Quantifying the Impacts of Mining Activities on Vegetation Ecology using Integrated Remote Sensing Approaches

Abstract

Understanding the impacts of mining activities on vegetation ecology is crucial, as vegetation plays a vital role in ecosystem stability, biodiversity conservation, and ecosystem services such as carbon sequestration and soil erosion prevention. Mining activities often result in significant vegetation cover loss, leading to ecosystem disruption, loss of biodiversity, among others. So, this thesis attempts to assess and quantify the direct and indirect impacts of mining activities on vegetation ecology using remote sensing approaches. Firstly, Chapter-4 aims to quantify the contributions of mining-induced FCL to carbon sequestration loss (CSL) and carbon dioxide (CO2) emissions from 2000 to 2019 using the proxy datasets. For FCL analysis, the global FCL data at 30 m spatial resolution, developed by Hansen et al. (2013), was employed in the Google Earth Engine (GEE) cloud platform. Furthermore, for CSL and CO₂ emissions assessment, Moderate Resolution Imaging Spectroradiometer (MODIS)-based Net Primary Productivity (NPP) data and Zhang and Liang (2020)-developed biomass datasets were used, respectively. The outcomes of the study exhibited approximately 16,785.90 km² FCL globally due to mining activities, resulting in an estimated CSL of ~ 36,363.17 Gg CO2/year and CO2 emissions of ~490,525.30 Gg CO2. Indonesia emerged as the largest contributor to mining-induced FCL, accounting for 3,622.78 km² of deforestation, or 21.58% of the global total. Brazil and Canada followed, with significant deforestation and CO2 emissions. Besides, the relative FCL was notably high in smaller countries like Suriname and Guyana, where mining activities constituted a significant proportion of total deforestation. Furthermore, Chapter-5 of the thesis employs medium-resolution (30 m) Landsat-series satellite datasets to derive vegetation greenness trends at a regional scale during 1988 – 2020 using the Google Earth Engine cloud platform. The Mann-Kendall test is used to characterize vegetation greenness trends in mining-dominated regions of Eastern India, specifically Jharkhand and Odisha states, during two distinct study periods: an earlier period from 1988 to 2004 and a later period from 2000 to 2020. The study outcomes revealed negative vegetation greenness trends covering approximately 2.97% and 3.91% of areas in Jharkhand state, and 5.68% and 3.20% of areas in Odisha state during 1988–2004 and 2000–2020, respectively. Anthropogenic activities, particularly opencast mining, have emerged as primary drivers of vegetation degradation, contributing to approximately 3–5.7% of vegetation destruction during the study epochs. The negative vegetation greenness trends are prominently observed in areas affected by mining, settlement encroachments, construction sites, roadways, and logging activities. Moreover, in mining-affected regions, climatic factors exhibit a lesser influence on vegetation greenness trends, accounting for ~ 27%, as opposed to forested areas, where they contribute to around 47% of the variation. Chapter-6 aimed to assess the extent of vegetation cover loss and associated vegetation primary productivity dynamics in the Rajmahal Hills of Jharkhand, India, caused by mining activity. Leveraging datasets on Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Vegetation Transpiration (VT), we analyzed the effects of mining activities on vegetation dynamics and productivity. The findings of the study revealed a substantial loss of vegetation cover, with ~ 340 km² of land converted to mining areas and a corresponding expansion of mining sites by ~ 54 km² between 1990 and 2020. Decadal analysis highlighted specific periods of intensified vegetation loss, with notable declines observed during 2000–2010, 2010–2020, and 2000–2020, totaling 3.06 km², 8.10 km², and 22.29 km², respectively. The conversion of vegetation to mining areas resulted in significant reductions in GPP, NPP, and VT, with daily losses ranging from 0.01 to 0.09 tonnes of carbon (tC) for GPP and 1.25 to 7.27 tC for NPP, along with corresponding VT losses of 5200 to 30,190 mm/day during the specified periods. Chapter-7 explores the potential of multi-sensors optical satellites (i.e., Landsat, Sentinel-2, and PlanetScope) and in-situ datasets for foliar dust approximation model development. Furthermore, this study investigates the impacts of foliar dust on the vegetation biochemical (e.g., chlorophyll content) and physiological processes (e.g., carbon sequestration, vegetation transpiration, etc.) using multi-source satellite/gridded datasets. The study highlighted the efficacy of near-infrared (NIR) and shortwave infrared1 (SWIR1) bands, along with specific radiometric indices such as the Global Environmental Monitoring Index (GEMI) and the Non-Linear Index (NLI), in precise foliar dust estimation. The comparative analysis of satellite sensors—Landsat-9, Landsat-8, Sentinel-2, and PlanetScope—reveals Landsat-9 as a robust performer. The study underscores the potential of satellite data and in-situ measurements for foliar dust estimation at the satellite footprint scale with considerable accuracy. Conversely, a negative correlation between foliar dust and various physiological parameters, including gross primary productivity (GPP), evapotranspiration (ET), water use efficiency (WUE), and a positive correlation with leaf temperature was observed. On average, the GPP loss, reductions in ET, and reduction in WUE per gram of foliar dust deposition were estimated as ~ 2 to 3 grams of carbon (gC), ~ 0.0005 to 0.0006 mm/m2/day, and ~ 0.0121 to 0.0207 gC/kg H2O, respectively. Besides, for every additional gram of foliar dust per square meter, leaf temperature was increased by ~ 0.0376 – 0.0454 K. Finally, Chapter-8 evaluates the impacts of aerosols on vegetation greenness. The COVID- 19 pandemic (SARS-COVID-19) had a devastating impact on human health, lives, and the global economy. However, the lockdowns imposed as a preventive measure led to reduced economic activities, which unexpectedly had positive effects on the environment. In Chapter-8, we used satellite data (such as NDVI for vegetation greenness, SIF for plant productivity, and AOD for air pollution) along with weather data (temperature, rainfall, and sunlight) to examine changes in vegetation during the lockdown period across India’s biogeographic regions. We compared these changes with average conditions from 2017 to 2019. The results showed that vegetation greenness and productivity increased significantly during the lockdown, by 37% and 55%, respectively. While weather factors like rainfall, temperature, and sunlight did play a role, they could not fully explain the improvement. This suggests that the reduced human activity during the lockdown was a key factor. Notably, areas around mining clusters saw the largest improvements in vegetation health—up to 78% in coal mining regions, 63% in iron ore mining regions, and 41% in stone mining areas. In summary, the COVID-19 lockdowns seemed to boost vegetation growth and health. Overall, this thesis highlights the profound and far-reaching effects of mining activities on vegetation ecology. The findings emphasize the urgent need for sustainable development in mining-affected regions, where the focus should be on conserving ecosystems, protecting biodiversity, and maintaining essential ecosystem services like carbon sequestration and water regulation. In this regard, remote sensing-based approaches, with their ability to monitor and assess environmental changes over large areas and timeframes, proven to be invaluable tools. They enable data-driven planning and decision-making, which are crucial for promoting sustainable mining practices. By integrating such innovative approaches into policymaking, we can ensure a balance between economic development and environmental conservation, fostering long-term sustainability and resilience in ecosystems impacted by mining.