Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series
AbstractReliable drought information is of utmost importance for efficient drought management. This paper presents a fully operational processing chain for mapping drought occurrence, extent and strength based on Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data at 250 m resolution. Illustrations are provided for the territory of Kenya. The processing chain was developed at BOKU (University of Natural Resources and Life Sciences, Vienna, Austria) and employs a modified Whittaker smoother providing consistent (de-noised) NDVI “Monday-images” in near real-time (NRT), with time lags between zero and thirteen weeks. At a regular seven-day updating interval, the algorithm constrains modeled NDVI values based on reasonable temporal NDVI paths derived from corresponding (multi-year) NDVI “climatologies”. Contrary to other competing approaches, an uncertainty range is produced for each pixel, time step and time lag. To quantify drought strength, the vegetation condition index (VCI) is calculated at pixel level from the de-noised NDVI data and is spatially aggregated to administrative units. Besides the original weekly temporal resolution, the indicator is also aggregated to one- and three-monthly intervals. During spatial and temporal aggregations, uncertainty information is taken into account to down-weight less reliable observations. Based on the provided VCI, Kenya’s National Drought Management Authority (NDMA) has been releasing disaster contingency funds (DCF) to sustain counties in drought conditions since 2014. The paper illustrates the successful application of the drought products within NDMA by providing a retrospective analysis applied to droughts reported by regular food security assessments. We also present comparisons with alternative products of the US Agency for International Development (USAID)’s Famine Early Warning Systems Network (FEWS NET). We found an overall good agreement (R2 = 0.89) between the two datasets, but observed some persistent (seasonal and spatial) differences that should be assessed against external reference information. View Full-Text
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Klisch, A.; Atzberger, C. Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sens. 2016, 8, 267.
Klisch A, Atzberger C. Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sensing. 2016; 8(4):267.Chicago/Turabian Style
Klisch, Anja; Atzberger, Clement. 2016. "Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series." Remote Sens. 8, no. 4: 267.
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