This datasets contains the satellite images used in this research article published in Remote Sensing (doi :10.3390/rs11212511). The images cover part of Leyte Island, the Philippines, before and after 2013 Typhoon Haiyan.Paper abstract:Natural disasters are projected to increase in number and severity, in part due to climatechange. At the same time a growing number of disaster risk reduction (DRR) and climate changeadaptation measures are being implemented by governmental and non-governmental organizations,and substantial post-disaster donations are frequently pledged. At the same time there has beenincreasing demand for transparency and accountability, and thus evidence of those measures having apositive e_ect. We hypothesized that resilience-enhancing interventions should result in less damageduring a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 yearperiod of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. Weused very high resolution optical images (<1 m), and created detailed land cover and land use mapsfor four epochs before and after the event, using a machine learning approach with extreme gradientboosting. The spatially and temporally highly variable recovery maps were then statistically relatedto detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assessthe impact of a 10 year land-planning intervention program by the German agency for technicalcooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives,motivations and drivers of the affected population. To some extent they also helped to overcomethe principal limitation of remote sensing, which can effectively describe but not explain the reasonsfor differential recovery. However, while a number of causal links between intervention parametersand reconstruction was found, the common notion that a resilient community should recover betterand more quickly could not be confirmed. The study also revealed a number of methodologicallimitations, such as the high cost for commercial image data not matching the spatially extensive butalso detailed scale of field evaluations, the remote sensing analysis likely overestimating damageand thus providing incorrect recovery metrics, and image data catalogues especially for more remotecommunities often being incomplete. Nevertheless, the study provides a valuable proof of conceptfor the synergies resulting from an integration of socio-economic survey data and remote sensingimagery for recovery assessment.

Date Submitted: 2020-09-17