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This study uses GIS to assess the extent of deforestation, forest degradation, and carbon stock in East Java, Indonesia. The dataset for this study required preprocessing and proper GIS analysis tools to track and quantify land cover changes, specifically deforestation and forest degradation from 1990 to 2022. The results show that East Java has experienced significant deforestation and forest degradation, with a total deforested area of approximately 180,000 hectares and a degraded forest area of around 270,000 hectares. Among the subregions, Malang had the highest deforestation rate, with a total area of 42,605 hectares, while Banyuwangi had the highest forest degradation, with around 64,782 hectares affected. Primary mangrove forest in East Java has decreased from approximately 15,000 hectares in 1990 to around 9,000 hectares in 2022. Furthermore, the study estimated the carbon stock changes over the study period. The total carbon stock in 1990 was around 196 million tonnes, but by 2022 it had decreased to approximately 156 million tonnes. The difference in carbon stock between 1990 and 2022 is approximately 40 million tonnes of carbon. Analyzing carbon stock with remote sensing and GIS will give faster results than the manual approach. These findings highlight the urgent need for effective forest management and conservation measures in East Java to mitigate further environmental degradation and carbon emissions. Integrating GIS-based forest monitoring techniques provides a valuable tool for assessing land cover changes and their impact on carbon stock, enabling informed decision-making for sustainable land use planning and conservation strategies. [ABSTRACT FROM AUTHOR]

Armed conflicts can lead to environmental degradation, thereby threatening the basis of people's livelihoods and wellbeing. Identifying areas where conflicts drive environmental degradation is important for designing effective recovery strategies, but this is inherently challenging in insecure contexts. We use a case study in Tigray, Ethiopia to illustrate how open-source satellite data can be used to support the identification of woody vegetation loss during armed conflicts in situations where ground-based assessments are difficult or impossible. Areas of potential woody vegetation loss extend across 930 km2 (approximately 4% of the area occupied by forest and other woody vegetation in Tigray) and appear to be concentrated mostly along major roads; however, vegetation recovery has continued during the war across a significantly larger area (approximately 2600 km2). Spatial patterns of woody vegetation loss appear to be unrelated to drought conditions and large-scale wildfires. Based on these observations and anecdotal evidence of deforestation, we propose that it may be conflict-driven deforestation, caused by increases in fuel wood demands, that are driving the woody vegetation losses in some areas of Tigray. Eventual recovery efforts will have to consider the loss in landscape health during the war in areas where woody vegetation has declined, and include efforts to restore this vegetation to ensure both food security and livelihoods. Open access satellite data, together with ground-based data collection, could inform such post-war restoration efforts by helping identify degraded areas at a regional scale. [ABSTRACT FROM AUTHOR]

Deforestation in the form of forest land use change (FLUC) increases the emission of greenhouse gases, disrupts the water cycle, dries the soil, and reduces the growth of plant products. This has a direct effect on the well-being of local communities whose livelihoods depend on the forest and threatens biodiversity. The systematic review aimed to analyze the studies conducted on the effects of FLUC on biodiversity ecosystem services (BECS) and human well-being (HWB) of local communities. The study utilized a qualitative content analysis (QCA) based on a deductive approach, which reviewed 114 scientific documents, particularly research articles, selected by searching keywords through a purposeful sampling method. The FLUC indicators in the two groups of dominant morphology (intensity, scale, pattern, and usage) and recessive morphology (function, property rights, and management mode) had 172 repetitions in the articles. Moreover, the BECS criteria (regulating, provisioning, supportive, and cultural services) and HWB (items related to Maslow's hierarchy of needs, subjective well-being, objective well-being, and preferences) had 125 and 148 repetitions, respectively. Results confirm the relationship and effects of FLUC on BECS and HWB, which emphasizes the mutual role of these variables in social, economic, and environmental studies in future research programs. An increase in FLUC can decline the performance and structure of BECS and have a negative impact on the HWB of those communities who depend on forest. Findings are presented in the form of a model that provides a comprehensive understanding of the relationships between FLUC, BECS, and HWB for relevant decision makers.

Armed conflicts can lead to environmental degradation, thereby threatening the basis of people’s livelihoods and well-being. Identifying areas where conflicts drive environmental degradation is important for designing effective recovery strategies, but this is inherently challenging in insecure contexts. We use a case study in Tigray, Ethiopia to illustrate how open-source satellite data can be used to support the identification of woody vegetation loss during armed conflicts in situations where ground-based assessments are difficult or impossible. Areas of potential woody vegetation loss extend across 930 km 2 (approximately 4% of the area occupied by forest and other woody vegetation in Tigray) and appear to be concentrated mostly along major roads; however, vegetation recovery has continued during the war across a significantly larger area (approximately 2600 km 2 ). Spatial patterns of woody vegetation loss appear to be unrelated to drought conditions and large-scale wildfires. Based on these observations and anecdotal evidence of deforestation, we propose that it may be conflict-driven deforestation, caused by increases in fuel wood demands, that are driving the woody vegetation losses in some areas of Tigray. Eventual recovery efforts will have to consider the loss in landscape health during the war in areas where woody vegetation has declined, and include efforts to restore this vegetation to ensure both food security and livelihoods. Open access satellite data, together with ground-based data collection, could inform such post-war restoration efforts by helping identify degraded areas at a regional scale.

Agricultural expansion is the main driver of deforestation in Myanmar. We analyzed the effectiveness of a national policy intervention on agricultural encroachment in state forests in Taungoo District in Myanmar from 2010 to 2020. The policy aims to stop agricultural encroachment and reforest encroached areas through farmers' participation in an agroforestry community forestry. We applied an integrated approach that involved a land cover change analysis together with a household survey about encroachment behavior. The remote sensing analysis for the years 2010, 2015 and 2020 showed the land cover change pattern and an increase in agricultural encroachment from 9.5% to 18.5%, while forests declined from 62.8% to 51.9%. The survey showed that most farmers (91%) believed that the policy intervention did not lead to a change in their encroachment behavior or farm size. The main reasons that incentivized encroachment were stated to be livelihood needs, immigration due to marriage and increased accessibility due to road construction. The main reason for reducing encroachment was plantation establishment, leading to a loss of land for encroaching farmers. In conclusion, the integrated approach showed that the policy intervention did not decrease encroachment, whereas other factors influenced encroachment behavior. We recommend solving interministerial conflicts of interest related to encroachment in Myanmar and using an integrated approach for future studies. [ABSTRACT FROM AUTHOR]

Less than half of the original two million km2 of the Brazilian Savanna natural vegetation, called the Cerrado Biome, remains standing. Given its climate and socio-biodiversity importance, more effective public policies are needed to protect the remaining natural areas. In this paper, we present the methodology and results of the DETER Cerrado, an early warning deforestation system within the Cerrado region. The findings support that DETER is effective in detecting a wider range of deforestation patch sizes, from the larger patches, heavily associated with agricultural expansion, to the smaller areas (>1 ha <10 ha). Nevertheless, 80% of the deforestation is concentrated in the 10-km radius zone from the DETER Alerts. This area was later detected by the Cerrado Deforestation Monitoring Project (PRODES), the system that accounts for the annual deforestation rate assessment, which highlights the capability of the DETER system to provide support to the surveillance of deforestation in the Cerrado. [ABSTRACT FROM AUTHOR]

Deforestation is a global issue; however, each deforestation phenomenon occurs within its own local context. Elucidating this context in detail is important to prevent deforestation and maintain sustainable ecosystem management. In this study, we analyzed the land cover changes, forest characteristics, and modeled the forest decline over the last two decades to reveal the pattern and affecting factors of deforestation in the Honam-Jeongmaek mountain range. Forests less than 50-years-old dominate the study area, indicating they were mainly regenerated after the 1970s. Reforestation policies such as planting trees have helped forest regeneration. In the study region, as deforestation occurred, agricultural and residential areas decreased, and barren and grassland increased. We applied the Weibull regression model to determine forest survivorship and covariates. The deforestation risks are significantly different among regions; protected areas lose less forest than non-protected areas but the losses in protected areas were also significant, with approximately 5% from 2000 to 2020. Areas of higher elevation and steep slopes experience less deforestation, whereas areas closer to the mountain ridge are at greater risk. With survival analysis, it is possible to assess the risk of deforestation quantitatively and predict long-term survival of forests. The findings and methods of this study could contribute to better forest management and policymaking.

Colombia is highly vulnerable to climate change which may be intensified due to the climatic effects of regional deforestation. Here, we quantify the impact of historical (1900–2011) land cover changes (LCC) and of global warming during ENSO events (CC) on precipitation, temperature and surface energy balance components by running the Weather Research and Forecasting model WRF v3.9 at 10 km resolution. We find that historical anthropogenic CC causes a mean temperature increase of 0.77 ± 0.02 °C in Colombia, which is more pronounced in high altitudes. Precipitation is enhanced by 0.98 ± 0.30 mm/day (+ 9%), particularly over forested areas and reduced at the Pacific coast. LCC imply a reduction of precipitation particularly above the Andes (− 0.48 ± 0.10 mm/day) and Caribbean Coast (− 0.67 ± 0.12 mm/day), where LCC effects dampen CC effects by 24% and 72%, respectively. La Niña tends to intensify LCC and CC effects in the Andes but dampens them at the Coast, roughly by a factor of two compared to El Niño impacts in both regions. At the subregional level, LCC and CC can have impacts of similar magnitude on precipitation highlighting the need to precisely account for both drivers in hydroclimatic assessments. Contrary to almost all observations and similar simulations with climate models, WRF simulates a cooling bias after historical deforestation in Colombia, even with alternative WRF land surface models. We identify two main sources of biases in the default WRF parametrization to explain this inaccuracy: (1) surface shortwave radiation reflected after deforestation is overestimated; (2) associated evapotranspiration loss is underestimated. Improved model representation and validation of tropical vegetation properties are necessary to provide more robust and confident projections. [ABSTRACT FROM AUTHOR]

Land conversion is a change in the function of a part or whole land area of its original function (as planned) into another function that becomes a negative impact (problem) on the environment and potential land itself. The study aims to analyze changes in land cover over five years with the image of Landsats 8 of 2016 and 2021 as well as analyze the factors of the cause of changes in land cover in the Limited Production Forest Area in Kulawi Subdistrict. The study used guided image analysis (supervised) and binary logistics regression analysis. The formation of spatial models of forest cover changes in the Kulawi Subdistrict Limited Production Forest Area using 5 modifier factors consisting of accessibility aspects i.e., settlement, population density, river, slope, road. Logit (p)=-0.24087+0.04249(x1)+0.2815(x2)-0.42492(x3)-0,.0480(x4)+0.04010(x5). The results of binary logistic regression analysis showed goodness of fit value of 5471996.86 chi-square values of 60287.17 and pseudo r2 of 0.30 greater than 0.2, indicating the model is worth using. Based on the results of binary logistics regression analysis, it is known that distance from roads, distance from settlement and population density are influential to changes in forest land cover.

Agricultural expansion is the main driver of deforestation in Myanmar. We analyzed the effectiveness of a national policy intervention on agricultural encroachment in state forests in Taungoo District in Myanmar from 2010 to 2020. The policy aims to stop agricultural encroachment and reforest encroached areas through farmers’ participation in an agroforestry community forestry. We applied an integrated approach that involved a land cover change analysis together with a household survey about encroachment behavior. The remote sensing analysis for the years 2010, 2015 and 2020 showed the land cover change pattern and an increase in agricultural encroachment from 9.5% to 18.5%, while forests declined from 62.8% to 51.9%. The survey showed that most farmers (91%) believed that the policy intervention did not lead to a change in their encroachment behavior or farm size. The main reasons that incentivized encroachment were stated to be livelihood needs, immigration due to marriage and increased accessibility due to road construction. The main reason for reducing encroachment was plantation establishment, leading to a loss of land for encroaching farmers. In conclusion, the integrated approach showed that the policy intervention did not decrease encroachment, whereas other factors influenced encroachment behavior. We recommend solving interministerial conflicts of interest related to encroachment in Myanmar and using an integrated approach for future studies.

The fusion of optical imagery with radar data can provide more accurate land cover change analysis of deforestation and tree-based agriculture. Radar data is limited temporally with most geographic areas not covered prior to 2007. This paper presents a new methodology to classify land cover change related to oil palm expansion that takes historic data limitations into account. Our approach utilizes Hansen's Global Forest Cover data, optical imagery, and texture information, to extract land cover information in Sumatra and Western Malaysia, where historical data is absent. Our method demonstrates how to accurately classify oil palm without radar data with overall accuracies for optical only experiments within 4.4% of optical plus radar classifications. Our results show agricultural land use was the primary driver of land cover change with the largest increase due to oil palm expansion (6.1%). Better estimations of oil palm expansion could be used in sustainable land management policies. [ABSTRACT FROM AUTHOR]

Tropical deforestation in the African continent plays a key role in the global carbon cycle and bears significant implications in terms of climate change and sustainable development. Especially in Sub-Saharan Africa, where more than two-thirds of the population rely on forest and woodland resources for their livelihoods, deforestation and land use changes for crop production lead to a substantial loss of ecosystem-level carbon stock. Unfortunately, the impacts of deforestation and land use change can be more critical than in any other region, but these are poorly quantified. We analyse changes in the main carbon pools (above- and below-ground, soil and litter, respectively) after deforestation and land use/land cover change, for the Jomoro District (Ghana), by assessing the initial reference level of carbon stock for primary forest and the subsequent stock changes and dynamics as a consequence of conversion to the secondary forest and to five different tree plantations (rubber, coconut, cocoa, oil palm, and mixed plantations) on a total of 72 plots. Results indicate overall a statistically significant carbon loss across all the land uses/covers and for all the carbon pools compared to the primary forest with the total carbon stock loss ranging between 35% and 85% but with no statistically significant differences observed in the comparison between primary forest and mixed plantations and secondary forest. Results also suggest that above-ground carbon and soil organic carbon are the primary pools contributing to the total carbon stocks but with opposite trends of carbon loss and accumulation. Strategies for sustainable development, policies to reduce emissions from deforestation and forest degradation, carbon stock enhancement (REDD+), and planning for sustainable land use management should carefully consider the type of conversion and carbon stock dynamics behind land use change for a win-win strategy while preserving carbon stocks potential in tropical ecosystems. [Display omitted] • Carbon stock dynamics due to land use/cover changes are understudied in Africa. • We estimated carbon stock dynamics across main land uses/covers in Western Ghana. • Primary forest and mixed plantations have higher carbon storage potential. • Aboveground and soil carbon are key contributors pools, oppositely affected by age. • To preserve carbon and local economies mixed plantation could be win-win option. [ABSTRACT FROM AUTHOR]

ABSTRACTThe fusion of optical imagery with radar data can provide more accurate land cover change analysis of deforestation and tree-based agriculture. Radar data is limited temporally with most geographic areas not covered prior to 2007. This paper presents a new methodology to classify land cover change related to oil palm expansion that takes historic data limitations into account. Our approach utilizes Hansen’s Global Forest Cover data, optical imagery, and texture information, to extract land cover information in Sumatra and Western Malaysia, where historical data is absent. Our method demonstrates how to accurately classify oil palm without radar data with overall accuracies for optical only experiments within 4.4% of optical plus radar classifications. Our results show agricultural land use was the primary driver of land cover change with the largest increase due to oil palm expansion (6.1%). Better estimations of oil palm expansion could be used in sustainable land management policies.

Changes in land cover are widespread in Indonesia. This tendency frequently causes annual deforestation rates to increase, which might lead to numerous natural calamities. This study will examine land cover changes, develop land cover prediction models, and examine the link between land cover changes and the Banjarmasin City and surrounding area flood disaster. Annual variations in land cover are determined using images from the GlobeLand30 satellite and a remote sensing method. Using the Cellular Automata -- Markov Chain approach, satellite imagery is analyzed to estimate land cover. The results indicate that built-up land and forests will have the most remarkable change in land cover from 2000 to 2020, whereas forests are expected to face deforestation of 356 km2 from 2020 to 2030. In 2021, deforestation produced catastrophic floods, with 111 flood locations in the plantation zone. The water has reached areas with low predicted flood risk. [ABSTRACT FROM AUTHOR]

Aims: Human‐induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location: Virtual Southeast Asia. Methods: Using 100 virtual species, we simulated ARCs with 100‐year land‐use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land‐use variable (conventional modelling protocols) and with a temporally dynamic land‐use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results: Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land‐use variable from predicting anthropogenic‐related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land‐use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land‐use variable consistently outperformed models without. Main conclusions: We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three‐step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations. [ABSTRACT FROM AUTHOR]

Deforestation is an environmental stressor that dramatically threatens biodiversity while having adverse, deep-rooted, socioeconomic impacts. Interventions to address deforestation require considerable long-term investments; therefore, assessing their impact is crucial. The UN Convention on Biological Diversity encourages establishing protected areas (PAs) as a strategy to conserve biodiversity, secure ecosystem services, support local livelihoods, and reduce climate change impacts. The Global Environment Facility (GEF) has supported the conservation and management of PAs, yet site accessibility and data availability present challenges in assessing PAs using traditional evaluation methods. We present a novel application of satellite Earth observations for evaluating the effectiveness and impact of PAs as a deforestation mitigation strategy. Geospatial analysis and ecological forecasting methodologies provide an efficient way to quantify land cover change, estimate above ground carbon stock, and evaluate ecosystem services provided by the PAs ex ante. We demonstrate the value of a geospatial approach through the evaluation of GEF-supported PAs in Kenya. [ABSTRACT FROM AUTHOR]

This study aims to map the changes in land use and land cover between 1970 and 2018, analyzing the influence of the protected area s (PAs) in the Cerrado biome, specifically in the area of the Jalapão Mosaic. Images from the Landsat 1-MSS, 5-TM, and 8-OLI satellites were used and processed in SPRING and ArcGIS software. The analyses were based on three approaches: (1) the boundary of the Jalapão Mosaic, (2) the PAs, and (3) a comparison between the PAs and their surroundings. The Jalapão Mosaic results demonstrated that 26% (≅ 8410 km2) of the area was burned, and 15.5% (4971 km2) was anthropized in at least one of the analyzed periods. Among the PAs, the Serra Geral do Tocantins Ecological Station (Integral Protection) presented the largest burned area (43.7% ≅ 3095 km2); however, there was no significant increase in the anthropized areas due to fire. Meanwhile, the anthropized areas in the Rio Preto and Serra da Tabatinga Environmental Protection Areas (Sustainable Use) increased by 27.5% and by 75%, respectively, due to agricultural expansion. By analyzing the two groups of PAs and their surroundings, it was observed that the loss of natural vegetation was restrained and fires were less intense in the Integral Protection Units; in the Sustainable Use Units, there was a significant increase in the anthropized areas. Furthermore, over 70% of the anthropized areas occurred in the surrounding areas, thus showing the importance of creating PAs. [ABSTRACT FROM AUTHOR]

Hydrological change is a conspicuous signal of land use intensification in human-dominated landscapes. We hypothesized that land conversion and land use change increase the availability of lentic habitats and associated biodiversity in Southern Amazonian landscapes through at least four drivers. River damming promotes the formation of reservoirs, which are novel permanent lentic water bodies. A rise in the water table driven by local deforestation promotes the expansion of shallow riparian floodplains. Soil compaction and the deliberate construction of cattle and drainage ponds promote the increase in temporary water bodies in interfluves. We tested these hypotheses using data on habitat characterization and biological surveys of amphibians and fish in forests, pastures and soybean fields in the headwaters of the Xingu River in Mato Grosso, Brazil. Lentic habitat availability sharply increased in deforested land, with consequences to freshwater biodiversity. Reservoir formation influenced both fish and amphibian assemblage structure. Fish species ranged from strongly favored to strongly disfavored by reservoir conditions. Amphibian richness and abundance increased in pasture and soybean streams relative to forests in proportion to the density of reservoirs in the landscape. Expansion of stream floodplains increased the abundance of Melanorivulus megaroni, a fish species indicator of shallow lentic habitats. Rainwater accumulation in temporary ponds and puddles, absent from well-drained forested interfluves, allowed the invasion of converted interfluves by twelve species of open-area amphibians. A literature review indicates that these four drivers of hydrological change are geographically widespread suggesting that we may be witnessing a major yet previously unaccounted form of habitat change in deforested Amazonia, affecting both biodiversity and human health.

Abstract Background Forest plays an important role in climate regulation and carbon sequestration. Komto Forest is one of the remnant natural forests found in Guto Gida district of East Wollega zone, Ethiopia, has been supporting the local community for construction, energy and household furniture. Currently, influenced by land use land cover change (LULCC), this forest has been declining at an alarming rate. Detecting LULCC and understanding the driving forces has important for supporting decision making processes. We examine variation in forest cover dynamics over the period 1991–2012 using Landsat TM image of 1991, ETM + of 2002 and OLI-TIRS of 2019. Results The LULCC detection results show that a dramatic increase of agricultural land from (24.78%) in 1991 to (33.5%) in 2019 with annual expansion rate (23.68%) per annum, where forest cover declined by 20.1% in 1991 and 37.38% in 2019 with annual decreasing rate of 4.18% per annum. Our finding indicates the increment of agricultural land, grassland, and settlement, while the dense and open forest cover shows a declining trend. The declining of forest coverage is likely to cause unpleasant environment and affects human wellbeing. Conclusions The massive declined in forest cover change are often associated with agricultural expansion in the periphery of the forest. Timber exploitation and charcoal production are other problems that contribute for the declining of forest coverage. Overall, our results suggest the need of participatory forest management and public awareness creation to sustain the Komto remnant forest.

The Nigerian environment changes due to humanity's anthropogenic activities. This study assessed the dynamics of land cover in Nigeria. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI C6 (250 m) and Landsat (30 m) images were acquired and processed. The results from the MODIS images indicated that the vegetation in several hot spots had changed over decades across Nigeria; two of these hot spots, one in the Northeast and one in the Southwest, were selected for further assessment using the Landsat images. From these, results indicated that there was an increase in the Woodland/Gallery Forest by 3.51 percent and a decrease in Sand Dunes by 3.42 percent between 1987 and 2018. In addition, in the Southwest, there was a decrease in the Less Dense Forest by 33.8 percent and an increase in the Built-up area by 22.8 percent between 1986 and 2016. This study concluded that some intervention projects adopted by the government of Nigeria to address the challenges of desertification and deforestation in the Northeast yielded some positive impacts; however, the case is different in the Southwest, and its vegetation has been depleted at an alarming rate. [ABSTRACT FROM AUTHOR]

As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation. [ABSTRACT FROM AUTHOR]

• There is currently no consensual definition on deforestation. • Existing definitions are not functional in decision-making contexts. • We propose a common framework as a tool to harmonize deforestation definitions. There is currently no consensual definition of deforestation. Given its importance in several research topics ranging from biodiversity to climate change, conservation, restoration, and emerging diseases as well as in comparative studies, it is necessary to provide an integrated framework towards a harmonized definition of deforestation. Here, we analyze and compare international and national reports on deforestation management (e.g., FAO, UNFCCC) and find important differences, including contrasting definitions of deforestation and conceptual ambiguities. We also present a straightforward, harmonized definition of deforestation that is linked to a specific methodology for decision-making studies. This proposal can contribute to informed decision-making on studies related to deforestation. [ABSTRACT FROM AUTHOR]

Natural protected areas (NPA) are portions of the Mexican territory where the original environment has not been modified and which are protected in order to conserve, restore, and develop biodiversity. Here, we evaluated the effectiveness of 18 NPA located within the Transmexican Volcanic Belt by analyzing deforestation and land use and land cover change (LULCC) between 2002 and 2014. An index of effectiveness (EI) was obtained by adding three parameters: (1) the percentage of the transformed area (TA%), (2) the rate of change inside the NPA (LULCC-R), and (3) the difference between the rate of change inside the NPA and outside (buffer). The sum of the three standardized parameters forms the EI, where NPA were classified as effective (index value > 2), weakly effective (index value between 1 and 2), and noneffective (index value < 1). In addition, we identified the most important transitions between categories of LULCC for each NPA. Only three NPA were classified as effective: Ciénegas de Lerma (EI = 2.29), Volcán Nevado de Colima (EI = 2.08), and Iztaccíhuatl-Popocatépetl (EI = 2.06). Eleven were weakly effective, and the remaining four were noneffective. Bosencheve was the least effective (with all parameters in 0). In terms of transition probabilities in each effectiveness class, effective NPA were characterized by transition probabilities of forest cover recovery. Weakly effective NPA presented exchange probabilities between forest cover and land use. Finally, in noneffective NPA, probabilities indicated transitions from forest cover to agricultural and livestock land use. Our results show that, in general, NPA are not currently effective in containing LULCC processes in the Transmexican Volcanic Belt. [ABSTRACT FROM AUTHOR]

Myanmar has been experiencing a significant amount of deforestation and forest degradation in recent years. Being a developing country, people are heavily dependent on its forest for sustenance and livelihood. This study examines a methodology to identify potential drivers and their relative significance for deforestation. The study was tested in one of the districts but could be applied in other areas of the country. The forest and non-forest land cover maps from the Japan Aerospace Exploration Agency (JAXA) for the years 2008 and 2016 were used in the study. It was derived that 46.54% of study area is still covered with forest, but there has been a significant decrease in forest area by 7.29% between the years 2008 and 2016. We examined a number of spatially explicit potential drivers of deforestation such as infrastructure, elevation, slope, deforested land, and population. As informed prevention awareness of deforestation, we projected future forest conditions using a cellular automation modeling technique for the years 2020, 2025 and 2030. We found that major physical and socioeconomic driving factors of deforestation such as proximity to infrastructure (reservoirs and roads), certain elevation levels, slope, proximity to previously deforested area and population density are strongly associated with neighborhood deforestation. The future projection showed a decrease in forest area by 13.8% from 2016 to 2030. This work therefore provides crucial information on forest landscape for forest management in the district. The projective scenario of study area generated by the model highlights the need for forest conservation and planning while addressing the key drivers of deforestation, giving direction for future potential areas of REDD+ implementation in the region. [ABSTRACT FROM AUTHOR]

This paper follows the evolution of the discourse of agricultural productivity, from its inception in colonial land-use mapping to current land-use changes, using a case study of the expansion of an oil palm company onto the territory of an indigenous community in the Peruvian Amazon that caused large-scale deforestation. Drawing on analytical tools of political ecology and insights from historical geographythis paper shows how current soil and land classification, forest usufruct rules, and forest designations under the 2011 Forest and Wildlife law stem from a colonial discourse of agricultural productivity. This discourse excludes other forms of land-use, such as indigenous forest management, and the identities and relationships bound up in those. This research contributes to an understanding of how the processes that seek to quantify and determine land-use change are historically embedded in western ideas of agricultural productivity. [ABSTRACT FROM AUTHOR]

Background: Forest plays an important role in climate regulation and carbon sequestration. Komto Forest is one of the remnant natural forests found in Guto Gida district of East Wollega zone, Ethiopia, has been supporting the local community for construction, energy and household furniture. Currently, influenced by land use land cover change (LULCC), this forest has been declining at an alarming rate. Detecting LULCC and understanding the driving forces has important for supporting decision making processes. We examine variation in forest cover dynamics over the period 1991–2012 using Landsat TM image of 1991, ETM + of 2002 and OLI-TIRS of 2019. Results: The LULCC detection results show that a dramatic increase of agricultural land from (24.78%) in 1991 to (33.5%) in 2019 with annual expansion rate (23.68%) per annum, where forest cover declined by 20.1% in 1991 and 37.38% in 2019 with annual decreasing rate of 4.18% per annum. Our finding indicates the increment of agricultural land, grassland, and settlement, while the dense and open forest cover shows a declining trend. The declining of forest coverage is likely to cause unpleasant environment and affects human wellbeing. Conclusions: The massive declined in forest cover change are often associated with agricultural expansion in the periphery of the forest. Timber exploitation and charcoal production are other problems that contribute for the declining of forest coverage. Overall, our results suggest the need of participatory forest management and public awareness creation to sustain the Komto remnant forest. [ABSTRACT FROM AUTHOR]

Independent smallholders who manage their own oil palm plantations without receiving technical assistance and agricultural inputs from oil palm estates or government have been increasing rapidly in Indonesia in recent years. However the magnitude of their impacts on tropical forest deforestation remains largely unevaluated. The objective of this study was to explore the history of land use, and the changes in land cover and status since the onset of oil palm plantation activities. The study was conducted from March to April 2016. Surveys were carried out in 30 ha of independent smallholder oil palm in Kampar District, Riau Province. To identify the land status, the Agreed functional forest classification (TGHK) and Provincial land use planning (RTRWP) maps were overlaid on images of the area of independent smallholder oil palm. Landsat images three years before oil palm was established were used to assess forest cover changes. Furthermore, oil palm smallholders and elders of the local community in the research area were interviewed to identify land use prior to oil palm. Our results showed that, based on land status, 47% of the area of independent smallholders’ oil palm derived from logged forest; that is the land changed in status from forest to oil palm plantation. The other 53% of oil palm area derived from non-forested land. The land use history before the establishment of independent smallholder oil palm mostly comprised general-purpose field activities and former forest-felling (forest concessions). The land cover before conversion into oil palm comprised rubber plantation, secondary forest, and shrub cover. From the results of our survey, we conclude that most of the oil palm plantations planted between 1990 and 2002 have their origins not in primary forest, but rather in degraded secondary forest, former fields, and shrub-land. These results imply that conversion of forest area into oil palm plantations is not the direct cause of deforestation in the tropical forests of Kampar, Riau Province.

After the year 2010, a century of rapid decline in Philippine forest cover was reversed due to a deliberate National Greening Program (NGP). Drivers that can sustain or counter this increase can be better investigated through the help of land change models. However, such models are not yet in mainstream use for planning in the Philippines. Hence, this study used two models – FOREST-SAGE and GEOMOD – to simulate forest conditions based on anthropogenic drivers and to evaluate model applicability in the Philippines. The performance of each model was assessed using the root-mean-square error (RMSE), mean absolute error (MAE), Kappa, national average tree cover percentage, and common deforestation hotspots with reference datasets. Validation with Climate Change Initiative Land Cover dataset (CCI-LC) yielded similar results between the models: 2015 tree cover maps with RMSE of 22–25% tree cover, MAE of 10–12% tree cover, and moderate agreement with reference map based on Kappa (0.4–0.6); and 2010–2015 change maps with RMSE of 8–9% tree cover, MAE of 1–2% tree cover, and agreement due to chance based on Kappa (0.01–0.03). Validation with MODerate Resolution Imaging Spectroradiometer-Vegetation Continuous Field (MODIS-VCF) maps yielded similar MAE results (10% tree cover) between the models. Validating FOREST-SAGE end-time and change maps with MODIS-VCF yielded better RMSE results than GEOMOD (RMSE of 13% tree cover for FOREST-SAGE; 22% tree cover for GEOMOD). However, GEOMOD tree cover maps yielded better Kappa than FOREST-SAGE (0.60 for GEOMOD; 0.01 for FOREST-SAGE). Results suggest that FOREST-SAGE is more applicable in the Philippines for provincial extent studies that aim to quantitively track forest cover change, while GEOMOD is more applicable for national extent studies that use categorical data. Results also suggest that input parameter settings must be improved to simulate spatial distribution of forest cover. [ABSTRACT FROM AUTHOR]

This study examined the existing forest management strategies and land cover change in one of nature forest reserves in Nigeria. It analysed freely available Landsat imageries for the assessment of land cover change between 1986 and 2014, and conducted key informant interviews on forest guards and an administrator in relevant ministry, for information on management practiced. Results showed that about 35.2% of the vegetal cover were lost within the study period; 80% of the interviewed forest guards attributed such loss to unrestricted access of the surrounding communities, 60% to logging or chain saw operation, and 40% to farming practices and bush burning in the reserve. Management practiced is that, which ensures passive input with active harvesting, but cannot be fixed within a specific sustainable strategy of the Food and Agriculture Organisation of the United Nations. The study concluded that given the management strategy practiced in the forest reserve, which also allows defaulters of forest reserve regulations to escape punishment because they can 'settle', indicates that corruption, rather than poverty, is the major drive of deforestation in the area. [ABSTRACT FROM AUTHOR]

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R2 between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.

A workshop on Agent-based Models of Land Use / Land Cover Change in Santa Barbara (hosted by CSISS) took place prior to and in conjunction with the National Academy of Sciences’ Arthur M. Sackler Colloquium on Adaptive Agents, Intelligence and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling. This took place at the Beckman Center, Irvine, California, 6-7 October 2001. This document features the schedule, summary of proceedings, and Position Papers by participants in the workshop.

Context: Understanding how the Northern Forest landscape has changed and is likely to change, both in terms of forest extent and forest configuration, has important implications for management. Objectives: We examined historical changes in forest pattern and extent to: (1) characterize recent forest cover change and potential drivers of that change, (2) identify areas vulnerable to future forest loss, (3) assess the impact of such loss on forest fragmentation, and (4) examine correlations between projected forest loss and socioeconomic variables to help inform future planning. Methods: We developed a cellular automata model to simulate changes in forest land cover in the Northern Forest region from 2015 to 2075. The model was parameterized from observed historical trends (1985 to 2015) and correlating spatial variables using Bayesian Weights of Evidence. Using our model outputs, we identified areas most vulnerable to change, and impacts of these changes on forest fragmentation. Results: Though we find an overall trend of decreasing forest area across the region, rates of change vary spatially and temporally, with an overall increase in forest cover between 2000 and 2015. Areas most attractive for development (e.g. high population density, low slope and elevation) were most likely to experience deforestation. Forest fragmentation increased during observed and simulated time steps, even during an observed period of net forest regeneration. Conclusions: Forest loss and fragmentation due to development represent a formidable threat to the Northern Forest. Historical trends indicate that simply increasing forest extent is not sufficient to restore forest connectivity in the region. [ABSTRACT FROM AUTHOR]

• A RCM is used to study the effect of LCC on climate extremes in Europe. • LCC significantly changes the occurrence frequency and duration of extreme climate. • Increasing understanding of land cover change-climate interactions. • Findings are helpful in developing future land management strategies in Europe. Extremes in climate and weather can pose significant challenges to economy, ecosystems and human health. Changes in land cover are one of the drivers for variability in frequency and magnitude of extreme climate at regional and local levels. In this study, a regional climate model (COSMO-CLM v4.8) is used to simulate effects in climate extremes from two different idealized land cover change scenarios in Europe. These two simulations involve abrupt large-scale conversion of today forestland to herbaceous vegetation (deforestation), and of today cropland to evergreen needle-leave forest (afforestation). A control simulation with today land cover distribution is used to identify differences in extreme climate. We find significant changes in extreme climate in both deforestation and afforestation simulations, with seasonal and spatial differences. Deforestation causes a warmer summer (with higher annual maximum temperature) and a colder winter (with lower annual minimum temperature). Afforestation slightly increases the average intensity of the hot extremes, although with high spatial variability (a reduction is common in several locations), and mitigates cold extremes in winter. Changes in extreme indices show that deforestation increases both the frequency and duration of hot and cold extremes, while afforestation causes a lower frequency of extreme cold climate. The two simulations show opposing results in the number of frozen days, as they increase for deforestation and decrease for afforestation. A drier climate is found after deforestation, whereas a wetter climate is observed after afforestation. In general, deforestation and afforestation increase the frequency of hot extreme climate as they reduce the return period and increase the return level. Overall, our findings show the potential critical effects that land cover changes can have on climate extremes, and the possible synergies that land management strategies and planning can have for climate change mitigation and adaptation at a regional scale. [ABSTRACT FROM AUTHOR]

To understand changes in land surface energy balance partitioning due to tropical deforestation, we use a physically based analytical formulation of the surface energy balance. Turbulent heat fluxes are constrained by the thermodynamic maximum power limit and a formulation for diurnal heat redistribution within the land‐atmosphere system. The derived turbulent fluxes of sensible and latent heat compare very well to in situ observations for sites with intact rainforest and soybean land cover in southeastern Amazonia. The equilibrium partitioning into sensible and latent heat flux compares well with observations for both sites, except for the soybean site during the dry season where water limitation needs to be explicitly accounted for. Our results show that tropical deforestation primarily affects the absorption of solar radiation and the water limitation of evapotranspiration, but not the overall magnitude of turbulent heat fluxes that is set by the thermodynamic maximum power limit. Plain Language Summary: Tropical deforestation impacts the local energy and water exchange between land surface and atmosphere, typically resulting in regionally warmer and drier climates. General circulation models still disagree in reproducing these changes and little has been done to derive them from first principles. Here, we present an alternative approach to describe the effects of tropical land conversion from forest to soy agriculture, based on a physical theory of land‐atmosphere interactions. We view land‐atmosphere exchange as the result of a heat engine strongly shaped by turbulent heat exchange. This provides a framework to derive analytical expressions of the turbulent fluxes from the limit by how much work this engine can maximally perform. By comparing these with observations from a tropical rainforest and a soybean field in Amazonia, we find that the diurnal variations of turbulent fluxes are very well estimated. This means that turbulent land‐atmosphere exchange is primarily constrained by the thermodynamic limit, irrespective of surface roughness and evapotranspiration, and suggests that one can estimate the primary impacts of tropical land use change from physical principles. Thus, using thermodynamic limits represents an alternative approach to investigate the highly complex nature of land‐atmosphere interactions and global change from first principles. Key Points: Diurnal variations of turbulent heat fluxes are well estimated by the maximum power limit for sites with contrasting land cover in AmazoniaLand cover affected primarily the absorption of solar radiation and the partitioning of turbulent fluxes into sensible and latent heatThermodynamic theory is well suited as a parsimonious approach to describe first‐order controls of land‐atmosphere interactions [ABSTRACT FROM AUTHOR]

Over the past 50 years, Costa Rica has experienced extensive landscape changes. It is, therefore, important to understand the historical changes that have occurred and how the forests will change in the future, which provides the objective of this study. To determine the historical deforestation trends and link them to the different biogeophysical and socioeconomic variables, forest maps from 1960 to 2013 were used in the Dinamica Environment for Geoprocessing Objects (Dinamica EGO) to create deforestation models for Costa Rica. The base model was built and analyzed for changes in landscape metrics such as patch size and distance between 1960 and 2013. After validation of the model's ability to replicate patterns, the landscape change was then projected to 2069. The historical model validated at 85% accuracy within 600 m for both the 2005-2013 and 1997-2005 iterations. All national future projections exhibited an increase in forest area, including the most extreme deforestation scenarios. Future projections are increasingly important given changes in the global socio-political structure, climatic change, and the ever-increasing globalization of capitalistic endeavors. The trajectory of the forest in Costa Rica can also serve as a way to track both these global pressures on the natural landscape, and as a proxy for how to manage deforestation in other similar political and geographic areas of the tropics. [ABSTRACT FROM AUTHOR]

Deforestation influences surface temperature locally ("local effects"), but also at neighboring or remote regions ("nonlocal effects"). Observations indicate that local effects induce a warming in most locations, while many climate models show a global mean cooling when simulating global deforestation. We show that a nonlocal cooling in models, which is excluded from observations, may strongly contribute to these conflicting results. For the MPI‐ESM, the globally averaged nonlocal cooling exceeds the globally averaged local warming by a factor of three, for global deforestation but also for realistic areal extents and spatial distributions of deforestation. Furthermore, the globally averaged nonlocal effects dominate the local effects in realistic scenarios across a range of climate models. We conclude that observations alone are not sufficient to capture the full biogeophysical effects, and climate models are needed to better understand and quantify the full effects of deforestation before they are considered in strategies for climate mitigation. Plain Language Summary: Deforestation influences surface temperature at the location of deforestation (local effects) and elsewhere (nonlocal effects). Only the local effects are included in observation‐based data sets, but in reality surface temperature may in addition be substantially influenced by the nonlocal effects. Using simulations in a climate model, we show that deforestation‐induced changes in the brightness of the surface influence surface temperature mainly nonlocally and thus may be largely overlooked in observation‐based data sets. The simulations show that the nonlocal effects have a larger impact on global average surface temperature than the local effects, independent of how much area is deforested and at which latitude the deforestation takes place. A better understanding of the nonlocal effects is essential before the full climate effects of deforestation can be included into international climate policies that aim at mitigating global climate warming. Key Points: Deforestation influences surface temperature also in locations that are not deforestedGlobally averaged, this temperature change may be stronger than the temperature change at deforested locationsThe surface cooling from changing surface albedo may mainly be nonlocal and thus underestimated in observation‐based estimates [ABSTRACT FROM AUTHOR]

The depletion of forests in conservation hotspots is a poorly known societal concern of the post‐conflict society of Angola. Rapid economic and population growth, fragile environmental regulation, enforcement, and planning threat these invaluable forests and their biodiversity. By mitigating climate change through a sustainable management of forests, Reducing Emissions from Deforestation and forest Degradation (REDD+) could also favour the preservation of biodiversity. This study addresses the depletion of forests in Angolan conservation 'hotspots' under a REDD+ perspective. In the escarpment forest, a centre of endemism, activity data, and emission factors were assessed between 2001 and 2014. Next, spatial factors associated with deforestation were identified using generalized linear modelling, and it was provided a view on how REDD+ may support sustainable forest management in conservation hotspots. Activity data based on an error‐corrected estimate indicated a deforested area of 4856.3 ± 653.1 ha between 2001 and 2014. Field mean above‐ground carbon content amounted to 89.4 ± 126.4 tCha‐1. Among the most relevant factors associated with deforestation were elevation, density of bare land, north–south exposure (aspect), and distance to trails. All negatively related to deforestation. Overall, the threatened escarpment forest conservation hotspot seems to have significant potential for reducing carbon emissions and beneficiate from REDD+. Yet the considerable uncertainty in our estimates limited the discussions in the magnitude of forest depletion and carbon emissions. In view of performance‐based payments for development, additional field surveying is necessary to boost the accuracies presented by new forest monitoring tools that can reduce technical difficulties and sustain the eligibility of Angola for international REDD+ funding sources. [ABSTRACT FROM AUTHOR]

The Air Telang Protected Forest (ATPF) is one of the most dynamic and essential coastal forest landscapes in South Sumatra, Indonesia, because of its location between multiple river outlets, including the Musi catchment—Sumatra’s largest and most dense lowland catchment area. While most ATPF areas are covered by mangroves, these areas have been experiencing severe anthropogenic-driven degradation and conversion. This study aims to evaluate land cover changes and associated carbon emissions in the ATPF over a 35-year period (1985–2020) by utilizing the available Landsat and Sentinel imagery from 1985, 2000, and 2020. Throughout the analysis period, we observed 63% (from 10,886 to 4059 ha) primary and secondary forest loss due to land use change. We identified three primary anthropogenic activities driving these losses, namely, land clearing for plantations and agriculture (3693 ha), coconut plantations (3315 ha), aquaculture (245 ha). We estimated that the largest carbon emissions were caused by coconut plantation conversion, with total carbon emissions of approximately 14.14 Mt CO2-eq. These amounts were almost 4 and 21 times higher than emissions from land clearing and aquaculture, respectively, as substantial soil carbon loss occurs once mangroves get transformed into coconut plantations. While coconut plantation expansion on mangroves could generate significant carbon stock losses and cleared forests become the primary candidate for restoration, our dataset could be useful for future land-based emission reduction policy intervention at a subnational level. Ultimately, our findings have direct implications for current national climate policies, through low carbon development strategies and emission reductions from the land use sector for 2030, as outlined in the Nationally Determined Contributions (NDCs).

Tropical vegetation influences local, regional, and global climates, largely through its relationship with the atmosphere, including seasonal patterns of photosynthesis and transpiration. Removal and replacement of natural vegetation can alter both of these processes. In the Amazon, land use/land cover change (LULCC; e.g. deforestation) started decades ago and is expected to continue, with potentially strong effects on climate. However, long-term data on tropical photosynthetic activity and transpiration are scarce, limiting our ability to estimate large-scale effects of LULCC. Here, we use remote sensing data to analyze the impact of LULCC on seasonal patterns of photosynthetic activity and transpiration in the southern Amazon. This region, naturally dominated by forest and Cerrado, has seen high rates of LULCC. Within each of these two ecosystems, we compare estimates of photosynthetic activity (from GOME-2 and GOSIF solar induced fluorescence, SIF) and transpiration (from the Global Land Evaporation Amsterdam Model, GLEAM) in paired sites with high and low rates of LULCC. In forest-dominated regions, deforestation has reduced photosynthetic activity and transpiration, particularly during the dry season, and replaced dry season greening with dry season browning. The SIF datasets disagree on wet season responses; SIF increases with deforestation according to GOME-2, but decreases according to GOSIF. In Cerrado-dominated areas, LULCC has increased photosynthetic activity during the wet season. In both ecosystems, LULCC has resulted in a higher seasonal or annual range of photosynthetic activity levels. The observed effects are often stronger in regions with more extensive LULCC. We found large differences between the two SIF products in both forest- and Cerrado-dominated pixels, with GOME-2 consistently providing higher maximum SIF values. These discrepancies merit further consideration. This analysis broadly characterizes the effects of LULCC on photosynthetic activity and transpiration in this region, and can be used to validate model representations of these effects.

Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region.

Large-scale deforestation may affect the surface energy budget and consequently climate by changing the physical properties of the land surface, namely biophysical effects. This study presents the potential energy budget change caused by deforestation in Northeastern China and its climate implications, which was evaluated by quantifying the differences in MODIS-observed surface physical properties between cropland and forest. We used the MODIS land products for the period of 2001–2010 in 112 cells of 0.75° × 0.75° each, within which only best quality satellite pixels over the pure forest and cropland pixels are selected for comparison. It is estimated that cropland has a winter (summer) mean albedo of 0.38 (0.16), which is 0.15 (0.02) higher than that of forest. Due to the higher albedo, cropland absorbs 16.84 W∙m−2 (3.08 W∙m−2) less shortwave radiation than forest. Compared to forest, cropland also absorbs 8.79 W∙m−2 more longwave radiation in winter and 8.12 W∙m−2 less longwave radiation in summer. In total, the surface net radiation of cropland is 7.53 W∙m−2 (11.2 W∙m−2) less than that of forest in winter (summer). Along with these radiation changes, the latent heat flux through evapotranspiration over cropland is less than that over forest, especially in summer (−19.12 W∙m−2). Average sensible heat flux increases in summer (7.92 W∙m−2) and decreases in winter (−8.17 W∙m−2), suggesting that conversion of forest to cropland may lead to warming in summer and cooling in winter in Northeastern China. However, the annual net climate effect is not notable because of the opposite sign of the energy budget change in summer and winter.

Land cover change processes are accelerating at the regional to global level. The remote sensing community has developed reliable and robust methods for wall-to-wall mapping of land cover changes; however, land cover changes often occur at rates below the mapping errors. In the current publication, we propose a cost-effective approach to complement wall-to-wall land cover change maps with a sampling approach, which is used for accuracy assessment and accurate estimation of areas undergoing land cover changes, including provision of confidence intervals. We propose a two-stage sampling approach in order to keep accuracy, efficiency, and effort of the estimations in balance. Stratification is applied in both stages in order to gain control over the sample size allocated to rare land cover change classes on the one hand and the cost constraints for very high resolution reference imagery on the other. Bootstrapping is used to complement the accuracy measures and the area estimates with confidence intervals. The area estimates and verification estimations rely on a high quality visual interpretation of the sampling units based on time series of satellite imagery. To demonstrate the cost-effective operational applicability of the approach we applied it for assessment of deforestation in an area characterized by frequent cloud cover and very low change rate in the Republic of Congo, which makes accurate deforestation monitoring particularly challenging.

Logging roads in the Congo Basin are often associated with forest degradation through fragmentation and access for other land uses. However, in concessions managed for timber production, secondary roads are usually closed after exploitation and are expected to disappear subsequently. Little is known about the effectiveness of this prescription and the factors affecting vegetation recovery rate on abandoned logging roads. In a novel approach we assessed logging roads as temporary elements in the forest landscape that vary in persistence depending on environmental conditions. We analyzed road persistence during the period 1986–2013 in adjacent parts of Cameroon, Central African Republic and Republic of Congo. Three successive phases of road recovery were identified on LANDSAT images: open roads with bare soil, roads in the process of revegetation after abandonment and disappeared roads no longer distinguishable from the surrounding forest. Field based inventories confirmed significant differences between all three categories in density and richness of woody species and cover of dominant herbs. We used dead‐end road segments, built for timber exploitation, as sampling units. Only 6% of them were identified as being re‐opened. Survival analyses showed median persistence of four years for open roads before changing to the revegetating state and 20 years for revegetating roads before disappearance. Persistence of revegetating roads was 25% longer on geologically poor substrates which might result from slower forest recovery in areas with lower levels of soil nutrient content. We highlight the contrast amongst forests growing on different types of substrate in their potential for ecosystem recovery over time after roads have been abandoned. Forest management plans need to take these constraints into account. Logging activities should be concentrated on the existing road network and sites of low soil resource levels should be spared from business‐as‐usual exploitation.

Mangrove forests in Southeast Asia provide many ecosystem services, though are experiencing extensive deforestation, especially in Myanmar. Recent political and economic reforms in Myanmar are projected to further increase development pressures on natural resources. Mangrove forests in the southern state of Tanintharyi have largely been spared clearance because of poor infrastructure and a volatile security situation, though this may change with plans for economic expansion. We quantified land cover dynamics and proximate drivers of mangrove deforestation in Tanintharyi between 1989 and 2014 using a remote sensing approach over five time intervals. Tanintharyi experienced low mangrove cover loss overall, as compared with other regions of Myanmar, with 3.2 per cent being deforested over 25 years. However, this statistic hides large site‐specific losses and gains, which varied significantly within the study area. Myeik district lost 210 km2 of mangrove over 25 years, but also gained 132 km2. Dawei and Kawthung had minimal net loss of mangroves because of extensive mangrove regeneration. This historical mangrove baseline is essential for supporting environmentally‐conscious development in Myanmar's southernmost state. [ABSTRACT FROM AUTHOR]

Forest monitoring is important for decision making of forest management and conservation. In Chile, the forest monitoring system was initiated in 1994, which has been continued through the implementation of regional updates. This has enabled a temporal monitoring of the distribution and surface covered by native forest. However, while regional updates have reported increase in the surface covered by native forest, other studies have shown an opposite trend. Therefore, the capacity of the forest monitoring system to measure the temporal variation in forest areas was evaluated. Specifically, a review of reports and official data of the national forest monitoring system was carried out through the fulfillment of three basic criteria: i) comparability, ii) replicability and iii) quality. According to our results, the Chilean forest monitoring system does not fulfill their basic requirements because: (i) methodologies have not been consistent over time; (ii) it does not provide a baseline of land cover or forest loss that allows comparisons with changing forest area; iii) there is not adequate error estimation and how it can affect the results and analysis of monitoring. The national forest monitoring system requires a redefinition of its aims and methods, guiding them to the long term by convening different stakeholders looking for a national agreement. [ABSTRACT FROM AUTHOR]

Jamaica experienced net deforestation during the period 2001–2010. Yet, the proximate causes of deforestation and the reasons why a forest transition might be absent, particularly at the local-level, which can explain national scale changes, have been little studied. Long-term changes in socio-economic and land cover data over 68-years, for the moist forest of the Cockpit Country, Jamaica, and predictors of land cover change, were assessed using generalized linear and non-linear regression models. These were used to determine if the classic theorized determinants of a forest transition were present at different spatial scales and to identify the proximate cause(s) of deforestation. Some theorized determinants were present (improvements in national and sub-national economic development, and a decline in agricultural production); however, rural/local scale population density and growth rates were stable. As such, pressure on the unprotected (non-reserves) marginal/agricultural lands in the Cockpit Country was not reduced and this prevented them from reverting to forest. Forest cover for the entire Cockpit Country and the non-reserves therefore fluctuated significantly over time (nonlinear trend), peaking twice (1980 and 2002). The implementation of trade liberalization policies in the 1980s resulted in increased yam cultivation, which was the proximate cause of forest cover change in the non-reserves (a significant non-linear relationship). As a result, deforestation increased significantly during this period and again during the 2000s, at which time it was driven largely by other factors. A forest transition is unlikely to occur via an economic pathway, but it can possibly be achieved via the enforcement of existing forest protection laws and active promotion of forest cover expansion. [ABSTRACT FROM AUTHOR]