Global forests lost 28.3 million hectares in 2023 and continue to be threatened by substantial natural and human disturbances. From a temporal perspective (over recent decades, or even recent centuries), we have witnessed spatial-temporal forest changes including changes in carbon storage, forest landscapes, and forest diversity and we have explored their possible drivers. From a spatial perspective, we seek to detect extreme events (deforestation, forest fire, insect outbreaks, droughts, and other forest disturbances) and collect new forest ecological data (e.g., tree canopy height, forest aboveground biomass, and forest soil biochemistry) to support forest management and planning. Remote sensing (RS) and geographic information systems (GISs) are cost-effective tools for long-term monitoring of forest change and large-scale detection of extreme events, thereby supporting forest management and planning. Novel applications of remote sensing and GISs could involve entirely new methods, such as deep learning or machine learning, or the use of high-quality sensors. They may also consist of updated applications of classical techniques and existing sensors.
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