Land Use Change and Trends
Mapping tropical forest degradation with deep learning and Planet NICFI dataBackgroundForest degradation, driven by logging, fire, and infrastructure expansion, represents a major yet under-detected source of carbon emissions in tropical forests. Unlike deforestation, degradation involves partial canopy loss and is difficult to capture using conventional remote sensing due to small-scale disturbances and rapid vegetation recovery. Existing global products often underestimate degradation or fail to attribute its causes. Advances in high-resolution satellite imagery and deep learning provide new opportunities to improve detection accuracy. Open access copy available |
DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series AnalysisBackgroundOpen access copy available |
Effects of different management regimes on mangrove ecosystem services in Java, IndonesiaBackgroundIndonesia’s mangrove forests have decreased from 4.5 million hectares (ha) to under 3 million ha since the 1980s, largely due to the lack of monetary value attributed to mangrove ecosystem services, leading to conversion into aquaculture. Developing a valuation system for mangroves that includes both economically valuable products (i.e., timber, food) and ecosystem services allows decision makers to better assess the impacts of management decisions on the important ecosystem services and properties provided by mangroves. Open access copy available |
Taking the pulse of Earth’s tropical forests using networks of highly distributed plotsBackgroundTropical forests play a critical but complex role in global carbon cycling, biodiversity conservation, and climate regulation. These complex dynamics are due to spatial heterogeneity and varying disturbance regimes. Traditional monitoring approaches often rely on remote sensing, which may not capture fine-scale ecological processes. In response, global scientific collaborations have developed extensive forest plot networks to monitor forest structure, biomass, and ecological changes over time. These distributed plots provide high-resolution, ground-based insights into tropical forest conditions across continents. Open access copy available |
Long-term (1990–2019) monitoring of forest cover changes in the humid tropicsBackgroundTropical moist forests are essential for biodiversity, climate regulation, and global carbon storage, yet they face increasing pressure from deforestation and degradation. Accurate, long-term monitoring of forest dynamics is necessary to support climate policies, including REDD+ and Nationally Determined Contributions (NDCs). Previous studies have provided partial insights, but a comprehensive spatial and temporal characterization of forest degradation and recovery remains limited. Advances in satellite imagery and cloud computing now enable consistent monitoring at pantropical scales. Open access copy available |
Belize National Forest Monitoring System 2001-2020BackgroundBelize’s diverse ecosystems, land tenure systems, and land-use dynamics require a robust and flexible National Forest Monitoring System (NFMS). Early efforts focused on establishing permanent forest inventory plots in the late 1990s to address data gaps in forest structure and carbon dynamics. Over time, Belize has expanded its forest monitoring framework to integrate both ground-based and remote sensing approaches, ensuring transparency, consistency, and national ownership of forest data systems. Open access copy available |
Framework for National Forest Monitoring SystemBackgroundOpen access copy available |
National forest monitoring system assessment tool – Quick guidanceBackgroundOpen access copy available |
Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 dataBackgroundAvailable with subscription or purchase |
Afforestation and Reforestation Have Varying Biodiversity Impacts Across and Within BiomesBackgroundAfforestation and reforestation (AR) are widely promoted as nature-based solutions (NbS) for carbon dioxide removal and climate mitigation. Global initiatives aim to expand forest cover significantly to meet climate targets. However, AR can produce unintended biodiversity impacts, particularly when implemented in ecosystems such as grasslands or savannas, where native species are not adapted to forest conditions. The ecological outcomes of AR vary across biomes and species, highlighting the need for spatially explicit, biodiversity-sensitive planning frameworks. Open access copy available |
