Deforestation Hotspots Research Articles

“Late-stage” deforestation enhances storm trends in coastal West Africa

Deforestation affects local and regional hydroclimate through changes in heating and moistening of the atmosphere. In the tropics, deforestation leads to warming, but its impact on rainfall is more complex, as it depends on spatial scale and synoptic forcing. Most studies have focused on Amazonia, highlighting that forest edges locally enhance convective rainfall, whereas rainfall decreases over drier, more extensive, deforested regions. Here, we examine Southern West Africa (SWA), an example of "late-stage" deforestation, ongoing since 1900 within a 300-km coastal belt. From three decades of satellite data, we demonstrate that the upward trend in convective activity is strongly modulated by deforestation patterns. The frequency of afternoon storms is enhanced over and downstream of deforested patches on length scales from 16 to 196 km, with greater increases for larger patches. The results are consistent with the triggering of storms by mesoscale circulations due to landscape heterogeneity. Near the coast, where sea breeze convection dominates the diurnal cycle, storm frequency has doubled in deforested areas, attributable to enhanced land-sea thermal contrast. These areas include fast-growing cities such as Freetown and Monrovia, where enhanced storm frequency coincides with high vulnerability to flash flooding. The proximity of the ocean likely explains why ongoing deforestation across SWA continues to increase storminess, as it favors the impact of mesoscale dynamics over moisture availability. The coastal location of deforestation in SWA is typical of many tropical deforestation hotspots, and the processes highlighted here are likely to be of wider global relevance.

Silvopastoral Systems Enhance Soil Health in the Amazon Region

Silvopastoral systems (SPS), an integrated farming system in which tropical grasses are combined with trees and shrubs, have been implemented in the last years in the Amazon region in order to mitigate the impacts generated by the traditional cattle ranching system. However, despite the multiple SPS’s benefits to soil and ecosystem, there is a paucity of comprehensive studies revealing the potential soil health (SH) restoration through SPS. Here, by developing an overall SH index using local native vegetation (Amazon rainforest) as a reference, we aimed to assess SH changes induced by the land transition from the traditional livestock production system to the SPS in the Colombian Amazon region. A chronosequence conformed by three areas: (i) native vegetation, (ii) traditional pasture and (iii) silvopastoral system was established in two study sites located in the Colombian Amazon, specifically in Caquetá State, the second hotspot of deforestation in the Amazon Basin. The results indicated high soil compaction and loss of macrofauna diversity and richness due to pasture management, causing a loss of 9% of soil capacity to function. In contrast, by integrating 31 soil indicators, our SH assessment revealed that SPS was an effective strategy for the recovery of SH, impacting positively multiple soil functions related to nutrient dynamics, water retention and supply, and biological activity.

Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks

Tropical dry forests harbor major carbon stocks but are disappearing rapidly across the globe as agriculture expands into them. Unfortunately, carbon emissions from deforestation in dry forests remain poorly understood as high spatial-temporal and vertical heterogeneity complicate biomass mapping. Here, we use a novel Gradient Boosted Regression framework to test the relative gains of combining optical (MODIS) and radar (Sentinel 1) time series, as well as lidar-based (GEDI) canopy-height information, to map biomass in tropical dry forests. We apply our approach across the entire Dry Chaco ecoregion (about 800,000 km2), using an extensive ground dataset of forest inventory plots for training and validation, to map above-ground biomass (AGB) for the year 2019. Our best AGB model had an r2 of 0.89 (RMSE = 15.1 t/ha) with an estimated AGB in remaining natural vegetation of 4.65 Gt (+/− 0.9 Gt). Seasonal metrics from EVI time-series, combined with seasonal Sentinel 1 metrics, had the highest predictive power, while adding GEDI-based canopy height did not improve models. Our resulting AGB maps had a much higher level of agreement with independent ground-data than global AGB products (agreements between r2 = 0.07–0.41), which all suffer from a huge, up to 14-fold, underestimation of AGB in the Chaco. Most of the remaining AGB stored in Chaco woodlands is found in Argentina (2.4 Gt AGB), followed by Paraguay (1.13 Gt AGB) and Bolivia (1.11 Gt AGB). Our results also highlight that 71% of the remaining AGB is located outside protected areas, and around half of the remaining AGB occurs on land utilized by traditional communities. Together, our analyses reveal substantial risk of continued high carbon emissions should agricultural expansion progress. Considerable co-benefits appear to exist between protecting traditional livelihoods and carbon stocks. Our map, the most accurate and fine-scale AGB map for this global deforestation hotspot, can serve as a basis for land-use and conservation planning aimed at leveraging such co-benefits. More broadly, our analyses reveal the considerable potential of combining time series of optical and radar data for a more reliable mapping of above-ground biomass in tropical dry forests and savannas.

Spatiotemporal analysis of forest cover change and associated environmental challenges: a case study in the Central Highlands of Vietnam

Spatiotemporal regression combining Theil-Sen median trend and Man-Kendall tests was applied to MODIS time-series data to quantify the trend and rate of change to forest cover in the Central Highlands, Vietnam from 2001 to 2019. Several MODIS data products, including Percent Tree Cover (PTC), Evapotranspiration (ET), Land Surface Temperature (LST), and Gross Primary Productivity (GPP) were selected as indicators for forest cover and climate and carbon cycle patterns. Emerging hot spot analysis was applied to identify patterns of long-term deforestation. Spatial regression analysis using Geographically Weighted Regression (GWR) was performed to understand variations in the relationship between vegetation changes and trends in LST, ET, and GPP. Our analysis reveals that deforestation occurred significantly in the study area with a total decrease of 14.5% in PTC and a total of 7314 deforestation hot spots were identified. Results indicate that forest cover loss explains 72.9%, 67.7%, and 89.4% of the changes in ET, GPP, and LST, respectively, and the levels of influence are heterogenous across space and dependent on the types of deforestation hot spots. The approach introduced in our study can be performed worldwide to address complex research questions about environmental challenges that emerge from deforestation.

Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning

Compared with disturbance maps produced at annual or multi-year time steps, monthly mapping of forest harvesting can provide more temporal details needed for studying the socio-economic drivers (e.g., differentiating salvage logging and slash-and-burn from other timber harvesting) of harvesting and characterizing the associated intra-annual carbon and hydrological dynamics. Frequent cloud cover limits the application of optical remote sensing in timely mapping of forest changes. The freely available Sentinel-1 synthetic aperture radar (SAR) sensor provides an unprecedented opportunity to achieve more frequent mapping of forest harvesting than ever before (i.e., at monthly interval). The unique landscape pattern of forest harvesting from Sentienl-1 data (i.e., how a harvested patch contrasts to surrounding intact forests) holds critical information for harvesting mapping but have not been fully explored. In this study, we propose a deep learning-based (i.e., U-Net) approach using the landscape pattern from Sentinel-1 data to produce monthly maps of forest harvesting in two deforestation hotspots - California, USA and Rondônia, Brazil – for as long as three years. Our results show that (1) our proposed approach is reliable (mean F1 scores (the geometric mean of user's and producer's accuracies) 0.74–0.78; mean IoU (the area of intersection over union between the prediction part and target part) 0.59–0.65) for monthly forest harvesting mapping with Sentinel-1 data, outperforming the traditional object-based approach (0.38–0.43 in IoU). The varying harvesting pattern from Sentinel-1 data can be recognized by the U-Net bottleneck block as whole entities, which is the key advantage of our proposed approach; (2) multi-temporal SAR filtering is helpful for improving the accuracies of our proposed approach (increased F1 and IoU for 0.04 and 0.06, respectively); (3) our proposed model can be trained using samples collected during a particular time period over one location and be fine-tuned using sparse local samples from a new area to achieve optimal performance, and hence can greatly reduce training data collection effort when applied to new study sites; (4) forest harvesting maps produced using our approach revealed substantial variations in monthly harvesting activities: in Rondônia, most of the forest harvest occurred in July/August (the dry season) and about 14% of the dry season harvesting were followed by fires (i.e., slash-and-burn); in California, the rates of forest harvesting were relatively stable, but abnormally high values could occur due to salvage logging after big fires. Our novel approach for mapping forest harvesting at monthly interval represents an important step towards timely monitoring of forest harvesting and assisting stakeholders in developing sustainable strategy of forest management, especially for regions with frequent cloud cover.

Adoption of sustainable silvopastoral practices in Argentina's Gran Chaco: A multilevel approach

The Gran Chaco is currently a global hotspot of deforestation and environmental degradation. Halting global warming and curbing biodiversity losses are urgent priorities and recent research suggests that sustainable smallholder production systems could contribute to maintain and restore key ecosystem services. This article examines the adoption of two silvopastoral practices in Argentina's Gran Chaco. We conducted a survey of 552 smallholders in three municipalities in the province of Salta. With the resulting data, we used multilevel models to assess adoption of the two practices. Our multilevel models indicate several factors that are associated with adoption, including: gender of the household head, year of establishment, literacy of the household head, membership in a producer organization, and socio-economic status. Our results suggest that paraje – groups of neighbouring households or joint settlements – are a good predictor of adoption, whereas nearby villages are only marginally associated with adoption. We conclude by highlighting the importance of accounting for local structures and groups of households in rural studies.

Dynamic Amazonia: The EU–Mercosur Trade Agreement and Deforestation

The trade agreement between the European Union and the Mercosur countries will increase deforestation in the Mercosur countries and Brazil, in particular, if ratified by member countries. We use a computable general equilibrium model to analyze how trade, land use, and agricultural production will change as a result of the agreement. We then use a statistical model to spatially allocate the predicted deforestation within the Brazilian Amazon. The models estimate that the agreement will cause additional deforestation in Brazil ranging from 56 to 173 thousand ha to accommodate increases in cropland area, depending on the level of governance, use of double-cropping techniques, and trade elasticity parameters. Most additional deforestation in Amazonia would be clustered near current deforestation hotspot areas. Some hotspots threaten the integrity of Indigenous lands and conservation units. Although a low deforestation scenario with gains in welfare is theoretically possible when high governance and multiple-cropping systems are in place, political challenges remain and cast doubt on Brazil’s ability to rein on illegal deforestation.

Land Use and Land Cover Changes in the Diversity and Life Zone for Uncontacted Indigenous People: Deforestation Hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon

Land use and land cover change (LULC) is an essential component for the monitoring environmental change and managing natural resources in areas of high natural and cultural biodiversity, such as the Amazon biome. This study was conducted in in the northern Amazon of Ecuador, specifically in the Diversity and Life Zone (DLZ) of the Yasuní Biosphere Reserve (YBR). The general aim was to investigate the territorial dynamics of land use/land cover changes to support policies for environmental and sociocultural protection in the DLZ. Specific objectives included (i) mapping LULC spatial and temporal dynamics in the DLZ in the period from 1999 to 2018, (ii) identifying sensitive LULC hotspots within the DLZ, and (iii) defining the possible policy implications for sustainable land use in the DLZ. Multitemporal satellite imagery from the Landsat series was used to map changes in LULC, which were divided into three-time stages (1999–2009, 2009–2018, 1999–2018). We adopted open-access Landsat images downloaded from the United States Geological Survey (USGS). The processes for assessing LULC in the DLZ included (1) data collection and analysis, (2) data processing for remote sensing, (3) thematic land cover, and (4) homogenization and vectorization of images. The results showed that in the period 1999–2018, most of the uses and land cover were transformed into pastures in the DLZ. Therefore, it is important to improve territorial planning, to avoid conflicts between indigenous populations, migrant settlers, and uncontacted indigenous populations that live in the DLZ, within the YBR.

Chaco region: Forest loss and fragmentation in the context of the territorial planning law. Remote sensing assessment in Formosa, Argentina application case

Agricultural expansion is the primary cause of forest loss and fragmentation. It threatens the conservation of its biodiversity as well as the capability to provide ecosystem services. Land-use policies, such as zonation programs, have been traditionally used as a tool for promoting a sustainable natural resource management; however, we still lack standardized methodologies that can be applied world-widely to achieve this purpose. In the current context of rampant deforestation over the tropical forests, there is an urgent need of identifying policies that steer agricultural land-use change into a reduced pressure on forests. This study focuses on the outcomes of the first territorial planning law in the Province of Formosa (Argentina) located within the Chaco region, one of the world’s deforestation hotspots. The research questions were: a) How did agriculture expand in Formosa before, during and after the enactment of the territorial planning law? b) Did the introduction of the law affect the spatial distribution of land-use change?; and c) How did the sanction of the law affect forest loss and forest fragmentation? Landsat imagery was used to map land-use change, and to calculate the cover loss and cover loss rate considering the zoning and physiognomic classification of the law. The forest fragmentation was evaluated in terms of forest loss spatial configuration, classified as perforation or shrinkage, forest edge generation, patch size distribution, and patch isolation. The territorial planning law effect over agricultural expansion was tested using a difference in difference model. After the law was passed, a reduced land-use pressure was observed for the forest within the conservation designated zone; however, the forest presented the highest cover loss rates among the physiognomic categories of the law. Land-use change within the conservation designated zone was predominantly made according to a perforation spatial configuration, which promoted the forest edge generation. The isolation between forest patches decreased and its size distribution changed towards a less large-patch-centered pattern, indicating that Formosa is experiencing an early fragmentation process. Overall, the territorial planning law in Formosa succeeded in the relief of land-use pressure on forest, but highlighted the need of incorporating spatial configuration guidelines for long-term forest conservation. The case of Formosa case could be useful in the design of future sustainable natural resource management policies and implies the importance of early natural resource planning.

Susceptibility of deforestation hotspots in Terai-Dooars belt of Himalayan Foothills: A comparative analysis of VIKOR and TOPSIS models

Once, the entire Terai-Dooars region of Himalayan Foothills was enveloped by the tropical and subtropical moist deciduous and broadleaf forest. Multiple anthropogenic activities are responsible for large scale deforestation, forest habitat fragmentation and biodiversity loss. The main objective of this current research is to identify the deforestation probable zones applying various multi-criteria decision analysis methods (AHP, TOPSIS and VIKOR). The main advantages of multi-criteria decision making methods are to determine and consider the suitable criteria by expert’s opinion. The advantages of AHP are subjectivity reduction, universality and inconsistency in data verification. Merits of VIKOR and TOPSIS models are rationality, simplicity, better computational proficiency and high efficiency to measure the performance of every alternative in a simple mathematical way. The result shows that the upper Terai-Dooars region has low deforestation probability while the lower part has high deforestation probability due to huge anthropogenic stress. It has been observed that the VIKOR model is the most reliable model with 73% accuracy rate. These MCDA methods (AHP, TOPSIS and VIKOR) have high flexibility and simplicity to modify the criteria and rank. They can provide logical and realistic results for solution of various problems as well as resource management.

Agricultural expansion and the ecological marginalization of forest-dependent people

Agricultural expansion into subtropical and tropical forests causes major environmental damage, but its wider social impacts often remain hidden. Forest-dependent smallholders are particularly strongly impacted, as they crucially rely on forest resources, are typically poor, and often lack institutional support. Our goal was to assess forest-smallholder dynamics in relation to expanding commodity agriculture. Using high-resolution satellite images across the entire South American Gran Chaco, a global deforestation hotspot, we digitize individual forest-smallholder homesteads (n = 23,954) and track their dynamics between 1985 and 2015. Using a Bayesian model, we estimate 28,125 homesteads in 1985 and show that forest smallholders occupy much larger forest areas (>45% of all Chaco forests) than commonly appreciated and increasingly come into conflict with expanding commodity agriculture (18% of homesteads disappeared; n = 5,053). Importantly, we demonstrate an increasing ecological marginalization of forest smallholders, including a substantial forest resource base loss in all Chaco countries and an increasing confinement to drier regions (Argentina and Bolivia) and less accessible regions (Bolivia). Our transferable and scalable methodology puts forest smallholders on the map and can help to uncover the land-use conflicts at play in many deforestation frontiers across the globe. Such knowledge is essential to inform policies aimed at sustainable land use and supply chains.

Roads and land tenure mediate the effects of precipitation on forest cover change in the Argentine Dry Chaco

Dry forests are among the most threatened ecosystems globally, due to agricultural expansion driven by the increasing demand for food, fibers, and energy in developed and emerging countries. Among these, the forests of the South American Gran Chaco are one of the global deforestation hotspots. The Argentine Dry Chaco has been the focus of several studies that assess the factors that drive forest conversion. However, these studies do not describe the causal relationships among these drivers and seldom use existing theory to select drivers. Here we employ a theory-driven approach to test the relative merits of alternative and complementary hypotheses to explain the drivers and mechanisms explaining the unequal spatial distribution of forest loss and maintenance in the Argentine Dry Chaco from 2000 to 2010. Using structural equation modeling, we quantified the direct and indirect effects of multiple drivers and compared the explanatory power and parsimony of these alternative hypotheses, i.e. the biophysical, infrastructure, socio-demographic, institutional, and the integration of them. For both forest loss and maintenance, the model containing infrastructural drivers had the best balance between parsimony and explanatory power. Integrated models, comprising a combination of drivers, had the highest explanatory power (R2 = 0.81 for forest maintenance, and R2 = 0.58 for forest loss). We show that biophysical constraints operate directly and indirectly: soil suitability had direct effects on forest cover maintenance, while precipitation affected it both directly and indirectly through influencing the institutional (land tenure) and infrastructure (road density). Indigenous communities positively affected forest maintenance both directly and indirectly mediated by non-private land tenure. Our results suggest that disentangling the structure of the relationships among drivers could increase our capacity for understanding and steering land-use change. Furthermore, policies for halting deforestation might increase their effectiveness by accounting for the mechanisms that underlie forest loss and maintenance.

Institutional arrangements and collective action: evidence from forest management in Zimbabwe

ABSTRACT This study examines the emergence and development of collective action and institutional arrangements in forest communities in Zimbabwe. The research adopted a qualitative approach using 87 key informant interviews and 1054 questionnaires administered to randomly selected households from deforestation hotspots in five districts. Based on a theoretical concept that collective action depends on resource system characteristics, actor networks, institutional arrangements and the external environment, the study explored patterns in the emergence of collective action. The study further explored actor networks to illustrate applicability of Social Network Analysis (SNA) as a proxy of collective action and institutional arrangements. Results suggest that actor networks reflect the existing strength and intensity of collective action and institutional arrangements. Forest tenure has a bearing on the intensity of collective action. Forest resources under state management, though more productive in terms of goods and services than those under communal management, have a low capacity for collective action and the emergence of new institutions. Results suggest that a high diversity of actors relates to a high intensity of collective action and an emergence of new institutional arrangements. These findings have a bearing on policy and forest management strategies for forest resources under community and state proprietorship.

Fading opportunities for mitigating agriculture-environment trade-offs in a south American deforestation hotspot

Strong trade-offs between agriculture and the environment occur in deforestation frontiers, particularly in the world's rapidly disappearing tropical and subtropical dry forests. Pathways to mitigate these trade-offs are often unclear, as well as how deforestation or different policies alter the option space of available pathways. Using a spatial optimization framework based on linear programming, we develop a landscape-scale possibility frontier describing trade-offs between agricultural profit, biodiversity, and carbon stock for the Argentinean Dry Chaco, a global deforestation hotspot. We use this framework to assess how current land-use zoning, as well as past and future land-use-trajectories, alter the option space to minimize trade-offs between biodiversity, carbon, and agriculture. Our analyses yield four major insights. First, we found substantial co-benefits between biodiversity and carbon, yet strong trade-offs of both with agriculture. Second, development according to the current zoning could lead to highly suboptimal socio-ecological outcomes; our analysis pinpoints how this zoning could be improved. Third, high landscape-scale multifunctionality can be achieved using different land-use strategies, but maintaining >40% of forest is essential in all of them, and silvopasture systems appear to be central for achieving high overall multifunctionality. Finally, our results suggest the window of opportunity is closing rapidly: recent land-use changes since 2000 have rapidly moved the Chaco within the option space, with forest extent declining towards critical thresholds for maintaining balanced, multifunctional landscapes. Our results emphasize that the time for sustainability planning in the Chaco is now. More broadly, we show how multi-criteria optimization can describe dynamic trade-offs between agriculture and the environment at landscape and regional scales. This can help to identify land-system tipping points that, once crossed, would inhibit more sustainable futures, and policies to avoid such potential traps.

Collaborative Governance Networks: A Case Study of Argentina’s Forest Law

Deforestation causes biodiversity loss and the eviction of small-scale ranchers and indigenous people. Accordingly, it is a global issue in environmental politics. This article analyzes a participatory governance system associated with the implementation of Argentina’s forest law in a hotspot of deforestation: the province of Salta in the Gran Chaco ecoregion. Specifically, this article investigates policy actors’ core beliefs, how they match with policy network clusters, and how this affects the implementation of the forest law. The study is based on a unique data set derived from extensive fieldwork and a network survey among all actors who participate in the policy forums. After defining three main core beliefs that describe policy actors’ motivations, we systematically analyze all key actors’ beliefs as well as their interactions in the various policy networks. This analysis shows that it is necessary to empirically identify coalitions based on both behavior and core beliefs to understand the limited implementation of the law. Our methodological approach holds promise for the analysis of other governance systems where multiple stakeholders engage in consensus-oriented decision-making.

Heterogeneity in Preferences for Nonfinancial Incentives to Engage Landholders in Native Vegetation Management

Most of Australia's native forest vegetation is located on private land, and conservation success often depends on landholders' participation in bush management programs. To understand landholders' preferences for these programs'attributes, we surveyed 251 landholders within historical deforestation hot spots across Queensland, Australia. Landholders were asked to make pairwise comparisons of 10 nonfinancial incentives and one financial compensation scheme. Based on a latent class analysis, we identify three distinct landholder classes. We discuss the implication of our results for the future design of native vegetation management and conservation policy instruments.

Intra-Annual Identification of Local Deforestation Hotspots in the Philippines Using Earth Observation Products

Like many other tropical countries, the Philippines has suffered from decades of deforestation and forest degradation during and even after the logging era. Several open access Earth Observation (EO) products are increasingly being used for deforestation analysis in support of national and international initiatives and policymaking on forest conservation and management. Using a combination of annual forest loss and near-real time forest disturbance products, we provide a comprehensive analysis of the deforestation events in three forest frontiers of the Philippines. A space-time pattern mining approach was used to map quarterly deforestation hotspots at 1 km pixel size (100 hectares), where hotspots are classified according to the spatial and temporal variability of the 2000–2020 deforestation in the study area. Our results revealed that 79–81% of the hotspots overlap with primary forests and 27–29% are inside the state-declared protected areas. The intra-annual analysis of deforestation in 2020 revealed an alarming trend, where most deforestation occurred between the 1st and 2nd quarter (92–94% in hotspot forests; 87–89% in non-hotspot forests), highly overlapping within the slash-and-burn farming season. We also found “new” hotspots (2020) formed mostly from landslide scars and partly from selective logging, the latter is believed to be underestimated. Our study paves the way for rapid and regular assessment of the country’s deforestation, useful for the respective environmental institutions who convene several times a year. Moreover, our findings assert the imperative of alternative livelihoods to upland farmers, efficient forest protection activities, and even the mitigation of landslide risks.

The impact of strictly protected areas in a deforestation hotspot

AbstractProtected areas are often thought of as a key conservation strategy for avoiding deforestation and retaining biodiversity; therefore, it is crucial to know how effective they are at achieving this purpose. Using a case study from Queensland, Australia, we identified and controlled for bias in allocating strictly protected areas (IUCN Class I and II) and evaluated their impact (in terms of avoiding deforestation) using statistical matching methods. Over the 30 years between 1988 and 2018, approximately 70,481 km2 of native forest was cleared in the study region. Using statistical matching, we estimated that 10.5% (1,447 km2) of Category I and II (strict) protected areas would have been cleared in the absence of protection. Put differently, 89.5% of strictly protected areas are unlikely to have been cleared, even if they were never protected. While previous studies have used statistical matching at a country or state level, we conducted an analysis that allows regional comparison across a single State. Our research indicates that strictly protected areas are marginally effective at preventing deforestation, and this likely due to biases in establishing protected areas on unproductive land.

Assessing the tropical forest cover change in northern parts of Sonitpur and Udalguri District of Assam, India

Sonitpur and Udalguri district of Assam possess rich tropical forests with equally important faunal species. The Nameri National Park, Sonai-Rupai Wildlife Sanctuary, and other Reserved Forests are areas of attraction for tourists and wildlife lovers. However, these protected areas are reportedly facing the problem of encroachment and large-scale deforestation. Therefore, this study attempts to estimate the forest cover change in the area through integrating the remotely sensed data of 1990, 2000, 2010, and 2020 with the Geographic Information System. The Maximum Likelihood algorithm-based supervised classification shows acceptable agreement between the classified image and the ground truth data with an overall accuracy of about 96% and a Kappa coefficient of 0.95. The results reveal a forest cover loss of 7.47% from 1990 to 2000 and 7.11% from 2000 to 2010. However, there was a slight gain of 2.34% in forest cover from 2010 to 2020. The net change of forest to non-forest was 195.17 km2 in the last forty years. The forest transition map shows a declining trend of forest remained forest till 2010 and a slight increase after that. There was a considerable decline in the forest to non-forest (11.94% to 3.50%) from 2000–2010 to 2010–2020. Further, a perceptible gain was also observed in the non-forest to the forest during the last four decades. The overlay analysis of forest cover maps show an area of 460.76 km2 (28.89%) as forest (unchanged), 764.21 km2 (47.91%) as non-forest (unchanged), 282.67 km2 (17.72%) as deforestation and 87.50 km2 (5.48%) as afforestation. The study found hotspots of deforestation in the closest areas of National Park, Wildlife Sanctuary, and Reserved Forests due to encroachments for human habitation, agriculture, and timber/fuelwood extractions. Therefore, the study suggests an early declaration of these protected areas as Eco-Sensitive Zone to control the increasing trends of deforestation.

Spatial-temporal variations in deforestation hotspots in Sumatra and Kalimantan from 2001-2018.

Tropical deforestation varies temporally and spatially which can inhibit the ability of existing protected areas to stem forest loss. Identifying the spatial–temporal distribution of deforestation and its concentration can help decision makers decide conservation priorities and leverage limited resources. This study assessed how topographic and anthropogenic variables affect deforestation patterns within and outside protected areas on the islands of Sumatra and Kalimantan in Indonesia. Emerging hotspot analysis (EHA) was used to evaluate spatial and temporal trends of forest loss on the Hansen annual forest loss data for these islands from 2001–2018. For the two islands, most hotspots were detected outside protected areas; those within protected areas were mainly concentrated at boundaries, where lower elevation/slope and high human pressure could be observed. New hotspots were identified within the three PAs in Sumatra, while three kinds of hotspots (consecutive, oscillating, and sporadic) were found in the two PAs of Kalimantan (Kutai and Teluk Kelumpang). Areas with high human pressure (average human footprint higher than 12) were covered by a high density of hotspots. The results identify specific areas where forest loss has emerged recently, which could indicate a conservation priority. It is suggested that new protected areas be established in locations showing intensifying and persistent hotspots—those where deforestation has occurred for ≥16 of 18 years of the study period.