Evaluating Resilience-Centered Development Interventions with Remote Sensing

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 effect. 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.

Post-Disaster Recovery Monitoring with Google Earth Engine

“…However, it has been shown that this assumption is not correct for all cases, and further investigation is needed to clearly determine the relationship between the speed of the post-disaster recovery and resilience [6].…”

UAV-Based Structural Damage Mapping: A Review

“…Image classification used for damage mapping increasingly made use of machine learning, in particular SVM and RF [41,54,69] or different boosting algorithms, such as AdaBoost [38] or XGBoost [58], and moving towards more advanced scene understanding and semantic processing. However, the features used were typically hand-crafted (such as HoG or Gabor, or other point feature descriptors related to spectral, textural, and geometrical properties [54]), and emerging work had shown that in deep learning approaches CNN could actually learn features and their representation directly from the image pixel values [70].…”

Towards Resilient Vital Infrastructure Systems: Challenges, Opportunities, and Future Research Agenda

“…However, in particular satellite images have been shown to have severe limitations in damage mapping (Kerle, 2010), mainly due to their comparatively limited spatial detail (resolution is at best cm for commercial imagery), but also their vertical perspective that severely limits the damage evidence that can be detected. Damage data can also be provided by drones, which yield more local observations that can be incorporated further in 3D modelling of the areas (Nex et al, 2019;Kerle et al, 2019a;. In particular, advances in machine learning have led to methods for accurate damage identification from drone data (Nex et al, 2019;Kerle et al, 2019a).…”

“…Damage data can also be provided by drones, which yield more local observations that can be incorporated further in 3D modelling of the areas (Nex et al, 2019;Kerle et al, 2019a;. In particular, advances in machine learning have led to methods for accurate damage identification from drone data (Nex et al, 2019;Kerle et al, 2019a).…”