ANNOUNCEMENTS
The state of Maharashtra is often praised for the great variety of landscapes it offers. A combination of extreme weather and growing urbanization has led to a rash of natural catastrophes in the state. In this study, our focus was identification of areas which are susceptible to dangers of floods and landslides using GIS and remote sensing; also, to analyze occurrences of landslide as a function of flooding using Universal Soil Loss Equation (USLE).
Maharashtra has majority of loamy and clayey textured soils, which undergoes runoff under consistent exposure to water hence soil loss was considered as an important factor to analyze the relationship between flooding and landslide. Total of 7 factors were taken for assessment of floods and landslides named degree of slope, distance from streams, vegetation cover, Land-use/land cover, distance from roads and rainfall. Slope and stream network were developed from SRTM data using DEM, it was also used in the USLE analysis. Sentinel-2 data was used for analysis as well as deriving MNDWI for validation of results. Accuracy assessment was conducted for flood susceptible map using MNDWI which gave overall accuracy of 75% with kappa coefficient of 0.67 showing. Landslide validation was done using NASA Landslide Viewer data and it also showed agreement with the results. Harmonized soil database or HWSD was used to derive soil texture data.
Results showed that majority of the area highly susceptible to both disasters was around 6% of the state and majorly concentrated toward western side where Sahyadri range lies. Annual soil loss was 7890 ton/ha/ year for the region with areas under high erosion overlapping with areas highly susceptible to the above-mentioned disasters. Total area of 21653.29Km^2 and 19370.84Km^2 was found to be under “high” and “very high” flood and landslide susceptible classes respectively. Using the research as a starting point, conservation and development planners at the landscape scale will be able to locate and map areas particularly vulnerable to key forces.
Keywords: GIS, Remote Sensing, USLE, SRTM, MNDWI.
