The in-situ foliar dust dataset for the Jharsuguda coal mining region in Odisha, India, offers a precise and localized measurement of dust accumulation on plant leaves, collected directly from areas most affected by mining activity. Field sampling focused on hotspots within key mining projects in the region, where I established 10 m * 10 m structured plots and carefully gathered dusty leaf samples from 30 sample plots (10 leaves from each plot). Each sample was weighed in the laboratory using high-precision electronic weighing machine to record the mass of dust adhered to the leaf surfaces; this involved an initial weighing, followed by thorough cleaning to remove all particulate matter using spongy foam and colour brush, and a final weighing to obtain the dust-free leaf mass. The difference provided the amount of dust retained by each sample, standardized by measuring the total leaf area so that results could be expressed as grams of dust per square meter. This dataset is designed to support applications such as monitoring dust pollution from coal mining, validating remote sensing models that estimate dust deposition from satellite imagery, and contributing valuable evidence for environmental impact studies. By providing detailed, ground-level data, it aids in refining the understanding of dust distribution patterns and their ecological implications, thereby offering critical insights for environmental planners, policymakers, and researchers working on pollution mitigation and the restoration of mining-impacted landscapes. 

The LULC dataset for the Rajmahal hills region in Jharkhand, India, provides a detailed and high-resolution assessment of spatial-temporal changes in land use and land cover over three decades, from 1990 to 2020. This dataset is derived from multi-temporal Landsat satellite imagery and employs robust classification techniques to map four principal LULC classes: vegetation, mining, waterbody, and others. Classification accuracy consistently exceeded 93% across all reference years, ensuring reliable interpretations for scientific and planning purposes. During this period, the Rajmahal hills—a geologically unique region rich in minerals and plant fossils—have undergone notable landscape transformation. The data reveal a significant reduction of approximately 340 km² in vegetation cover and a remarkable expansion of mining areas by about 54 km². Detailed analysis shows that the most intense increase in mining activity and related vegetation loss occurred after 2010, with the northern, eastern, western, and southern zones experiencing the greatest impacts. This LULC time series is highly valuable for monitoring mining impacts, analyzing ecosystem change, and supporting policy interventions aimed at sustainable land management. Researchers and practitioners can use it to quantify land cover trends, model ecosystem services, assess carbon and water cycle disruption, and inform decisions aligned with biodiversity conservation and sustainable development goals. The Rajmahal hills LULC dataset provides a critical baseline for restoration planning and for understanding the ongoing challenges facing this ecologically sensitive and culturally significant region

The vegetation greenness trends dataset focuses on mining-dominated regions in eastern India, particularly in Jharkhand and Odisha, covering the years 1988 to 2020. Developed using high-resolution (30-meter) Landsat satellite imagery and processed through Google Earth Engine, this dataset analyzes changes in vegetation health using the Normalized Difference Vegetation Index (NDVI) and statistical methods like the Mann–Kendall trend test. It captures long-term shifts in vegetation across two key periods: 1988–2004 and 2000–2020, offering valuable insights into how intensive human activities, especially opencast coal mining, have influenced ecological patterns. The findings highlight that mining has significantly contributed to vegetation loss, accounting for up to 5.7% decline in areas surrounding mining sites. While certain zones show signs of vegetation recovery, either through planned reclamation or natural processes, the overall greenness trends suggest that ecological restoration remains limited and inconsistent. Interestingly, the dataset also shows that in regions impacted by mining, vegetation decline is less influenced by climatic variables such as rainfall, temperature, and soil moisture, underlining human activity as the dominant driver of ecosystem change. This dataset serves as a critical resource for researchers, policymakers, and environmental planners seeking to understand and mitigate the ecological impacts of coal mining in one of India’s most resource-rich but environmentally vulnerable regions.