Remote Sensing and GIS-Based Agricultural Drought Assessment: the Case of Waghimra Zone, Amhara Regional State, Ethiopia

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Addis Ababa University


In Ethiopia drought is the most prominent hazard affecting economy of the country. Agricultural drought has been a recurrent phenomenon in many part of the country. GIS and Remote Sensing plays an important role for near real time monitoring of agricultural drought condition over large areas. This study was conducted to assess agricultural drought condition in Waghimra Zone using Remote sensing and GIS techniques. Enhanced/expedited/expandable MODIS (eMODIS) NDVI data and monthly rainfall data from 2000 to 2016 were utilized as an input data while crop yield data were utilized as ground truth data for validate strength of drought indices. Normalized Vegetation Index (NDVI), Vegetation condition index, NDVI anomaly were applied to assess spatiotemporal variation of agricultural drought while Standard Precipitation Index(SPI) which is derived from rainfall data were applied to assess spatiotemporal variation of metrological drought. Drought risk map was prepared by combining agricultural and metrological drought. To validate drought indices crop yield anomaly was calculated by using ground truth data of crop yield. Correlation analysis was computed between NDVI and rainfall, NDVI anomaly and crop yield anomaly, VCI and crop yield anomaly and SPI and crop yield anomaly. Results revealed that year 2009 and 2015 were of drought years while 2001 and 2007 were wet years. The result also shows there is good correlation between NDVI and rainfall (r=0.71), NDVI anomaly and crop yield anomaly (0.53), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Drought risk severity map was computed from 2000 to 2016 by integrating agricultural and metrological drought frequency maps. Combined drought risk map showed that 8%, 56%, and 35% of study area were vulnerable to very severe, severe and moderate drought condition respectively. This figure indicates that study area is high vulnerable to drought. Thus, besides mapping drought vulnerable areas integrating socioeconomic data in order to better understand vulnerable factors were recommended.



Agricultural Drought, eMODIS NDVI, GIS, Remote Sensing, SPI, Waghimra Zone