Integrating Surface Deformation and Ecological Indicators for Mining Environment Assessment: A Novel MDECI Approach.
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| Title: | Integrating Surface Deformation and Ecological Indicators for Mining Environment Assessment: A Novel MDECI Approach. |
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| Authors: | Zhang, Lei1,2,3 (AUTHOR), Su, Qiaomei1,2 (AUTHOR), Zhang, Bin1,2,3 (AUTHOR) binzhang@tyut.edu.cn, Xue, Hongwen4 (AUTHOR), Zuo, Zhengkang1,5 (AUTHOR), Li, Yanpeng1,2,5 (AUTHOR), Zheng, He2,5 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1272. 27p. |
| Subjects: | Deformation of surfaces, Environmental indicators, Radar interferometry, Bioindicators, Spatial analysis (Statistics) |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? A Mining Deformation–Ecology Coupling Index (MDECI) was developed by integrating InSAR-derived surface stability with multi-spectral indicators. A non-linear "unimodal" response mechanism was identified in the Datong Coalfield, revealing an Ecological Turning Point (ETP) at −100 mm where mining ecosystems transition to structural degradation. What are the implications of the main findings? MDECI significantly outperforms traditional models by maintaining a stable Average Correlation Coefficient (ACC) of 0.63–0.75, achieving a 30.3% performance lead (0.628 vs. 0.482) during environmental interference while remaining highly consistent with existing benchmarks (correlation > 0.9). The −100 mm threshold establishes a quantitative boundary for mining intensity control, providing an early-warning basis to prevent ecosystems from crossing the degradation turning point. Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial 'micro-disturbance gain' (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A Mining Deformation–Ecology Coupling Index (MDECI) was developed by integrating InSAR-derived surface stability with multi-spectral indicators. A non-linear "unimodal" response mechanism was identified in the Datong Coalfield, revealing an Ecological Turning Point (ETP) at −100 mm where mining ecosystems transition to structural degradation. What are the implications of the main findings? MDECI significantly outperforms traditional models by maintaining a stable Average Correlation Coefficient (ACC) of 0.63–0.75, achieving a 30.3% performance lead (0.628 vs. 0.482) during environmental interference while remaining highly consistent with existing benchmarks (correlation > 0.9). The −100 mm threshold establishes a quantitative boundary for mining intensity control, providing an early-warning basis to prevent ecosystems from crossing the degradation turning point. Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial 'micro-disturbance gain' (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091272 |
