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Title: Spatio-Temporal Patterns of NDVI in Response to Rainfall Variability by Using Remote Sensing Approaches in Drought-Prone Area of Awash Basin, Ethiopia.
???metadata.dc.contributor.*???: ErmiasTeferi (PhD)
Dembel, Bonta
Keywords: Rainfall variability, Mann-Kendal, Theil-Sen Slope
Issue Date: Jun-2017
Publisher: AAU
Abstract: Global and local rainfall variability and change has a great impact on vegetation dynamics at spatial and temporal scales. This study was conducted with the main aim to assess the spatio-temporal patterns of vegetation and rainfall as well as vegetation response to rainfall variability by incorporating satellite-derived vegetation index and rainfall estimate for the period of 30 years from 1983 – 2012 in the Awash basin, Ethiopia. Two satellite derived data have been used; a non-stationary satellite 15-day MVC GIMMS NDVI3g time series data onboard of NOAA-AVHRR and monthly TAMSAT RFE v2 of the Metosat satellite data were obtained and processed to achieve the stated objective. Two types of long term inter-annual trend analysis i.e. monotonic trend analysis and linear trend (Theil-Sen slope estimator) as well as one trend significance test (Mann-Kendal significance test Z-score) has been performed on both NDVI3g and RFE. Similarly, Harmonic analysis and Thiel-Sen slope estimator have been used to assess the seasonal trend analysis of NDVI and rainfall in the study area for the studied 30 years period (1983 – 2012). Likewise, linear modeling has been also employed to investigate the response of vegetation to rainfall variability from 1983 – 2012 in the Awash basin. The results from inter-annual trend analysis showed that, the major vegetation classes revealed a decreasing trends and increasing trends of rainfall with different significant percentage in the whole basin as well as for different major land cover types. On the other hand, the seasonal patterns and trends also showed an increasing trend of rainfall and vegetation in the study area. The correlation between vegetation responses to rainfall was 30 days lag (lag1) for short term lag relationship; and below 30 days (lag0) is for long term inter-annual lag relationship. Therefore, the findings of this study can be used for early warning system like drought forecasting
Appears in Collections:Thesis - Geography

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