Mapping the terraces on the Loess Plateau based on a deep learning-based model at 1.89 m resolution.

ABSTRACT: Terraces on the Loess Plateau play essential roles in soil conservation, as well as agricultural productivity in this region. However, due to the unavailability of high-resolution (<10 m) maps of terrace distribution for this area, current research on these terraces is limited to specific regions. We developed a deep learning-based terrace extraction model (DLTEM) using texture features of the terraces, which have not previously been applied regionally. The model utilizes the UNet++ deep learning network as its framework, with high-resolution satellite images, a digital elevation model, and GlobeLand30 as the interpreted data and topography and vegetation correction data sources, respectively, and incorporates manual correction to produce a 1.89 m spatial resolution terrace distribution ma

SUBMITTER: Lu Y 

PROVIDER: S-EPMC9981555 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature