Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF)

This Special Issue highlights the evolving role of remote sensing in monitoring land use, land-use change, and forestry (LULUCF), a critical component of greenhouse gas (GHG) accounting and climate policy. As demands for transparent and spatially explicit reporting increase, Earth observation (EO) data-particularly from programmes such as Copernicus-are becoming central to national inventories and monitoring, reporting, and verification (MRV) systems. The contributions demonstrate a clear shift from experimental research to operational applications. High-resolution satellite data (e.g., Sentinel-2), cloud-based platforms such as Google Earth Engine, and machine learning methods-especially Random Forest-are widely used to produce consistent, large-scale land-use classifications. A recurring theme is the integration of multi-source datasets, including spectral, topographic, and ancillary information, to improve accuracy and better represent complex land-use categories. The papers also emphasize validation, methodological harmonization, and alignment with LULUCF reporting frameworks. Overall, this collection underlines the growing maturity and policy relevance of EO-based approaches for robust, scalable, and transparent LULUCF monitoring.