Mapping, Remote Sensing, and GIS
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 |
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 |
User-Driven Land Cover Change Prediction Map Tool for Land Conservation PlanningBackgroundEffective conservation planning requires forward-looking tools that anticipate land cover change, rather than relying solely on historical analysis. Rapid urbanization and land-use change threaten ecosystems and biodiversity, particularly in regions experiencing development pressure. Traditional models often lack accessibility for nontechnical users, limiting their application in real-world decision-making. Integrating machine learning with user-friendly platforms can enhance stakeholder engagement and improve conservation outcomes. Open access copy available |
Integrating satellite-based forest disturbance alerts improves detection timeliness and confidenceBackgroundSatellite-based forest monitoring systems are essential for detecting deforestation and supporting climate change mitigation efforts. Multiple alert systems exist, including Global Land Analysis and Discovery (GLAD)-Landsat, GLAD-Sentinel-2, and RADD, each with distinct capabilities and limitations related to sensor type and environmental conditions. Optical systems struggle under cloud cover, while radar systems may miss certain disturbance signals. This creates uncertainty for users and highlights the need for integrated monitoring approaches. Open 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 |
