High-efficiency supervision method for green geological exploration based on remote sensing
MA Shibin1,2,3(), PI Yingnan1,2,3(), WANG Jia1,2,3, ZHANG Kun1,2,3, LI Shenghui1,2,3, PENG Xi1,2,3
1. Institute of Geological Survey of Qinghai Province, Xining 810012, China 2. Qinghai-Tibet Plateau During the North Qilian Geology and Mineral Resources Laboratory of Qinghai Province, Xining 810012, China 3. Qinghai Remote Sensing Big Data Engineering Technology Research Center, Xining 810012, China
Resources serve as the material guarantee of the existence and development of human society. However, as a basis for resource discovery, geological exploration tends to damage the ecological environment. With the official release of the Specification for Green Geological Survey and Mineral Exploration (DZ/T 0374—2021) in June 2021, green geological exploration has been officially promoted to the national level and was implemented nationwide in China. However, the supervision of green geological exploration faces many difficulties and challenges in practice. To meet the demands of responsible entities for the supervision, inspection, and management of green geological exploration projects, this study proposed a high-efficiency supervision method based on remote sensing. By applying this method to a polymetallic survey project in Qinghai Province, this study expounded the specific implementation process of the method, as well as its effectiveness in the supervision services for geological exploration projects. As indicated by the results, the method proposed in this study allows for ascertaining the basic external environment of the project area, following the project layout and implementation, and verifying the consistency with the project plan. In addition, through quantitative information investigation, this method allows for the full identification of the disturbance and damage to the ecological environment and its restoration during the project implementation. Therefore, this study can provide effective technical support and basic data for evaluating the performance of green geological exploration.
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2023, Vol. 35 
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