IMPACTOF CLIMATECHANGEAND DROUGHT ANALYSISON AGRICULTURE IN SABARKANTHA DISTRICTUSING GEOINFORMATICS TECHNOLOGY

Drought is a long duration dry period in natural Climate Cycle.it is defined as “Severe water shortage”. In recent years, Geographic Information System (GIS) and Remote Sensing (RS) have played a key role in studying different types of hazards either man-made or natural. This Study stresses upon the use of GIS and RS in the field of climate change and drought impact on agriculture. Different indices were computed using Landsat-7 data of January 2002 and Landsat-8 data of February 2018 as well as meteorological data for drought severity assessment in Sabarkanta district, Gujrat State. The indic... Mehr ...

Verfasser: Manan Parmar*1
Shital Shukla1",M.H.Kalubarme2
Dokumenttyp: Artikel
Erscheinungsdatum: 2019
Verlag/Hrsg.: Zenodo
Schlagwörter: Deference Vegetation Index (NDVI) / Standardized Precipitation Index (SPI) / Aridity Index (AI)
Sprache: unknown
Permalink: https://search.fid-benelux.de/Record/base-29678344
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.5281/zenodo.2751054

Drought is a long duration dry period in natural Climate Cycle.it is defined as “Severe water shortage”. In recent years, Geographic Information System (GIS) and Remote Sensing (RS) have played a key role in studying different types of hazards either man-made or natural. This Study stresses upon the use of GIS and RS in the field of climate change and drought impact on agriculture. Different indices were computed using Landsat-7 data of January 2002 and Landsat-8 data of February 2018 as well as meteorological data for drought severity assessment in Sabarkanta district, Gujrat State. The indices generated include Normalize Difference Vegetation Index (NDVI) for 2002 and 2018 and meteorological data based Standardized Precipitation Index (SPI), and Aridity Index (AI). Correlation analysis was performed between NDVI, SPI and AI. SPI and AI values were incorporated to get the spatial pattern of meteorological based drought. NDVI threshold were Identified to get the Agricultural Drought risk. Large historical datasets are required to study drought condition of the study area, to study complex interrelationship between spatial data and meteorological data. In this study, the drought prone areas in the Sabarkantha district were identified by using RS and GIS technology and drought risk areas were delineate by integration of satellite images and meteorological information in GIS environment.