Mapping and interpreting spatio-temporal trends in vegetation restoration following mining disturbances in large-scale surface coal mining areas

The direct removal of surface vegetation during surface coal mining has a negative impact on the surrounding ecological environment. Effective vegetation restoration is essential to mitigate these impacts. Therefore, accurate monitoring and assessment of vegetation restoration following mining disturbance is critical for ecological protection in mining areas. This study employs the Detecting Breakpoints and Estimating Segments in Trend (DBEST) to map the historical patterns of vegetation disturbance and subsequent recovery at the Shendong coal base. This is the first large-scale application of DBEST for such purposes. To examine the spatio-temporal trends in post-mining vegetation restoration, the Years to Recovery (Y2R) and amount of NDVI recovery were calculated based on the Normalized Difference Vegetation Index (NDVI) time-series. The results show that the DBEST has an accuracy of 0.90 in detecting vegetation destruction and 0.78 in detecting restoration. These findings highlight the substantial potential of this algorithm for monitoring vegetation disturbance in mining areas. The total area of vegetation destruction within the Shendong coal base is 449.65 km 2 , and the restoration area is 156.62 km 2 . Between 1992 and 2017, 46.90% of the disturbed areas achieved 80% of the pre-mining vegetation level, exceeding the average restoration level in China. The average Y2R was 4.68 years. Furthermore, NDVI restoration showed an initial increase followed by a decline with longer Y2R values, suggesting that while early restoration efforts were more effective, long-term restoration efficiency decreased. This finding emphasizes the necessity of concentrating on the restoration process at each stage of the planning and implementation of revegetation projects, particularly regarding the difficulties associated with long-term restoration. This is crucial for the development of more comprehensive and sustainable strategies.

Authors

Publication Details

Journal
Frontiers in Environmental Science
Published
2025-03-11
DOI
https://doi.org/10.3389/fenvs.2025.1531424
Citations
1
Primary Topic
Rangeland and Wildlife Management
Type
article
Field-Weighted Citation Impact
1.72
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article

Mapping and interpreting spatio-temporal trends in vegetation restoration following mining disturbances in large-scale surface coal mining areas

Yicong Zhang, Chengye Zhang, Yaling Xu, Guozhu Yang et al.
1 citations
Frontiers in Environmental Science
Rangeland and Wildlife Management
1.72
article

Mapping and interpreting spatio-temporal trends in vegetation restoration following mining disturbances in large-scale surface coal mining areas

Yicong Zhang, Chengye Zhang, Yaling Xu, Guozhu Yang, Junting Guo
article en
1 citations

Abstract

The direct removal of surface vegetation during surface coal mining has a negative impact on the surrounding ecological environment. Effective vegetation restoration is essential to mitigate these impacts. Therefore, accurate monitoring and assessment of vegetation restoration following mining disturbance is critical for ecological protection in mining areas. This study employs the Detecting Breakpoints and Estimating Segments in Trend (DBEST) to map the historical patterns of vegetation disturbance and subsequent recovery at the Shendong coal base. This is the first large-scale application of DBEST for such purposes. To examine the spatio-temporal trends in post-mining vegetation restoration, the Years to Recovery (Y2R) and amount of NDVI recovery were calculated based on the Normalized Difference Vegetation Index (NDVI) time-series. The results show that the DBEST has an accuracy of 0.90 in detecting vegetation destruction and 0.78 in detecting restoration. These findings highlight the substantial potential of this algorithm for monitoring vegetation disturbance in mining areas. The total area of vegetation destruction within the Shendong coal base is 449.65 km 2 , and the restoration area is 156.62 km 2 . Between 1992 and 2017, 46.90% of the disturbed areas achieved 80% of the pre-mining vegetation level, exceeding the average restoration level in China. The average Y2R was 4.68 years. Furthermore, NDVI restoration showed an initial increase followed by a decline with longer Y2R values, suggesting that while early restoration efforts were more effective, long-term restoration efficiency decreased. This finding emphasizes the necessity of concentrating on the restoration process at each stage of the planning and implementation of revegetation projects, particularly regarding the difficulties associated with long-term restoration. This is crucial for the development of more comprehensive and sustainable strategies.

Frontiers in Environmental ScienceVol. 13
Openalex Percentile: Top 19%
Rangeland and Wildlife Management
1.72
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Mapping and interpreting spatio-temporal trends in vegetation restoration following mining disturbances in large-scale surface coal mining areas — Yicong Zhang, Chengye Zhang, et al. · Frontiers in Environmental Science (2025) | TGRS Research Map | TGRS