Mapping Risk Areas with EO data in the Indonesian City of Semarang

The application of EO data for climate resilience and disaster management in urban areas relates to various processes. A specific case for the World Bank City Planning Lab programme which was part of the EO4SD Urban project focused on the provision of Flood History and -Risk as well as Land Motion (subsidence) to stakeholders in Indonesia. In addition, related EO products such as Land Use and its Change as well as Transport Infrastructure are also produced, as they are highly relevant for assessing sustainability of cities with respect to climate resilience and disaster management. The full operational mapping exercise is demonstrated for the Indonesian city of Semarang. Semarang is located at the north coast of Java, Indonesia, in an area of active land subsidence. Flooding is common; specifically the coastal zones have experienced increased flooding due to the sinking land. In future, potential sea level rise can further aggravate the situation. In a first step, separate mapping products were derived from various EO data sources. We used VHR and HR optical satellite data for the classification of detailed land use classes and transport infrastructure. The Land Use/Land Cover product for Semarang has an overall mapping accuracy of 85.32% with a confidence interval (CI) ranging from 83.04% to 87.59% at a 95% CI.For flood history, we employed all available historic EO data from optical, but also from RADAR systems. In addition, also non-EO data such as maps, in-situ observations, reports, etc. were used to complete the flood history assessment. For assessing the currently ongoing terrain subsidence, time-series data from Sentinel-1 was used in an interferometric processing chain. In the second step, we generated the current flood risk map by combining the flood history with the subsidence map. Based on this combination, we re-evaluate existing risk areas and identified potential new risk areas. One shortcoming in this evaluation was the lack of a highly accurate terrain model, which is not available for the area. However, rough terrain information was also included in the expert evaluation system to calculate risk areas. Finally, in a third step, all individual products were combined in a GIS to generate meaningful spatial analytics, such as the share and length of transport infrastructure prone to flooding and/or to damage due to subsidence. Another example is the share of LU class “Residential and Public Urban Fabric Areas”. From these areas, 83 % are not affected by flood risk; about 8 % of the areas are in medium risk areas, 7 % in high and almost 2 % in very high flood risk areas. The preparation and work done for this article is part of the European Space Agency supported Programme “Earth Observation for Sustainable Development (EO4SD) - Urban”. Joanneum Research would like to thank especially the World Bank City Planning Labs programme and Team for their support; this includes Gayatri Singh the Task Team Leader, Champaka Rajagopal and Aurora Dias Lokita who facilitated the user interactions and data/information collection from the City.