Article describes how forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County, which is a mountainous area with rich biodiversity and is witnessing rapid development of economic forests in Yunnan province of China.

Article describes how forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County.

This article presents a study of urban expansion in the Beijing–Tianjin–Hebei plain based on ALOS Global Digital Surface Model “ALOS World 3D-30 m” (AW3D30 DSM), Shuttle Radar Topography Mission (SRTM) DSM, and Landsat 7 ETM+ images. The method proposed in this paper can provide rapid and large-scale statistics to study urban construction expansion in the world.