<abstract xmlns="http://www.w3.org/1999/xhtml">

Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area.
We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021.
The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances.
The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth.
Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.
</abstract>

Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area.
We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021.
The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances.
The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth.
Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Mining Revue

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we...

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Publié en ligne: 05 avr. 2023

Pages: 26 - 41

DOI: https://doi.org/10.2478/minrv-2023-0003

Mots clés
coal mine, NDVI, LandTrendr algorithm, Google Earth engine, restoration of vegetation, solar farm

© 2023 Vancho Adjiski et al., Published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Continuous Monitoring of the Mining Activities, Restoration Vegetation Status and Solar Farm Growth in Coal Mine Region Using Remote Sensing Data

Publié en ligne: 05 avr. 2023

Pages: 26 - 41

DOI: https://doi.org/10.2478/minrv-2023-0003

Mots cléscoal mine, NDVI, LandTrendr algorithm, Google Earth engine, restoration of vegetation, solar farm

© 2023 Vancho Adjiski et al., Published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Mots clés
coal mine, NDVI, LandTrendr algorithm, Google Earth engine, restoration of vegetation, solar farm