Change detection of wetland restoration in China’s Sanjiang National Nature Reserve using STANet method based on GF-1 and GF-6 images

Abstract

Wetlands are important ecosystems, and detecting changes in wetlands plays a very important role in the management of wetland resources. Therefore, we selected Sanjiang National Nature Reserve as the study area and proposed a method in which bi-temporal GaoFen (GF) remote sensing data was combined with a spatial–temporal attention neural network (STANet) model, coupled with the selection of spectral variables. We used the bi-temporal images from GF-1 and GF-6 and compared three band selection methods, Relief F, principal component analysis, and RGB combination, and two STANet-based change detection methods (BAM: basic spatial–temporal attention module and PAM: pyramid spatial–temporal attention module) to improve the change detection of the wetland restoration. The results show that the accuracy of wetland change detection is related to different combinations of spectral variable selection and STANet models. The combination of Relief F feature selection and the BAM model led to the highest accuracy of the wetland change detection, 71.72 % for F1-Score and 76.92 % for MIou. The vector results of the change detection statistics show that the area of the Sanjiang National Nature Reserve cultivated lands converted to wetlands is 46.05 km2. This study implies that the STANet method has the capacity of detecting complex wetland restoration. Red, Bule, NIR and Red Edge1 bands of GaoFen data can provide effective information in the detection of changes in wetlands. The results of the study can be used as a reference for rational planning of wetland restoration, seeking a balance between cultivated lands and wetlands, which is important for the sustainable development of wetland.