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Drought is a natural disaster that impacts the economy, the environment, society, and agriculture. Drought occurs due to a rainfall deficit. This paper discusses the characteristics of drought for Davanagere district of Karnataka state using the remotely sensed data. The Davanagere district falls under the central dry agro-climatic zone and is categorized as a drought-prone area. The average rainfall for the whole district is 325mm. The monsoon generally reaches the district by June and lasts till September. Although the total rainfall is not high, the district benefits both from the southwest and northeast monsoons. The Davanagere district is declared as a drought-prone area in the year 2017. It is imperative to monitoring drought to prevent and mitigate the effects of drought. Drought assessment using remote sensing-based indices has been widely in practiced in recent years for monitoring droughts. The present work demonstrates the establishment of relationship between different indices such as NDVI & LST, VCI, VHI, and LST & VCI, for autumn season of year 2000, 2010, and 2020, VHI & SPI and LST & VHI from May to November of 21 years (2000-2020). The correlation between NDVI & VCI, VHI, and LST & VCI shows a positive for the autumn season, whereas VHI & SPI gives no relationship because SPI is purely dependent on rainfall and hence VHI is dependent on precipitation. Similarly, LST and VHI relation shows a negative correlation from May to November.
"Drought Using Remote Sensing Data and Analyzing Relation Between Different Indices: A Case Study of Davanagere District of Karnataka State, India", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 9, page no.734 - 741, September-2022, Available :http://www.ijrti.org/papers/IJRTI2209098.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator