The present study evaluates the impact of reservoir on changing the landscape pattern in the Urmodi River watershed of Satara district, Maharashtra, India, by employing Remote Sensing (RS)-based change detection technique. To map the change in surface cover due to the reservoir, we have used Landsat TM and Landsat 8 imagery which represents before-dam and after-dam status of land cover in the year 1996 and 2016. The supervised classification used with the maximum likelihood algorithm was employed to generate the thematic map. Further, to detect the change, SAGA GIS software was used which has an inbuilt tool of the confusion matrix (grid-based) to represent changes in two grids. The tool has significant importance in showing the detection of the transformation of land cover in previous to next condition. The technique is found to be scientific and robust with more precision as compared to the traditional methods. The rate of change in land cover of agriculture land shows −2.50%, barren land 0.31%, settlement 6.64%, and forest 2.20% change per annum. The water body area has a sudden increment in the year 2016 with coverage of 11.86 km 2 as compared to a previous area of 0.64 km 2 in the year 1996, and is not considered under the rate of change as it was a one-time activity. The study has a potential role in analyzing the land change dynamics.
Soil loss by water erosion is a common type of land degradation issue in the hilly regions of the world. The present study investigates the soil erosion risk due to change in Land Use Land Cover (LULC) brought in by the construction of dam in a hilly watershed of the River Urmodi embracing Kaas Plateau, a world heritage site. The Revised Universal Soil Loss Equation (RUSLE) model was used with change detection analysis for soil erosion estimation. Thematic layers required for the computation of factors required in RUSLE were gathered using remote sensing (RS) and Geographical Information Science techniques. The changes in LULC affect the soil erosion phenomena and therefore, the present study analyzes drastic alteration in the hydrological nature of the watershed brought by the construction of reservoir through damming the Urmodi River. For assessment of the impact of the dam we have used RS data with 30 m spatial resolution, which includes Digital Elevation Model and Landsat TM and Landsat 8 bands. Using RUSLE, soil risk zone was mapped and the Change Detection in zones were computed. The very simple logic of 'soil erosion class as a type of land cover' was successfully applied to perform the change transition for the Urmodi river watershed. Confusion matrix technique used for analyzing the change, has the ability to show the previous and next condition of zone type very affectively with good precision. Along with Change Detection technique, soil erosion class transition operation was also performed to assess the conversion of zones. This gave the important output regarding increased conversion from very slight to very severe risk zone by 70.22 km 2 . The investigation concludes that there was extensive change in very severe (> 80 ton ha −1 year −1 ) soil vulnerable risk zone in the last 17 years with the rise of 14.87%. The conversion of lower risk category areas to higher risk category areas of soil erosion was detected all over the watershed. Undertaking such studies revealed the changes brought in the soil erosion susceptible zones by building of dams. This study will be a crucial help to policy and decision makers for proper planning of watershed with consideration to LULC in the event of dam construction on the rivers flowing in hilly terrain.
Soil Organic Carbon (SOC) plays a vital role in the global carbon cycle, affecting soil fertility and agricultural sustainability. Our study focused on areas with low SOC, where increasing its levels could enhance soil health and carbon management. We used an earth science approach to analyze SOC density and stock in different land classes of the Urmodi River watershed in Maharashtra, India. Using GIS, we estimated SOC from soil samples collected up to 20 cm depth, and produced maps of SOC, SOC density (SOCD), and SOC stock. Our results showed that natural vegetation areas, such as closed and open forests, had a higher nutritional status of SOC and SOC stock, indicating the importance of land cover history on soil health. We found the average SOC to be 1.4 g/kg−1, SOCD to be 57.36 kg/m−2, SOC stock to be 3.46 ton/ha−1. We also depicted the relationship between elevation and SOCD using a scatterplot, revealing the distribution of SOC across different elevations. Our study demonstrates the use of modern geoscientific analysis tools to understand the physical and chemical properties of soil, which can be useful in assessing soil health. Overall, our results provide valuable insights into the distribution of SOC and its relationship with other geo-chemical aspects at a regional scale.
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