Land Use/Land Cover Change With Impact On Land Surface Temperature: A Case Study Of Mkda Planning Area, West Bengal, India

The type of surface influences the temperature of a surface. If it is made of concrete or another hard material, the temperature will be higher. Hence it is essential to study the land surface temperature (LST) of urban areas. The LST is an important parameter in the estimation of radiation budgets and heat balance and is a controlling factor of dynamic climate changes. In this work, we made an effort to identify the LST of the Midnapore Kharagpur Development Authority planning region. Multi-temporal images acquired by Landsat 7 ETM+, Landsat 5 TM and Landsat 8 using OLI sensors on 3 May 2001, 7 May 2011 and 29 May 2019, respectively, were corrected for radiometric and geometric errors and processed to extract LULC classes and LST. Thermal remote sensing can be used to monitor the temperature and local climate of urban areas. This study has shown that the temperature varies across the surface according to land use. It was found that the urbanized area increased from 6.79% (40.39 sq. km) to 11.6% (69.2 sq. km) between 2001 and 2011 and from 11.6% (69.2 sq. km) to 17.22 % (102.79 sq. km) between 2011 and 2019. The LST study has shown that there has been a tremendous change in the spatial pattern of the temperature between 2001 and 2019. Whereas in 2001 the highest temperature did not exceed 34°C, by 2019 it had increased by nearly 8°C, reaching 41.29°C. So, the findings of this study are significant

Evaluation of soil loss estimation using the RUSLE model and SCS-CN method in hillslope mining areas

Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses the Revised Universal Soil Loss Equation (RUSLE) and SCS-CN (Soil Conservation Service - Curve Number) process to estimate in Kiruburu and Meghahatuburu mining sites areas. The geospatial model of annual average soil loss rate was determined by integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity and runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management(C) and conservation practice (P) factors were calculated to determine their effects on annual soil erosion in the study area. The coefficient of determination (r2) was 0.834, which indicates a strong correlation of soil loss with runoff and rainfall. Sub -watersheds 5,9,10 and 2 experienced high level of highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell) to determine the critical soil loss areas (Sub-watershed 9 and 5). Total soil erosion area was classified into five class, slight (10,025 ha), moderate (3125 ha), high (973 ha), very high (260 ha) and severe (53 ha). The resulting map shows greatest soil erosion of >40 t h-1 y-1 (severe) through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the verification of soil erosion results

Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region

This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using AHP model based on multi-criteria analysis in a GIS platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The assessment results of FHR show that of the total existing forest area, 2.85% area under very high, 13.63% high, 31.98% moderate, 32.68% low, and 18.87% are under very low categories. According to the assessment and prediction FHR results, the very high-risk classes were found at mines surrounding forest compartments. The sensitivity analysis showed that some factors were more sensitive to FHR. The correlation results showed a negative relationship between FHR and distance from mines and foliar dust concentration. This work will provide a basic guideline for effective planning and management in forestry studies for the mining-affected region