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

Article

Peng, C., Li, Q., Li, J., Kang, H., Zhang, C., Tang, J. and Banerjee, B.. 2025. "An Automated Identification Method of Disturbance Ranges of Surface Coal Mines on Vegetation Based on the Fitting of NDVI Spatial Trajectory." Land Degradation and Development. 36 (7), pp. 2458-2473. https://doi.org/10.1002/ldr.5508
Article Title

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

ERA Journal ID5264
Article CategoryArticle
AuthorsPeng, C., Li, Q., Li, J., Kang, H., Zhang, C., Tang, J. and Banerjee, B.
Journal TitleLand Degradation and Development
Journal Citation36 (7), pp. 2458-2473
Number of Pages16
Year2025
PublisherJohn Wiley & Sons
Place of PublicationUnited Kingdom
ISSN1085-3278
1099-145X
Digital Object Identifier (DOI)https://doi.org/10.1002/ldr.5508
Web Address (URL)https://onlinelibrary.wiley.com/doi/abs/10.1002/ldr.5508
Abstract

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.

Keywordsmining disturbance; curve fitting; surface coal mines; spatial trajectory; NDVI
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401905. Mining engineering
410402. Environmental assessment and monitoring
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Byline AffiliationsChina University of Mining and Technology, China
State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, China
Peking University, China
School of Surveying and Built Environment
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