Environmental Impacts of Opencast Coal Mining in the Margherita-Ledo Region, Assam, India: A Temporal Analysis using NDVI, NDBI and LST

Environmental Impacts of Opencast Coal Mining in the Margherita-Ledo Region, Assam, India: A Temporal Analysis using NDVI, NDBI and LST | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental Impacts of Opencast Coal Mining in the Margherita-Ledo Region, Assam, India: A Temporal Analysis using NDVI, NDBI and LST Chandra Kumar Dutta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8397493/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The coal mining is responsible for landscape and environment degradation. The open-cast mining in Makum-Ledo area has more impact on environmental degradation then underground coal mining. Unscientific and unauthorized mining led degradation in quality of air, water, soil, changes in landform, land use/land cover, vegetation distribution mainly due to open crust mining activity. Desertification of the coal field region degradation led to the biodiversity influencing Land Use and Land Cover (LULC), land, water and air pollution. The temporal change in land cover is revealed by NDVI and NDBI of the area, the LST of the study area reveals that the decrease in forest cover for mining activities increases land surface temperature. The new areas are includes since 1993 to 2024, which the area loss of top soil due to opencast mining, the dumping of overburden material, and the deposition of coal dust due to wind erosion. Apart from that, opencast and underground mining, as well as the construction of coal-related businesses, have a direct impact on the environment (including crops in agricultural land). The study focuses to evaluation of the environmental impact and estimate the present status of machineries active in the study area. The study provides an important insight on temporal change in the study area and open scope for further scientific investigation of various factors unaddressed in this paper for future researchers. The paper would be a primary evaluation of the temporal change for the stakeholders to plan strategies for its maintenance and development in scientific way. Coal mining open-cast environment impact NDVI NDBI and LST Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION The natural stuff present in the earth surface is turned into resources, as its utility is known to humans [ 48 ]. The utility of substances depends upon the need, knowledge, and skill of human. Coal is the important power resources, in demand worldwide. The excavation of coal is done widely both open-cast and underground mining. The mining procedure of coal field depends upon the reservoir of mineral deposit, as the greater the depth of the deposit reveals the underground mining and if the deposit present in outer crust develops open-cast mining. Resources are not distributed evenly in the earth surface [ 5 ], Indian resources are even not identified and estimated due to uneven distribution, and it is still to explore [ 14 ]. The excavation of mineral resources led the degradation of natural environment ant its biodiversity [ 38 , 30 ]. The increasing demand of mineral resources led to over exploitation and reveals the environmental; consequences as degradation and pollution of land, water and air [ 29 ]. The unconsolidated eroded loose material from the mining activity contributes to the increases the sediment load in streams resulting sedimentation of river bed [ 9 , 46 ]. The chemical properties of the water bodies around the mining site influence to acidification of water reserves led to contamination of water. The fine sediments distributed by the influence of agents of degradation, reduces the soil quality, the dumping of overburden material, and the deposition of coal dust due to wind erosion. The impact on environment pollution, soil infertility, land degradation, water security and air quality are the primary observations [ 48 ]. Apart from this biodiversity treaty, animal and plant dispersal, human health, socioeconomic aspects, shifting of occupational structure of the inhabitants nearer to the mines are a greater interest of concerns [ 27 , 17 ]. The environment degradation in mining region due to unscientific and riskless excavation of mineral resources let deforestation, and land degradation [ 51 , 44 ]. The acquisition of leases for mining in the Assam coal fields predominantly involved interior barren lands, agricultural farms, and government-controlled fallow and forested areas. Approximately 10% of the total lease area was utilized for the establishment of underground mining operations, residential complexes, and civic amenities, necessitating the conversion of forest, agricultural, and fallow lands. This land conversion aimed to support common facility development with minimal disruption to the soil and vegetation cover [ 31 , 41 ]. However, this process inevitably led to the displacement or disturbance of the area's indigenous biological species due to human settlement, noise pollution from heavy machinery, and extensive construction activities [ 1 , 10 ] The study is an initial attempt to evaluate the changes that the environment faces due to opencast mining in the region, also the investigate the ground reality of deforestation due to mining [ 4 ], The goggle earth satellite imageries were consulted to find geographical attributes of the mining region with a one (1 km) kilometer buffer from the ring road surrounding the coalmining region as an area of interest (AOI). The study is limited to certain geographical processes of evaluating environmental impact by investigating on NDVI and NDBI and LST of the study area [ 2 ]. The study provides an important insight on temporal change in the study area and open scope for further scientific investigation of various factors unaddressed in this paper for future researchers. The paper would be a primary evaluation of the temporal change for the stakeholders to plan strategies for its maintenance and development in scientific way. 1.1 Research gap and objectives The search for literature regarding the environmental changes due to coal mining and it’s found that the mining locations were investigated with physical and chemical parameters to evaluate land, soil, vegetation and water degradation. Few gap found and attempted to address in this paper includes, (a) field data of water, air and soil quality [ 20 ]. Whereas the area are not easily accessible so remote sensing platform were not considered. (b) The use of remote sensing and GIS [ 35 ] were mainly is used to identify and evaluate on land use land cover changes by open cast mining [ 26 , 24 ]. (c) Evaluate temporal change by implementing remote sensing platform methods were moistly used for changes due to urbanisation by Normalized Difference Vegetation Index (NDVI) [ 6 , 11 ], Normalized Difference Built-up Index (NDBI) [ 15 ] and Land Surface Temperature (LST) [ 3 , 25 , 42 ]. These paper attempt to use for evaluation of temporal change in open cast mining region. The Raster reclassification on land surface temperature if year 1993, 2003, 2013, 2023 and 2024 was re-analyzed with Fuzzy overlay and weighted overlay to find the most vulnerable areas of the study area. The primary objective of the study is to investigate the impact of open cast mining on the surrounding environment. To evaluate the temporal change in vegetation and built-up areas in respect to land surface temperate of the mining region, and to evaluate the current trend on open cast mining in the study region. The study is significant because it provides a resource for researchers who wish to investigate the impact of open cast mining on environments. The stakeholders would find interest to evaluate and plan strategic management plan for the environment sustainability in mining regions. This study introduces novel elements absent in prior Margherita-Ledo research, which has emphasized soil impacts or broad LULC without multi-index integration [ 2 ]. First, it provides the longest temporal NDVI/NDBI/LST analysis (1993–2024), capturing recent expansions like highly exposed areas (+ 7.84 km²). Second, fuzzy and weighted LST overlays identify high-risk zones, revealing inverse vegetation-LST relationships refined for mining contexts. Third, ground-truthing via high-res imagery quantifies machinery (e.g., 198 excavators active in 2024), correlating operations to degradation—offering stakeholders actionable insights for restoration beyond descriptive monitoring. 1.2 Study Area The study area selected for the research is an active open cast coal field of Assam and Arunachal Pradesh. It is situated in the foothill areas of the Patkai hills of lower Arunachal Himalayas, it is termed as Mergherita-Ledo in particular and coalfield [ 4 ]. Coal mining activities are commonly operates by the North Eastern Coalfields, Coal India Limited (CIL) for Margherita coal field generally known as ″the Ledo-Makum Coal Field" [ 43 , 37 , 8 ]. Mainly confined to sub-divisions or Colliery namely, Lido OCP, Tipong Colliery, Tikak Colliery and Tirap Colliery. The Ledo OCP ( area 0.85 Sq. km.) situated in Northern part of Makum Coalfield situated at 10 kms North East of Margherita town. The area is connected to the rest of the country by NH.38 and Indian railways which extends upto Tirap-Siding. Ledo Mechanised OCP block, covering an area of 0.85 sq km, is situated in the northern part of Makum coalfield and is defined by 27 o 17’ 25” and 27 o 17’ 50” North latitude and 95 o 0’ 45” to 95 o 45’ 45 ” East longitude falling in the Survey of India Topographical map No.83M/1 [ 28 ] (Fig. 1 ). 2. MATERIALS AND METHODS The methodology involves recent data analysis using high resolution satellite imageries. Remote sensing data from the Landsat satellite mission have been derived from U.S. Geological Survey interface [ 36 ]. The principles of imaging of the Thematic Mapper (TM) for 1993, Thematic Mapper plus (ETM+) for 2003, and Operational Land Imager (OLI) for 2013, 2023 and 2024 are well documented. Data derived from open course using added plugins in QGIS and ArcGIS interfaces to generate high resolution multispectral image. The image used has orbit path 134, row 41 with cloud cover of less than 10%; the projection coordinate system was UTM (Universal Transverse Mercator); the geographic coordinate system was WGS84; the zone was 46 North. Spatial information are digitized in Google Earth platform and imported to GIS interface for further analysis. Land use land cover of the study area was computed adopting supervised classification and used to analyze changes due to human interference [ 40 ]. To identify the active coal fields physical variables were considered from image include earth movers, excavators, and vehicles used for carrying extracted coal for justification. 2.1 Vegetation indices An index is a simple graphical indicator that can be used to analyze remote sensing data for a particular type of land class. For creating such indices we try to find out the strongest (maximum) reflectance band and weakest (minimum) reflectance bands among the bands available [ 19 ]. Using the strongest reflectance in the numerator and the weakest reflectance in the denominator, the normalized ratio of these two bands can give the highest contrast for the particular land type with minimum background noise. 1) Normalized Difference Vegetation Index : Area of Interest (AOI) is curved out with a 1km buffer over a ring road around coal mining areas for evaluate the change in vegetation concentration and assess the impact of coal mining region over the past three decades. NDVI [ 21 ], images were derived for each date using the following equation: \(\:NDVI=NIR-RED\:/\:NIR+RED\) (i) where RED and NIR are the spectral reflectance of vegetation in the red band and the near-infrared band, respectively. The range of NDVI varies from − 1 to + 1, whereas − 1 corresponds to water and barren surfaces and + 1 to very dense forests. The temporal difference of the NDVI result would reveals the impact of land cover change due to mining activities. 2) Normalized Difference Built-up Index : Built-up areas are manipulated through arithmetic algorithm on different spectrum of bands to evaluate NDBI from Landsat 8 Imagery. Hence the formula for calculating NDBI through Landsat 8 is [ 21 ]: \(\:NDBI=SWIR1-NIR/\:SWIR1+NIR\) (ii) SWIR1 is the shortwave infrared band i.e. band 6 and NIR is the near infrared band 5 of Landsat 8/9. The value of the index ranges from − 1 to + 1. 3) Land Surface Temperature : The land surface temperature is derived by calculation of radiances with different spectral bands in Landsat-5, 8 and 9 imageries. The thermal infrared digital number (DN) value is used to compute top of atmosphere radiance (TOA) using radiance rescaling factors. Further processed to derive top of atmosphere’s brightness temperature (BT) considering the metadata of landsat imagery. The proportion of vegetation (PV) was derived considering NDVI metadata and product to obtain land surface emissivity (LSE) which is an important component to compute land surface temperature [ 21 ]. Table 1 Showing equation used to derive LST Particulars Equation/ Reference Expression Top of Atmosphere (TOA) radiance \(\:TOA=\:ML*Qcal+AL-Ol\) [ 34 , 7 , 32 ] ML: DN value of Radiance_ Multiplicative _Band _x; AL: DN value of Radiance_Add_Band_x; Qcal: DN value of Band_x; Ol: Correction value from Band_x . TOA-Brightness Temperature (BT) \(\:BT=K2/Ln(K1\left(TOA+1\right)-273.15(constant)\) [ 7 ] K1: DN value of K1_ Constant_Band-x , K2: DN value of K2_ Constant_Band-x , TOA: Top of Atmosphere’s DN value Ln: Mathematical algorithm in Math_raster calculator Proportion of Vegetation (PV) \(\:PV=\left[\right(NDVI-NDVImin)/(NDVImax-NDVImin\left)\right]\) 2 [ 39 , 52 ] NDVI: Out put of NDVI algorithm NDVImin: Minimum DN value of NDVI NDVImax: Maximum DN value of NDVI Emissivity (E) of Land surface \(\:E=0.004*PV+0.986\) [ 39 , 34 ] E: Land surface emissivity , PV: Dm value of outcome from PV 0.986 : corresponds to be a correction value of the earth’s equator. Land Surface Temperature (LST) \(\:\text{L}\text{S}\text{T}\:=\:\text{B}\text{T}/(\:1\:+\:({\lambda\:}\:\text{*}"BT"\:/\text{C}2)\text{*}\:\text{l}\text{n}(\text{E})\) [ 50 , 34 ] BT: DN value of outcome of TOA-Brightness Temperature (BT) , λ = central wavelength (in µm) C2 : Landsat thermal band; ρ = 1.438 * 10 − 2 m K.= 14388 4. Fuzzy and Weighted Overlay : The LST of the temporal range were further classified using fuzzy and weighted overlay model to identify the most effected parts of the region, the flowchart reveals the structure of the model applied. 3. RESULTS AND DISCUSSION 3.1 Environmental change in the study region 1993 to 2024 All the experiments were carried out in QGIS 3.23 and ArcGis 10.3 with data from open source database available online viz. http://earthexplorer.usgs.gov/ for Landsat 5, 8 and 9. Further geographical data of the mining region were derived from digitization from additional plugin in QGIS with tiles of higher resolution satellite images from open street map standard (OSM), and Google satellite. In order to investigate the change the NDVI and NDBI of the targeted year were analyzed. The NDVI (Normalized Difference Vegetation Index) data indicates significant fluctuations in areas influenced by human activities over the observation period from 1993 to 2024. Key drivers of these changes include land use for agriculture, fisheries, settlement, infrastructural development, and mining activities. Particularly, the clearances of natural forests for construction of roadways and open-cast mining operations in the Mergherita-Ledo coalfield region have notably impacted the vegetation [ 45 ]. The trend of NDVI reflects that the mining activities on Mergherita-Ledo coal field region are increasing its areal extension since 1993 to 2024. The opencast mine are remained abundant and undergoes some replenishment plan for restoration of ecology [ 23 ] by the Coal India Limited, North- Eastern Coal Field agencies. The observation year 1993 has been considered to be the base year for the investigation and simultaneously a decadal year viz, 2003, 2013 and 2023 were considered and to know the current trend 2024 data was used for illustrate the current trend of mining in the region. As comparisons the decadal change was computed by deducting then years result to previous year, and to identify overall change 2024 result was deducted to the result of the base year (1993) to illustrate the ultimate change. The NDVI categories classified the areas into five distinct vegetation density classes: Highly Exposed, Exposed, Low Vegetation, Dense Vegetation, and Very Dense Vegetation (Table -). Highly Exposed areas have shown a general trend of increase from 1.60 km² in 1993 to 9.44 km² in 2024, with some fluctuations due to mining activities and road communication issues. Exposed areas have steadily increased from 9.31 km² in 1993 to 25.85 km² in 2024, reflecting ongoing human activities and land use changes. Low Vegetation areas have generally decreased from 38.52 km² in 1993 to 28.71 km² in 2024, likely due to conversion to other land use types and degradation. Dense Vegetation areas have significantly decreased from 48.01 km² in 1993 to 29.43 km² in 2024, which aligns with increased deforestation and land clearance activities. Very Dense Vegetation areas have shown an overall increase from 16.61 km² in 1993 to 20.62 km² in 2024, despite some fluctuations, possibly due to reforestation or natural re-growth efforts (Table 2 , Fig. 3 ). In analyzing the Mergherita-Ledo coalfield region's landscape over the past three decades, the data reveals a pronounced relationship between human activity and changes in vegetation cover. The most notable fluctuations correspond to areas experiencing intense mining activities. Specifically, regions classified as Highly Exposed have expanded significantly, primarily due to the prevalence of opencast mining. This type of mining involves the removal of large areas of surface vegetation and soil, drastically altering the natural landscape. Consequently, these operations have led to a sharp decrease in areas categorized as Dense Vegetation, highlighting the extent of deforestation driven by infrastructure development and other land use changes associated with mining [ 12 ]. Conversely, the data also indicates an increase in Very Dense Vegetation areas, suggesting some successful efforts at ecological restoration and replenishment [ 23 ]. This uptick in vegetation density is particularly noticeable in zones previously disturbed by mining but subsequently abandoned. The natural regeneration of these areas, possibly supplemented by human-led restoration initiatives, showcases a positive trend towards ecological recovery. The use of Normalized Difference Vegetation Index (NDVI) data has been instrumental in this analysis, providing a detailed visualization of how human activities, especially mining, have substantially reshaped the region's vegetation cover. NDVI, a satellite-based measurement, captures the density of green vegetation and thus serves as a vital tool in monitoring environmental changes over time [ 13 ]. The data underscores the dramatic transformation in the Mergherita-Ledo coalfield, emphasizing the severe environmental impact of unchecked mining activities. These findings underscore the critical need for sustainable land management practices in the region. The marked reduction in dense vegetation areas and the corresponding rise in exposed surfaces due to mining activities highlight the urgency of implementing effective restoration plans. Such plans should aim to mitigate environmental degradation, promote the regeneration of natural habitats, and balance the region's economic needs with ecological sustainability. In conclusion, the insights gained from the NDVI data not only reveal the past and present state of vegetation in the Mergherita-Ledo coalfield but also point towards the essential measures required to ensure a more sustainable and environmentally sound future for the region. Table 2 Temporal analysis of NDVI (1993–2024) NDVI Area in Km 2 Areal change during (in km 2 ) Years 1993 2003 2013 2023 2024 2003 − 1993 2013 − 2003 2023 − 2013 2023 − 2013 2024 − 1993 Highly Exposed 1.60 7.74 7.12 10.77 9.44 6.14 -0.62 3.65 -1.33 7.84 Exposed 9.31 15.49 19.00 23.13 25.85 6.18 3.51 4.13 2.72 16.54 Low Vegetation 38.52 28.18 31.77 28.91 28.71 -10.34 3.59 -2.86 -0.20 -9.81 Dense Vegetation 48.01 43.08 31.18 28.93 29.43 -4.92 -11.90 -2.25 0.49 -18.58 Very Dense Vegetation 16.61 19.59 24.98 22.33 20.62 2.98 5.39 -2.64 -1.71 4.01 The table illustrates the changes in aerial coverage of various land cover types over multiple decades, showing trends from 1993 to 2024 (Table 2 , Fig. 3 , 4 ). From 1993 to 2003, the highly exposed areas increased by 6.14, while exposed areas saw a similar increase of 6.18. In contrast, low vegetation areas significantly decreased by -10.34, and dense vegetation areas also decreased by -4.92. However, very dense vegetation areas increased by 2.98. Between 2003 and 2013, there was a slight decrease in highly exposed areas by -0.62, whereas exposed areas continued to increase, this time by 3.51. Low vegetation areas rebounded, increasing by 3.59, while dense vegetation areas experienced a significant decline of -11.90. Very dense vegetation areas saw a substantial increase of 5.39. From 2013 to 2023, highly exposed areas increased again by 3.65, and exposed areas continued their upward trend with an increase of 4.13. Low vegetation areas decreased by -2.86, and dense vegetation areas decreased by -2.25. Very dense vegetation areas decreased by -2.64. In the most recent period from 2023 to 2024, highly exposed areas decreased slightly by -1.33, while exposed areas continued to increase, adding 2.72. Low vegetation areas saw a small decrease of -0.20. Dense vegetation areas increased slightly by 0.49, and very dense vegetation areas decreased by -1.71. Over the entire period from 1993 to 2024, highly exposed areas showed a net increase of 7.84, and exposed areas had a significant net increase of 16.54. Low vegetation areas experienced a net decrease of -9.81, and dense vegetation areas saw a substantial net decrease of -18.58. Very dense vegetation areas had a net increase of 4.01 (Table 2 , Fig. 4 ). Overall, the data suggests a trend of increasing exposed and highly exposed areas over the decades, accompanied by a decline in low and dense vegetation areas. 3.2 NDBI Change during 2003 to 2024 The Normalized Difference Built-up Index (NDBI) is a valuable tool for geographers to assess the extent of human activities on the Earth's surface, particularly in terms of infrastructure development in residential and industrial areas. This index leverages the difference in reflectance values between the Shortwave Infrared (SWIR) and Near Infrared (NIR) spectrums, effectively distinguishing built-up regions from vegetated ones due to their distinct spectral characteristics (Table 1 ). Specifically, built-up areas exhibit high reflectance in the SWIR range and lower values in the NIR range, which contrasts sharply with the reflectance patterns typical of vegetated areas, thus making them easily identifiable. The analysis of NDBI data over the study period, starting from 2003, reveals a significant and consistent increase in the extent of built-up areas. The baseline measurement in 2003 showed that built-up areas covered approximately 7.60 km². This figure steadily rose over the subsequent years, reaching 9.19 km² by 2013, 9.91 km² by 2023, and further expanding to 12.09 km² by February 2024, as captured by the latest satellite imagery (date of satellite imagery used). This progression represents a substantial increase of 4.49 km² in built-up areas over the two-decade span (2003 to 2024) (Fig. 5 ). The growth in built-up areas underscores the intensification of human activity in the region, driven by factors such as urban expansion, infrastructure development, and industrialization. This trend is particularly evident in regions undergoing rapid economic development, where the demand for housing, commercial spaces, and industrial facilities has spurred significant land use changes. The increase in built-up areas is a direct indicator of the region's evolving socio-economic landscape, reflecting a shift from natural land cover to urban and industrial uses [ 18 ]. The consistent upward trajectory in built-up area highlights the need for careful urban planning and sustainable development strategies. As built-up areas expand, they often encroach upon natural landscapes, leading to habitat fragmentation, loss of biodiversity, and other environmental challenges. Therefore, the data derived from NDBI not only provide a quantitative measure of urban growth but also serve as a crucial input for policy-makers and urban planners. These stakeholders can use such information to balance development with environmental conservation, ensuring that growth is managed in a way that minimizes negative impacts on the natural environment. The NDBI analysis offers a clear and quantifiable view of the expansion of built-up areas from 2003 to 2024, highlighting the ongoing transformation of the landscape due to human activities. This information is essential for understanding the broader implications of urbanization and industrial growth, and for guiding future land use decisions in a manner that supports sustainable development objectives. The graph and table (Fig. 6 ) illustrate the changes in the Normalized Difference Built-up Index (NDBI) from 2003 to 2024, measured in square kilometers. The blue bars represent the total area of built-up land for each specified year, while the red bars show the incremental changes in NDBI compared to the previous measurements. In 2003, the built-up area was 7.60 sq. km, with no recorded change as considered the base year for the observation. By 2013, the area increased to 9.19 sq. km, marking a change of 1.58 sq. km. In 2023, the built-up area slightly rose to 9.91 sq. km, with a change of 0.73 sq. km. The largest increase occurred by 2024, with the built-up area expanding to 12.09 sq. km, resulting in a significant change of 2.17 sq. km. This trend highlights a consistent and accelerating growth in built-up areas over the 21-year period. 3.3 Land Surface Temperature (LST) Change during 1993 to 2024 The changes in the Normalized Difference Built-up Index (NDBI) from 2003 to 2024 highlight significant environmental transformations and increased human pressure due to opencast mining in the Margherita-Ledo coal mine region. The graph indicates a consistent increase in built-up areas, reflecting the infrastructural demands that have led to the clearance of natural vegetation. This reduction in green space, combined with dust and pollutants from mining activities, has adversely impacted the local ecology. By 2003, the built-up area was 7.60 sq. km. This expanded to 9.19 sq. km by 2013, an increase of 1.58 sq. km. The growth continued, albeit more modestly, to 9.91 sq. km in 2023, with a change of 0.73 sq. km. The most substantial increase occurred by 2024, with the built-up area reaching 12.09 sq. km, an additional 2.17 sq. km. This trend underscores the escalating human activity and its environmental consequences. The rising land surface temperature (LST) in the region, primarily due to infrastructural development and mining expansion, reveals the urgent issue of environmental change since 1993. Coal India Limited has recognized this challenge, implementing replenishment plans for exposed areas and re-planting abandoned coal fields with local vegetation. These efforts have positively impacted the region, but there remains a need for further restoration to support the area's wildlife and ensure ecological sustainability (Fig. 7 ). The table and maps (Table 3 , Fig. 7 ) illustrate the changes in Land Surface Temperature (LST) in the Margherita-Ledo coal mine region over the years 1993, 2003, 2013, 2023, and 2024, segmented into five categories: Very Low, Low, Moderate, High, and Very High LST. In 1993, the majority of the area had Very Low (16.61 sq. km) and Low (48.01 sq. km) LST, totaling 64.62 sq. km. Moderate LST covered 38.52 sq. km, while High and Very High LST were minimal, at 9.31 sq. km and 1.60 sq. km, respectively. By 2003, there was a notable shift: the Very Low LST area expanded significantly to 43.68 sq. km, and the Low LST area increased slightly to 53.00 sq. km. In contrast, the Moderate LST area drastically decreased to 14.22 sq. km, while High LST reduced to 2.46 sq. km, and Very High LST shrunk to 0.69 sq. km. In 2013, the distribution showed further changes with Very Low LST decreasing to 33.40 sq. km and Low LST to 34.80 sq. km. The Moderate LST area rebounded to 30.51 sq. km. High LST areas surged to 13.50 sq. km, and Very High LST increased to 1.84 sq. km. By 2023, the Very Low LST area decreased to 30.85 sq. km, and Low LST increased slightly to 39.57 sq. km. The Moderate LST area slightly reduced to 26.37 sq. km. The High LST area rose to 14.44 sq. km, and Very High LST grew to 2.81 sq. km. In 2024, the changes are more pronounced. The Very Low LST area decreased significantly to 22.56 sq. km, and Low LST further decreased to 32.30 sq. km. The Moderate LST area increased to 29.50 sq. km. The High LST area surged to 22.22 sq. km, and the Very High LST area saw a dramatic rise to 7.46 sq. km. These maps and data reflect the impact of infrastructural development and mining activities on the region’s thermal profile. The increasing areas of High and Very High LST from 2013 to 2024 indicate rising temperatures due to the expansion of coal mines and reduction of vegetation, highlighting the need for sustainable practices to mitigate these effects (Table 3 , Fig. 7 ). Table 3 Temporal analysis of land surface temperature change (1993–2024) LST 1993 2003 2013 2023 2024 Change Particulars Area (km2) 2003 − 1993 2013 − 2003 2023 − 2013 2024 − 2023 Very Low 16.61 43.68 33.40 30.85 22.56 27.07 -10.28 -2.55 -8.29 Low 48.01 53.00 34.80 39.57 32.30 4.99 -18.20 4.77 -7.27 Moderate 38.52 14.22 30.51 26.37 29.50 -24.30 16.29 -4.14 3.13 High 9.31 2.46 13.50 14.44 22.22 -6.84 11.04 0.93 7.78 Very High 1.60 0.69 1.84 2.81 7.46 -0.91 1.15 0.97 4.65 Total Area 114 114 114 114 114 The Table 3 and Fig. 8 shows the changes in LST (Land Surface Temperature) across different categories as Very Low, Low, Moderate, High, and Very High—over several time periods. Between 1993 and 2003, the most significant decrease in temperature was observed in the Moderate category, with a notable reduction of -24.30. From 2003 to 2013, there was a general decrease in temperature across all categories except for Moderate and High, where temperatures increased by 16.29 and 11.04, respectively. From 2013 to 2023, the temperature changes were more mixed, with increases in the Very Low, Low, and High categories, while Moderate and Very High categories experienced slight decreases. Finally, between 2023 and 2024, there was a noticeable increase in temperature in the High and Very High categories, with increases of 7.78 and 4.65, respectively, while the Very Low and Low categories continued to see decreases. Overall, the data indicates a trend towards higher temperatures in the more extreme categories over recent years. 3.4 LST Analysis: Fuzzy Overlay and Weighted Overlay The model run of LST [ 47 ] with Fuzzy overlay and Weighted overlay tool [ 22 ] reveals a significance result (Fig. 8 ) reveals, the weighted overlay representation of the LST is more suitable than the fuzzy overlay. Table 4 Temporal analysis of land surface temperature change (1993–2024) FUZZY OVERLAY WEIGHTER OVERLAY Area (km2) % Area (km2) % 51.64 45.27 25.78 22.61 48.11 42.18 38.77 34.00 12.67 11.11 34.75 30.47 1.59 1.39 13.97 12.25 0.06 0.05 0.78 0.68 114 100 114 100 The Table 4 presents a temporal analysis of land surface temperature changes in the Margherita-Ledo opencast coal mining region from 1993 to 2024, using two different methods: Fuzzy Overlay and Weighted Overlay. For the Fuzzy Overlay method, the data is categorized into five area ranges with corresponding percentages, indicating how the area of each temperature range contributes to the total land surface area. For instance, the largest category, with 51.64 km², represents 45.27% of the area. The Weighted Overlay method, on the other hand, shows a different distribution of areas and percentages. The largest category here is 38.77 km², constituting 34.00% of the total area. Both methods total 114 km², but the proportions differ, illustrating variations in how temperature changes are captured by each overlay technique. The fuzzy overlay refined it to higher values of land surface temperature, mainly concentrates on the exposed ground and concrete infrastructure. Whereas the weighted overlay represents the actual variants of land temperature with reference to the surface temperature, it reveals the areas with low vegetation have higher LST and the areas with rich vegetation posses Low LST. The LST has inverse relationship with vegetation cover. The areas with concrete infrastructure and exposed surface due to human intervention due to agriculture, mining and deforestation have the trend of increasing LST respectively. The region is associated with forest ecology as well as wildlife dwelling within needs a balance and sustainable environment [ 16 ]. The Coal India Limited on the other hand adopts replenishment strategies of vegetation cover to restore the ecosystem. 3.5 Ground parameters of the study area The ground reality of the coal field is been verified through open access satellite imageries in QGIS interface with additional plug-in. The GIS interface provides high resolution imageries, which represents the present world’s status. To validate the status of open cast mining in the study area, it is found authentic, as it provides details about the geographical parameters present in the real world scenario. The Road, places, exposed coal field, water bodies, and even the presence of mining machineries are easily identified and digitized for collection of real time data. The presence of machineries including Dozers, Dumber, Excavators, tankers and communication and transportation vehicles reveals that the coal field is active and expanding to new dimensions. Since 2015 the official publication by Coal India Limited North Eastern Coalfield reveals that only few machinery was used in the coalmines (Table 6 , Fig. 9 . 10). The mining region has different infrastructure development supporting the livelihood of the population around. The places are mainly categorized by their main operational activities as, commercial, health, mining, and official, Police station, Railway station, recreational and residential. Commercial places (3 nos.) includes market facilities mainly Mergherita, Ledo town and Ledo bus stand were the hub for glossaries and machineries in the coal mines. Three health centers are identified viz. Coal India Hospital, Ledo Tea Estate Hospital and Baragolai Colliery Hospital in the study area. Seven places considered in mining areas includes Ledo coalfield, Ledo Coal Para, Ledo Tikak NC, Namdang coal mine, Molong Pathar No. 1(Fig. 10 ). Five places are identifies as official centers of mining region include Coal Heritage Park and Museum, North East coalfields. CIL, Tikak Colliery agent and project offices. The area has one police station and three railway stations provide services in coal mining activities. The associates residential areas in towns and mining includes main complexes Matikhanda Coal India Colony, Lekhapani, Mergherita and ledo township as well as coal India recreational places includes golf field ( 207571 m 2 ) and Patkai stadium. Table 5 Table showing important geographical properties Geographical properties Area (km 2 ) Settlement 11.830 Coal field 10.128 Gulf field 0.208 Water bodies 0.469 Total area (AOI) 114.000 The study area contain 11.830 km 2 of area under settlment includes the mergherita - ledo township and settlements along coal fields. The degitized area composit the coal field occupies 10.128 km 2 of area with 0.459 km 2 of waterbodies associate with coalfield (Table 5 ). For finding the raal activity in the study area the high resolution satellite imageries are used to identidy and mechineries used in coal field to verify its activeless in QGIS interface. The result is shone in Fig. 11 and Table 6 . The Ledo coal fiels is shown in Fig. 11 a reveals less mechinmeries so identified as inactive coalfiels with 5 dumpers and 3 excavatoes and 10 fourwheelers mainly paked in the warehouse of the coalfield fpr maintainence. Tikak colliary posses highest mechinaries reveals its activeness. It contain 1 Dozer at work site, 68 bumbers in active field, 72 excavators, 3 fuel tankers in mining sites and 13 four wheelers operating for the operational activities. The second highest mechineary is located in Namdang colliary and coal mines. It is composed of 36 dumpers and 59 active excavator, 1 fuel tank, 3 water tank and 11 four wheelers active in operational activities. Table 6 Present machinery status of the coal fields reflecting its activeness. Sl.No Item Available Ledo Tikak Namdang Molong Pathar Total (2024) 2015 2024 1 Dozer 2 0 1 0 0 1 2 Dumper 12 5 68 36 9 118 3 Excavator 2 3 72 59 64 198 4 Fuel Tanker 1 1 3 1 2 7 5 Four Wheelers 1 10 13 11 0 34 6 Water Tanker(Amw) 1 0 0 3 0 3 Status In-active Active Active Active 361 Sources: Coal India Limited North Eastern Coal fields report 2015 @ https://neccoal.co.in/ledo-ocp/ 4. Conclusion The analysis of the Margherita-Ledo coalfield presents a stark warning about the severe environmental degradation that has occurred due to extensive mining and urbanization over the past three decades. The data highlights a troubling increase in exposed areas and a corresponding decrease in vegetation, indicating a growing ecological imbalance. The rapid urbanization, marked by the rise in the Normalized Difference Built-up Index (NDBI), and the resulting increase in land surface temperatures (LST), point to the intensifying human impact on the region's climate. The contrasting results from different temperature analysis methods, such as Fuzzy Overlay and Weighted Overlay, reveal the complexity of monitoring these changes, while the presence of heavy machinery in active coalfields further emphasizes the ongoing industrial strain on the environment. Although there have been efforts by Coal India Limited to mitigate these impacts through vegetation restoration, the data suggests these efforts have fallen short in reversing the ecological damage. This situation underscores the urgent need for a comprehensive, integrated land management strategy that balances economic development with ecological restoration. Without immediate and effective intervention, the Margherita-Ledo coalfield faces the risk of continued environmental degradation, which could have long-term negative consequences for both the environment and the local economy. Declarations Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used generative AI in order to streamline the language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. 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08:10:51","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151637,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/ca449989bcd619e0b8f7955a.html"},{"id":100370264,"identity":"80752e18-d31a-4ca9-bed4-320e122850b9","added_by":"auto","created_at":"2026-01-16 08:04:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":607972,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the Margherita-Ledo Coal field.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/8af04e2573e66f0c349f2326.png"},{"id":100229391,"identity":"44f6fe77-3e63-4347-a8b5-69b751bd0446","added_by":"auto","created_at":"2026-01-14 11:12:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182043,"visible":true,"origin":"","legend":"\u003cp\u003eCharts showing model for LST’s Fuzzy and Weighted Overlay\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/69f1e45d8abf5f8fefc8920d.png"},{"id":100371605,"identity":"163b20fd-98fd-4c77-9488-1756eea56092","added_by":"auto","created_at":"2026-01-16 08:10:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466233,"visible":true,"origin":"","legend":"\u003cp\u003eMap Showing NDVI of the study area, a) 1993; b) 2003; c) 2013; d) 2023 and e) 2024.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/baab475c4394bd69b921b0f7.png"},{"id":100229393,"identity":"cc22368a-591e-483e-b75b-200fc6ca206f","added_by":"auto","created_at":"2026-01-14 11:12:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68645,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of aerial change during observation period 1993 to 2024\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/5cfc7bd391324fd8db3c4322.png"},{"id":100370142,"identity":"4a8850cc-3747-48e3-aac2-0ef61c57bfa0","added_by":"auto","created_at":"2026-01-16 08:00:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":274103,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing NDBI change since 2003 to 2024\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/e9a921dfb5a77971b4611cde.png"},{"id":100229396,"identity":"193b309e-d7c8-4ba2-ad68-3fba84c3308a","added_by":"auto","created_at":"2026-01-14 11:12:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46153,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of NDBI change during 2003-2024\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/740b91be5afa1bbe0a188081.png"},{"id":100371579,"identity":"69113389-840b-4e97-abfa-5e344eced6e0","added_by":"auto","created_at":"2026-01-16 08:10:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":461043,"visible":true,"origin":"","legend":"\u003cp\u003eMap Showing LST of the study area, a) 1993; b) 2003; c) 2013; d) 2023 and e) 2024.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/86028d3bb455c5e6a6abaad5.png"},{"id":100229403,"identity":"550be3da-9584-42fc-a830-21058bc29160","added_by":"auto","created_at":"2026-01-14 11:12:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":57183,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of LST change during 2003-2024\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/29fe291da5307c2805343a71.png"},{"id":100229398,"identity":"ad2cd15a-21a6-4c67-a38d-686627f318d5","added_by":"auto","created_at":"2026-01-14 11:12:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":355914,"visible":true,"origin":"","legend":"\u003cp\u003eMap Showing LST compression, a) Fuzzy overlay and, b) Weighted overlay.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/e67ac32694a1d163ceb7e4ed.png"},{"id":100370708,"identity":"624f666e-8f4f-456b-89fa-79ab5863819e","added_by":"auto","created_at":"2026-01-16 08:07:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":298961,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing Geographical parameters\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/eeb8c407c25c06749e59700b.png"},{"id":100229420,"identity":"6c3009f0-ee61-476f-8e3c-430e8c8e4876","added_by":"auto","created_at":"2026-01-14 11:12:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":308095,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical parameters a) Ledo, b)Tikak, c) Namdang and d) Molong Pathar coal mines (Source: Digitized with Google plug-in in QGIS)\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/6c36a68078d55830387a92cd.png"},{"id":102962288,"identity":"71e89a77-5c8d-4535-9e3f-4aa433943815","added_by":"auto","created_at":"2026-02-19 04:07:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3795410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8397493/v1/d7841941-8521-413e-880d-15f12db6ef9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Impacts of Opencast Coal Mining in the Margherita-Ledo Region, Assam, India: A Temporal Analysis using NDVI, NDBI and LST","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe natural stuff present in the earth surface is turned into resources, as its utility is known to humans [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The utility of substances depends upon the need, knowledge, and skill of human. Coal is the important power resources, in demand worldwide. The excavation of coal is done widely both open-cast and underground mining. The mining procedure of coal field depends upon the reservoir of mineral deposit, as the greater the depth of the deposit reveals the underground mining and if the deposit present in outer crust develops open-cast mining. Resources are not distributed evenly in the earth surface [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Indian resources are even not identified and estimated due to uneven distribution, and it is still to explore [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The excavation of mineral resources led the degradation of natural environment ant its biodiversity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The increasing demand of mineral resources led to over exploitation and reveals the environmental; consequences as degradation and pollution of land, water and air [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The unconsolidated eroded loose material from the mining activity contributes to the increases the sediment load in streams resulting sedimentation of river bed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The chemical properties of the water bodies around the mining site influence to acidification of water reserves led to contamination of water. The fine sediments distributed by the influence of agents of degradation, reduces the soil quality, the dumping of overburden material, and the deposition of coal dust due to wind erosion. The impact on environment pollution, soil infertility, land degradation, water security and air quality are the primary observations [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Apart from this biodiversity treaty, animal and plant dispersal, human health, socioeconomic aspects, shifting of occupational structure of the inhabitants nearer to the mines are a greater interest of concerns [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The environment degradation in mining region due to unscientific and riskless excavation of mineral resources let deforestation, and land degradation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The acquisition of leases for mining in the Assam coal fields predominantly involved interior barren lands, agricultural farms, and government-controlled fallow and forested areas. Approximately 10% of the total lease area was utilized for the establishment of underground mining operations, residential complexes, and civic amenities, necessitating the conversion of forest, agricultural, and fallow lands. This land conversion aimed to support common facility development with minimal disruption to the soil and vegetation cover [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, this process inevitably led to the displacement or disturbance of the area's indigenous biological species due to human settlement, noise pollution from heavy machinery, and extensive construction activities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe study is an initial attempt to evaluate the changes that the environment faces due to opencast mining in the region, also the investigate the ground reality of deforestation due to mining [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], The goggle earth satellite imageries were consulted to find geographical attributes of the mining region with a one (1 km) kilometer buffer from the ring road surrounding the coalmining region as an area of interest (AOI). The study is limited to certain geographical processes of evaluating environmental impact by investigating on NDVI and NDBI and LST of the study area [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The study provides an important insight on temporal change in the study area and open scope for further scientific investigation of various factors unaddressed in this paper for future researchers. The paper would be a primary evaluation of the temporal change for the stakeholders to plan strategies for its maintenance and development in scientific way.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research gap and objectives\u003c/h2\u003e \u003cp\u003eThe search for literature regarding the environmental changes due to coal mining and it\u0026rsquo;s found that the mining locations were investigated with physical and chemical parameters to evaluate land, soil, vegetation and water degradation. Few gap found and attempted to address in this paper includes, (a) field data of water, air and soil quality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Whereas the area are not easily accessible so remote sensing platform were not considered. (b) The use of remote sensing and GIS [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were mainly is used to identify and evaluate on land use land cover changes by open cast mining [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. (c) Evaluate temporal change by implementing remote sensing platform methods were moistly used for changes due to urbanisation by Normalized Difference Vegetation Index (NDVI) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Normalized Difference Built-up Index (NDBI) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Land Surface Temperature (LST) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These paper attempt to use for evaluation of temporal change in open cast mining region. The Raster reclassification on land surface temperature if year 1993, 2003, 2013, 2023 and 2024 was re-analyzed with Fuzzy overlay and weighted overlay to find the most vulnerable areas of the study area. The primary objective of the study is to investigate the impact of open cast mining on the surrounding environment. To evaluate the temporal change in vegetation and built-up areas in respect to land surface temperate of the mining region, and to evaluate the current trend on open cast mining in the study region. The study is significant because it provides a resource for researchers who wish to investigate the impact of open cast mining on environments. The stakeholders would find interest to evaluate and plan strategic management plan for the environment sustainability in mining regions. This study introduces novel elements absent in prior Margherita-Ledo research, which has emphasized soil impacts or broad LULC without multi-index integration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. First, it provides the longest temporal NDVI/NDBI/LST analysis (1993\u0026ndash;2024), capturing recent expansions like highly exposed areas (+\u0026thinsp;7.84 km\u0026sup2;). Second, fuzzy and weighted LST overlays identify high-risk zones, revealing inverse vegetation-LST relationships refined for mining contexts. Third, ground-truthing via high-res imagery quantifies machinery (e.g., 198 excavators active in 2024), correlating operations to degradation\u0026mdash;offering stakeholders actionable insights for restoration beyond descriptive monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Study Area\u003c/h2\u003e \u003cp\u003eThe study area selected for the research is an active open cast coal field of Assam and Arunachal Pradesh. It is situated in the foothill areas of the Patkai hills of lower Arunachal Himalayas, it is termed as Mergherita-Ledo in particular and coalfield [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Coal mining activities are commonly operates by the North Eastern Coalfields, Coal India Limited (CIL) for Margherita coal field generally known as \u0026Prime;the Ledo-Makum Coal Field\" [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Mainly confined to sub-divisions or Colliery namely, Lido OCP, Tipong Colliery, Tikak Colliery and Tirap Colliery. The Ledo OCP ( area 0.85 Sq. km.) situated in Northern part of Makum Coalfield situated at 10 kms North East of Margherita town. The area is connected to the rest of the country by NH.38 and Indian railways which extends upto Tirap-Siding. Ledo Mechanised OCP block, covering an area of 0.85 sq km, is situated in the northern part of Makum coalfield and is defined by 27\u003csup\u003eo\u003c/sup\u003e 17\u0026rsquo; 25\u0026rdquo; and 27\u003csup\u003eo\u003c/sup\u003e 17\u0026rsquo; 50\u0026rdquo; North latitude and 95\u003csup\u003eo\u003c/sup\u003e 0\u0026rsquo; 45\u0026rdquo; to 95\u003csup\u003eo\u003c/sup\u003e 45\u0026rsquo; 45 \u0026rdquo; East longitude falling in the Survey of India Topographical map No.83M/1 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003eThe methodology involves recent data analysis using high resolution satellite imageries. Remote sensing data from the Landsat satellite mission have been derived from U.S. Geological Survey interface [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The principles of imaging of the Thematic Mapper (TM) for 1993, Thematic Mapper plus (ETM+) for 2003, and Operational Land Imager (OLI) for 2013, 2023 and 2024 are well documented. Data derived from open course using added plugins in QGIS and ArcGIS interfaces to generate high resolution multispectral image. The image used has orbit path 134, row 41 with cloud cover of less than 10%; the projection coordinate system was UTM (Universal Transverse Mercator); the geographic coordinate system was WGS84; the zone was 46 North. Spatial information are digitized in Google Earth platform and imported to GIS interface for further analysis. Land use land cover of the study area was computed adopting supervised classification and used to analyze changes due to human interference [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To identify the active coal fields physical variables were considered from image include earth movers, excavators, and vehicles used for carrying extracted coal for justification.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Vegetation indices\u003c/h2\u003e \u003cp\u003eAn index is a simple graphical indicator that can be used to analyze remote sensing data for a particular type of land class. For creating such indices we try to find out the strongest (maximum) reflectance band and weakest (minimum) reflectance bands among the bands available [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Using the strongest reflectance in the numerator and the weakest reflectance in the denominator, the normalized ratio of these two bands can give the highest contrast for the particular land type with minimum background noise.\u003c/p\u003e \u003cp\u003e1) \u003cem\u003eNormalized Difference Vegetation Index\u003c/em\u003e: Area of Interest (AOI) is curved out with a 1km buffer over a ring road around coal mining areas for evaluate the change in vegetation concentration and assess the impact of coal mining region over the past three decades. NDVI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], images were derived for each date using the following equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:NDVI=NIR-RED\\:/\\:NIR+RED\\)\u003c/span\u003e \u003c/span\u003e (i)\u003c/p\u003e \u003cp\u003ewhere RED and NIR are the spectral reflectance of vegetation in the red band and the near-infrared band, respectively. The range of NDVI varies from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, whereas \u0026minus;\u0026thinsp;1 corresponds to water and barren surfaces and +\u0026thinsp;1 to very dense forests. The temporal difference of the NDVI result would reveals the impact of land cover change due to mining activities.\u003c/p\u003e \u003cp\u003e2) \u003cem\u003eNormalized Difference Built-up Index\u003c/em\u003e: Built-up areas are manipulated through arithmetic algorithm on different spectrum of bands to evaluate NDBI from Landsat 8 Imagery. Hence the formula for calculating NDBI through Landsat 8 is [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:NDBI=SWIR1-NIR/\\:SWIR1+NIR\\)\u003c/span\u003e \u003c/span\u003e (ii)\u003c/p\u003e \u003cp\u003eSWIR1 is the shortwave infrared band i.e. band 6 and NIR is the near infrared band 5 of Landsat 8/9. The value of the index ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1.\u003c/p\u003e \u003cp\u003e3) \u003cem\u003eLand Surface Temperature\u003c/em\u003e: The land surface temperature is derived by calculation of radiances with different spectral bands in Landsat-5, 8 and 9 imageries. The thermal infrared digital number (DN) value is used to compute top of atmosphere radiance (TOA) using radiance rescaling factors. Further processed to derive top of atmosphere\u0026rsquo;s brightness temperature (BT) considering the metadata of landsat imagery. The proportion of vegetation (PV) was derived considering NDVI metadata and product to obtain land surface emissivity (LSE) which is an important component to compute land surface temperature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShowing equation used to derive LST\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticulars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation/ Reference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop of Atmosphere (TOA) radiance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:TOA=\\:ML*Qcal+AL-Ol\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eML: DN value of Radiance_ Multiplicative _Band _x;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAL: DN value of Radiance_Add_Band_x;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eQcal: DN value of Band_x;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eOl: Correction value from Band_x\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOA-Brightness Temperature (BT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:BT=K2/Ln(K1\\left(TOA+1\\right)-273.15(constant)\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eK1: DN value of K1_ Constant_Band-x\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e\u003cem\u003eK2: DN value of K2_ Constant_Band-x\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e\u003cem\u003eTOA: Top of Atmosphere\u0026rsquo;s DN value\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLn: Mathematical algorithm in Math_raster calculator\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of Vegetation (PV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PV=\\left[\\right(NDVI-NDVImin)/(NDVImax-NDVImin\\left)\\right]\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNDVI: Out put of NDVI algorithm\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNDVImin: Minimum DN value of NDVI\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNDVImax: Maximum DN value of NDVI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmissivity (E) of Land surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E=0.004*PV+0.986\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE: Land surface emissivity\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e\u003cem\u003ePV: Dm value of outcome from PV\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.986 : corresponds to be a correction value of the earth\u0026rsquo;s equator.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Surface Temperature (LST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{S}\\text{T}\\:=\\:\\text{B}\\text{T}/(\\:1\\:+\\:({\\lambda\\:}\\:\\text{*}\u0026quot;BT\u0026quot;\\:/\\text{C}2)\\text{*}\\:\\text{l}\\text{n}(\\text{E})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBT: DN value of outcome of TOA-Brightness Temperature (BT)\u003c/em\u003e,\u003c/p\u003e \u003cp\u003eλ\u0026thinsp;=\u0026thinsp;central wavelength (in \u0026micro;m)\u003c/p\u003e \u003cp\u003e\u003cem\u003eC2\u003c/em\u003e: Landsat thermal band; ρ\u0026thinsp;=\u0026thinsp;1.438 * 10\u0026thinsp;\u0026minus;\u0026thinsp;2\u0026nbsp;m\u0026nbsp;K.= 14388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e4.\u003cem\u003eFuzzy and Weighted Overlay\u003c/em\u003e: The LST of the temporal range were further classified using fuzzy and weighted overlay model to identify the most effected parts of the region, the flowchart reveals the structure of the model applied.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Environmental change in the study region 1993 to 2024\u003c/h2\u003e \u003cp\u003eAll the experiments were carried out in QGIS 3.23 and ArcGis 10.3 with data from open source database available online viz. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"http://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for Landsat 5, 8 and 9. Further geographical data of the mining region were derived from digitization from additional plugin in QGIS with tiles of higher resolution satellite images from open street map standard (OSM), and Google satellite. In order to investigate the change the NDVI and NDBI of the targeted year were analyzed. The NDVI (Normalized Difference Vegetation Index) data indicates significant fluctuations in areas influenced by human activities over the observation period from 1993 to 2024. Key drivers of these changes include land use for agriculture, fisheries, settlement, infrastructural development, and mining activities. Particularly, the clearances of natural forests for construction of roadways and open-cast mining operations in the Mergherita-Ledo coalfield region have notably impacted the vegetation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe trend of NDVI reflects that the mining activities on Mergherita-Ledo coal field region are increasing its areal extension since 1993 to 2024. The opencast mine are remained abundant and undergoes some replenishment plan for restoration of ecology [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] by the Coal India Limited, North- Eastern Coal Field agencies. The observation year 1993 has been considered to be the base year for the investigation and simultaneously a decadal year viz, 2003, 2013 and 2023 were considered and to know the current trend 2024 data was used for illustrate the current trend of mining in the region. As comparisons the decadal change was computed by deducting then years result to previous year, and to identify overall change 2024 result was deducted to the result of the base year (1993) to illustrate the ultimate change.\u003c/p\u003e \u003cp\u003eThe NDVI categories classified the areas into five distinct vegetation density classes: Highly Exposed, Exposed, Low Vegetation, Dense Vegetation, and Very Dense Vegetation (Table -). Highly Exposed areas have shown a general trend of increase from 1.60 km\u0026sup2; in 1993 to 9.44 km\u0026sup2; in 2024, with some fluctuations due to mining activities and road communication issues. Exposed areas have steadily increased from 9.31 km\u0026sup2; in 1993 to 25.85 km\u0026sup2; in 2024, reflecting ongoing human activities and land use changes. Low Vegetation areas have generally decreased from 38.52 km\u0026sup2; in 1993 to 28.71 km\u0026sup2; in 2024, likely due to conversion to other land use types and degradation. Dense Vegetation areas have significantly decreased from 48.01 km\u0026sup2; in 1993 to 29.43 km\u0026sup2; in 2024, which aligns with increased deforestation and land clearance activities. Very Dense Vegetation areas have shown an overall increase from 16.61 km\u0026sup2; in 1993 to 20.62 km\u0026sup2; in 2024, despite some fluctuations, possibly due to reforestation or natural re-growth efforts (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn analyzing the Mergherita-Ledo coalfield region's landscape over the past three decades, the data reveals a pronounced relationship between human activity and changes in vegetation cover. The most notable fluctuations correspond to areas experiencing intense mining activities. Specifically, regions classified as Highly Exposed have expanded significantly, primarily due to the prevalence of opencast mining. This type of mining involves the removal of large areas of surface vegetation and soil, drastically altering the natural landscape. Consequently, these operations have led to a sharp decrease in areas categorized as Dense Vegetation, highlighting the extent of deforestation driven by infrastructure development and other land use changes associated with mining [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, the data also indicates an increase in Very Dense Vegetation areas, suggesting some successful efforts at ecological restoration and replenishment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This uptick in vegetation density is particularly noticeable in zones previously disturbed by mining but subsequently abandoned. The natural regeneration of these areas, possibly supplemented by human-led restoration initiatives, showcases a positive trend towards ecological recovery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe use of Normalized Difference Vegetation Index (NDVI) data has been instrumental in this analysis, providing a detailed visualization of how human activities, especially mining, have substantially reshaped the region's vegetation cover. NDVI, a satellite-based measurement, captures the density of green vegetation and thus serves as a vital tool in monitoring environmental changes over time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The data underscores the dramatic transformation in the Mergherita-Ledo coalfield, emphasizing the severe environmental impact of unchecked mining activities. These findings underscore the critical need for sustainable land management practices in the region. The marked reduction in dense vegetation areas and the corresponding rise in exposed surfaces due to mining activities highlight the urgency of implementing effective restoration plans. Such plans should aim to mitigate environmental degradation, promote the regeneration of natural habitats, and balance the region's economic needs with ecological sustainability. In conclusion, the insights gained from the NDVI data not only reveal the past and present state of vegetation in the Mergherita-Ledo coalfield but also point towards the essential measures required to ensure a more sustainable and environmentally sound future for the region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal analysis of NDVI (1993\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eArea in Km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eAreal change during (in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2003\u0026thinsp;\u0026minus;\u0026thinsp;1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2013\u0026thinsp;\u0026minus;\u0026thinsp;2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2023\u0026thinsp;\u0026minus;\u0026thinsp;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2023\u0026thinsp;\u0026minus;\u0026thinsp;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2024\u0026thinsp;\u0026minus;\u0026thinsp;1993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly Exposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-9.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-18.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Dense Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe table illustrates the changes in aerial coverage of various land cover types over multiple decades, showing trends from 1993 to 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). From 1993 to 2003, the highly exposed areas increased by 6.14, while exposed areas saw a similar increase of 6.18. In contrast, low vegetation areas significantly decreased by -10.34, and dense vegetation areas also decreased by -4.92. However, very dense vegetation areas increased by 2.98. Between 2003 and 2013, there was a slight decrease in highly exposed areas by -0.62, whereas exposed areas continued to increase, this time by 3.51. Low vegetation areas rebounded, increasing by 3.59, while dense vegetation areas experienced a significant decline of -11.90. Very dense vegetation areas saw a substantial increase of 5.39. From 2013 to 2023, highly exposed areas increased again by 3.65, and exposed areas continued their upward trend with an increase of 4.13. Low vegetation areas decreased by -2.86, and dense vegetation areas decreased by -2.25. Very dense vegetation areas decreased by -2.64. In the most recent period from 2023 to 2024, highly exposed areas decreased slightly by -1.33, while exposed areas continued to increase, adding 2.72. Low vegetation areas saw a small decrease of -0.20. Dense vegetation areas increased slightly by 0.49, and very dense vegetation areas decreased by -1.71. Over the entire period from 1993 to 2024, highly exposed areas showed a net increase of 7.84, and exposed areas had a significant net increase of 16.54. Low vegetation areas experienced a net decrease of -9.81, and dense vegetation areas saw a substantial net decrease of -18.58. Very dense vegetation areas had a net increase of 4.01 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Overall, the data suggests a trend of increasing exposed and highly exposed areas over the decades, accompanied by a decline in low and dense vegetation areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 NDBI Change during 2003 to 2024\u003c/h2\u003e \u003cp\u003eThe Normalized Difference Built-up Index (NDBI) is a valuable tool for geographers to assess the extent of human activities on the Earth's surface, particularly in terms of infrastructure development in residential and industrial areas. This index leverages the difference in reflectance values between the Shortwave Infrared (SWIR) and Near Infrared (NIR) spectrums, effectively distinguishing built-up regions from vegetated ones due to their distinct spectral characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, built-up areas exhibit high reflectance in the SWIR range and lower values in the NIR range, which contrasts sharply with the reflectance patterns typical of vegetated areas, thus making them easily identifiable. The analysis of NDBI data over the study period, starting from 2003, reveals a significant and consistent increase in the extent of built-up areas. The baseline measurement in 2003 showed that built-up areas covered approximately 7.60 km\u0026sup2;. This figure steadily rose over the subsequent years, reaching 9.19 km\u0026sup2; by 2013, 9.91 km\u0026sup2; by 2023, and further expanding to 12.09 km\u0026sup2; by February 2024, as captured by the latest satellite imagery (date of satellite imagery used). This progression represents a substantial increase of 4.49 km\u0026sup2; in built-up areas over the two-decade span (2003 to 2024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe growth in built-up areas underscores the intensification of human activity in the region, driven by factors such as urban expansion, infrastructure development, and industrialization. This trend is particularly evident in regions undergoing rapid economic development, where the demand for housing, commercial spaces, and industrial facilities has spurred significant land use changes. The increase in built-up areas is a direct indicator of the region's evolving socio-economic landscape, reflecting a shift from natural land cover to urban and industrial uses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The consistent upward trajectory in built-up area highlights the need for careful urban planning and sustainable development strategies. As built-up areas expand, they often encroach upon natural landscapes, leading to habitat fragmentation, loss of biodiversity, and other environmental challenges. Therefore, the data derived from NDBI not only provide a quantitative measure of urban growth but also serve as a crucial input for policy-makers and urban planners. These stakeholders can use such information to balance development with environmental conservation, ensuring that growth is managed in a way that minimizes negative impacts on the natural environment. The NDBI analysis offers a clear and quantifiable view of the expansion of built-up areas from 2003 to 2024, highlighting the ongoing transformation of the landscape due to human activities. This information is essential for understanding the broader implications of urbanization and industrial growth, and for guiding future land use decisions in a manner that supports sustainable development objectives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe graph and table (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) illustrate the changes in the Normalized Difference Built-up Index (NDBI) from 2003 to 2024, measured in square kilometers. The blue bars represent the total area of built-up land for each specified year, while the red bars show the incremental changes in NDBI compared to the previous measurements. In 2003, the built-up area was 7.60 sq. km, with no recorded change as considered the base year for the observation. By 2013, the area increased to 9.19 sq. km, marking a change of 1.58 sq. km. In 2023, the built-up area slightly rose to 9.91 sq. km, with a change of 0.73 sq. km. The largest increase occurred by 2024, with the built-up area expanding to 12.09 sq. km, resulting in a significant change of 2.17 sq. km. This trend highlights a consistent and accelerating growth in built-up areas over the 21-year period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Land Surface Temperature (LST) Change during 1993 to 2024\u003c/h2\u003e \u003cp\u003eThe changes in the Normalized Difference Built-up Index (NDBI) from 2003 to 2024 highlight significant environmental transformations and increased human pressure due to opencast mining in the Margherita-Ledo coal mine region. The graph indicates a consistent increase in built-up areas, reflecting the infrastructural demands that have led to the clearance of natural vegetation. This reduction in green space, combined with dust and pollutants from mining activities, has adversely impacted the local ecology. By 2003, the built-up area was 7.60 sq. km. This expanded to 9.19 sq. km by 2013, an increase of 1.58 sq. km. The growth continued, albeit more modestly, to 9.91 sq. km in 2023, with a change of 0.73 sq. km. The most substantial increase occurred by 2024, with the built-up area reaching 12.09 sq. km, an additional 2.17 sq. km. This trend underscores the escalating human activity and its environmental consequences. The rising land surface temperature (LST) in the region, primarily due to infrastructural development and mining expansion, reveals the urgent issue of environmental change since 1993. Coal India Limited has recognized this challenge, implementing replenishment plans for exposed areas and re-planting abandoned coal fields with local vegetation. These efforts have positively impacted the region, but there remains a need for further restoration to support the area's wildlife and ensure ecological sustainability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe table and maps (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) illustrate the changes in Land Surface Temperature (LST) in the Margherita-Ledo coal mine region over the years 1993, 2003, 2013, 2023, and 2024, segmented into five categories: Very Low, Low, Moderate, High, and Very High LST. In 1993, the majority of the area had Very Low (16.61 sq. km) and Low (48.01 sq. km) LST, totaling 64.62 sq. km. Moderate LST covered 38.52 sq. km, while High and Very High LST were minimal, at 9.31 sq. km and 1.60 sq. km, respectively. By 2003, there was a notable shift: the Very Low LST area expanded significantly to 43.68 sq. km, and the Low LST area increased slightly to 53.00 sq. km. In contrast, the Moderate LST area drastically decreased to 14.22 sq. km, while High LST reduced to 2.46 sq. km, and Very High LST shrunk to 0.69 sq. km. In 2013, the distribution showed further changes with Very Low LST decreasing to 33.40 sq. km and Low LST to 34.80 sq. km. The Moderate LST area rebounded to 30.51 sq. km. High LST areas surged to 13.50 sq. km, and Very High LST increased to 1.84 sq. km. By 2023, the Very Low LST area decreased to 30.85 sq. km, and Low LST increased slightly to 39.57 sq. km. The Moderate LST area slightly reduced to 26.37 sq. km. The High LST area rose to 14.44 sq. km, and Very High LST grew to 2.81 sq. km. In 2024, the changes are more pronounced. The Very Low LST area decreased significantly to 22.56 sq. km, and Low LST further decreased to 32.30 sq. km. The Moderate LST area increased to 29.50 sq. km. The High LST area surged to 22.22 sq. km, and the Very High LST area saw a dramatic rise to 7.46 sq. km. These maps and data reflect the impact of infrastructural development and mining activities on the region\u0026rsquo;s thermal profile. The increasing areas of High and Very High LST from 2013 to 2024 indicate rising temperatures due to the expansion of coal mines and reduction of vegetation, highlighting the need for sustainable practices to mitigate these effects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal analysis of land surface temperature change (1993\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticulars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2003\u0026thinsp;\u0026minus;\u0026thinsp;1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2013\u0026thinsp;\u0026minus;\u0026thinsp;2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2023\u0026thinsp;\u0026minus;\u0026thinsp;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2024\u0026thinsp;\u0026minus;\u0026thinsp;2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-8.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-7.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-24.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the changes in LST (Land Surface Temperature) across different categories as Very Low, Low, Moderate, High, and Very High\u0026mdash;over several time periods. Between 1993 and 2003, the most significant decrease in temperature was observed in the Moderate category, with a notable reduction of -24.30. From 2003 to 2013, there was a general decrease in temperature across all categories except for Moderate and High, where temperatures increased by 16.29 and 11.04, respectively. From 2013 to 2023, the temperature changes were more mixed, with increases in the Very Low, Low, and High categories, while Moderate and Very High categories experienced slight decreases. Finally, between 2023 and 2024, there was a noticeable increase in temperature in the High and Very High categories, with increases of 7.78 and 4.65, respectively, while the Very Low and Low categories continued to see decreases. Overall, the data indicates a trend towards higher temperatures in the more extreme categories over recent years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 LST Analysis: Fuzzy Overlay and Weighted Overlay\u003c/h2\u003e \u003cp\u003eThe model run of LST [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] with Fuzzy overlay and Weighted overlay tool [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reveals a significance result (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveals, the weighted overlay representation of the LST is more suitable than the fuzzy overlay.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal analysis of land surface temperature change (1993\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFUZZY OVERLAY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWEIGHTER OVERLAY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a temporal analysis of land surface temperature changes in the Margherita-Ledo opencast coal mining region from 1993 to 2024, using two different methods: Fuzzy Overlay and Weighted Overlay. For the Fuzzy Overlay method, the data is categorized into five area ranges with corresponding percentages, indicating how the area of each temperature range contributes to the total land surface area. For instance, the largest category, with 51.64 km\u0026sup2;, represents 45.27% of the area. The Weighted Overlay method, on the other hand, shows a different distribution of areas and percentages. The largest category here is 38.77 km\u0026sup2;, constituting 34.00% of the total area. Both methods total 114 km\u0026sup2;, but the proportions differ, illustrating variations in how temperature changes are captured by each overlay technique. The fuzzy overlay refined it to higher values of land surface temperature, mainly concentrates on the exposed ground and concrete infrastructure. Whereas the weighted overlay represents the actual variants of land temperature with reference to the surface temperature, it reveals the areas with low vegetation have higher LST and the areas with rich vegetation posses Low LST. The LST has inverse relationship with vegetation cover. The areas with concrete infrastructure and exposed surface due to human intervention due to agriculture, mining and deforestation have the trend of increasing LST respectively. The region is associated with forest ecology as well as wildlife dwelling within needs a balance and sustainable environment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Coal India Limited on the other hand adopts replenishment strategies of vegetation cover to restore the ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ground parameters of the study area\u003c/h2\u003e \u003cp\u003eThe ground reality of the coal field is been verified through open access satellite imageries in QGIS interface with additional plug-in. The GIS interface provides high resolution imageries, which represents the present world\u0026rsquo;s status. To validate the status of open cast mining in the study area, it is found authentic, as it provides details about the geographical parameters present in the real world scenario. The Road, places, exposed coal field, water bodies, and even the presence of mining machineries are easily identified and digitized for collection of real time data. The presence of machineries including Dozers, Dumber, Excavators, tankers and communication and transportation vehicles reveals that the coal field is active and expanding to new dimensions. Since 2015 the official publication by Coal India Limited North Eastern Coalfield reveals that only few machinery was used in the coalmines (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. 10). The mining region has different infrastructure development supporting the livelihood of the population around. The places are mainly categorized by their main operational activities as, commercial, health, mining, and official, Police station, Railway station, recreational and residential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCommercial places (3 nos.) includes market facilities mainly Mergherita, Ledo town and Ledo bus stand were the hub for glossaries and machineries in the coal mines. Three health centers are identified viz. Coal India Hospital, Ledo Tea Estate Hospital and Baragolai Colliery Hospital in the study area. Seven places considered in mining areas includes Ledo coalfield, Ledo Coal Para, Ledo Tikak NC, Namdang coal mine, Molong Pathar No. 1(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Five places are identifies as official centers of mining region include Coal Heritage Park and Museum, North East coalfields. CIL, Tikak Colliery agent and project offices. The area has one police station and three railway stations provide services in coal mining activities. The associates residential areas in towns and mining includes main complexes Matikhanda Coal India Colony, Lekhapani, Mergherita and ledo township as well as coal India recreational places includes golf field ( 207571 m\u003csup\u003e2\u003c/sup\u003e) and Patkai stadium.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable showing important geographical properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographical properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoal field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGulf field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal area (AOI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study area contain 11.830 km\u003csup\u003e2\u003c/sup\u003e of area under settlment includes the mergherita - ledo township and settlements along coal fields. The degitized area composit the coal field occupies 10.128 km\u003csup\u003e2\u003c/sup\u003e of area with 0.459 km\u003csup\u003e2\u003c/sup\u003e of waterbodies associate with coalfield (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For finding the raal activity in the study area the high resolution satellite imageries are used to identidy and mechineries used in coal field to verify its activeless in QGIS interface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result is shone in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The Ledo coal fiels is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea reveals less mechinmeries so identified as inactive coalfiels with 5 dumpers and 3 excavatoes and 10 fourwheelers mainly paked in the warehouse of the coalfield fpr maintainence. Tikak colliary posses highest mechinaries reveals its activeness. It contain 1 Dozer at work site, 68 bumbers in active field, 72 excavators, 3 fuel tankers in mining sites and 13 four wheelers operating for the operational activities. The second highest mechineary is located in Namdang colliary and coal mines. It is composed of 36 dumpers and 59 active excavator, 1 fuel tank, 3 water tank and 11 four wheelers active in operational activities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePresent machinery status of the coal fields reflecting its activeness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSl.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLedo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTikak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNamdang\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMolong Pathar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (2024)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDozer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDumper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e118\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcavator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e198\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuel Tanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFour Wheelers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Tanker(Amw)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIn-active\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eActive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eActive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eActive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e361\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSources: Coal India Limited North Eastern Coal fields report 2015 @\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://neccoal.co.in/ledo-ocp/\u003c/span\u003e\u003cspan address=\"https://neccoal.co.in/ledo-ocp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe analysis of the Margherita-Ledo coalfield presents a stark warning about the severe environmental degradation that has occurred due to extensive mining and urbanization over the past three decades. The data highlights a troubling increase in exposed areas and a corresponding decrease in vegetation, indicating a growing ecological imbalance. The rapid urbanization, marked by the rise in the Normalized Difference Built-up Index (NDBI), and the resulting increase in land surface temperatures (LST), point to the intensifying human impact on the region's climate. The contrasting results from different temperature analysis methods, such as Fuzzy Overlay and Weighted Overlay, reveal the complexity of monitoring these changes, while the presence of heavy machinery in active coalfields further emphasizes the ongoing industrial strain on the environment. Although there have been efforts by Coal India Limited to mitigate these impacts through vegetation restoration, the data suggests these efforts have fallen short in reversing the ecological damage. This situation underscores the urgent need for a comprehensive, integrated land management strategy that balances economic development with ecological restoration. Without immediate and effective intervention, the Margherita-Ledo coalfield faces the risk of continued environmental degradation, which could have long-term negative consequences for both the environment and the local economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used \u003cstrong\u003egenerative AI\u0026nbsp;\u003c/strong\u003ein order to streamline the language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration missing:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author, upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: No funding\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAboda C, J\u0026uacute;l\u0026iacute;usd\u0026oacute;ttir M, Byakagaba P et al. 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The open-cast mining in Makum-Ledo area has more impact on environmental degradation then underground coal mining. Unscientific and unauthorized mining led degradation in quality of air, water, soil, changes in landform, land use/land cover, vegetation distribution mainly due to open crust mining activity. Desertification of the coal field region degradation led to the biodiversity influencing Land Use and Land Cover (LULC), land, water and air pollution. The temporal change in land cover is revealed by NDVI and NDBI of the area, the LST of the study area reveals that the decrease in forest cover for mining activities increases land surface temperature. The new areas are includes since 1993 to 2024, which the area loss of top soil due to opencast mining, the dumping of overburden material, and the deposition of coal dust due to wind erosion. Apart from that, opencast and underground mining, as well as the construction of coal-related businesses, have a direct impact on the environment (including crops in agricultural land). The study focuses to evaluation of the environmental impact and estimate the present status of machineries active in the study area. The study provides an important insight on temporal change in the study area and open scope for further scientific investigation of various factors unaddressed in this paper for future researchers. The paper would be a primary evaluation of the temporal change for the stakeholders to plan strategies for its maintenance and development in scientific way.\u003c/p\u003e","manuscriptTitle":"Environmental Impacts of Opencast Coal Mining in the Margherita-Ledo Region, Assam, India: A Temporal Analysis using NDVI, NDBI and LST","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 11:11:59","doi":"10.21203/rs.3.rs-8397493/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9be1489f-636c-43af-bcc3-7fd2201431f4","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T07:26:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 11:11:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8397493","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8397493","identity":"rs-8397493","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}