Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data

Land cover and land use (LCLU) is an important component of the Earth system. It affects ecosystem services, water conservation, infectious disease outbreaks, food security, the climate system, urban forms, etc. In the last few decades, remote sensing technology has evolved dramatically for better radiometric, spatial, and spectral resolution image products. Such advances in remote sensing, together with a variety of machine- and deep learning algorithms and multiple high-performance cloud computing platforms, further provide the potential and opportunities for fine-resolution, global-scale, and dynamic LCLU mapping. In addition, along with the development of drone technology, the (near) real-time monitoring of land surface in different sectors, such as forestry and agriculture, has attracted much attention in recent years. The improved documenting of LCLU leads the scientific community to further explore the driving factors of LCLU changes and reveal the underlying mechanisms for better land use planning. This Reprint synthesizes recent advances in LCLU mapping, change detection, accuracy assessment, and driver analysis, providing important insights into the remote sensing of LCLU.