For a growing economy like India, most of its energy resources are obtained through extractive processes such as mining of coal and other minerals. Mining can however have many negative social and ecological impacts if it is not well regulated. Illegal mining or inadequate reclamation of abandoned mines can amplify these impacts, emphasizing the need to develop methods that can monitor changes in the land-use patterns in and around mining sites. We develop a method using machine learning on freely available satellite data to monitor the extent of mines, and augment it with outputs from land use and land cover classification, deforestation detection, and PM2.5 particulate matter estimation from remote sensing data to track land-use and ecological changes taking place in the proximity of mining sites. We provide evaluation results of our mining delineation classifier, a feasibility check of this suite of tools to monitor mining areas over a period of four years, and a temporal characterization study over 628 mines in India that were granted a clearance for operations during the period 2006 to 2012. We further use this suite of monitoring tools to compare socio-economic development and health indicators across mining and non-mining areas, across various states in India, to study whether extractive processes of mining benefit the immediate population in their neighbourhood.