Identifying and monitoring tailings ponds by integrating multi-source geographic data and high-resolution remote sensing images: A case study of Gejiu City, Yunnan Province

LIU Xiaoliang; WANG Zhihua; XING Jianghe; ZHOU Rui; YANG Xiaomei; LIU Yueming; ZHANG Junyao; MENG Dan

doi:10.6046/zrzyyg.2022480

2024 Vol. 36, No. 1

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Article navigation > Remote Sensing for Natural Resources > 2024 > 36(1): 103-109

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LIU Xiaoliang, WANG Zhihua, XING Jianghe, ZHOU Rui, YANG Xiaomei, LIU Yueming, ZHANG Junyao, MENG Dan. 2024. Identifying and monitoring tailings ponds by integrating multi-source geographic data and high-resolution remote sensing images: A case study of Gejiu City, Yunnan Province. Remote Sensing for Natural Resources, 36(1): 103-109. doi: 10.6046/zrzyyg.2022480

Citation:

LIU Xiaoliang, WANG Zhihua, XING Jianghe, ZHOU Rui, YANG Xiaomei, LIU Yueming, ZHANG Junyao, MENG Dan. 2024. Identifying and monitoring tailings ponds by integrating multi-source geographic data and high-resolution remote sensing images: A case study of Gejiu City, Yunnan Province. Remote Sensing for Natural Resources, 36(1): 103-109. doi: 10.6046/zrzyyg.2022480

Identifying and monitoring tailings ponds by integrating multi-source geographic data and high-resolution remote sensing images: A case study of Gejiu City, Yunnan Province

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
;2. University of Chinese Academy of Sciences, Beijing 100049, China
;3. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
;4. Image Sky Beijing Technology Co., Ltd., Beijing 100086, China

More Information

Corresponding author: XING Jianghe

Received Date: 12 December 2022

Revised Date: 04 March 2023

Publish Date: 13 March 2024

Abstract

Abstract

Tailings ponds are considerable hazard sources with high potential energy. Ascertaining the number and distribution of tailings ponds in a timely manner through rapid identification and monitoring of their spatial extents is critical for the environmental supervision and governance of tailings ponds in China. Due to the lack of pertinence for potential targets, identifying tailings ponds based on solely remote sensing images is prone to produce confusion between tailings ponds and exposed surfaces, resulting in significant errors in practical applications. This study proposed an extraction method for tailings ponds, which integrated enterprise directory, multi-source geographic data (e.g., data from spatial distribution points, digital elevation model (DEM), and road networks), and high-resolution remote sensing images. The application of this method in Gejiu City, Yunnan Province indicates that integrating multi-source geographic data can effectively exclude the interferential areas without tailings ponds, with the precision and recall rates of the extraction results reaching 83.9% and 72.4%, respectively. The method proposed in this study boasts significant application prospects in high-frequency and automated monitoring of tailings ponds nationwide.
- multi-source geographic data /
- remote sensing /
- object-oriented classification /
- tailings pond /
- multi-scale segmentation