Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan

Abstract

The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km2 of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts.

Construction of Improved Semantic Segmentation Model and Application to Extraction of Anthropogenically Disturbed Parcels With Soil Erosion From Remote Sensing Images

“…𝐿(𝑌, 𝑃) = − (5) where 𝑦 𝑛,𝑐 ∈ 𝑌 and 𝑝 𝑛,𝑐 ∈ 𝑃 are the target labels and predicted probabilities of the cth type and nth pixel in one batch; Y and P are the actual values and predicted values of ADPSE, respectively, and C and N denote the number of types and pixels in the dataset in one batch, respectively. Implementation details.…”

“…Based on multi-temporal high-resolution remote sensing imagery, Nascimento et al used spectral bands, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), light detection and ranging (LIDAR) digital terrain model, and slope map to establish thresholds for distinguishing open-cast mining complexes with an overall accuracy reaching up to 90% [4].Ali et al used the coma cap transform and the Kmeans algorithm to implement image classification in the GIS program, and thus identified surface coal mine area in the study area. [5]. He et al proposed a tree-root algorithm coupled with an extreme learning machine to construct an open-pit mine classification model and achieved a desirable extraction effect, and their method could monitor the changes in the mine areas in real time [6].…”

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

“…where H + 2 is the Moore-Penrose generalized inverse matrix of H 2 . The final output of the VTELM algorithm is expressed by Equation (13).…”

“…Hai et al [12] realized the identification and monitoring of surface elements in open-pit mining areas based on multi-source remote sensing information. Ali et al [13] monitored coal mining operations and assessed soil reclamation based on remote sensing information. Li et al [14] accurately monitored rare earth mining in rare earth mining areas based on high-resolution remote sensing images.…”