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

Urban 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 subdistrict...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (9), p.2010
Main Authors: Zhang, Hui, Liu, Xiaoqian, Xie, Yingkai, Gou, Qiang, Li, Rongrong, Qiu, Yanqing, Hu, Yueming, Huang, Bo
Format: Article
Language:English
Subjects:
Case studies
Cities
Climate change
Coefficients
Disaster recovery
Disasters
Elevation
Flooding
Floods
Freshwater resources
Infrastructure
Jakarta
Natural disasters
nighttime light satellite imagery
Population density
Principal components analysis
Radar imaging
Recessions
recovery
Remote sensing
Resilience
SAR
Satellite imagery
Society
Spatial data
Sustainable development
Synthetic aperture radar
Topology
urban resilience
Urban studies
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Summary:Urban 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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14092010