Title: A drought monitoring method in the Yellow River basin based on boundary extraction of remote sensing images

Authors: Yang Liu; Xiaodong Shi; Ge Zhang; Shaofeng Zhao; Ze Wang

Addresses: Cloud Computing and Big Data Institute, Henan University of Economics and Law, Zhengzhou Henan, 450046, China ' Cloud Computing and Big Data Institute, Henan University of Economics and Law, Zhengzhou Henan, 450046, China ' Cloud Computing and Big Data Institute, Henan University of Economics and Law, Zhengzhou Henan, 450046, China ' Cloud Computing and Big Data Institute, Henan University of Economics and Law, Zhengzhou Henan, 450046, China ' College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou Gansu, 730050, China

Abstract: In order to improve the extraction accuracy of remote sensing image boundary information and improve the fitting degree between drought monitoring results and actual results, this study designed a drought monitoring method in the Yellow River basin based on boundary extraction of remote sensing images. ETM+ Landsat satellite was selected to collect real-time remote sensing images comprehensively. After geometric correction and radiometric correction, edge information is extracted from remote sensing image data. Then the spatial inversion process of remote sensing index and surface temperature characteristics is established, and the drought monitoring level of the Yellow River basin is set after the elevation correction. According to the experiment, the accuracy of the method to extract the image boundary information is always above 91.7%, and the fitting degree between the obtained drought monitoring results and the actual results is always above 0.94, indicating that the method effectively achieves the design expectation.

Keywords: Yellow River basin; drought conditions; remote sensing monitoring; remote sensing image; boundary extraction.

DOI: 10.1504/IJETM.2024.135559

International Journal of Environmental Technology and Management, 2024 Vol.27 No.1/2, pp.23 - 36

Received: 27 Jul 2022
Accepted: 07 Dec 2022
Published online: 18 Dec 2023 *

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