Application Of Remote Sensing For Delineation Of Drought Vulnerable Areas In Amhara Region

Drought is the most complex but least understood of all natural hazards. It is broadlyrndefined as “severe water shortage”. Low rainfall and fall in agricultural production hasrnmainly caused droughts. A droughts impact constitutes losses of life, human sufferingrnand damage to economy and environment. Droughts have been a recurring feature of thernEthiopian climate therefore study of historical droughts may help in the delineation ofrnmajor areas facing drought risk and thereby management plans can be formulated by therngovernment authorities to cope with the disastrous effects of this hazard. The Amhararnregion is prone to extreme climate events such as drought. Successive years of low andrnerratic rainfall have left large areas of the region in severe drought that resulted inrncrop failure, water shortage and has raised serious food security concerns for thernregion. Drought assessment and monitoring based on available weather data are tediousrnand time consuming. Beside that the data are not available in time to enable relativelyrnaccurate and timely large scale drought detection and monitoring. But, the satellite sensorrndata are consistently available, cost effective and can be used to detect the onset ofrndrought, its duration and magnitude. In the present work an effort has been made tornderive drought vulnerable areas facing agricultural drought by use of temporal imagesrnfrom NOAA-AVHRR (8km) and MODIS (500m) based Normalized DifferencernVegetation Index (NDVI) (1981- 2007) and (2000 to 2003) respectively. A deviation ofrnthe current NDVI with the long-term mean NDVI, and the Vegetation ConditionrnIndex (VCI) derived from the AVHRR and MODIS used in this study for droughtrndetection, monitoring and real time prediction. The results clearly indicate that therntemporal and spatial characteristics of drought in Amhara region detected and mappedrnby the DEVNDVI, and VCI indices. These results were validated by ground truth data suchrnas precipitation and agricultural crop yield. The validation result shows that therernis a strong correlation between the satellite derived indices and the ground truthrndata, both precipitation and agricultural production yield for most of thernZones Amhara region. Correlation and regression analysis was performed betweenrnNDVI, drought indices, precipitation and agricultural yield. The NDVI and rainfall wasrnfound to be highly correlated in water limiting areas. Apart from this, the highest NDVIrainfallrncorrelation associated with three -month time lag shows rainfall event inducedrnvegetation growth in subsequent periods. The NDVI-rainfall correlation was found to bernhighly influenced by mean rainfall condition and vegetation types. It is thereforernconcluded that temporal variations of NDVI are closely linked with precipitation. Therninter sensor relationships were also developed based on data from specific months andrnthe monthly models explain up to 95 percent of variability in the data of two sensors

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