High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms

Abstract

Abstract It is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points located in sandy land and remote sensing images of high-spectral and spatial resolution Sentinel-1, Sentinel-2, and GDEM (V3), this article made a research on data of large-scale and high-precision mapping classification of this area between 2015 and 2020 by using convolutional neural network deep learning algorithm. The results showed that the types of aeolian sandy landform in the west of Horqin Sandy Land mainly include longitudinal dune, flat sandy land, mild undulating sand land, nest-shaped land, parabolic dune, barchan dune, and dune chain, with an area of 1735.62, 51.32, 251.38, 902.07, 49.57, and 101.63 km2. Among them, longitudinal dune, barchan dune, and dune chain have the largest area, while parabolic dunes and flat sand land are smaller. Between 2015 and 2020, the area of aeolian landforms was reduced by 89.27 km2 and transformed into an oasis from a desert. This study adopted remote sensing data by high-resolution Sentinel and GDEM (V3) and convolutional neural network deep learning algorithm to map the aeolian landforms effectively. The precision of aeolian landform classification and Kappa coefficient in the western part of Horqin Sandy Land is as high as 95.51% and 0.8961. Combined with Sentinel-1, Sentinel-2, and GDEM (V3), the deep learning algorithm based on the convolution neural network can timely and effectively monitor the changes of sand dunes, which can be used for large-scale aeolian landforms.