Data Integration for Climate Vulnerability Mapping in West Africa

The integration of climate, biophysical, and socioeconomic data in an overall vulnerability framework allows for the identification of "hotspots" of vulnerability. These maps can be used to target adaptation and disaster risk management interventions. The African and Latin American Resilience to Climate Change (ARCC) project, funded by USAID, conducted vulnerability mapping efforts in West Africa. The focus was on integrating remotely sensed and socioeconomic data. Data inputs included a range of sensor data, such as MODIS NDVI, Landsat, SRTM elevation, and DMSP-OLS night-time lights, as well as high-resolution poverty, conflict, and infrastructure data. Two basic methods were used: one in which each layer was transformed into standardized indicators in an additive approach, and another in which remote sensing data were used to contextualize the results of composite indicators. The benefits of data integration include the ability to identify areas of high vulnerability and to understand the determinants of vulnerability. However, challenges arise from the need to balance the complexity of the data with the need for simplicity and clarity in the maps. Remote sensing data have the potential to fill important data gaps in data-poor developing country contexts. The authors used a spatial index approach to map the general vulnerability of the population in Mali. The approach separated vulnerability to climate stressors into three components: exposure, sensitivity, and adaptive capacity. The authors used a range of 18 spatial indicators, including climate variables, socioeconomic data, and remote sensing data. The indicators were transformed into standardized indicators in an additive approach, and then averaged to produce sub-indices for exposure, sensitivity, and adaptive capacity. The results of the vulnerability mapping efforts in Mali and Coastal West Africa highlighted the importance of considering the interplay between climate variability, population sensitivity, and adaptive capacities. The maps can be used to inform adaptation planning and decision-making, and to identify areas where additional support is needed. The authors conclude that remote sensing data can be a useful tool for illuminating regions that are vulnerable to climate change, but that more critical perspectives are needed to address the limitations of the methods used. The spatial vulnerability index construction methods described in this paper are widely used in the literature and have been found to be useful to policy audiences seeking to better understand the factors contributing to vulnerability. However, the authors acknowledge that vulnerability mapping and the quantification of vulnerability is not without shortcomings, and that more critical perspectives are provided in other publications.