A Study of Sandy Land Changes in the Chifeng Region from 1990 to 2020 Based on Dynamic Convolution

Hongbo Zhu, Bing Zhang (), Xinyue Chang, Weidong Song, Jiguang Dai and Jia Li
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Hongbo Zhu: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Bing Zhang: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Xinyue Chang: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Weidong Song: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Jiguang Dai: School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Jia Li: Dalian Huangbohai Marine Surveying Data Information Co., Ltd., Dalian 116000, China

Sustainability, 2023, vol. 15, issue 17, 1-15

Abstract: Desertification is the process of land degradation and the reduction or destruction of biological potential in arid, semi-arid, and semi-humid areas, and its impact on agricultural development and the ecological environment cannot be ignored. Therefore, many researchers have aimed to achieve the acquisition of large-scale sandy land areas using sandy land extraction algorithms based on remote sensing images. However, the sandy land extraction accuracy needs to be improved because of the variable contour features in the remote sensing images and the easy confusion with targets such as the Gobi and bare ground areas. In this study, we combine the dynamic convolution with a U-Net model and propose the DU-Net sandy land extraction model. The method is based on dynamic convolution, which can adaptively learn the complex features of the target and build a dynamic convolutional neural network to achieve high-accuracy extraction of complex targets. DU-Net achieved 86.32% in IoU, 93.22% in precision, 94.5% in recall, and 92.66% in F 1 -score in sandy land extraction accuracy, which are 4.68%, 2.33%, 3.09%, and 2.76% improvements, respectively, compared with the U-Net static neural network. Based on this, we obtained the spatial and temporal evolution trends of sandy land areas based on Landsat images in the Chifeng region in the Inner Mongolia Autonomous Region, China. Meanwhile, in order to investigate the mechanism of spatial and temporal changes in the sandy land area in the study region over the past 30 years, the direct and indirect effects of seven climatic and human socioeconomic activity factors on the changes in the sandy land area in the study region were evaluated based on a structural equation model. The results show that the sandy area in the Chifeng region tended to first increase and then decrease over the study period, with the sandy land area reaching its maximum around the year 2000. In addition, the main driving factor for the change in the sandy land area in the Chifeng region has been human socioeconomic activities, with climatic conditions as the secondary driving factor. The method proposed in this paper realizes the rapid extraction of sandy land areas with high accuracy at a large scale and with a long time series and provides a basis for assessing the effectiveness of ecosystem restoration projects.

Keywords: dynamic convolution; changes in sandy land area; structural equation model; driving force analysis; U-Net (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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