Combining Deep Learning and Hydrological Analysis for Identifying Check Dam Systems from Remote Sensing Images and DEMs in the Yellow River Basin

Mengqi Li, Wen Dai (), Mengtian Fan, Wei Qian, Xin Yang, Yu Tao and Chengyi Zhao
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Mengqi Li: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Wen Dai: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Mengtian Fan: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Wei Qian: School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 211800, China
Xin Yang: School of Geography, Nanjing Normal University, Nanjing 210023, China
Yu Tao: School of Geographical Information and Tourism, Chuzhou University, Chuzhou 239000, China
Chengyi Zhao: School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

IJERPH, 2023, vol. 20, issue 5, 1-16

Abstract: Identifying and extracting check dams is of great significance for soil and water conservation, agricultural management, and ecological assessment. In the Yellow River Basin, the check dam, as a system, generally comprises dam locations and dam-controlled areas. Previous research, however, has focused on dam-controlled areas and has not yet identified all elements of check dam systems. This paper presents a method for automatically identifying check dam systems from digital elevation model (DEM) and remote sensing images. We integrated deep learning and object-based image analysis (OBIA) methods to extract the dam-controlled area’s boundaries, and then extracted the location of the check dam using the hydrological analysis method. A case study in the Jiuyuangou watershed shows that the precision and recall of the proposed dam-controlled area extraction approach are 98.56% and 82.40%, respectively, and the F1 score value is 89.76%. The completeness of the extracted dam locations is 94.51%, and the correctness is 80.77%. The results show that the proposed method performs well in identifying check dam systems and can provide important basic data for the analysis of spatial layout optimization and soil and water loss assessment.

Keywords: check dam system extraction; object-based image analysis; Yellow River Basin; terrain analysis; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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