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• W3216391473 abstract "Drought is one of the main disasters that act as a silent killer among Sir Lanka and it is the disaster that affects the highest number of people over the country. Therefore, it requires systematic and scientific investigation to study the magnitude of the drought. Monaragala district can be identified as one of the high drought-affected areas in the country which is an intermingling of dry and intermediate ecological zones where most of the people occupy in the agricultural sector. Further different types of droughts can be identified such as Meteorological drought, Hydrological drought, Agricultural drought, and physical Drought. The integration between Remote sensing techniques and Geographic Information Systems was used for the investigation. Decile index-based precipitation analysis was implemented in order to analyze the meteorological drought and stream density and distribution of water resources used for hydrological analysis. Integration between 15 years of Moderate Resolution Imaging Spectroradiometer remote sensing images was for drought detection by using the Normalized Difference Vegetation Index and Vegetation Condition Index for the study. In conclusion finally, raster overlay analysis was performed by using the ArcMap 10.1 software environment in order to perform a combined analysis of the drought. As a result of the study presented a complete drought risk assessment map for the Monaragala district. Rendering to the results inclusive, Monaragala district illustrations moderately drought conditions while highlighting a server hydrological drought state. The final drought map shows that 6.55% of the area experienced server drought, 58.22% of the area faces moderate drought, 35.13% slight drought while nearly 0.1% faces no drought situation." @default.
• W3216391473 created "2021-12-06" @default.
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• W3216391473 date "2021-11-19" @default.
• W3216391473 modified "2023-10-14" @default.
• W3216391473 title "Multicriteria Drought Severity Analysis in Monaragala District Sri Lanka by Utilizing Remote Sensing and GIS" @default.
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• W3216391473 doi "https://doi.org/10.1002/9781119793403.ch9" @default.
• W3216391473 hasPublicationYear "2021" @default.
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