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Addis Ababa University (2017)

Remote Sensing and Gis Based Characterization of Agriculturral Drought Conditions in North Wollo Zone Amhara Regional State, Ethiopia

Gebre, Eshetu

Titre : Remote Sensing and Gis Based Characterization of Agriculturral Drought Conditions in North Wollo Zone Amhara Regional State, Ethiopia

Auteur : Gebre, Eshetu

Université de soutenance : Addis Ababa University

Grade : Masters of Science in Remote Sensing and Geo-informatics 2017

Résumé partiel
Drought is the most complex but least understood of all natural hazards. Major food production in Ethiopia, especially in the Amhara Region, North Wollo Zone, is almost fully dependent on rain-fed agriculture and the area is often hit by periodic droughts. This drought causes serious economic, social, food security and environmental problems. Arid climatic conditions in North Wollo Zone are characterized by erratic rainfall and successive drought years together with high rate of moisture deficiency has adversely affected the agricultural production levels. Thereby increases drought risk. In this study, the Standardized Precipitation Evaporation Index (SPEI), Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), were applied to characterize the agricultural drought conditions in North Wollo Zone from 2000 to 2015. Correlation analysis was performed between NDVI and SPEI, rainfall and NDVI, VCI and rainfall and NDVI and Crop Yield Anomaly and SPEI and Crop Yield Anomaly. SPEI values were interpolated to get the spatial pattern of meteorological based drought. Ground based crop yield data was used to evaluate the drought monitoring index. Finally, the combined drought severity map was generated by overlaying the agricultural and meteorological drought severity maps. The results showed that there was good correlation between rainfall and NDVI (r=0.71), VCI and Rainfall (r = 0.77), NDVI and SPEI (r=0.82) and NDVI and Crop Yield anomaly, (r=0.78) and SPEI and Crop Yield Anomaly (r=8.3). Analysis result of Spatial pattern of long term seasonal average rainfall and NDVI from 2000 to 2015 years, showed that there was a large variation during the main cropping season and the corresponding NDV1 values was also almost similar.

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