Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China

Xiangyan Tan; Congr Cheng; Dongjie Chen; Yuyao Chen; Diwen Cai

doi:doi:10.11648/j.earth.20261505.13

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Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China

Xiangyan Tan

, Congr Cheng

, Dongjie Chen

, Yuyao Chen

, Diwen Cai^*

Published in Earth Sciences (Volume 15, Issue 5)

Received: 6 October 2026 Accepted: 15 October 2026 Published: 28 October 2026

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Abstract

Increasing open-pit mining is considered to have a remarkable impact on the regional ecological environments due to drastic surface excavation and vegetation damage, therefore there is an urgent need for large-scale disturbance monitoring and restoration effect evaluation. Taking the Huolinhe Mining Coalfield as an example, this study employed the LandTrendr algorithm integrated with the Google Earth Engine (GEE) platform to detect mining disturbances and vegetation recovery using the Normalized Difference Vegetation Index (NDVI) calculated from long-term Landsat images. The results show that: (1) the extent of mining disturbance expanded before 2010 and remained broadly stable until 2017, after which it decreased following the implementation of the restoration project from 2018 onwards. (2) Breakpoints were identified in all years within the study period, and the detection rate for mining-disturbed areas exceeded 96% in the validation years of 2000, 2010, 2017, and 2021. (3) Vegetation recovery started in two periods, 2010-2012 and 2017-2019, the earlier period followed the severe drought of 2010 and is attributed to natural recovery, whereas the larger latter period coincided with the implementation of artificial restoration projects. (4) A lag of approximately one growing season was found between the implementation of reclamation engineering and the first detectable vegetation recovery, so that detection in the implementation year identified only 14.9% to 30.0% of the implemented restoration area, whereas a one year lag adjustment increased the value to 70.0% to 85.1%. (5) NDVI increased in the mining area between 2017 and 2022, and the increase was spatially concentrated in the restoration project area, which is consistent with a positive vegetation-cover response associated with the restoration projects. The results indicate that LandTrendr is an effective tool in change detection and can support rapid identification of large-scale disturbance in mining areas. The results provide a basis for subsequent monitoring of vegetation cover and for the sustainable development of a typical mining area, and they define the conditions under which annual mining-disturbance monitoring can be interpreted at face value.

Published in	Earth Sciences (Volume 15, Issue 5)
DOI	10.11648/j.earth.20261505.13
Page(s)	234-246
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

LandTrendr, Google Earth Engine, Open-pit Mining, Ecological Disturbance, Vegetation Recovery

References

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[6]	Gao, Z., Liu, Z., Wu, Z., et al. Soil nematode communities and functions: Ecological package restoration responses in damaged desert steppe open-pit mining ecosystems. Land Degradation & Development, 2022, 33(7), 1015-1028. https://doi.org/10.1002/ldr.4208
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[12]	Parelius, E. J. A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images. Remote Sensing, 2023, 15(8), 2092. https://doi.org/10.3390/rs15082092
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Tan, X., Cheng, C., Chen, D., Chen, Y., Cai, D. (2026). Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sciences, 15(5), 234-246. https://doi.org/10.11648/j.earth.20261505.13

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ACS Style

Tan, X.; Cheng, C.; Chen, D.; Chen, Y.; Cai, D. Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sci. 2026, 15(5), 234-246. doi: 10.11648/j.earth.20261505.13

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AMA Style

Tan X, Cheng C, Chen D, Chen Y, Cai D. Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sci. 2026;15(5):234-246. doi: 10.11648/j.earth.20261505.13

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@article{10.11648/j.earth.20261505.13,
  author = {Xiangyan Tan and Congr Cheng and Dongjie Chen and Yuyao Chen and Diwen Cai},
  title = {Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China},
  journal = {Earth Sciences},
  volume = {15},
  number = {5},
  pages = {234-246},
  doi = {10.11648/j.earth.20261505.13},
  url = {https://doi.org/10.11648/j.earth.20261505.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20261505.13},
  abstract = {Increasing open-pit mining is considered to have a remarkable impact on the regional ecological environments due to drastic surface excavation and vegetation damage, therefore there is an urgent need for large-scale disturbance monitoring and restoration effect evaluation. Taking the Huolinhe Mining Coalfield as an example, this study employed the LandTrendr algorithm integrated with the Google Earth Engine (GEE) platform to detect mining disturbances and vegetation recovery using the Normalized Difference Vegetation Index (NDVI) calculated from long-term Landsat images. The results show that: (1) the extent of mining disturbance expanded before 2010 and remained broadly stable until 2017, after which it decreased following the implementation of the restoration project from 2018 onwards. (2) Breakpoints were identified in all years within the study period, and the detection rate for mining-disturbed areas exceeded 96% in the validation years of 2000, 2010, 2017, and 2021. (3) Vegetation recovery started in two periods, 2010-2012 and 2017-2019, the earlier period followed the severe drought of 2010 and is attributed to natural recovery, whereas the larger latter period coincided with the implementation of artificial restoration projects. (4) A lag of approximately one growing season was found between the implementation of reclamation engineering and the first detectable vegetation recovery, so that detection in the implementation year identified only 14.9% to 30.0% of the implemented restoration area, whereas a one year lag adjustment increased the value to 70.0% to 85.1%. (5) NDVI increased in the mining area between 2017 and 2022, and the increase was spatially concentrated in the restoration project area, which is consistent with a positive vegetation-cover response associated with the restoration projects. The results indicate that LandTrendr is an effective tool in change detection and can support rapid identification of large-scale disturbance in mining areas. The results provide a basis for subsequent monitoring of vegetation cover and for the sustainable development of a typical mining area, and they define the conditions under which annual mining-disturbance monitoring can be interpreted at face value.},
 year = {2026}
}

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TY  - JOUR
T1  - Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China
AU  - Xiangyan Tan
AU  - Congr Cheng
AU  - Dongjie Chen
AU  - Yuyao Chen
AU  - Diwen Cai
Y1  - 2026/10/28
PY  - 2026
N1  - https://doi.org/10.11648/j.earth.20261505.13
DO  - 10.11648/j.earth.20261505.13
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 234
EP  - 246
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20261505.13
AB  - Increasing open-pit mining is considered to have a remarkable impact on the regional ecological environments due to drastic surface excavation and vegetation damage, therefore there is an urgent need for large-scale disturbance monitoring and restoration effect evaluation. Taking the Huolinhe Mining Coalfield as an example, this study employed the LandTrendr algorithm integrated with the Google Earth Engine (GEE) platform to detect mining disturbances and vegetation recovery using the Normalized Difference Vegetation Index (NDVI) calculated from long-term Landsat images. The results show that: (1) the extent of mining disturbance expanded before 2010 and remained broadly stable until 2017, after which it decreased following the implementation of the restoration project from 2018 onwards. (2) Breakpoints were identified in all years within the study period, and the detection rate for mining-disturbed areas exceeded 96% in the validation years of 2000, 2010, 2017, and 2021. (3) Vegetation recovery started in two periods, 2010-2012 and 2017-2019, the earlier period followed the severe drought of 2010 and is attributed to natural recovery, whereas the larger latter period coincided with the implementation of artificial restoration projects. (4) A lag of approximately one growing season was found between the implementation of reclamation engineering and the first detectable vegetation recovery, so that detection in the implementation year identified only 14.9% to 30.0% of the implemented restoration area, whereas a one year lag adjustment increased the value to 70.0% to 85.1%. (5) NDVI increased in the mining area between 2017 and 2022, and the increase was spatially concentrated in the restoration project area, which is consistent with a positive vegetation-cover response associated with the restoration projects. The results indicate that LandTrendr is an effective tool in change detection and can support rapid identification of large-scale disturbance in mining areas. The results provide a basis for subsequent monitoring of vegetation cover and for the sustainable development of a typical mining area, and they define the conditions under which annual mining-disturbance monitoring can be interpreted at face value.
VL  - 15
IS  - 5
ER  -

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Author Information

Xiangyan Tan

School of Geographical Sciences, Lingnan Normal University, Zhanjiang, China

http://orcid.org/0009-0009-4772-3226
Congr Cheng

School of Geographical Sciences, Lingnan Normal University, Zhanjiang, China

http://orcid.org/0009-0007-6641-9292
Dongjie Chen

School of Geographical Sciences, Lingnan Normal University, Zhanjiang, China

http://orcid.org/0009-0000-5569-7720
Yuyao Chen

School of Geographical Sciences, Lingnan Normal University, Zhanjiang, China

http://orcid.org/0009-0001-0465-3119
Diwen Cai

School of Geographical Sciences, Lingnan Normal University, Zhanjiang, China

Contact Email

http://orcid.org/0000-0001-7065-3742

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APA Style

Tan, X., Cheng, C., Chen, D., Chen, Y., Cai, D. (2026). Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sciences, 15(5), 234-246. https://doi.org/10.11648/j.earth.20261505.13

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ACS Style

Tan, X.; Cheng, C.; Chen, D.; Chen, Y.; Cai, D. Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sci. 2026, 15(5), 234-246. doi: 10.11648/j.earth.20261505.13

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AMA Style

Tan X, Cheng C, Chen D, Chen Y, Cai D. Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China. Earth Sci. 2026;15(5):234-246. doi: 10.11648/j.earth.20261505.13

Copy | Download

@article{10.11648/j.earth.20261505.13,
  author = {Xiangyan Tan and Congr Cheng and Dongjie Chen and Yuyao Chen and Diwen Cai},
  title = {Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China},
  journal = {Earth Sciences},
  volume = {15},
  number = {5},
  pages = {234-246},
  doi = {10.11648/j.earth.20261505.13},
  url = {https://doi.org/10.11648/j.earth.20261505.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20261505.13},
  abstract = {Increasing open-pit mining is considered to have a remarkable impact on the regional ecological environments due to drastic surface excavation and vegetation damage, therefore there is an urgent need for large-scale disturbance monitoring and restoration effect evaluation. Taking the Huolinhe Mining Coalfield as an example, this study employed the LandTrendr algorithm integrated with the Google Earth Engine (GEE) platform to detect mining disturbances and vegetation recovery using the Normalized Difference Vegetation Index (NDVI) calculated from long-term Landsat images. The results show that: (1) the extent of mining disturbance expanded before 2010 and remained broadly stable until 2017, after which it decreased following the implementation of the restoration project from 2018 onwards. (2) Breakpoints were identified in all years within the study period, and the detection rate for mining-disturbed areas exceeded 96% in the validation years of 2000, 2010, 2017, and 2021. (3) Vegetation recovery started in two periods, 2010-2012 and 2017-2019, the earlier period followed the severe drought of 2010 and is attributed to natural recovery, whereas the larger latter period coincided with the implementation of artificial restoration projects. (4) A lag of approximately one growing season was found between the implementation of reclamation engineering and the first detectable vegetation recovery, so that detection in the implementation year identified only 14.9% to 30.0% of the implemented restoration area, whereas a one year lag adjustment increased the value to 70.0% to 85.1%. (5) NDVI increased in the mining area between 2017 and 2022, and the increase was spatially concentrated in the restoration project area, which is consistent with a positive vegetation-cover response associated with the restoration projects. The results indicate that LandTrendr is an effective tool in change detection and can support rapid identification of large-scale disturbance in mining areas. The results provide a basis for subsequent monitoring of vegetation cover and for the sustainable development of a typical mining area, and they define the conditions under which annual mining-disturbance monitoring can be interpreted at face value.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Monitoring of Open-pit Mining Disturbance and Restoration Using GEE-LandTrendr: A Case Study of Huolinhe Coalfield in Inner Mongolia, China
AU  - Xiangyan Tan
AU  - Congr Cheng
AU  - Dongjie Chen
AU  - Yuyao Chen
AU  - Diwen Cai
Y1  - 2026/10/28
PY  - 2026
N1  - https://doi.org/10.11648/j.earth.20261505.13
DO  - 10.11648/j.earth.20261505.13
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 234
EP  - 246
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20261505.13
AB  - Increasing open-pit mining is considered to have a remarkable impact on the regional ecological environments due to drastic surface excavation and vegetation damage, therefore there is an urgent need for large-scale disturbance monitoring and restoration effect evaluation. Taking the Huolinhe Mining Coalfield as an example, this study employed the LandTrendr algorithm integrated with the Google Earth Engine (GEE) platform to detect mining disturbances and vegetation recovery using the Normalized Difference Vegetation Index (NDVI) calculated from long-term Landsat images. The results show that: (1) the extent of mining disturbance expanded before 2010 and remained broadly stable until 2017, after which it decreased following the implementation of the restoration project from 2018 onwards. (2) Breakpoints were identified in all years within the study period, and the detection rate for mining-disturbed areas exceeded 96% in the validation years of 2000, 2010, 2017, and 2021. (3) Vegetation recovery started in two periods, 2010-2012 and 2017-2019, the earlier period followed the severe drought of 2010 and is attributed to natural recovery, whereas the larger latter period coincided with the implementation of artificial restoration projects. (4) A lag of approximately one growing season was found between the implementation of reclamation engineering and the first detectable vegetation recovery, so that detection in the implementation year identified only 14.9% to 30.0% of the implemented restoration area, whereas a one year lag adjustment increased the value to 70.0% to 85.1%. (5) NDVI increased in the mining area between 2017 and 2022, and the increase was spatially concentrated in the restoration project area, which is consistent with a positive vegetation-cover response associated with the restoration projects. The results indicate that LandTrendr is an effective tool in change detection and can support rapid identification of large-scale disturbance in mining areas. The results provide a basis for subsequent monitoring of vegetation cover and for the sustainable development of a typical mining area, and they define the conditions under which annual mining-disturbance monitoring can be interpreted at face value.
VL  - 15
IS  - 5
ER  -

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