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International Institute for Geo-Information Science and Earth Observation (ITC) 2017

Exploring an alternative approach for deriving NDVI-based forage scarcity in the framework of index-based livestock insurance in East Africa

De Oto, L.H. (Lucas Herman)

Titre : Exploring an alternative approach for deriving NDVI-based forage scarcity in the framework of index-based livestock insurance in East Africa

Auteur : De Oto, L.H. (Lucas Herman)

Etablissement de soutenance : University of Twente International Institute for Geo-Information Science and Earth Observation (ITC)

Grade : Master of Science in Geo-Information Science and Earth Observation 2017

Résumé
Recurrent drought represents a major threat in arid and semi-arid regions of East Africa. Prolonged lack of water availability may trigger widespread livestock mortality due to forage scarcity and disease outbreaks. Pastoralists in these regions depend entirely on their herds for subsistence and therefore, they are severely affected by this events. To protect them against this peril, index- based insurance products constitute an innovative intervention. Under this scheme, indemnities are payed based on objectively measured variables which are highly correlated with the loss being insured. A satellite- derived product that is frequently used as a proxy in this framework is the normalized difference vegetation index (NDVI). The Index Based Insurance for Livestock (IBLI), developed by the International Livestock Research Institute (ILRI) uses area- aggregated NDVI values from Enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) to calculate a seasonal forage scarcity index based on which indemnities are determined per administrative unit. Although the index has been tested and proved to be correlated with the actual livestock losses experienced by pastoralists, the early spatial aggregation of NDVI values hides spatial variability within the units which may negatively impact the performance of the product. In Ethiopia, the Geodata for Innovative Agricultural Credit Insurance Schemes (GIACIS) project uses a different insurance scheme that first groups pixels based on a similar NDVI temporal behaviour and then pools the pixel-level data within the clusters to generate statistics and derive indemnities. The present research integrates the index design logic of GIACIS into IBLI and proposes an alternative design for IBLI which accounts for ecological variability within the administrative units. First, an unsupervised classification has been performed on NDVI series of the study area using the Iterative Self-Organized Unsupervised Clustering Algorithm ( ISODATA ) . Then, the resulting classes have been evaluated in terms of significance for forage production in order to discard those that are irrelevant from further analysis. Trigger and exit points have been set for the retained classes, then used to calculate payouts per pixel. Finally, the indemnities were aggregated per spat ial unit. The results have been contrasted against spatially-aggregated monthly household survey data on drought outcome parameters from different sample sites within the study area. The proposed design has a slightly stronger correspondence to available l ivestock mortality data for selected areas. Although further validation is required, the integration of two existing methods may provide a sound basis for an insurance product with lower basis risk.

Version intégrale (ITC )

Page publiée le 10 avril 2018, mise à jour le 13 octobre 2018