Quantifying Mining-Induced Phenological Disturbance and Soil Moisture Regulation in Semi-Arid Grasslands Using HLS Time Series

Yanling Zhao (), Shenshen Ren and Yanjie Tang
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Yanling Zhao: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Shenshen Ren: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Yanjie Tang: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Land, 2025, vol. 14, issue 10, 1-19

Abstract: Coal mining disturbances in semi-arid grasslands affect land surface phenology (LSP), impacting ecosystem functions, restoration target setting, and carbon sequestration; however, the magnitude and spatial extent of these disturbances and their detectability across vegetation indices (VIs), remain insufficiently constrained. We developed and applied a streamlined quantitative framework to delineate the extent and intensity of mining-induced phenological disturbance and to compare the sensitivity and stability of commonly used VIs. Using Harmonized Landsat Sentinel (HLS) surface reflectance data over the Yimin mine, we reconstructed multitemporal VI trajectories and derived phenological metrics; directional phenology gradients were used to delineate disturbance, and VI responsiveness was evaluated via mean difference (MD) and standard deviation (SD) between affected and control areas. Research findings indicate that the impact of mining extends to an area approximately four times the size of the mining site, with the start of season (SOS) in affected areas occurring about 10 days later than in unaffected areas. Responses varied markedly among VIs, with the Modified Soil-Adjusted Vegetation Index (MSAVI) exhibiting the highest spectral stability under disturbance. This framework yields an information-rich quantification of phenological impacts attributable to mining and provides operational guidance for index selection and the prioritization of restoration and environmental management in semi-arid mining landscapes.

Keywords: remote sensing; mining disturbance; vegetation phenology; soil water content (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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