A Novel Index to Detect Opencast Coal Mine Areas From Landsat 8 OLI/TIRS

摘要

Detection, classification, and monitoring of surface mining region from satellite images have vast challenges. Surface mining industry has huge effect on social, economical, ecological, and environmental welfare of a country. Machine learning techniques are used to capture the unique characteristics of surface mining region from satellite images. In opencast coal mines, raw coals stay exposed to the environment. Reflectance measures in certain wavelengths of such areas are distinguishable from other non-coal areas. These surface mining areas can be detected through satellite images using this cue. It has been found in this paper, that the spectral index derived from SWIR-I (1.566-1.651 mu m) and SWIR-II (2.107-2.294 mu m) bands of Landsat 8 exhibit distinctive features to detect high mineral areas of coal seams. Spectral plots of reflectance values of different land cover classes with respect to reflectance values of coal are found to be different. This paper presents a novel index to detect surface coal mines. Coal quarry and coal dump regions are considered as the true positive regions. The accuracy of the index has been validated with high-resolution Google Earth ground truth images and statistical measures. The robustness of the index is analyzed through seasonal variations. The average accuracy of the proposed method over the season is found to be 86.24%. The proposed index can be further used for classification and area monitoring of opencast coal mine regions.