General
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 |
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 |
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 |
Tropical dry forest land use/land cover change detection using semi-supervised deep learning algorithms and remote sensingBackgroundOpen access copy available |
Remote sensing of drylands: An overviewBackgroundOpen access copy available |
A systematic review on remote sensing of dryland ecological integrity: Improvement in the spatiotemporal monitoring of vegetation is requiredBackgroundOpen access copy available |
