Forests around the world are under immense pressure from human land use and climate change, however vastly improved remote sensing techniques can help identify where forests are under greatest stress from a wide range of human-caused and climate risks.
Forest stability is a key component of ecosystem integrity and primary forests. Current remote sensing products largely focus on deforestation rather than forest degradation, and depend on machine learning calibrated with extensive field measurements. To address this, we used MODIS time series to develop a novel approach for mapping forest stability across forest biomes.
Using remote sensing time series, we found increasing trends in fire and logging disturbances in primary forests of the Angara case study region. We also found large increases in fires closer to human settlements, roads, and logged sites.