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 climate
change. At the same time a growing number of disaster risk reduction (DRR) and climate change
adaptation measures are being implemented by governmental and non-governmental organizations,
and substantial post-disaster donations are frequently pledged. At the same time there has been
increasing demand for transparency and accountability, and thus evidence of those measures having a
positive e_ect. We hypothesized that resilience-enhancing interventions should result in less damage
during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year
period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We
used very high resolution optical images (<1 m), and created detailed land cover and land use maps
for four epochs before and after the event, using a machine learning approach with extreme gradient
boosting. The spatially and temporally highly variable recovery maps were then statistically related
to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess
the impact of a 10 year land-planning intervention program by the German agency for technical
cooperation (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 overcome
the principal limitation of remote sensing, which can effectively describe but not explain the reasons
for differential recovery. However, while a number of causal links between intervention parameters
and reconstruction was found, the common notion that a resilient community should recover better
and more quickly could not be confirmed. The study also revealed a number of methodological
limitations, such as the high cost for commercial image data not matching the spatially extensive but
also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage
and thus providing incorrect recovery metrics, and image data catalogues especially for more remote
communities often being incomplete. Nevertheless, the study provides a valuable proof of concept
for the synergies resulting from an integration of socio-economic survey data and remote sensing
imagery for recovery assessment.