Spatiotemporal dynamics of ecological quality and driving factors in a coal mining area: A case study of the Binchang mining area

With the rapid expansion of mining activities and urbanization, the contradiction between human activities and the ecological environment in mining areas has become increasingly prominent. However, existing Remote Sensing Ecological Index (RSEI)-based studies mostly focus on urban and wetland ecosystems, with limited insights into the entire ecological evolution and its driving mechanisms in ecologically fragile coal mining areas. To fill this gap, this study took the Binchang mining area, a core production base of the Huanglong coal base, as the study region, used 30 m resolution Landsat 5/7/8 imagery (1990–2020) from the Google Earth Engine (GEE) platform to construct the RSEI by fusing four core indicators (greenness, wetness, dryness, heat) via principal component analysis; 30 m annual China Land Cover Dataset (CLCD) to characterize land-use transitions; 12 natural and anthropogenic driving factors, combined with the GeoDetector model and Moran's I index, to quantify the driving mechanisms and spatial autocorrelation characteristics of ecological quality. The quantitative results show that: (1) the mean RSEI of the study area increased from 0.462 to 0.595 during 1990–2020, with a total increase of 28.89%, indicating an overall improvement in ecological quality; (2) areas with ecological improvement accounted for 69.38% of the total region, which far exceeded the 29.60% of ecological degradation areas, showing a general trend of ecological recovery; (3) cultivated land, forest land and grassland dominated the study area (over 90% of the total area), while construction land expanded rapidly with a total growth rate of 19.22%, mainly at the expense of cultivated land; (4) land-use type was the dominant single factor driving RSEI variation (mean q = 0.25), and its synergistic interaction with slope showed a continuously enhanced effect (q up to 0.52 in 2020), especially after large-scale mining in 2005. Compared with the standard RSEI application framework, this study advances the mining area ecological assessment by constructing an integrated analysis framework, which effectively isolates the ecological impact of mining activities and reveals the novel synergistic driving relationship between land-use change and topographic conditions in mining areas. This study provides critical scientific support for terrain-sensitive land-use planning and targeted ecological restoration in ecologically fragile coal mining areas worldwide. • Long-term spatiotemporal monitoring of RSEI in the Binchang mining area. • Quantitative identification of drivers of ecological quality using Geodetector. • Linking mining activities to ecological quality via land-use change.

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Publication Details

Journal
Ecological Indicators
Published
2026-05-09
DOI
https://doi.org/10.1016/j.ecolind.2026.114920
Primary Topic
Environmental Quality and Pollution
Type
article
Field-Weighted Citation Impact
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article

Spatiotemporal dynamics of ecological quality and driving factors in a coal mining area: A case study of the Binchang mining area

Wanqiang Yao, Junliang Zheng, Ma Xiongwei, Yong Zhang et al.
Ecological Indicators
Environmental Quality and Pollution
article

Spatiotemporal dynamics of ecological quality and driving factors in a coal mining area: A case study of the Binchang mining area

Wanqiang Yao, Junliang Zheng, Ma Xiongwei, Yong Zhang, Qiao Purui, Li You, Cheng Qianjin
article en

Abstract

With the rapid expansion of mining activities and urbanization, the contradiction between human activities and the ecological environment in mining areas has become increasingly prominent. However, existing Remote Sensing Ecological Index (RSEI)-based studies mostly focus on urban and wetland ecosystems, with limited insights into the entire ecological evolution and its driving mechanisms in ecologically fragile coal mining areas. To fill this gap, this study took the Binchang mining area, a core production base of the Huanglong coal base, as the study region, used 30 m resolution Landsat 5/7/8 imagery (1990–2020) from the Google Earth Engine (GEE) platform to construct the RSEI by fusing four core indicators (greenness, wetness, dryness, heat) via principal component analysis; 30 m annual China Land Cover Dataset (CLCD) to characterize land-use transitions; 12 natural and anthropogenic driving factors, combined with the GeoDetector model and Moran's I index, to quantify the driving mechanisms and spatial autocorrelation characteristics of ecological quality. The quantitative results show that: (1) the mean RSEI of the study area increased from 0.462 to 0.595 during 1990–2020, with a total increase of 28.89%, indicating an overall improvement in ecological quality; (2) areas with ecological improvement accounted for 69.38% of the total region, which far exceeded the 29.60% of ecological degradation areas, showing a general trend of ecological recovery; (3) cultivated land, forest land and grassland dominated the study area (over 90% of the total area), while construction land expanded rapidly with a total growth rate of 19.22%, mainly at the expense of cultivated land; (4) land-use type was the dominant single factor driving RSEI variation (mean q = 0.25), and its synergistic interaction with slope showed a continuously enhanced effect (q up to 0.52 in 2020), especially after large-scale mining in 2005. Compared with the standard RSEI application framework, this study advances the mining area ecological assessment by constructing an integrated analysis framework, which effectively isolates the ecological impact of mining activities and reveals the novel synergistic driving relationship between land-use change and topographic conditions in mining areas. This study provides critical scientific support for terrain-sensitive land-use planning and targeted ecological restoration in ecologically fragile coal mining areas worldwide. • Long-term spatiotemporal monitoring of RSEI in the Binchang mining area. • Quantitative identification of drivers of ecological quality using Geodetector. • Linking mining activities to ecological quality via land-use change.

Ecological IndicatorsVol. 187
Xi'an University of Science and Technology (CN)
Openalex Percentile: Top 5%
Environmental Quality and Pollution
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