An Automated Identification Method of Disturbance Ranges of Surface Coal Mines on Vegetation Based on the Fitting of NDVI Spatial Trajectory

Bibliographic Data

ID21647710
AuthorsChuanying Peng (College of Geoscience and Surveying Engineering China University of Mining and Technology‐Beijing Beijing China), Quansheng Li (0000-0003-3765-8909, State Key Laboratory of Water Resource Protection and Utilization in Coal Mining Beijing China), Jun Li (0000-0001-5824-6626, College of Geoscience and Surveying Engineering China University of Mining and Technology‐Beijing Beijing China, corresponding author), Hui Kang (0000-0002-5979-1658, School of Software and Microelectronics Peking University Beijing China), Chengye Zhang (0000-0002-4902-8704, College of Geoscience and Surveying Engineering China University of Mining and Technology‐Beijing Beijing China), Jiahao Tang (0009-0008-9114-3504, College of Geoscience and Surveying Engineering China University of Mining and Technology‐Beijing Beijing China), Bikram Banerjee (School of Surveying and Built Environment University of Southern Queensland Toowoomba Australia), Bikram Pratap Banerjee (0000-0002-5542-3751, University of Southern Queensland)
Year2025
Volume36
Issue7
Pages2458-2473
Publication date2025-04-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.5508
OpenAlexW4407420622
LanguageEN
Citations received2
References cited49

Accurately and efficiently identifying the vegetation disturbance ranges in surface coal mines is of great significance for determining the scope of land degradation and mitigating land degradation. The objective of this article is to propose an automated method for identifying disturbance ranges of surface coal mines on vegetation based on the fitting of NDVI spatial trajectory (called Disran_SpaTFit). The process of the proposed method includes preparing the NDVI spatial trajectory dataset, designing the curve conceptual function model, fitting the spatial trajectory, and selecting the optimal model to identify disturbance ranges. With the Shendong coal base in China as the study area, the mining disturbance ranges of 106 surface coal mines were automatically identified. The results show that: (1) The accuracy of the automated identification of mining disturbance distances was 91.1%, with a mean absolute error of 109 m. (2) Disran_SpaTFit is widely applicable to various heterogeneous coal mines. 96.62% of the NDVI spatial trajectories (1229 out of 1272 in total) were confirmed to match one of the four curve models designed in Disran_SpaTFit. (3) The ranges of mining disturbance in the 106 surface mines exhibit significant spatial heterogeneity across different directions and extend a certain distance away from the open‐cut area. (4) Disran_SpaTFit is able to accurately identify the ranges of mining disturbances for different years, covering the changes before and during mining activities. The results in this article demonstrate that the proposed Disran_SpaTFit provides an effective tool for identifying disturbance ranges of various surface coal mines, which is of importance for ecological assessment and restoration management in mining areas

Geometry · Geomorphology · Normalized Difference Vegetation Index · Physics · Remote sensing · Trajectory · Vegetation Index · Environmental Science · Mathematics · Remote Sensing and Land Use · Ecology · Geology

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