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

International Soil and Water Conservation Research 2018年第1期6卷 31-42页

作者： Narayan Kayet khanindra pathak Abhisek Chakrabarty Satiprasad Sahoo Department of Mining Engineering Indian Institute of TechnologyKharagpurIndia Department of Remote Sensing&GIS Vidyasagar UniversityMidnaporeIndia School of Water Resources Indian Institute of Technology KharagpurIndia

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.

关键词： Soil erosion Runoff RUSLE SVM Remote sensing GIS

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Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk(FFR)mapping in Melghat Tiger Reserve(MTR)forest

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Journal of Forestry Research 2020年第2期31卷 565-579页

作者： Narayan Kayet Abhisek Chakrabarty khanindra pathak Satiprasad Sahoo Tanmoy Dutta Bijoy Krishna Hatai Indian Institute of Technology Kharagpur KharagpurWest BengalIndia Vidyasagar University MidnaporeWest BengalIndia Jadavpur University KolkataWest BengalIndia Odisha Space Application Center BhubaneswarOdishaIndia

A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central *** of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS *** results from FR and AHP show similar *** FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR *** results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.

关键词： Forest fire risk(FFR) Remote sensing GIS FR AHP Sensitivity analysis Validation

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Latent Variable Modeling Approach for Assessing Social Impacts of Mine Closure

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Open Journal of Applied Sciences 2014年第14期4卷 573-587页

作者： Mallikarjun Rao Pillalamarry khanindra pathak Department of Mining and Process Engineering Polytechnic of Namibia Windhoek Namibia Department of Mining Engineering Indian Institute of Technology Kharagpur India

Mining stimulates environmental and economic impacts on the neighboring community right from the inception to the closure of its operations. The society in the neighborhood of mining gradually adopts a characteristic life-style that is highly influenced by the mining. In order to sustain the societal development beyond the mine closure, it is necessary to plan post mining activities in the area. Thus, it is essential to predict the impacts of mine closure well before the closure. Many societal and family attributes are affected by mine closure. Impact on these attributes is reflected on the overall quality of life of the neighboring community. There are no adequate indicators and/or methodology available to measure social impacts of mine closure on a neighboring community. This paper made an attempt to develop such methodology to predict the degree of adverse effects of mine closure on the quality of life of neighboring communities using the Structural Equation Modeling (SEM) and the Latent Variables Interaction Model (LVM).

关键词： Mine Closure Social Impacts Structural Equation Modeling Latent Variable Modeling

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