand side, we have the countries (grouped by region) where deforestation occurs, and on the right, we have the countries and regions where these products are consumed. The paths between these end boxes indicate where emissions are being traded — the wider the bar, the more emissions are embedded in these products.

Latin America exports around 23% of its emissions; that means more than three-quarters are generated for products that are consumed within domestic markets. The Asia-Pacific region — predominantly Indonesia and Malaysia — exports a higher share: 44%. As we will see later, this is dominated by palm oil exports to Europe, China, India, North America, and the Middle East. Deforestation in Africa is mainly driven by local populations and markets; only 9% of its emissions are exported.

Since international demand is driving one-third of deforestation emissions, we have some opportunity to reduce emissions through global consumers and supply chains. However, most emissions are driven by domestic markets, which means that policies in major producer countries will be key to tackling this problem.

How much deforestation emissions is each country responsible for?

Let’s now focus on the consumers of products driving deforestation. After we adjust for imports and exports, how much CO 2 from deforestation is each country responsible for?

Rather than looking at total figures by country (if you’re interested, we have mapped them here ), we have calculated the per capita footprint. This gives us an indication of the impact of the average person’s diet. Note that this only measures the emissions from tropical deforestation — it doesn’t include any other emissions from agricultural production, such as methane from livestock or rice or the use of fertilizers.

In the chart, we see deforestation emissions per person, measured in tonnes of CO 2 per year. For example, the average German generated half a tonne (510 kilograms) of CO 2 per person from domestic and imported foods.

At the top of the list, we see Brazil and Indonesia, which are some of the major producer countries. The fact that the per capita emissions after trade are very high means that a lot of their food products are consumed by people in Brazil and Indonesia. The diet of the average Brazilian creates 2.7 tonnes of CO 2 from deforestation alone. That’s more than the country’s CO 2 emissions from fossil fuels , which are around 2.2 tonnes per person.

But we also see that some countries which import a lot of food have high emissions. Luxembourg has the largest footprint at nearly three tonnes per person. Imported emissions are also high for Taiwan, Belgium, and the Netherlands at around one tonne.

The average across the EU was 0.3 tonnes of CO 2 per person. To put this in perspective, that would be around one-sixth of the total carbon footprint of the average EU diet. 29

Beef, soybeans, and palm oil are the key drivers of deforestation

We know where deforestation emissions are occurring and where this demand is coming from. But we also need to know what products are driving this. This helps consumers understand what products they should be concerned about and allows us to target specific supply chains.

As we covered in a previous article , 60% of tropical deforestation is driven by beef, soybean, and palm oil production. We should look not only at where these foods are produced but also at where the consumer demand is coming from.

In the chart here, we see the breakdown of deforestation emissions by product for each consumer country. The default is shown for Brazil, but you can explore the data for a range of countries using the “Change country” button.

We see very clearly that the large Brazilian footprint is driven by its domestic demand for beef. In China, the biggest driver is demand for ‘oilseeds’ — which is the combination of soy imported from Latin America and palm oil imported from Indonesia and Malaysia.

Across the US and Europe, the breakdown of products is more varied. But, overall, oilseeds and beef tend to top the list for most countries.

Bringing all of these elements together, we can focus on a few points that should help us prioritize our efforts to end deforestation. Firstly, international trade does play a role in deforestation — it’s responsible for almost one-third of emissions. By combining our earlier Sankey diagram and breakdown of emissions by-product, we can see that we can tackle a large share of these emissions through only a few key trade flows. Most traded emissions are embedded in soy and palm oil exports to China and India, as well as beef, soy, and palm oil exports to Europe. The story of both soy and palm oil is complex — and it’s not obvious that eliminating these products will fix the problem. Therefore, we look at them both individually in more detail to better understand what we can do about it.

However, international markets alone cannot fix this problem. Most tropical deforestation is driven by the demand for products in domestic markets. Brazil’s emissions are high because Brazilians eat a lot of beef. Africa’s emissions are high because people are clearing forests to produce more food. This means interventions at the national level will be key: this can include a range of solutions, including policies such as Brazil’s soy moratorium, the REDD+ Programme to compensate for the opportunity costs of preserving these forests, and improvements in agricultural productivity so countries can continue to produce more food on less land.

FAO. 2020. Global Forest Resources Assessment 2020 – Key findings. Rome. https://doi.org/10.4060/ca8753en

Estimates vary, but most date the end of the last ice age to around 11,700 years ago.

Kump, L. R., Kasting, J. F., & Crane, R. G. (2004). The Earth System (Vol. 432). Upper Saddle River, NJ: Pearson Prentice Hall.

Year-to-year data on forest change comes with several issues: either data at this resolution is not available, or year-to-year changes can be highly variable. For this reason, data sources — including the UN Food and Agriculture Organization — tend to aggregate annual losses as the average over five-year or decadal periods.

Williams, M. (2003). Deforesting the earth: from prehistory to global crisis. University of Chicago Press.

The data for 1990 to 2020 is from the latest assessment: the UN’s Global Forest Resources Assessment 2020.

FAO (2020). Global Forest Resources Assessment 2020: Main report. Rome. https://doi.org/10.4060/ca9825en .

Mather, A. S., Fairbairn, J., & Needle, C. L. (1999). The course and drivers of the forest transition: the case of France. Journal of Rural Studies, 15(1), 65-90.

Mather, A. S., & Needle, C. L. (2000). The relationships of population and forest trends. Geographical Journal, 166(1), 2-13.

It estimated that the net change in forests without plantations was 121 million hectares. With plantations included — as is standard for the UN’s forest assessments — this was 102 million hectares.

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., … & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4), 044009.

The area of India is around 330 million hectares. The combined losses in the 1990s and 2000s were 309 million hectares. Just 6% less than the size of India.

The area of Spain is around 51 million hectares. Double this area is around 102 million hectares — a little under 110 million hectares.

The UN Food and Agriculture Organization (FAO) Forest Resources Assessment estimates global deforestation, averaged over the five-year period from 2015 to 2020, was 10 million hectares per year.

If we sum countries’ imported deforestation by World Bank income group , we find that high-income countries were responsible for 14% of imported deforestation; upper-middle-income for 52%; lower-middle income for 23%; and low income for 11%.

Mather, A. S. (2004). Forest transition theory and the reforesting of Scotland . Scottish Geographical Journal, 120(1-2), 83-98.

England is similar: in the late 11th century, 15% of the country was forested, and over the following centuries, two-thirds were cut down. By the 19th century, the forest area had been reduced to a third of what it once was. But it was then that England reached its transition point, and since then, forests have doubled in size.

National Inventory of Woodland and Trees, England (2001). Forestry Commission. Available here .

This was first coined by Alexander Mather in the 1990s. Mather, A. S. (1990). Global forest resources . Belhaven Press.

This diagram is adapted from the work of Hosonuma et al. (2012).

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7 (4), 044009.

Rudel, T. K. (1998). Is there a forest transition? Deforestation, reforestation, and development . Rural Sociology , 63 (4), 533-552.

Rudel, T. K., Coomes, O. T., Moran, E., Achard, F., Angelsen, A., Xu, J., & Lambin, E. (2005). Forest transitions: towards a global understanding of land use change . Global Environmental Change , 15 (1), 23-31.

Cuaresma, J. C., Danylo, O., Fritz, S., McCallum, I., Obersteiner, M., See, L., & Walsh, B. (2017). Economic development and forest cover: evidence from satellite data . Scientific Reports , 7 , 40678.

Noriko Hosonuma et al. (2012) looked at this distribution for low-to-middle-income subtropical countries and also studied the many drivers of forest loss.Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7 (4), 044009.

Pendrill, F., Persson, U. M., Godar, J., & Kastner, T. (2019). Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition . Environmental Research Letters , 14 (5), 055003.

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., ... & Tuanmu, M. N. (2015). Mapping tree density at a global scale . Nature , 525 (7568), 201-205.

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss . Science , 361 (6407), 1108-1111.

Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., ... & Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries . Environmental Research Letters , 7(4), 044009.

Hosonuma et al. (2012) gathered this data from a range of sources, including country submissions as part of their REDD+ readiness activities, Center for International Forestry Research (CIFOR) country profiles, UNFCCC national communications, and scientific studies.

Scheffers, B. R., Joppa, L. N., Pimm, S. L., & Laurance, W. F. (2012). What we know and don’t know about Earth's missing biodiversity . Trends in Ecology & Evolution , 27(9), 501-510.

Maxwell, S. L., Fuller, R. A., Brooks, T. M., & Watson, J. E. (2016). Biodiversity: The ravages of guns, nets, and bulldozers . Nature, 536(7615), 143.

Lewis, S. L. (2006). Tropical forests and the changing earth system . Philosophical Transactions of the Royal Society B: Biological Sciences , 361(1465), 195-210.

Tyukavina, A., Hansen, M. C., Potapov, P. V., Stehman, S. V., Smith-Rodriguez, K., Okpa, C., & Aguilar, R. (2017). Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013 . Science Advances , 3 (4), e1601047.

Lewis, S. L., Edwards, D. P., & Galbraith, D. (2015). Increasing human dominance of tropical forests . Science , 349 (6250), 827-832.

To do this, they quantified where deforestation was occurring due to the expansion of croplands, pasture, and tree plantations (for logging) and what commodities were produced on this converted land. Then, using a physical trade model across 191 countries and around 400 food and forestry products, they could trace them through to where they are physically consumed, either as food or in industrial processes.

Pendrill, F., Persson, U. M., Godar, J., Kastner, T., Moran, D., Schmidt, S., & Wood, R. (2019). Agricultural and forestry trade drives a large share of tropical deforestation emissions . Global Environmental Change , 56 , 1-10.

In 2012 — the mid-year of this period — global emissions from fossil fuels, industry, and land use change was 40.2 billion tonnes. Deforestation was therefore responsible for [2.6 / 40.2 * 100 = 6.5%].

The carbon footprint of diets across the EU varies from country to country, and estimates vary depending on how much land use change is factored into these figures. Notarnicola et al. (2017) estimate that the average EU diet, excluding deforestation, is responsible for 0.5 tonnes of CO 2 per person. If we add 0.3 tonnes to this figure, deforestation would account for around one-sixth [0.3 / (1.5+0.3) * 100 = 17%].

Notarnicola, B., Tassielli, G., Renzulli, P. A., Castellani, V., & Sala, S. (2017). Environmental impacts of food consumption in Europe . Journal of Cleaner Production , 140 , 753-765.

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A Study of Deforestation In India

Deforestation is major threat that affects the environment in the world. The deforestation make so many bad impact on the earth, this may lead to the end of life in earth. There are some causes behind the deforestation. Here in this study it deals with impacts and causes of deforestation.

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Deforestation: Case Studies

Deforestation is putting our planet at risk, as the following case studies exemplify. It is responsible for at least 10 per cent of global greenhouse gas emissions 1 and wipes out 137 species of plants, animals and insects every day 2 . The deplorable practice degenerates soil, losing half of the world’s topsoil over the past 150 years. 3 Deforestation also leads to drought by reducing the amount of water in the atmosphere. 4

Since the 1950s, deforestation has accelerated significantly, particularly in the tropics. 5 This is primarily due to rapid population growth and a resultant increase in demand for food and resources. 6 Agriculture drives about 80 per cent of deforestation today, as land is cleared for livestock, growing animal feed or other crops. 7 The below deforestation case studies of Brazil’s Amazon rainforest and the Congo Basin provide further insights into modern deforestation. 

Deforestation case study: Brazil

Nearly two-thirds of the Amazon rainforest – the largest rainforest in the world – is within Brazil’s national borders. 8 Any examination of deforestation case studies would be incomplete without considering tree felling in Brazil. 

History of deforestation in Brazil

Humans first discovered the Amazon rainforest about 13,000 years ago. But, it was the arrival of Europeans in the late 15th century that spurred the conversion of the forest into farmland. Nevertheless, the sheer size of the Amazon meant that the rainforest remained largely intact until the early 20th century. It was in the latter half of the 20th century that things began to change. 9

Industrial activities and large-scale agriculture began to eat away the southern and eastern fringes of the Amazon, from the 1950s onwards. 10 Deforestation in Brazil received a significant boost in 1964 when a military dictatorship took power and declared the jungle a security risk. 11 By the 1970s, the government was running television ads encouraging land conversion, provoking millions to migrate north into the forest. 12 Settlements replaced trees, and infrastructure began to develop. Wealthy tycoons subsequently bought the land for cattle ranches or vast fields of soy. 13

By the turn of the 21st century, more than 75 per cent of deforestation in the Amazon was for cattle ranching. But, environmentalists and Indigenous groups drew international attention to the devastation caused and succeeded in curtailing it by 2004. Between 2004 and the early 2010s, annual forest cover loss in Brazil reduced by about 80 per cent. The decline is attributed to “increased law enforcement, satellite monitoring, pressure from environmentalists, private and public sector initiatives, new protected areas, and macroeconomic trends”. 14  

Brazil’s deforestation of the Amazon rainforest since 2010

Unfortunately, however, efforts to curtail deforestation in Brazil’s Amazon have stalled since 2012. 15 Tree felling and land conversion have been trending upwards ever since. The economic incentive for chopping the rainforest down has overcome the environmental benefits of leaving it standing. 16 Political movements and lax government legislation have leveraged this to their advantage. President Jair Bolsonaro won the 2018 election with a promise to open up the Amazon to business. 17 Since his inauguration, the rate of deforestation has leapt by as much as 92 per cent. 18

However, there is still hope for the Amazon rainforest. Bolsonaro’s principal international ally was US President Trump. Now that environmentally-conscious Joe Biden has replaced him in the White House, international pressure regarding deforestation will increase heavily. 19 Biden has made this clear with a promise of USD $20 billion to protect the Amazon. 20

The impact of continued deforestation in Brazil

For its three million plant and animal species and one million Indigenous inhabitants, it is imperative that Amazonian deforestation is massively and immediately reduced. 21 As much as 17 per cent of the Amazon has been lost already. 22 If this proportion increases to over 20 per cent, a tipping point will be reached. 23 This will irreversibly break the water cycle, and at least half of the remaining forest will become savannah. 24

Impact on climate change

Losing the Amazon would also mean losing the fight against climate change. Despite the rampant deforestation in recent years, the remaining Amazon rainforest still absorbs between 5 to 10 per cent of all human CO2 emissions. 25 Cutting trees down increases anthropogenic emissions. When felled, burned or left to rot, trees release sequestered carbon. 26 A combination of reducing greenhouse gas emissions and preserving existing forests is crucial to preventing dangerous levels of global warming. 27  

Deforestation case study: The Congo Basin

The Congo Basin is the second-largest rainforest in the world. 28 It has been described as the ‘second lungs’ of the Earth because of how much carbon dioxide it absorbs and how much oxygen it produces. 29 But, just as the world’s first lungs – the Amazon – is being destroyed by humans, the Congo’s rainforest is also suffering heavy casualties. 30

60 per cent of the Congo Basin is located within the Democratic Republic of the Congo (DRC). 31 The DRC is one of the world’s largest and poorest countries, though it has immense economic resources. 32 Natural resources have fuelled an ongoing war that has affected all the neighbouring countries and claimed as many as six million lives. 33 The resultant instability combined with corruption and poor governance have led to an ever-increasing rate of deforestation within the DRC’s borders. 34

Deforestation in the Democratic Republic of the Congo (DRC)

Compared to the Amazon and Southeast Asia, deforestation in the Congo Basin has been low over the past few decades. 35 Nevertheless, great swathes of primary forest have been lost. Between 2000 and 2014, an area of forest larger than Bangladesh was destroyed. 36 From 2015 until 2019, 6.37 million hectares of tree cover was razed. 37 In 2019 alone, 475,000 hectares of primary forest disappeared, placing the DRC second only to Brazil for total deforestation that year. 38 Should the current rate of deforestation continue, all primary forest in the Congo Basin will be gone by the end of the century. 39

Drivers of deforestation in the DRC’s Congo Basin

Over the past 20 years, the biggest drivers of deforestation in the DRC has been small-scale subsistence agriculture. Clearing trees for charcoal and fuelwood, urban expansion and mining have also contributed to deforestation. Industrial logging is the most common cause of forest degradation. It opens up deeper areas of the forest to commercial hunting. There has been at least a 60 per cent drop in the region’s forest elephant populations over the past decade due to hunting and poaching. 40  

Between 2000 and 2014, small-scale farming contributed to about 90 per cent of the DRC’s deforestation. This trend has not changed in recent years. The majority of small-scale forest clearing is conducted with simple axes by people with no other livelihood options. The region’s political instability and ongoing conflict are therefore inciting the unsustainable rate of deforestation within the Congo Basin. 41

In future, however, industrial logging and land conversion to large-scale agriculture will pose the greatest threats to the Congo rainforest. 42 There are fears that demand for palm oil, rubber and sugar production will promote a massive increase in deforestation. 43 The DRC’s population is also predicted to grow to almost 200 million people by 2050. 44 This increase will threaten the remaining rainforest further, as they try to earn a living in a country deprived of opportunities. 45

The impact of deforestation in the Congo Basin

80 million people depend upon the Congo Basin for their existence. It provides food, charcoal, firewood, medicinal plants, and materials for building and other purposes. But, this rainforest also indirectly supports people across the whole of sub-Saharan Africa. Like all forests, it is instrumental in regulating rainfall, which can affect precipitation hundreds of miles away. The Congo Basin is a primary source of rainfall for the Sahel region, doubling the amount of rainfall in the air that passes over it. 46

The importance of the Congo Basin’s ability to increase precipitation cannot be understated. Areas such as the Horn of Africa are becoming increasingly dry. Drought in Ethiopia and Somalia has put millions of people on emergency food and water rations in recent years. Destroying the DRC’s rainforest would create the largest humanitarian crisis on Earth. 47  

It would also be devastating for biodiversity. The Congo Basin shelters some 10,000 animal species and more than 600 tree species. 48 They play a hugely important role in the forest, which has consequences for the entire planet. For instance, elephants, gorillas, and other large herbivores keep the density of small trees very low through predation. 49 This results in a high density of tall trees in the Congo rainforest. 50 Larger trees store more carbon and therefore help to prevent global warming by removing this greenhouse gas from the atmosphere. 51  

Preserve our forests

Preserving the Amazon and Congo Basin rainforests is vital for tackling climate change, as these deforestation case studies demonstrate. We must prioritise protecting and enhancing our existing trees if we are to limit the global temperature increase to 1.5°C, as recommended by the IPCC. 52

• Rainforest Alliance. (2018). What is the Relationship Between Deforestation And Climate Change? [online] Available at: https://www.rainforest-alliance.org/articles/relationship-between-deforestation-climate-change.
• www.worldanimalfoundation.com. (n.d.). Deforestation: Clearing The Path For Wildlife Extinctions. [online] Available at: https://www.worldanimalfoundation.com/advocate/wild-earth/params/post/1278141/deforestation-clearing-the-path-for-wildlife-extinctions#:~:text=Seventy%20percent%20of%20the%20Earth.
• World Wildlife Fund. (2000). Soil Erosion and Degradation | Threats | WWF. [online] Available at: https://www.worldwildlife.org/threats/soil-erosion-and-degradation.
• Butler, R.A. (2001). The impact of deforestation. [online] Mongabay. Available at: https://rainforests.mongabay.com/09-consequences-of-deforestation.html.
• The Classroom | Empowering Students in Their College Journey. (2009). The History of Deforestation. [online] Available at: https://www.theclassroom.com/the-history-of-deforestation-13636286.html.
• Greenpeace USA. (n.d.). Agribusiness & Deforestation. [online] Available at: https://www.greenpeace.org/usa/forests/issues/agribusiness/.
• Yale.edu. (2015). The Amazon Basin Forest | Global Forest Atlas. [online] Available at: https://globalforestatlas.yale.edu/region/amazon.
• Time. (2019). The Amazon Rain Forest Is Nearly Gone. We Went to the Front Lines to See If It Could Be Saved. [online] Available at: https://time.com/amazon-rainforest-disappearing/.
• Butler, R. (2020). Amazon Destruction. [online] Mongabay.com. Available at: https://rainforests.mongabay.com/amazon/amazon_destruction.html.
• the Guardian. (2020). Amazon deforestation surges to 12-year high under Bolsonaro. [online] Available at: https://www.theguardian.com/environment/2020/dec/01/amazon-deforestation-surges-to-12-year-high-under-bolsonaro.
• Earth Innovation Institute. (2020). Joe Biden offers $20 billion to protect Amazon forests. [online] Available at: https://earthinnovation.org/2020/03/joe-biden-offers-20-billion-to-protect-amazon-forests/.
• Brazil’s Amazon: Deforestation “surges to 12-year high.” (2020). BBC News. [online] 30 Nov. Available at: https://www.bbc.co.uk/news/world-latin-america-55130304.
• Carbon Brief. (2020). Guest post: Could climate change and deforestation spark Amazon “dieback”? [online] Available at: https://www.carbonbrief.org/guest-post-could-climate-change-and-deforestation-spark-amazon-dieback.
• Union of Concerned Scientists (2012). Tropical Deforestation and Global Warming | Union of Concerned Scientists. [online] www.ucsusa.org. Available at: https://www.ucsusa.org/resources/tropical-deforestation-and-global-warming#:~:text=When%20trees%20are%20cut%20down.
• Milman, O. (2018). Scientists say halting deforestation “just as urgent” as reducing emissions. [online] the Guardian. Available at: https://www.theguardian.com/environment/2018/oct/04/climate-change-deforestation-global-warming-report.
• Bergen, M. (2019). Congo Basin Deforestation Threatens Food and Water Supplies Throughout Africa. [online] World Resources Institute. Available at: https://www.wri.org/blog/2019/07/congo-basin-deforestation-threatens-food-and-water-supplies-throughout-africa.
• www.esa.int. (n.d.). Earth from Space: “Second lungs of the Earth.” [online] Available at: https://www.esa.int/Applications/Observing_the_Earth/Earth_from_Space_Second_lungs_of_the_Earth [Accessed 26 Feb. 2021].
• Erickson-Davis, M. (2018). Congo Basin rainforest may be gone by 2100, study finds. [online] Mongabay Environmental News. Available at: https://news.mongabay.com/2018/11/congo-basin-rainforest-may-be-gone-by-2100-study-finds/.
• Mongabay Environmental News. (2020). Poor governance fuels “horrible dynamic” of deforestation in DRC. [online] Available at: https://news.mongabay.com/2020/12/poor-governance-fuels-horrible-dynamic-of-deforestation-in-drc/ [Accessed 26 Feb. 2021].
• DR Congo country profile. (2019). BBC News. [online] 10 Jan. Available at: https://www.bbc.co.uk/news/world-africa-13283212.
• Butler, R.A. (2001). Congo Deforestation. [online] Mongabay. Available at: https://rainforests.mongabay.com/congo/deforestation.html.
• Mongabay Environmental News. (2020). Poor governance fuels “horrible dynamic” of deforestation in DRC. [online] Available at: https://news.mongabay.com/2020/12/poor-governance-fuels-horrible-dynamic-of-deforestation-in-drc/.
• Butler, R. (2020). The Congo Rainforest. [online] Mongabay.com. Available at: https://rainforests.mongabay.com/congo/.
• Editor, B.W., Environment (n.d.). Large trees are carbon-storing giants. www.thetimes.co.uk. [online] Available at: https://www.thetimes.co.uk/article/large-trees-are-more-valuable-carbon-stores-than-was-thought-k8hnggzs8#:~:text=The%20world [Accessed 26 Feb. 2021].
• IPCC (2018). Summary for Policymakers — Global Warming of 1.5 oC. [online] Ipcc.ch. Available at: https://www.ipcc.ch/sr15/chapter/spm/.

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Bengaluru: More than five million large-sized farmland trees, used in agroforestry, which are usually grown individually in farms and offer multiple socio-ecological benefits, were felled between 2018 and 2022 in India to make land available for farming, a new study has said. The team of Denmark-based researchers, who evaluated Indian agroforestry practices, also found that around 11 percent of India’s large trees, mapped previously in 2010-2011, had disappeared by 2018.

The study, published this week in the journal Nature Sustainability , noted that areas where most trees had disappeared were in the states of Telangana and Maharashtra, where some hotspots lost nearly half their trees. Smaller hotspot areas were also observed in eastern Madhya Pradesh around Indore, with the highest losses generally occurring in central India.

Researchers used satellite views over the years for their study. High-definition satellite images were obtained from 2010 to 2022, and the crown areas or the canopy size of trees were measured to inform size.

Further analysis revealed that these trees were mature and quite old, and the high loss rate of mature trees in less than a decade is “unexpected”. 

Authors of the study corroborated their findings with detailed interviews with local villagers in Telangana, Andhra Pradesh, Kerala, Maharashtra, Uttar Pradesh, Haryana, and Kashmir. Unanimous responses indicated that giant trees were felled to expand paddy fields to boost crop yields. 

According to researchers, the study “reveals a concerning trajectory, documenting a considerable depletion of large farmland trees over the past decade in parts of India”.

They found the results “particularly unsettling”, in the context of the growing awareness about agroforestry playing a crucial role in climate adaptation and mitigation strategies, including sustaining livelihoods and protection of crops. They also mentioned that their estimation of loss of trees is “conservative” due to the satellite observation methods used. 

The study, however, clarifies that the findings do not contradict official reports stating that India’s tree cover has increased in recent years because large plantations of trees were not considered for the study, especially due to their lack of contribution to biodiversity.

The team also made an interactive tool with satellite imagery available online, showing the outcomes of its research.

Also Read: Making of a geomagnetic storm — ISRO puts out data on solar ejections captured from Earth, Moon & space

Observed patterns in farming practices

While wildfires, fungal infections, droughts and insects tend to cause naturally-occurring tree mortality in forests, human misuse of land resources and anthropogenic climate change have steeply accelerated tree die-offs. However, there has been minimal research yet about die-offs outside forested areas, especially in areas like drylands or farms. 

Agroforestry trees are a crucial part of tree planting and restoration today, which is a land-use practice that incorporates widely beneficial trees in agricultural areas. India has historically adopted a number of traditional agroforestry practices on farms. 

These trees include Azadirachta indica (neem), Cocos nucifera (coconut), Madhuca longifolia (mahua), Artocarpus heterophyllus (jackfruit), Prosopis cineraria (ghaf/khejri/jammi), Acacia nilotica (babul), Dalbergia sissoo (shisham), Syzygium cumini (jamun), Sesbania grandiflora (agati/katurai), Albizia procera (karoi), and more. They provide a number of ecological benefits and services like soil fertilisation, protection of crops, shade, provision of fruits, fibre, medicine, etc. 

Today, 56 percent of India is covered by farmland, the largest agricultural cover in the world, while 20 percent of the country is covered by forest vegetation.

Trees, like the neem and jackfruit, have been traditionally grown on farms, rising up to great heights and living for hundreds of years. Many, in recent years, have succumbed to fungal and other infections, the researchers noted, but such numbers are hard to track as there exists no inventory of large trees in India in non-forested areas. 

For the study, the authors excluded groups and perennial plantations of trees like palm and rubber, which have large closed canopies that are typically classified as tree cover. They used high resolution satellite images for the period of 2010-2011, and then for the years 2018-2022. Comparison of these yielded the observation about the trees that went missing between 2011 and 2018. 

Data showed that 22 million trees from the previous dataset remained, but nearly 2.5 million individual trees disappeared between 2011 and 2018. In total, a loss of about 11 percent of trees was observed. Additionally, large trees in the dataset for 2018-2022 dropped in huge numbers. 

The authors stated that between 2018 and 2022, 5.6 million trees disappeared. Mature trees across most of the country faced a loss of 5-10 percent, except areas in central India. Satellite imagery accompanying the paper shows large losses in parts of Telangana and Maharashtra.

The researchers observed that infections have a smaller role to play in the disappearance of trees, and detailed interviews with villagers from multiple states show that changing agricultural practices led to tree felling to expand paddy fields. This was especially common in areas where new water supply systems were built. 

Climate change caused by humans and physical felling of trees for intensification of agriculture are credited for this massive agroforestry tree loss in India.

The scientists explained that such changes lead to simplification of not just natural, but also cultural landscapes. This, in turn, leads to loss of biodiversity, release of carbon, and disappearance of indigenous knowledge about the ecological working of agroforestry systems that were in use for decades or centuries. 

The paper also notes, “despite receiving limited attention, diverse and complex multifunctional landscapes are still maintained by indigenous peoples and smallholders”, referring to farmers with less than 2 hectares of land, who make up 86 percent of India’s farmers.

(Edited by Mannat Chugh)

Also Read: Astronomy conferences generated 42,000 tonnes CO2 in 2019, virtual or local meets could help cut this

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What EU Deforestation Regulation compliance readiness looks like

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With the EU Deforestation Regulation (EUDR), the European Union (EU) aims to ensure certain products and commodities imported, traded within or exported from the EU have not led to deforestation or forest degradation, and have been produced in compliance with the legislation of the country of origin.

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