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Article: Assessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study

TitleAssessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study
Authors
Keywordsflooding
Jakarta
nighttime light satellite imagery
recovery
SAR
urban resilience
Issue Date2022
Citation
Remote Sensing, 2022, v. 14, n. 9, article no. 2010 How to Cite?
AbstractUrban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.
Persistent Identifierhttp://hdl.handle.net/10722/329808
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Hui-
dc.contributor.authorLiu, Xiaoqian-
dc.contributor.authorXie, Yingkai-
dc.contributor.authorGou, Qiang-
dc.contributor.authorLi, Rongrong-
dc.contributor.authorQiu, Yanqing-
dc.contributor.authorHu, Yueming-
dc.contributor.authorHuang, Bo-
dc.date.accessioned2023-08-09T03:35:29Z-
dc.date.available2023-08-09T03:35:29Z-
dc.date.issued2022-
dc.identifier.citationRemote Sensing, 2022, v. 14, n. 9, article no. 2010-
dc.identifier.urihttp://hdl.handle.net/10722/329808-
dc.description.abstractUrban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.-
dc.languageeng-
dc.relation.ispartofRemote Sensing-
dc.subjectflooding-
dc.subjectJakarta-
dc.subjectnighttime light satellite imagery-
dc.subjectrecovery-
dc.subjectSAR-
dc.subjecturban resilience-
dc.titleAssessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.3390/rs14092010-
dc.identifier.scopuseid_2-s2.0-85129272675-
dc.identifier.volume14-
dc.identifier.issue9-
dc.identifier.spagearticle no. 2010-
dc.identifier.epagearticle no. 2010-
dc.identifier.eissn2072-4292-
dc.identifier.isiWOS:000795392200001-

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