Remote sensing-based proxies for urban disaster risk management and resilience: A review

© 2018 by the authors. Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery

A Review of Remote Sensing-Based Proxies and Data Processing Methods for Urban Disaster Risk Management

Disaster risk management (DRM) and reduction has been gaining in importance as a result of increasing impacts of natural disasters. Reliable and informative data are the foundation of any comprehensive and effective DRM. Synoptic and multi-type remote sensing has become an essential tool for rapid acquiring of geospatial data, particularly for complex and dynamic urban areas. Accordingly, it has been used for the assessment of all components of the disaster risk cycle, ranging from disaster preparedness to rapid damage assessment. However, due to the complex and multifaceted characteristics of many urban elements, in particular social and economic activities and functions, accurate risk assessment that takes account of the varied and complex set of vulnerabilities and their associated dynamics continues to be very difficult, and direct remote sensing observations are frequently insufficient. Therefore, methods have been developed to indirectly estimate the risk, utilizing image-based proxies. In recent years, using proxies has become a predominant way for such measurements in the DRM field for both pre- and post-disaster phases, at times with similar proxies being used for both situations. For example, the presence of vegetation in urban areas is used as a proxy for both pre-event social vulnerability and for post-disaster recovery assessments. In addition, existing proxies do not sufficiently address all assessment requirements, e.g. there is no proxy for building-based functional damage assessment. Another persistent challenge is the extraction those proxies as a basis for automating the urban DRM process. Although several remote sensing data processing methods have been developed to derive information for DRM in recent years, extracting proxies from remote sensing data requires more accurate results in detecting objects and features. In this study we carried out a comprehensive review of remote sensing-based proxies for different urban DRM phases, identified duplications on efforts, inconsistencies in terminology, but also remaining gaps. With a specific focus on post-disaster recovery assessment, which particularly relies on measures to assess the progress in functions and processes, the review was then used as a basis for the development of new proxies and indicators. The focus is on developing robust proxies to go beyond the physical evaluation perspective, and to extract socio- economic information and functional assessment of urban areas using new strategies, such as multiple-proxies approach, and fusing object- and pattern-based proxies from various remote sensing data, including very-high resolution satellite and aerial images, drone data, LiDAR data. In addition, the reliability of current remote sensing data processing methods in extracting proxies will be discussed, and accordingly how remote sensing data processing methods can contribute to developing reliable proxies will be demonstrated (e.g. using new pattern recognition, texture, and object detection methods)

Synthesis and characterization of ZnO nanostructures using palm olein as biotemplate

Background: A green approach to synthesize nanomaterials using biotemplates has been subjected to intense research due to several advantages. Palm olein as a biotemplate offers the benefits of eco-friendliness, low-cost and scale-up for large scale production. Therefore, the effect of palm olein on morphology and surface properties of ZnO nanostructures were investigated. Results: The results indicate that palm olein as a biotemplate can be used to modify the shape and size of ZnO particles synthesized by hydrothermal method. Different morphology including flake-, flower- and three dimensional star-like structures were obtained. FTIR study indicated the reaction between carboxyl group of palm olein and zinc species had taken place. Specific surface area enhanced while no considerable change were observed in optical properties. Conclusion: Phase-pure ZnO particles were successfully synthesized using palm olein as soft biotemplating agent by hydrothermal method. The physico-chemical properties of the resulting ZnO particles can be tuned using the ratio of palm olein to Zn cation

Post-disaster recovery monitoring with Google Earth Engine

Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments

Agent-based modelling of post-disaster recovery with remote sensing data

Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households’ decision to relocate after a disaster

Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information

Post-disaster recovery (PDR) is a complex, long-lasting, resource intensive, and poorly understood process. PDR goes beyond physical reconstruction (physical recovery) and includes relevant processes such as economic and social (functional recovery) processes. Knowing the size and location of the places that positively or negatively recovered is important to effectively support policymakers to help readjust planning and resource allocation to rebuild better. Disasters and the subsequent recovery are mainly expressed through unique land cover and land use changes (LCLUCs). Although LCLUCs have been widely studied in remote sensing, their value for recovery assessment has not yet been explored, which is the focus of this paper. An RS-based methodology was created for PDR assessment based on multi-temporal, very high-resolution satellite images. Different trajectories of change were analyzed and evaluated, i.e., transition patterns (TPs) that signal positive or negative recovery. Experimental analysis was carried out on three WorldView-2 images acquired over Tacloban city, Philippines, which was heavily affected by Typhoon Haiyan in 2013. Support vector machine, a robust machine learning algorithm, was employed with texture features extracted from the grey level co-occurrence matrix and local binary patterns. Although classification results for the images before and four years after the typhoon show high accuracy, substantial uncertainties mark the results for the immediate post-event image. All land cover (LC) and land use (LU) classified maps were stacked, and only changes related to TPs were extracted. The final products are LC and LU recovery maps that quantify the PDR process at the pixel level. It was found that physical and functional recovery can be mainly explained through the LCLUC information. In addition, LC and LU-based recovery maps support a general and a detailed recovery understanding, respectively. It is therefore suggested to use the LC and LU-based recovery maps to monitor and support the short and the long-term recovery, respectively

Dynamics of floating objects at high particulate Reynolds numbers

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Evaluating Resilience-Centered Development Interventions with Remote Sensing

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