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M.Tech / M.E / PhD Thesis | Civil Engineering | India | Volume 7 Issue 2, February 2018

Remote Sensing Based Agricultural Drought Assessment in Krishnagiri District

Parthibanraja.A ^[2] | Purushothaman.B.M ^[3]

Abstract: Increasing temperature and altered precipitation patterns, leads to the extreme weather events like Drought which drasticallyaffects the agricultural production. Agricultural drought is nothing but the decline in the productivity of crops due to irregularities in the rainfall as well as decrease in the soil moisture, which in turn affects the economy of the nation. As the Indian agriculture is largely dependent on the Monsoon, a slight change in it affects the production as well as the crop yield drastically. The agricultural drought monitoring, assessment as well as management can be done more accurately with the help of geospatial techniques like Remote Sensing. Krishnagiri is an important district in the part of Tamilnadu. The study area falls between North latitudes 12 16 & #039, N & 12 88 & #039, N and East longitude 77 50 & #039, E & 78 55 & #039, E (Fig.1) and covers an area about 5143 km2It is a drought prone region and falls within the most arid band of the country. The district relies on the traditional agricultural based economy, hence the impact of drought on the agriculture not only affects the production but also the livelihood of common man. The purpose of the study is to analyze the vegetation stress in the region krishnagiri district with the calculation of NDVI values and the land surface change classification). The MODIS data is used for the calculation of NDVI as well as Land surface temperature. The Combination of (NDVI) normalized difference vegetation index and LST, provides very useful information for agricultural drought monitoring and early warning system for the farmers. By calculating the correlation between rainfall analysis and NDVI, it can be clearly noticed that they show a high negative correlation. The correlation between Rainfall analysis and NDVI is -0.635 for the -0.586 for the year 2017. The LST when correlated with the vegetation index it can be used to detect the agricultural drought of a region, as demonstrated in this work.

Keywords: Remote sensing, GIS, Agricultural drought, NDVI, land use land cover map, MODIS-Drought assementmap, Rainfall analysis

Edition: Volume 7 Issue 2, February 2018,

Pages: 150 - 156

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