Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing

Li, Xuecao; Yu, Le; Xu, Yidi; Yang, Jun; Gong, Peng

doi:10.1007/s11434-016-1167-y

Cited by 21 publications

(11 citation statements)

References 47 publications

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“…Decrease in vegetation cover ratio of an area demonstrates the ecological damage ratio. In a different study, Li, et al [152] use vegetation cover change to assess the long-term effects of a hurricane in the urban natural environment recovery in New Orleans after Hurricane Katrina. Specifically, while using MODIS and Landsat images, Li and colleagues assess the period from immediately after the event to ten years later to discover that a decade later the vegetation cover is still at a lower level than before Katrina, indicating that the area has not yet fully recovered.…”

Section: Socio-economic Statusmentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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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.2 of 30 threats, to reduce own overall vulnerability, and to allow the community to recover from adverse impacts when they occur. Decades of disaster research offer extensive findings in this respect [4][5][6][7]. Remote sensing (RS)-as an effective and rapid tool for monitoring large areas-is essential for the acquisition of geospatial data, which in turn constitutes the basis for risk assessment and management. RS is widely used for various aspects of the DRM, ranging from vulnerability [8] to rapid damage assessments [9], for diverse areas ranging from coastal ecosystems [10] to complex urban settings [11], and for disasters as diverse as landslides [12,13] or cyclones [14].Numerous methods have been developed to extract information from RS data to identify, characterize, or quantify different phases of the disaster risk cycle: response, recovery, prevention/mitigation, and preparedness [15]. However, early studies predominantly considered the physical side of the assessments for both ...

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Section: Socio-economic Statusmentioning

confidence: 99%

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

2018

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“…Landsat 8 imagery has been used to monitor the recovery process in New Orleans and the surrounding area based on remote sensing ten years after Hurricane Katrina (Li et al, 2016). In this study, results indicated that on average conditions for vegetation in the areas that were affected have not recovered fully as compared to the conditions before the disaster.…”

Section: Introductionmentioning

confidence: 87%

Sar Coherence Change Detection of Urban Areas Affected by Disasters Using Sentinel-1 Imagery

Washaya

Balz

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The study focuses on two study areas: San Juan in Puerto Rico, which was affected by Hurricane Maria in September 2017, and Sarpol Zahab in Iran, which was one of the towns affected by an earthquake in November 2017. In our study, we generate coherence images, and classify them into areas of 'change' and 'no-change'. A statistical analysis is made by converting the coherence results into point data, creating street blocks for the study areas and integrating the point data into the street blocks to calculate the standard deviation over the whole stack of images. Additionally, Landsat imagery is used to create land-use classes, convert them to polygons and integrate the polygon classes to the coherence maps to determine the average coherence loss per class for each disaster. Results show 65% loss in coherence after the earthquake in Sarpol-e-Zahab and 75% loss in Puerto Rico after the Hurricane. Land-use classes show coherence losses to below 0.5 for each disaster.

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“…It can quickly and accurately analyze the dynamic changes in the interpretation of landforms, textures, colors, or other features in towns, rice fields, gardens, woodlands, and specific locations. (Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Dynamic monitoring algorithm of natural resources in scenic spots based on MODIS Remote Sensing technology

Li¹

2021

Earth sci. res. j.

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A dynamic monitoring algorithm of natural resources in scenic spots based on MODIS remote sensing technology is proposed to improve natural resources monitoring accuracy in scenic spots. The remote sensing images of scenic spots obtained by MODIS were preprocessed by TM image processing, atmospheric correction, and other technologies to get high-precision remote sensing images. The remote sensing images of scenic spots were segmented by the multi-scale segmentation method, and then the hierarchical supervision classification method was used. The change points of natural resources were extracted. The resource changes and independent variables of scenic spots were analyzed based on the least square method to realize the dynamic monitoring of natural resources in scenic locations. The experimental results show that the technique can accurately monitor the dynamic changes of forest resources and water resources in scenic spots, and the monitoring results have high accuracy.

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Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing

Cited by 21 publications

References 47 publications

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

Sar Coherence Change Detection of Urban Areas Affected by Disasters Using Sentinel-1 Imagery

Dynamic monitoring algorithm of natural resources in scenic spots based on MODIS Remote Sensing technology

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