Mapping of areas with a high-risk for natural disasters (risk management) or after disaster events such as floods, landslides, or wind storms (emergency response) are often performed over large scales with Earth Observation (EO) data from space. After mapping the (possible) geographical impact of a disaster, the affected population or damaged infrastructure is of keen interest. For the pre-disaster risk management, different damage models have to be compared with the respective exposure to get a full picture of the possible impacts. In post-disaster emergency response, the number of affected inhabitants and the impact on infrastructure is important for the coordination of rescue and mitigation efforts.
Freely available population data sets (e.g., WorldPop) or land cover information (e.g., Landcover30) offer the opportunity to conduct geospatial analysis of beforehand created hazard or disaster extent maps. The recommended practice focuses on a feasible approach based on open source software for creating exposure maps.
- Exposure mapping of areas near rivers which might be affected by a flooding - together with population density or land cover.
- Exposure mapping of areas which have a high risk for landslides such as build-up areas at the top or below of slopes without protection mechanisms - together with population density or land cover.
- Exposure mapping regarding other types of hazards for which a risk map is created.
- Mapping the exposure of population and land use when a disaster extent map is available.
- Relatively quick and easy-to-use procedure.
- Besides of the beforehand created hazard or hazard risk map, the practice does not need specific near real-time data that might be difficult to acquire.
- Several sources of uncertainties: the accuracy of the exposure map relies on the geometric and thematic accuracy of both the hazard/disaster extent map and the auxiliary data.
- When using global auxiliary data sets, the accuracy is not necessarily always homogeneous and might be difficult to estimate for a specific area of interest. Global data sets include a lot of data from sometimes different sources. For example, population data sets can consist of disaggregated census data, settlement or landcover information, space-based nightlight maps, and other geodata. In the case of census data, countries might have different measurement basis and statistical approaches.
- Due to the relatively coarse resolution of population data, hazard classifications close to settlements or of non built-up areas within a settlement might cause significant overestimations in terms of the affected population.
General description of the workflow:
(0) Prerequisites: Hazard or disaster extent map and auxiliary data.
(1) Data preparation.
(2) Geospatial analysis.