Operational forest monitoring is essential for the effective implementation of national and international initiatives to reduce deforestation and forest degradation. Such monitoring systems are especially important within Brazilian humid tropical primary forests, where they support enforcement of national policies to prevent deforestation. Several satellite-based forest disturbance monitoring systems are operating in Brazil, including MapBiomas Alerta (MBA), Tree Cover Loss supported by Global Forest Watch (TCL), and Tropical Moist Forest developed by the European Commission’s Joint Research Centre (TMF). These systems differ in their forest disturbance definitions, input satellite data, and change detection methodologies. As a result, their annual estimates of primary forest disturbance are not fully consistent, which complicates the implementation of forest conservation policy and introduces uncertainties and potential bias in greenhouse gas emissions accounting. In this study, we followed good practice recommendations to evaluate the performance of these monitoring products to detect humid tropical primary forest disturbances in 2023 and 2024 using a probability reference sample. We compared the map-based and sample-based disturbance areas for each product and estimated their sensitivity and precision for primary forest disturbance detection. Our analysis showed that the MBA product mapped 64%–65% of the sample-based reference deforested area while maintaining the highest mapping precision among the three systems. The TCL product detected the highest percentage of the reference deforested area (82%–94%), while maintaining high precision for mapping high-severity, stand-replacement disturbances. TMF outperformed other products in capturing low-severity disturbances. The detailed comparative evaluation of the three operational monitoring systems highlights their respective strengths and limitations and explains the differences in forest disturbance reporting. Our results provide guidance for researchers, policymakers, and practitioners in selecting the most appropriate products for specific applications.

Quantifying the drivers of tree cover loss globally provides a synoptic understanding of pressures on the world's forests. Existing information about tree cover loss drivers relies on maps of coarse spatial and thematic resolution. In this study, we quantified the global extent of tree cover loss in 2018 at the scale of individual disturbances and provided a comprehensive accounting of land use outcomes using a global probability sample of 600 5 × 5-kilometer blocks mapped with high-resolution (3- to 10-meter) satellite data. Out of 277 thousand square kilometers of estimated global tree cover loss, nearly a third (29.0%) was due to long-term conversion of tree cover to other land uses, including conversion of natural tree cover to pasture (15.0%), cropland (6.4%), and nontimber tree plantations (3.8%).

Ontario’s forests are changing rapidly under the pressure of industrial logging, coupled with the effects of climate change, which is facilitating increased wildfires. The reduction of mature and old-growth forests negatively affects ecosystem functions, including wildlife habitat suitability and long-term carbon storage. Here, we present an operational mapping tool to identify and monitor the extent of high-carbon primary forests (HCPF) to support their protection and track progress toward the Kunming-Montréal Global Biodiversity Framework’s Target 3. We define HCPF as mature and old-growth, naturally regenerated forests that have not experienced tree canopy disturbance over the past 50 years and are located within unfragmented landscape patches with a minimum area of 1,000 ha. By integrating existing maps, satellite-based products, and detailed manual image interpretation, we mapped 28.4 Mha of HCPF in Ontario for 2020. HCPF comprised 80.7% of all forests outside industrial timber concessions, whereas within private and industrial forests, their percentage was much lower (29.7%). They represented less than 2% of all forests within the Mixedwood Plains ecozone of southern Ontario. The HCPF vegetation carbon stock density is higher than that of fragmented and recently disturbed forests by 7% within the entire province, and by 18% within the region of industrial forestry. Ontario’s unfragmented landscapes contain more than 72% of the total soil organic carbon stock of the province, illustrating their importance for long-term carbon storage. Unfragmented landscapes are essential for the survival of Ontario’s woodland caribou populations; however, their percentage within most caribou population ranges was below the management threshold of 65% and continued to decline. From 2021 to 2025, Ontario’s HCPF area decreased by 3.5% (0.2 Mha/yr) due to wildfires and logging. Within the region of industrial forestry, the proportion of 2021–2025 logging within HCPF exceeded that in non-HCPF forests, and the annual logging area within HCPF increased by 55%. This acceleration of logging and associated road construction illustrates that HCPF, as opposed to secondary forests, continue to be the major source of timber in Ontario. The HCPF method provides the tools and data required to accurately assess and monitor mature and old-growth primary forests, and to develop effective conservation strategies and policies at the provincial and national levels. As demonstrated for Ontario, the HCPF method can be applied to all provinces to facilitate the protection of some of the last remaining primary forests in Canada.

High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed an openly available, annual, 10 m spatial resolution maize and soybean map product over the Contiguous United States (CONUS) from 2019 to 2022 (available at https://glad.umd.edu/dataset/mapping-crops-10-m-resolution-united-states, last access: 26 December 2025). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10 d analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10 d ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95 % with standard errors of less than 1 %. User's accuracies (UAs) and producer's accuracies (PAs) for maize were higher than 91 % and 84 % across the years, and UAs and PAs for soybean were greater than 88 % and 82 %, respectively. To illustrate the substantial improvement of the 10 m map over existing datasets, e.g., the 30 m Cropland Data Layer (CDL), we aggregated the 10 m maps to 30 m spatial resolution and quantified the number of mixed pixels that can be reduced by improving the mapping from 30 to 10 m. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1 % to 7 %, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 8 % and 9 %, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10 m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales. The dataset is also available at 10.6084/m9.figshare.28934993.v2 (Li et al., 2025).

Food security worldwide is increasingly threatened by population growth, shifting diets, geopolitical conflicts, and climate change impacts. Annual operational cropland monitoring is required to support the United Nations Zero Hunger Sustainable Development Goal. Landsat satellite data provide a foundation for such global, independent, high-cadence monitoring at 30-m spatial resolution suitable for agricultural policy and management interventions, policy responses, and market adjustments. Here, we used Landsat Analysis Ready Data developed by the Global Land Analysis and Discovery Lab (GLAD-ARD) and machine learning to map global cropland extent annually from 2015 to 2024. We showed that the global cropland area increased by more than 6% over the past decade. By combining sample-based cropland area estimates from our research and the earlier analysis (2003-2019), we estimate that the global cropland area has expanded by nearly 14% since 2003. Between 2015 and 2024, Africa accounted for the largest regional increase (+24.5 Mha). At the national scale, Brazil experienced the largest gain (+16.5 Mha) and Morocco the largest loss (-0.38 Mha). A third (33.3%) of all new cropland was established through natural vegetation clearing or irrigation expansion within natural drylands. The overall accuracies of the 2015 and 2024 cropland maps were 97.8% (Standard Error 0.3%) and 97.3% (SE 0.4%), respectively. Despite cropland expansion, population growth has outpaced cropland gains; between 2015 and 2024, per-capita cropland area declined from 0.166 to 0.161 ha per person. Our data illustrate the combined effects of changes in land use priorities, climate, water supply, international trade, and armed conflicts on global cropland extent dynamics during the last decade.

Global forests provide key ecosystem services, from climate regulation to biodiversity habitat, but are under increasing pressure from the combined impacts of climate and land use change. Here, we show that forest disturbance due to fire is growing globally, with the most dramatic increases in intact forest landscapes, highlighting an existential threat to remaining high biomass, high biodiversity forests. The global annual area of forest disturbance due to fire for 2023 and 2024 was highest since the beginning of monitoring in 2001. Compared to 2002-2022 average annual forest disturbance due to fire, the 2023-2024 average was 2.2 times higher globally and 3 times higher in the Tropics. More than ¼ of all 2024 forest disturbance from fire occurred in tropical forests. We found a statistically significant increasing trend of forest disturbance due to fire from 2002 to 2024 in all climate domains except Subtropical. High forest, low deforestation tropical countries were not exempt, with Guyana and the Republic of the Congo experiencing record forest disturbance due to fire. Our results agree with recently estimated increases in global forest fire emissions and active fire detections. The unprecedented scale of fires in the world's most remote forests is a potential harbinger of ecosystem tipping points. Protecting these remaining unfragmented high conservation value forests from this threat poses a daunting and as yet undeveloped policy and capacity challenge.

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency's (ESA's) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

Indonesia has experienced rapid primary forest loss, second only to Brazil in modern history. We examined the fates of Indonesian deforested areas, immediately after clearing and over time, to quantify deforestation drivers in Indonesia. Using time-series satellite data, we tracked degradation and clearing events in intact and degraded natural forests from 1991 to 2020, as well as land use trajectories after forest loss. While an estimated 7.8 Mha (SE = 0.4) of forest cleared during this period had been planted with oil palms by 2020, another 8.8 Mha (SE = 0.4) remained unused. Of the 28.4 Mha (SE = 0.7) deforested, over half were either initially left idle or experienced crop failure before a land use could be detected, and 44% remained unused for 5 y or more. A majority (54%) of these areas were cleared mechanically (not by escaped fires), and in cases where idle lands were eventually converted to productive uses, oil palm plantations were by far the most common outcome. The apparent deliberate creation of idle deforested land in Indonesia and subsequent conversion of idle areas to oil palm plantations indicates that speculation and land banking for palm oil substantially contribute to forest loss, although failed plantations could also contribute to this dynamic. We also found that in Sumatra, few lowland forests remained, suggesting that a lack of remaining forest appropriate for palm oil production, together with an extensive area of banked deforested land, may partially explain slowing forest loss in Indonesia in recent years.

European forests are among the most extensively studied ecosystems in the world, yet there are still debates about their recent dynamics. We modeled the changes in tree canopy height across Europe from 2001 to 2021 using the multidecadal spectral data from the Landsat archive and calibration data from Airborne Laser Scanning (ALS) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidars. Annual tree canopy height was modeled using regression tree ensembles and integrated with annual tree canopy removal maps to produce harmonized tree height map time series. From these time series, we derived annual tree canopy extent maps using a >= 5 m tree height threshold. The root-mean-square error (RMSE) for both ALS-calibrated and GEDI-calibrated tree canopy height maps was <= 4 m. The user's and producer's accuracies estimated using reference sample data are >= 94% for the tree canopy extent maps and >= 80% for the annual tree canopy removal maps. Analyzing the map time series, we found that the European tree canopy extent area increased by nearly 1% overall during the past two decades, with the largest increase observed in Eastern Europe, Southern Europe, and the British Isles. However, after the year 2016, the tree canopy extent in Europe declined. Some regions reduced their tree canopy extent between 2001 and 2021, with the highest reduction observed in Fennoscandia (3.5% net decrease). The continental extent of tall tree canopy forests (>= 15 m height) decreased by 3% from 2001 to 2021. The recent decline in tree canopy extent agrees with the FAO statistics on timber harvesting intensification and with the increasing extent and severity of natural disturbances. The observed decreasing tree canopy height indicates a reduction in forest carbon storage capacity in Europe.

Agricultural intensification and forest conservation are often seen as incompatible. Agricultural interventions can help boost food security for poor rural communities but in certain cases can exacerbate deforestation, known as the rebound effect. We tested whether coupling agricultural interventions with participatory forest zoning could improve food security and promote forest conservation in the Democratic Republic of the Congo. Simple agricultural interventions led to a >60% increase in cassava yields and a spill-over effect of improved cassava variety uptake in non-intervention zones. Household surveys conducted at the end of the 8 year project implementation period revealed that households that received agricultural interventions had more favorable attitudes toward forest zoning and conservation. The surveys also showed that farmers in the intervention domain practiced less land-intensive field and fallow management strategies compared to those practiced in the non-intervention domain. However, an 18 year time series analysis of Landsat satellite data revealed that agricultural expansion persisted in areas both with and without intervention assistance, and there is risk of a rebound effect. Approximately 70% of the tree cover loss that occurred outside of the agricultural areas was located within a 3 km buffer zone surrounding the outermost edges of the agricultural areas, which suggested that the majority of tree cover loss was caused by agricultural expansion. Within that 3 km buffer, average annual tree cover loss during the post-intervention period was higher in the intervention domain compared to the non-intervention domain (0.17% yr −1 compared to 0.11% yr −1 respectively, p < 0.001), suggesting risk of a rebound effect. The disconnection between household perceptions of zoning adherence and actual behavior indicates the importance of strengthening governance structures for community-based monitoring and enforcement.

Forest fires contribute to global greenhouse gas emissions and can negatively affect public health, economic activity, and provision of ecosystem services. In boreal forests, fires are a part of the ecosystem dynamics, while in the humid tropics, fires are largely human-induced and lead to forest degradation. Studies have shown changing fire dynamics across the globe due to both climate and land use change. However, global trends in fire-related forest loss remain uncertain due to the lack of a globally consistent methodology applied to high spatial resolution data. Here, we create the first global 30-m resolution satellite-based map of annual forest loss due to fire. When producing this map, we match the mapped area of forest loss due to fire to the reference area obtained using a sample-based unbiased estimator, thus enabling map-based area reporting and trend analysis. We find an increasing global trend in forest loss due to fire from 2001 to 2019, driven by near-uniform increases across the tropics, subtropical, and temperate Australia, and boreal Eurasia. The results quantify the increasing threat of fires to remaining forests globally and may improve modeling of future forest fire loss rates under various climate change and development scenarios.

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.

The conversion of natural land cover into human-dominated land use systems has significant impacts on the environment. Global mapping and monitoring of human-dominated land use extent via satellites provides an empirical basis for assessing land use pressures. Here, we present a novel 2019 global land cover, land use, and ecozone map derived from Landsat satellite imagery and topographical data using derived image feature spaces and algorithms suited per theme. From the map, we estimate the spatial extent and dispersion of land use disaggregated by climate domain and ecozone, where dispersion is the mean distance of land use to all land within a subregion. We find that percent of area under land use and distance to land use follow a power law that depicts an increasingly random spatial distribution of land use as it extends across lands of comparable development potential. For highly developed climate/ecozones, such as temperate and sub-tropical terra firma vegetation on low slopes, area under land use is contiguous and remnant natural land cover have low areal extent and high fragmentation. The tropics generally have the greatest potential for land use expansion, particularly in South America. An exception is Asian humid tropical terra firma vegetated lowland, which has land use intensities comparable to that of temperate breadbaskets such as the United States' corn belt. Wetland extent is inversely proportional to land use extent within climate domains, indicating historical wetland loss for temperate, sub-tropical, and dry tropical biomes. Results highlight the need for planning efforts to preserve natural systems and associated ecosystem services. The demonstrated methods will be implemented operationally in quantifying global land change, enabling a monitoring framework for systematic assessments of the appropriation and restoration of natural land cover.

Spatiotemporally consistent data on global cropland extent is essential for tracking progress towards sustainable food production. In the present study, we present an analysis of global cropland area change for the first two decades of the twenty-first century derived from satellite data time-series. We estimate that, in 2019, the cropland area was 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C year −1 . From 2003 to 2019, cropland area increased by 9% and cropland NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019, global per-capita cropland area decreased by 10% due to population growth. However, the per-capita annual cropland NPP increased by 3.5% as a result of intensified agricultural land use. The presented global, high-resolution, cropland map time-series supports monitoring of natural land appropriation at the local, national and international levels.

Spatiotemporally consistent multidecadal data on forest extent and structure is the key for quantifying carbon storage, GHG emissions, degradation, and recovery of tropical forests. Consistently processed long-term Landsat data record is the only tool that enables such capacity. Here, we prototyped regional-level 2000–2019 forest structure monitoring and pantropical forest structure mapping for the year 2019 through the integration of the Landsat analysis-ready data with airborne lidar and Global Ecosystem Dynamics Investigation (GEDI) data. The presented approach can support the data needs of many global, regional, and national climate change mitigation and sustainable development initiatives.

Biodiversity hotspots are conservation priority areas that feature exceptionally high levels of species endemism and high levels of habitat loss. The Guinean Forests of West Africa hotspot, home to a quarter of all the mammal species of Africa, has experienced high levels of forest loss within its protected areas. Here, we analyzed tree cover loss and its proximate drivers within Guinée Forestière, a high biodiversity region within the Guinean Forests of West Africa hotspot, both inside and outside protected areas. Using Landsat analysis ready data and a regionally calibrated, annual forest change detection model, we mapped tree cover loss occurring across this region from 2000 to 2018. We quantified the area of tree cover loss and identified proximate drivers using a statistical sample of reference data. The total tree cover loss in Guinée Forestière between years 2000 and 2018 was 10,907 km 2 (SE 889 km 2 ), which consists of approximately 25% of the region’s total land area. Of this total loss, 364 km 2 (SE 91 km 2 ) occurred within protected areas of high biodiversity value. Tree cover loss was not consistent across high biodiversity areas and did not appear to be related to protected area classification. Smallholder agriculture (subsistence and cash crop farming) was the primary driver of tree cover loss across Guinée Forestière. This research provides multitemporal spatial data on tree cover dynamics that is required for effective implementation of sustainable management and biodiversity conservation strategies within the broader socioecological landscape of Guinée Forestière. We also highlight important limitations to consider and address when using remote sensing to automate change detection across landscapes.

Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R-2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R-2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.

Spatiotemporally consistent data on global cropland extent is a key to tracking progress toward hunger eradication and sustainable food production1,2. Here, we present an analysis of global cropland area and change for the first two decades of the 21st century derived from satellite data time-series. We estimate 2019 cropland area to be 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C yr-1. From 2003 to 2019, cropland area increased by 9% and crop NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019 global population growth outpaced cropland area expansion, and per capita cropland area decreased by 10%. However, the per capita annual crop NPP increased by 3.5% as a result of intensified agricultural land use. The presented global high-resolution cropland map time-series supports monitoring of sustainable food production at the local, national, and international levels.