Land use change directly increased soil erosion risk, which is a very sensitive environmental issue in Central India. To evaluate the response of land use changes on soil erosion risk, research was implemented using remote sensing techniques, coupled with ground information, to develop an integrated modelling approach to study the factors driving land use changes in the Man River basin, Central India. Results were used to assess the impact of land use change on soil erosion risk. First, a series of sub methods were applied to monitor and verify land use land cover change in the study area which included pre-processing, classification and assessment of land use transaction from 1971 to 2013 using Landsat time series imagery. Additionally, an independent spatial assessment of deforestation, forest degradation and responsible drivers for the period 2009-2013 was conducted to enable a deeper analysis of forestry activates using the GIS based direct interpretation approach. The research also developed a robust accuracy assessment method to check the quality of the 2009 and 2013 classification maps using good quality Google Earth TM imagery and a field measured GPS dataset. These approaches were largely based on the GOFC- GOLD (2010) and IPCC good recommendations for land use land cover mapping and verification. The information obtained from an accuracy assessment was also used to estimate deforestation area and construct confidence intervals that reflect the uncertainty of the area estimates obtained. Such analysis is rarely applied in current published verification assessments. In the second phase of the study, a Geo-spatial interface for process-based Water Erosion Prediction Project (GeoWEPP) was implemented, to estimate the response of land use and land cover change on soil erosion risk in several scenarios derived from both ground and satellite based precipitation, DEMs and vegetation change. GeoWEPP was used at the hillslope scale in three selected watersheds within the Man River basin using Landsat, LISSIII, Cartosat-1, ASTER, SRTM, TRMM and ground based datasets. The results highlight that the study developed a realistic approach using remote sensing techniques to understand the pattern and process of landscape change in the Man River basin and its response on soil erosion risk. Over the last four decades, forest and agriculture areas were found to be the most dynamic land use /land cover categories. During the last four decades, around 54200 ha (33.7 %) forest area has been decreased due to the expansion of agriculture, forest harvesting and infrastructure development. The direct interpretation approach estimated similar patterns of deforestation and forest degradation associated with iii drivers for the 2009 to 2013 time period, but this approach also provided more accurate and location specific information than automatic analysis. The overall correspondence between the map and reference data are a good measure for 2009 and 2013; 94.03 % and 92.8 % respectively. User‘s and producer‘s accuracies of individual classes range from 75 % to 99 %. Using the accuracy assessment data and a simple set of equations, an error-adjusted estimate of the area of deforestation was obtained (± 95% confidence interval) of 23382 ± 550 ha. The estimated average annual soil loss for all three watersheds is 21 T/ha which was found to be comparable to similar studies carried out in the study region. The highest soil loss rates occurred in areas of agriculture (301 T. /ha /yr) and fallow land (158 T/ha/yr), while the lowest rates were recorded in forest land (33.45 T/ha/yr). Agriculture extension (316.5 ha) due to forest harvesting (234 ha) in the last four decades is one of the significant drivers to speed up soil erosion (7.37 T/ha/yr.) in all three watersheds. The spatial pattern of erosion risk indicates that areas with forest cover have minimum rates of soil erosion, while areas with extensive human intervention such as agriculture and fallow land, have high estimated rates of soil erosion. The different DEMs generated varied topographic and hydrologic attributes, which in turn led to significantly different erosion simulations. GeoWEPP using Cartosat-1 (30 m) and SRTM (90 m) produced the most accurate estimation of soil loss which was close to similar already published studies in the area. TRMM rainfall data has good to use as a rainfall parameter for soil erosion risk mapping in study area. Overall, the integrated approach using remote sensing and GIS allowed a clear understanding of the factors that drive land use/land cover change to be developed and enabled the impact of this change on soil erosion risk in the Man River basin, Central India to be assessed.