Comment on "High-resolution global maps of 21st-century forest cover change"

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

Mapping the diversity of land uses following deforestation across Africa

African forest are increasingly in decline as a result of land-use conversion due to human activities. However, a consistent and detailed characterization and mapping of land-use change that results in forest loss is not available at the spatial-temporal resolution and thematic levels suitable for decision-making at the local and regional scales; so far they have only been provided on coarser scales and restricted to humid forests. Here we present the first high-resolution (5 m) and continental-scale mapping of land use following deforestation in Africa, which covers an estimated 13.85% of the global forest area, including humid and dry forests. We use reference data for 15 different land-use types from 30 countries and implement an active learning framework to train a deep learning model for predicting land-use following deforestation with an F1-score of [Formula: see text] for the whole of Africa. Our results show that the causes of forest loss vary by region. In general, small-scale cropland is the dominant driver of forest loss in Africa, with hotspots in Madagascar and DRC. In addition, commodity crops such as cacao, oil palm, and rubber are the dominant drivers of forest loss in the humid forests of western and central Africa, forming an "arc of commodity crops" in that region. At the same time, the hotspots for cashew are found to increasingly dominate in the dry forests of both western and south-eastern Africa, while larger hotspots for large-scale croplands were found in Nigeria and Zambia. The increased expansion of cacao, cashew, oil palm, rubber, and large-scale croplands observed in humid and dry forests of western and south-eastern Africa suggests they are vulnerable to future land-use changes by commodity crops, thus creating challenges for achieving the zero deforestation supply chains, support REDD+ initiatives, and towards sustainable development goals.

Situating defaunation in an operational framework to advance biodiversity conservation

Anthropogenic pressures are causing the widespread loss of wildlife species and populations, with adverse consequences for ecosystem functioning. This phenomenon has been widely but inconsistently referred to as defaunation. A cohesive, quantitative framework for defining and evaluating defaunation is necessary for advancing biodiversity conservation. Likening defaunation to deforestation, we propose an operational framework for defaunation that defines it and related terms, situates defaunation relative to intact communities and faunal degradation, and encourages quantitative, ecologically reasonable, and equitable measurements. We distinguish between defaunation, the conversion of an ecosystem from having wild animals to not having wild animals, and faunal degradation, the process of losing animals or species from an animal community. The quantification of context-relevant defaunation boundaries or baselines is necessary to compare faunal communities over space and time. Situating a faunal community on the degradation curve can promote Global Biodiversity Framework targets, advancing the 2050 Vision for Biodiversity.

Tropical dry woodland loss occurs disproportionately in areas of highest conservation value

Tropical and subtropical dry woodlands are rich in biodiversity and carbon. Yet, many of these woodlands are under high deforestation pressure and remain weakly protected. Here, we assessed how deforestation dynamics relate to areas of woodland protection and to conservation priorities across the world's tropical dry woodlands. Specifically, we characterized different types of deforestation frontier from 2000 to 2020 and compared them to protected areas (PAs), Indigenous Peoples' lands and conservation areas for biodiversity, carbon and water. We found that global conservation priorities were always overrepresented in tropical dry woodlands compared to the rest of the globe (between 4% and 96% more than expected, depending on the type of conservation priority). Moreover, about 41% of all dry woodlands were characterized as deforestation frontiers, and these frontiers have been falling disproportionately in areas with important regional (i.e. tropical dry woodland) conservation assets. While deforestation frontiers were identified within all tropical dry woodland classes of woodland protection, they were lower than the average within protected areas coinciding with Indigenous Peoples' lands (23%), and within other PAs (28%). However, within PAs, deforestation frontiers have also been disproportionately affecting regional conservation assets. Many emerging deforestation frontiers were identified outside but close to PAs, highlighting a growing threat that the conserved areas of dry woodland will become isolated. Understanding how deforestation frontiers coincide with major types of current woodland protection can help target context-specific conservation policies and interventions to tropical dry woodland conservation assets (e.g. PAs in which deforestation is rampant require stronger enforcement, inactive deforestation frontiers could benefit from restoration). Our analyses also identify recurring patterns that can be used to test the transferability of governance approaches and promote learning across social-ecological contexts.

Madagascar's fire regimes challenge global assumptions about landscape degradation

Narratives of landscape degradation are often linked to unsustainable fire use by local communities. Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity viewed as threatened by unsustainable anthropogenic fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we contribute a comparative approach to determining relationships between regional fire regimes and global patterns and trends, applied to Madagascar using MODIS remote sensing data (2003-2019). Rather than a global exception, we show that Madagascar's fire regimes are similar to 88% of tropical burned area with shared climate and vegetation characteristics, and can be considered a microcosm of most tropical fire regimes. From 2003-2019, landscape-scale fire declined across tropical grassy biomes (17%-44% excluding Madagascar), and on Madagascar at a relatively fast rate (36%-46%). Thus, high tree loss anomalies on the island (1.25-4.77× the tropical average) were not explained by any general expansion of landscape-scale fire in grassy biomes. Rather, tree loss anomalies centred in forests, and could not be explained by landscape-scale fire escaping from savannas into forests. Unexpectedly, the highest tree loss anomalies on Madagascar (4.77×) occurred in environments without landscape-scale fire, where the role of small-scale fires (<21 h [0.21 km² ]) is unknown. While landscape-scale fire declined across tropical grassy biomes, trends in tropical forests reflected important differences among regions, indicating a need to better understand regional variation in the anthropogenic drivers of forest loss and fire risk. Our new understanding of Madagascar's fire regimes offers two lessons with global implications: first, landscape-scale fire is declining across tropical grassy biomes and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with tropical forest loss, indicating a need for socio-ecological context in framing new narratives of fire and ecosystem degradation.

Biases and limitations of Global Forest Change and author-generated land cover maps in detecting deforestation in the Amazon

Studying land use change in protected areas (PAs) located in tropical forests is a major conservation priority due to high conservation value (e.g., species richness and carbon storage) here, coupled with generally high deforestation rates. Land use change researchers use a variety of land cover products to track deforestation trends, including maps they produce themselves and readily available products, such as the Global Forest Change (GFC) dataset. However, all land cover maps should be critically assessed for limitations and biases to accurately communicate and interpret results. In this study, we assess deforestation in PA complexes located in agricultural frontiers in the Amazon Basin. We studied three specific sites: Amboró and Carrasco National Parks in Bolivia, Jamanxim National Forest in Brazil, and Tambopata National Reserve and Bahuaja-Sonene National Park in Peru. Within and in 20km buffer areas around each complex, we generated land cover maps using composites of Landsat imagery and supervised classification, and compared deforestation trends to data from the GFC dataset. We then performed a dissimilarity analysis to explore the discrepancies between the two remote sensing products. Both the GFC and our supervised classification showed that deforestation rates were higher in the 20km buffer than inside the PAs and that Jamanxim National Forest had the highest deforestation rate of the PAs we studied. However, GFC maps showed consistently higher rates of deforestation than our maps. Through a dissimilarity analysis, we found that many of the inconsistencies between these datasets arise from different treatment of mixed pixels or different parameters in map creation (for example, GFC does not detect reforestation after 2012). We found that our maps underestimated deforestation while GFC overestimated deforestation, and that true deforestation rates likely fall between our two estimates. We encourage users to consider limitations and biases when using or interpreting our maps, which we make publicly available, and GFC’s maps.

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.

Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data

Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and gain (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in “forest gain after loss (forest recovery)” and “forest gain with no loss (forest newborn)”, accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection.

Uncertainty of Historic GLAD Forest Data in Temperate Climates and Implications for Forest Change Modelling

Forest loss and degradation are central problems in the context of climate change and biodiversity conservation. The identification of areas of loss relies on accurate base maps. Central datasets in this context are the products of the Global Land Analysis & Discovery (GLAD) project. Although the GLAD forest cover products are primarily intended to serve as a near real-time flag for areas of forest loss, its historic datasets are increasingly also being used in ways that go beyond this initial focus. To date, very little information is available on the performance of GLAD data in temperate regions. This study aims to address this research gap by comparing the GLAD baseline forest cover maps for the years 2000 and 2010 with UK national forest datasets. The results showed substantial commission errors, which highlight potential problems when deviating from the GLAD datasets’ intended use. GLAD data appear to be less useful in regions with a high proportion of medium to low-density canopy cover. In such cases, its application in forest models should only be used in conjunction and cross-calibration with good quality reference data.

A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region

Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and associated phenology features between planted forests and other vegetation types. In this study, taking advantage of the Google Earth Engine (GEE) platform and taking the Ningxia Hui Autonomous Region in northwestern China as an example, we developed an approach to map planted forests in an arid region by applying long-term features of the NDVI derived from dense Landsat time series. Our land cover map achieved a satisfactory accuracy and relatively low uncertainty, with an overall accuracy of 93.65% and a kappa value of 0.92. Specifically, the producer (PA) and user accuracies (UA) were 92.48% and 91.79% for the planted forest class, and 93.88% and 95.83% for the natural forest class, respectively. The total planted forest area was estimated as 3608.72 km2 in 2020, accounting for 20.60% of the study area. The proposed mapping approach can facilitate assessment of the restoration effects of ecological engineering and research on ecosystem services and stability of planted forests.

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.

Early warning sign of forest loss in protected areas

As humanity is facing the double challenge of species extinctions and climate change, designating parts of forests as protected areas is a key conservation strategy.^1-4 Protected areas, encompassing 14.9% of the Earth's land surface and 19% of global forests, can prevent forest loss but do not do so perfectly everywhere.^5-12 The reasons why protection only works in some areas are difficult to generalize: older and newer parks, protected areas with higher and lower suitability for agriculture, and more and less strict protection can be more effective at preventing forest loss than their counterparts.^6,8,9,12-16 Yet predicting future forest loss within protected areas is crucial to proactive conservation. Here, we identify an early warning sign of subsequent forest loss, based on forest loss patterns in strict protected areas and their surrounding landscape worldwide, from 2000 to 2018.^17,18 We found that a low level in the absolute forest cover immediately outside of a protected area signals a high risk of future forest loss inside the protected area itself. When the amount of forest left outside drops to <20%, the protected area is likely to experience rates of forest loss matching those in the wider landscape, regardless of its protection status (e.g., 5% loss outside will be matched by 5% loss inside). This knowledge could be used to direct funding to protected areas threatened by imminent forest loss, helping to proactively bolster protection to prevent forest loss, especially in countries where detailed information is lacking.

Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework

Despite substantial conservation efforts, the loss of ecosystems continues globally, along with related declines in species and nature's contributions to people. An effective ecosystem goal, supported by clear milestones, targets and indicators, is urgently needed for the post-2020 global biodiversity framework and beyond to support biodiversity conservation, the UN Sustainable Development Goals and efforts to abate climate change. Here, we describe the scientific foundations for an ecosystem goal and milestones, founded on a theory of change, and review available indicators to measure progress. An ecosystem goal should include three core components: area, integrity and risk of collapse. Targets-the actions that are necessary for the goals to be met-should address the pathways to ecosystem loss and recovery, including safeguarding remnants of threatened ecosystems, restoring their area and integrity to reduce risk of collapse and retaining intact areas. Multiple indicators are needed to capture the different dimensions of ecosystem area, integrity and risk of collapse across all ecosystem types, and should be selected for their fitness for purpose and relevance to goal components. Science-based goals, supported by well-formulated action targets and fit-for-purpose indicators, will provide the best foundation for reversing biodiversity loss and sustaining human well-being.

Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery

The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in LULC maps has been emphasized, especially for tropical areas, due to their differences in biodiversity and ecosystem services provision. In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. The most accurate results were obtained with the MS + SAR U-net, where the highest overall accuracy (0.76) and average F1-score (0.58) were achieved. Although MS + SAR and MS U-nets gave similar results for almost all of the classes, for old-growth plantations and secondary forest, the addition of the SAR band caused an F1-score increment of 0.08–0.11 (0.62 vs. 0.54 and 0.45 vs. 0.34, respectively). Consecutively, in comparison with the MS + SAR RF, the MS + SAR U-net obtained higher F1-scores for almost all the classes. Our results show that using the U-net with a combined input of SAR and MS images enabled a higher F1-score and accuracy for a detailed LULC map, in comparison with other evaluated methods.

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

Satellite-based deforestation alerts with training and incentives for patrolling facilitate community monitoring in the Peruvian Amazon

Despite substantial investments in high-frequency, remote-sensed forest monitoring in the Amazon, early deforestation alerts generated by these systems rarely reach the most directly affected populations in time to deter deforestation. We study a community monitoring program that facilitated transfer of early deforestation alerts from the Global Forest Watch network to indigenous communities in the Peruvian Amazon and trained and incentivized community members to patrol forests in response to those alerts. The program was randomly assigned to 39 of 76 communities. The results from our analysis suggest that the program reduced tree cover loss, but the estimated effects from the experiment are imprecise: We estimate a reduction of 8.4 ha per community in the first year (95% CI [-19.4, 2.6]) and 3.3 ha in the second year (95% CI: [-13.6, 7.0]) of monitoring. The estimated reductions were largest in communities facing the largest threats. Data from monitoring records and community surveys provide evidence about how the program may affect forest outcomes. Community members perceived that the program's monitors were new authorities with influence over forest management and that the monitors' incentivized patrols were substitutes for traditional, unincentivized citizen patrols that suffer from free riding and inhibit timely community detection of and responses to deforestation. Should our findings be replicated elsewhere, they imply that externally facilitated community-based monitoring protocols that combine remote-sensed early deforestation alerts with training and incentives for monitors could contribute to sustainable forest management.

Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

This study assessed the accuracy of land cover change (2000–2018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random sampling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73–77%) compared to the map based on visual interpretation (overall accuracy 80–84%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

Mapping the deforestation footprint of nations reveals growing threat to tropical forests

Deforestation, a significant threat to biodiversity, is accelerated by global demand for commodities. Although prior literature has linked deforestation to global supply chains, here we provide a fine-scale representation of spatial patterns of deforestation associated with international trade. Using remote sensing data and a multi-region input-output model, we quantify and map the spatiotemporal changes in global deforestation footprints over 15 years (2001-2015) at a 30-m resolution. We find that, while many developed countries, China and India have obtained net forest gains domestically, they have also increased the deforestation embodied in their imports, of which tropical forests are the most threatened biome. Consumption patterns of G7 countries drive an average loss of 3.9 trees per person per year. Some of the hotspots of deforestation embodied in international trade are also biodiversity hotspots, such as in Southeast Asia, Madagascar, Liberia, Central America and the Amazonian rainforest. Our results emphasize the need to reform zero-deforestation policies through strong transnational efforts and by improving supply chain transparency, public-private engagement and financial support for the tropics.

Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

Biting mosquitos spread the malaria parasite to humans. Along the Mekong River in Southeast Asia, spending time in the surrounding forest increases a person's risk of malaria. This has led to a debate about whether deforestation in this area, which is called the Greater Mekong Sub-region (GMS), will increase or decrease malaria transmission. The answer to the debate is not clear because some malaria-transmitting mosquitos thrive in heavily forested areas, in particular in the GMS, while others prefer less forested areas.

Scientists studying malaria in the Amazon in South America suspect that malaria transmission increases shortly after deforestation but decreases six to eight years later. Some studies have tested this ‘frontier malaria’ theory but the results have been conflicting. Fewer studies have tested this theory in Southeast Asia. But deforestation has been blamed for recent malaria outbreaks in the GMS.

Using data on malaria testing and forest cover in the GMS, Rerolle et al. show that deforestation around villages increases malaria transmission in the first two years and decreases malaria rates later. This trend was driven mostly by a type of malaria called Plasmodium falciparum and was less strong for Plasmodium vivax. The location of deforested areas also mattered. Deforestation within one to 10 kilometer of villages did not affect malaria rates. Deforestation further away in about a 30 kilometer radius did affect malaria transmission. Rerolle et al. suggest this may be because villagers have to spend longer times trekking through forests to hunt or harvest wood when the wider area is deforested.

Currently, National Malaria Control Programs in the GMS focus their efforts on reducing forest-related transmission. This study strengthens the evidence supporting this approach. The results also suggest that different malaria elimination strategies may be necessary for different types of malaria parasite. Using this new information could help malaria control programs better target resources or educate villagers on how to protect themselves. The innovative methods used by Rerolle et al. reveal a more complex role of deforestation in malaria transmission and may inspire other scientists to think more carefully about environmental drivers of malaria.

Long-term (1990-2019) monitoring of forest cover changes in the humid tropics

Accurate characterization of tropical moist forest changes is needed to support conservation policies and to quantify their contribution to global carbon fluxes more effectively. We document, at pantropical scale, the extent and changes (degradation, deforestation, and recovery) of these forests over the past three decades. We estimate that 17% of tropical moist forests have disappeared since 1990 with a remaining area of 1071 million hectares in 2019, from which 10% are degraded. Our study underlines the importance of the degradation process in these ecosystems, in particular, as a precursor of deforestation, and in the recent increase in tropical moist forest disturbances (natural and anthropogenic degradation or deforestation). Without a reduction of the present disturbance rates, undisturbed forests will disappear entirely in large tropical humid regions by 2050. Our study suggests that reinforcing actions are needed to prevent the initial degradation that leads to forest clearance in 45% of the cases.

Reforestation and Deforestation in Northern Luzon, Philippines: Critical Issues as Observed from Space

Among the richest in biodiversity globally has been the Philippine rainforest, which used to cover about 90% of the country’s land area. During the last few decades, the forest cover has been reduced to less than 10% of the original, only a fraction of which is old-growth forest. The negative impacts of deforestation led to the launching of the National Greening Program (NGP) that involved the planting of more than a billion seedlings over a few million hectares of land from 2011 to 2016. To assess the success of the NGP, satellite data from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) were analyzed before, during, and after the NGP. Reforestation in the NGP sites was examined concurrently with observed deforestation in Luzon using forest loss data derived from Landsat for the period 2001 to 2018. The results show that losses declined from 2011 to 2015 but increased from 2016 to 2018. Because of such losses, the net effect is a balance of reforestation and deforestation or no significant gain from the NGP. Case studies were done in three sites in the Sierra Madre forest, where half of the remaining old-growth forest is located, using a combination of Landsat and Very High Resolution (VHR) data. The Landsat data were classified into closed forest, open forest, and other vegetation cover types. The conversion from one vegetation cover type to another was evaluated through the use of the Sankey Diagram. While some non-forest types became open or closed forests, the loss of open or closed forests is more pronounced. VHR data reveal critical issues happening within the NGP sites during the NGP period. More comprehensive data from MODIS also confirm that there was no significant increase in the forest cover in Luzon, Sierra Madre, and Cordillera from 2001 to 2018.

Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

Recording Urban Land Dynamic and Its Effects during 2000–2019 at 15-m Resolution by Cloud Computing with Landsat Series

Cities, the core of the global climate change and economic development, are high impact land cover land use change (LCLUC) hotspots. Comprehensive records of land cover land use dynamics in urban regions are essential for strategic climate change adaption and mitigation and sustainable urban development. This study aims to develop a Google Earth Engine (GEE) application for high-resolution (15-m) urban LCLUC mapping with a novel classification scheme using pan-sharpened Landsat images. With this approach, we quantified the annual LCLUC in Changchun, China, from 2000 to 2019, and detected the abrupt changes (turning points of LCLUC). Ancillary data on social-economic status were used to provide insights on potential drivers of LCLUC by examining their correlation with change rate. We also examined the impacts of LCLUC on environment, specifically air pollution. Using this approach, we can classify annual LCLUC in Changchun with high accuracy (all above 0.91). The change detection based on the high-resolution wall-to-wall maps show intensive urban expansion with the compromise of cropland from 2000 to 2019. We also found the growth of green space in urban regions as the result of green space development and management in recent years. The changing rate of different land types were the largest in the early years of the observation period. Turning points of land types were primarily observed in 2009 and 2010. Further analysis showed that economic and industry development and population migration collectively drove the urban expansion in Changchun. Increasing built-up areas could slow wind velocity and air exchange, and ultimately led to the accumulation of PM2.5. Our implement of pan-sharpened Landsat images facilitates the wall-to-wall mapping of temporal land dynamics at high spatial resolution. The primary use of GEE for mapping urban land makes it replicable and transferable by other users. This approach is a first crucial step towards understanding the drivers of change and supporting better decision-making for sustainable urban development and climate change mitigation.

Conceptual Links between Landscape Diversity and Diet Diversity: A Roadmap for Transdisciplinary Research

Malnutrition linked to poor quality diets affects at least 2 billion people. Forests, as well as agricultural systems linked to trees, are key sources of dietary diversity in rural settings. In the present article, we develop conceptual links between diet diversity and forested landscape mosaics within the rural tropics. First, we summarize the state of knowledge regarding diets obtained from forests, trees, and agroforests. We then hypothesize how disturbed secondary forests, edge habitats, forest access, and landscape diversity can function in bolstering dietary diversity. Taken together, these ideas help us build a framework illuminating four pathways (direct, agroecological, energy, and market pathways) connecting forested landscapes to diet diversity. Finally, we offer recommendations to fill remaining knowledge gaps related to diet and forest cover monitoring. We argue that better evaluation of the role of land cover complexity will help avoid overly simplistic views of food security and, instead, uncover nutritional synergies with forest conservation and restoration.

Potential PGPR Properties of Cellulolytic, Nitrogen-Fixing, Phosphate-Solubilizing Bacteria in Rehabilitated Tropical Forest Soil

In the midst of the major soil degradation and erosion faced by tropical ecosystems, rehabilitated forests are being established to avoid the further deterioration of forest lands. In this context, cellulolytic, nitrogen-fixing (N-fixing), phosphate-solubilizing bacteria are very important functional groups in regulating the elemental cycle and plant nutrition, hence replenishing the nutrient content in forest soils. As is the case for other potential plant growth-promoting (PGP) rhizobacteria, these functional bacteria could have cross-functional abilities or beneficial traits that are essential for plants and can improve their growth. This study was conducted to isolate, identify, and characterize selected PGP properties of these three functional groups of bacteria from tropical rehabilitated forest soils at Universiti Putra Malaysia Bintulu Sarawak Campus, Malaysia. The bacteria were isolated based on their colonial growth on respective functional media, identified using both molecular and selected biochemical properties, and were assessed for their functional quantitative activities as well as PGP properties based on seed germination tests and indole-3-acetic acid (IAA) production. Out of the 15 identified bacterial isolates that exhibited beneficial phenotypic traits, a third belong to the genus Burkholderia and a fifth to Stenotrophomonas sp., with both genera consisting of members from two different functional groups. The results of the experiments confirm the multiple PGP traits of some selected bacterial isolates based on their respective high functional activities, root and shoot lengths, and seedling vigor improvements when bacterized on mung bean seeds, as well as significant IAA production. The results of this study suggest that these functional bacterial strains could potentially be included in bio-fertilizer formulations for crop growth on acid soils.

Potential PGPR Properties of Cellulolytic, Nitrogen-Fixing, Phosphate-Solubilizing Bacteria in Rehabilitated Tropical Forest Soil

In the midst of the major soil degradation and erosion faced by tropical ecosystems, rehabilitated forests are being established to avoid the further deterioration of forest lands. In this context, cellulolytic, nitrogen-fixing (N-fixing), phosphate-solubilizing bacteria are very important functional groups in regulating the elemental cycle and plant nutrition, hence replenishing the nutrient content in forest soils. As is the case for other potential plant growth-promoting (PGP) rhizobacteria, these functional bacteria could have cross-functional abilities or beneficial traits that are essential for plants and can improve their growth. This study was conducted to isolate, identify, and characterize selected PGP properties of these three functional groups of bacteria from tropical rehabilitated forest soils at Universiti Putra Malaysia Bintulu Sarawak Campus, Malaysia. The bacteria were isolated based on their colonial growth on respective functional media, identified using both molecular and selected biochemical properties, and were assessed for their functional quantitative activities as well as PGP properties based on seed germination tests and indole-3-acetic acid (IAA) production. Out of the 15 identified bacterial isolates that exhibited beneficial phenotypic traits, a third belong to the genus Burkholderia and a fifth to Stenotrophomonas sp., with both genera consisting of members from two different functional groups. The results of the experiments confirm the multiple PGP traits of some selected bacterial isolates based on their respective high functional activities, root and shoot lengths, and seedling vigor improvements when bacterized on mung bean seeds, as well as significant IAA production. The results of this study suggest that these functional bacterial strains could potentially be included in bio-fertilizer formulations for crop growth on acid soils.

A global synthesis reveals biodiversity-mediated benefits for crop production

Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.

Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica

Global tree cover products are widely used in analyses of deforestation, fragmentation, and connectivity, but are rarely critically assessed. Inaccuracies in these products could have consequences for future decision making, especially in data-poor regions like the tropics. In this study, potential biases in global and regional tree cover products were assessed across a diverse tropical country, Costa Rica. Two global tree cover products and one regional national forest cover map were evaluated along biophysical gradients in elevation, precipitation, and agricultural land cover. To quantify product accuracy and bias, freely available high-resolution imagery was used to validate tree and land cover across these gradients. Although the regional forest cover map was comparable in accuracy to a widely-used global forest map (the Global Forest Change of Hansen et al., also known as the GFC), another global forest map (derived from a cropland dataset) had the highest accuracy. Both global and regional forest cover products showed small to severe biases along biophysical gradients. Unlike the regional map, the global GFC map strongly underestimated tree cover (>10% difference) below 189 mm of precipitation and at elevations above 2000 m, with a larger bias for precipitation. All map products misclassified agricultural fields as forest, but the GFC product particularly misclassified row crops and perennial erect crops (banana, oil palm, and coffee), with maximum tree cover in agricultural fields of 89%–100% across all crops. Our analysis calls into further question the utility of the GFC product for global forest monitoring outside humid regions, indicating that, in tropical regions, the GFC product is most accurate in areas with high, aseasonal rainfall, low relief, and low cropland area. Given that forest product errors are spatially distributed along biophysical gradients, researchers should account for these spatial biases when attempting to analyze or generate forest map products.

Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017

Myanmar, abundant in natural resources, is one of the countries with high forest cover in Southeast Asia. Along with its rapid socio-economic development, however, the construction of large-scale infrastructure, expansion of agricultural land, and an increasing demand for timber products have posed serious threats to the forests and significantly affected regional sustainable development. However, the geographical environment in Myanmar is complex, resulting in the lack of long-term sequence of land cover data products. Based on 30 years’ Landsat satellite remote sensing imagery data and the land cover data extracted by a mixed classification method, this paper examined the spatial and temporal evolution characteristics of forest cover in Myanmar and investigated driving factors of the spatio-temporal evolution. Results show that the forest cover has decreased by 110,621 km2 in the past 30 years with the annual deforestation rate of 0.87%. Cropland expansion is the main reason for the deforestation throughout the study period. The study can provide basic information of the forest cover data to the Myanmar government for ecological environment protection. At the same time, it can provide important support to the “Belt and Road” initiative to invest in the region’s economy.

Impacts of forestation and deforestation on local temperature across the globe

Changing forest cover is a key driver of local climate change worldwide, as it affects both albedo and evapotranspiration (ET). Deforestation and forestation are predicted to have opposing influences on surface albedo and ET rates, and thus impact local surface temperatures differently. Relationships between forest change, albedo, ET, and local temperatures may further vary regionally, as the strengths of warming by albedo effects and cooling by ET effects vary with latitude. Despite these important relationships, the magnitude of forest cover effects on local surface temperature across the globe remains unclear. Using recently-released global forest change data, we first show that forestation and deforestation have pervasive and opposite effects on LST, ET and albedo worldwide. Deforestation from 2000 to 2010 caused consistent warming of 0.38 ± 0.02 (mean ± SE) and 0.16 ± 0.01°C in tropical and temperate regions respectively, while forestation caused cooling in those regions of -0.18 ± 0.02 and -0.19 ± 0.02°C. Tropical forests were particularly sensitive to the climate effects of forest change, with forest cover losses of ~50% associated with increased LST of 1.08 ± 0.25°C, whereas similar forest cover gains decreased LST by -1.11 ± 0.26°C. Secondly, based on a new structural equation model, we show that these changes on LST were largely mediated by changes in albedo and ET. Finally, based on this model, we show that predicted forest changes in Brazil associated with a business-as-usual land use scenario through 2050 may increase LST up to 1.45°C. Our results contribute to a better understanding of the mechanistic inter-relationships between forest change and changes in albedo, ET and LST, and provide additional evidence that forestation has the potential to reverse deforestation impacts on local climate, especially in tropical and temperate regions.

Assessing Harvested Sites in a Forested Boreal Mountain Catchment through Global Forest Watch

Global Forest Watch (GFW) provides a global map of annual forest cover loss (FCL) produced from Landsat imagery, offering a potentially powerful tool for monitoring changes in forest cover. In managed forests, FCL primarily provides information on commercial harvesting. A semi-autonomous method for providing data on the location and attributes of harvested sites at a landscape level was developed which could significantly improve the basis for catchment management, including risk mitigation. FCL in combination with aerial images was used for detecting and characterising harvested sites in a 1607 km2 mountainous boreal forest catchment in south-central Norway. Firstly, the forest cover loss map was enhanced (FCLE) by removing small isolated forest cover loss patches that had a high probability of representing commission errors. The FCLE map was then used to locate and assess sites representing annual harvesting activity over a 17-year period. Despite an overall accuracy of >98%, a kappa of 0.66 suggested only a moderate quality for detecting harvested sites. While errors of commission were negligible, errors of omission were more considerable and at least partially attributed to the presence of residual seed trees on the site after harvesting. The systematic analysis of harvested sites against aerial images showed a detection rate of 94%, but the area of the individual harvested site was underestimated by 29% on average. None of the site attributes tested, including slope, area, altitude, or site shape index, had any effect on the accuracy of the area estimate. The annual harvest estimate was 0.6% (standard error 12%) of the productive forest area. On average, 96% of the harvest was carried out on flat to moderately steep terrain (<40% slope), 3% on steep terrain (40% to 60% slope), and 1% on very steep terrain (>60% slope). The mean area of FCLE within each slope category was 1.7 ha, 0.9 ha, and 0.5 ha, respectively. The mean FCLE area increased from 1.0 ha to 3.2 ha on flat to moderate terrain over the studied period, while the frequency of harvesting increased from 249 to 495 sites per year. On the steep terrain, 35% of the harvesting was done with cable yarding, and 62% with harvester-forwarder systems. On the very steep terrain (>60% slope), 88% of the area was harvested using cable yarding technology while harvesters and forwarders were used on 12% of the area. Overall, FCL proved to be a useful dataset for the purpose of assessing harvesting activity under the given conditions.

Understanding Land Cover Change in a Fragmented Forest Landscape in a Biodiversity Hotspot of Coastal Ecuador

Understanding forest cover changes is especially important in highly threatened and understudied tropical dry forest landscapes. This research uses Landsat images and a Random Forest classifier (RF) to map old-growth, secondary, and plantation forests and to evaluate changes in their coverage in Ecuador. We used 46 Landsat-derived predictors from the dry and wet seasons to map these forest types and to evaluate the importance of having seasonal variables in classifications. Initial RF models grouped old-growth and secondary forest as a single class because of a lack of secondary forest training data. The model accuracy was improved slightly from 92.8% for the wet season and 94.6% for the dry season to 95% overall by including variables from both seasons. Derived land cover maps indicate that the remaining forest in the landscape occurs mostly along the coastline in a matrix of pastureland, with less than 10% of the landscape covered by plantation forests. To obtain secondary forest training data and evaluate changes in forest cover, we conducted a change analysis between the 1990 and 2015 images. The results indicated that half of the forests present in 1990 were cleared during the 25-year study period and highlighted areas of forest regrowth. We used these areas to extract secondary forest training data and then re-classified the landscape with secondary forest as a class. Classification accuracies decreased with more forest classes, but having data from both seasons resulted in higher accuracy (87.9%) compared to having data from only the wet (85.8%) or dry (82.9%) seasons. The produced cover maps classified the majority of previously identified forest areas as secondary, but these areas likely correspond to forest regrowth and to degraded forests that structurally resemble secondary forests. Among the few areas classified as old-growth forests are known reserves. This research provides evidence of the importance of using bi-seasonal Landsat data to classify forest types and contributes to understanding changes in forest cover of tropical dry forests.

Payments for ecosystem services in Mexico reduce forest fragmentation

Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances. A key emerging policy to protect forests is payments for ecosystem services (PES), which offers compensation to landowners for environmental stewardship. Mexico was one of the first countries to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico's PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the PES effects varied among forest types, among socioeconomic zones, or compared to the protected areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We summarized fragmentation for different forest types and in four socioeconomic zones. We then used matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and compared the effects of the PES program with that of protected areas. We found that the area covered by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area. Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical forest lost the most core areas (-17%), while oak forest lost the least (-2%). Our matching analysis found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones. We conclude that the PES was successful in slowing forest fragmentation at the regional and country level. However, the program could be improved by targeting areas where forest changes are more frequent, especially in southern Mexico. Fragmentation analyses should be implemented in other areas to monitor the outcomes of protection programs such as REDD+ and PES.

A Co-Wife for the Cow: Gender Dimensions of Land Change and Livelihood Shift among Loita Maasai of Southern Kenya

Gender dimensions are fundamental to human/environment systems. I use gender to investigate land change in a forested area of conservation concern in the pastoral rangelands of Kenya Maasailand. Mixed methods reveal a narrative arc from the mid-1970s culminating in a transformation of land, livelihood, and culture by 2014. Empirical findings expand current understandings of livelihood shift in Maasailand to include gender dimensions critical to livelihood success. Remotely sensed satellite data and qualitative evidence expose simplistic narratives about environmental conditions in Loita Forest and Maasai women's social status. I argue that gender deserves more attention in land-change studies because of its linkages to resource utilization and drivers of forest decline around the world.

Assessing environmental impacts and change in Myanmar's mangrove ecosystem service value due to deforestation (2000-2014)

Myanmar is one of the mangrove-richest countries in the world, providing valuable ecosystem services to people. However, due to deforestation driven primarily by agricultural expansion, Myanmar's mangrove forest cover has declined dramatically over the past few decades, while what remains is still under pressure. To support management planning, accurate quantification of mangrove forest cover changes on a national scale is needed. In this study, we quantified Myanmar's mangrove forest cover changes between 2000 and 2014 using remotely sensed data, examined the environmental impacts of such changes, and estimated the changes in the economic values of mangrove ecosystem services in the country. Results indicate that Myanmar had a net mangrove loss of 191,122 ha over the study period. Since 2000, Myanmar has been losing mangrove forest cover at an alarming rate of 14,619 ha/year (2.2%/year). The loss was predominant in Rakhine and Ayeyarwady. The observed mangrove forest cover loss has resulted in decreased evapotranspiration, carbon stock, and tree cover percentage. Due to deforestation, Myanmar also suffered a net loss of 2,397 million US$/year in its mangrove ecosystem service value (i.e. 28.7% decrease from 2000), in which maintenance of fisheries nursery populations and habitat and coastal protection were among those services that were greatly affected. We suggest that intensive reforestation and mangrove protection programs be implemented immediately. Agroforestry and community forestry programs are encouraged in areas that are under immense pressure from paddy field expansion, fuelwood extraction, charcoal production, and fish and shrimp farming activities. Potential alternative sustainable solutions should include intensive government-led private forest plantations or community-owned forest plantations to be developed with care by local farmers, nongovernmental organizations, and business owners.

Congo Basin forest loss dominated by increasing smallholder clearing

A regional assessment of forest disturbance dynamics from 2000 to 2014 was performed for the Congo Basin countries using time-series satellite data. Area of forest loss was estimated and disaggregated by predisturbance forest type and direct disturbance driver. An estimated 84% of forest disturbance area in the region is due to small-scale, nonmechanized forest clearing for agriculture. Annual rates of small-scale clearing for agriculture in primary forests and woodlands doubled between 2000 and 2014, mirroring increasing population growth. Smallholder clearing in the Democratic Republic of the Congo alone accounted for nearly two-thirds of total forest loss in the basin. Selective logging is the second most significant disturbance driver, contributing roughly 10% of regional gross forest disturbance area and more than 60% of disturbance area in Gabon. Forest loss due to agro-industrial clearing along the Gulf of Guinea coast more than doubled in the last half of the study period. Maintaining natural forest cover in the Congo Basin into the future will be challenged by an expected fivefold population growth by 2100 and allocation of industrial timber harvesting and large-scale agricultural development inside remaining old-growth forests.

Classifying drivers of global forest loss

Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary loss from forestry or wildfire. Using satellite imagery, we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015. Our results indicate that 27% of global forest loss can be attributed to deforestation through permanent land use change for commodity production. The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26%), shifting agriculture (24%), and wildfire (23%). Despite corporate commitments, the rate of commodity-driven deforestation has not declined. To end deforestation, companies must eliminate 5 million hectares of conversion from supply chains each year.

Evaluating the ability of community-protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data

Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Medium-resolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high-resolution LiDAR data have been used to analyze the impact of human-induced forest degradation on forest structure and AGB. The findings of this work indicate that while community-protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.

Identifying important conservation areas for the clouded leopard Neofelis nebulosa in a mountainous landscape: Inference from spatial modeling techniques

The survival of large carnivores is increasingly precarious due to extensive human development that causes the habitat loss and fragmentation. Habitat selection is influenced by anthropogenic as well as environmental factors, and understanding these relationships is important for conservation management. We assessed the environmental and anthropogenic variables that influence site use of clouded leopard Neofelis nebulosa in Bhutan, estimated their population density, and used the results to predict the species’ site use across Bhutan. We used a large camera‐trap dataset from the national tiger survey to estimate for clouded leopards, for the first time in Bhutan, (1) population density using spatially explicit capture–recapture models and (2) site‐use probability using occupancy models accounting for spatial autocorrelation. Population density was estimated at D^Bayesian=0.40 (0.10 SD) and D^maximum−likelihood=0.30 (0.12 SE) per 100 km². Clouded leopard site use was positively associated with forest cover and distance to river while negatively associated with elevation. Mean site‐use probability (from the Bayesian spatial model) was ψ^spatial=0.448 (0.076 SD). When spatial autocorrelation was ignored, the probability of site use was overestimated, ψ^nonspatial=0.826 (0.066 SD). Predictive mapping allowed us to identify important conservation areas and priority habitats to sustain the future of these elusive, ambassador felids and associated guilds. Multiple sites in the south, many of them outside of protected areas, were identified as habitats suitable for this species, adding evidence to conservation planning for clouded leopards in continental South Asia.

Human infectious disease burdens decrease with urbanization but not with biodiversity

Infectious disease burdens vary from country to country and year to year due to ecological and economic drivers. Recently, Murray et al. (Murray CJ et al 2012 Lancet380, 2197-2223. (doi:10.1016/S0140-6736(12)61689-4)) estimated country-level morbidity and mortality associated with a variety of factors, including infectious diseases, for the years 1990 and 2010. Unlike other databases that report disease prevalence or count outbreaks per country, Murray et al. report health impacts in per-person disability-adjusted life years (DALYs), allowing comparison across diseases with lethal and sublethal health effects. We investigated the spatial and temporal relationships between DALYs lost to infectious disease and potential demographic, economic, environmental and biotic drivers, for the 60 intermediate-sized countries where data were available and comparable. Most drivers had unique associations with each disease. For example, temperature was positively associated with some diseases and negatively associated with others, perhaps due to differences in disease agent thermal optima, transmission modes and host species identities. Biodiverse countries tended to have high disease burdens, consistent with the expectation that high diversity of potential hosts should support high disease transmission. Contrary to the dilution effect hypothesis, increases in biodiversity over time were not correlated with improvements in human health, and increases in forestation over time were actually associated with increased disease burden. Urbanization and wealth were associated with lower burdens for many diseases, a pattern that could arise from increased access to sanitation and healthcare in cities and increased investment in healthcare. The importance of urbanization and wealth helps to explain why most infectious diseases have become less burdensome over the past three decades, and points to possible levers for further progress in improving global public health.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Effect of oil palm sustainability certification on deforestation and fire in Indonesia

Many major corporations and countries have made commitments to purchase or produce only "sustainable" palm oil, a commodity responsible for substantial tropical forest loss. Sustainability certification is the tool most used to fulfill these procurement policies, and around 20% of global palm oil production was certified by the Roundtable on Sustainable Palm Oil (RSPO) in 2017. However, the effect of certification on deforestation in oil palm plantations remains unclear. Here, we use a comprehensive dataset of RSPO-certified and noncertified oil palm plantations (∼188,000 km2) in Indonesia, the leading producer of palm oil, as well as annual remotely sensed metrics of tree cover loss and fire occurrence, to evaluate the impact of certification on deforestation and fire from 2001 to 2015. While forest loss and fire continued after RSPO certification, certified palm oil was associated with reduced deforestation. Certification lowered deforestation by 33% from a counterfactual of 9.8 to 6.6% y-1 Nevertheless, most plantations contained little residual forest when they received certification. As a result, by 2015, certified areas held less than 1% of forests remaining within Indonesian oil palm plantations. Moreover, certification had no causal impact on forest loss in peatlands or active fire detection rates. Broader adoption of certification in forested regions, strict requirements to avoid all peat, and routine monitoring of clearly defined forest cover loss in certified and RSPO member-held plantations appear necessary if the RSPO is to yield conservation and climate benefits from reductions in tropical deforestation.

Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild

Wilderness areas, defined as areas free of industrial scale activities and other human pressures which result in significant biophysical disturbance, are important for biodiversity conservation and sustaining the key ecological processes underpinning planetary life-support systems. Despite their importance, wilderness areas are being rapidly eroded in extent and fragmented. Here we present the most up-to-date temporally inter-comparable maps of global terrestrial wilderness areas, which are essential for monitoring changes in their extent, and for proactively planning conservation interventions to ensure their preservation. Using maps of human pressure on the natural environment for 1993 and 2009, we identified wilderness as all 'pressure free' lands with a contiguous area >10,000 km2. These places are likely operating in a natural state and represent the most intact habitats globally. We then created a regionally representative map of wilderness following the well-established 'Last of the Wild' methodology; which identifies the 10% area with the lowest human pressure within each of Earth's 60 biogeographic realms, and identifies the ten largest contiguous areas, along with all contiguous areas >10,000 km2.