SDGSAT-1夜间灯光数据揭示的2023年土耳其-叙利亚地震后的城市恢复模式

IF 11.4 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Yu Gong , Xi Li , Deren Li , Xubing Zhang
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引用次数: 0

摘要

土耳其-叙利亚地震于2023年2月发生,是该地区近20年来遭受的最严重地震。震后重建工作仍在进行中,当地社区继续与挑战作斗争。本研究对土耳其的四个城市(即安塔基亚、基里可汗、萨曼达格和努尔达吉)进行了调查,以跟踪灾后恢复过程。我们采用了电力恢复百分比指标,该指标来源于可持续发展科学卫星1号(SDGSAT-1)获取的微光图像,该图像记录了夜间灯光(NTL),以跟踪地震后一年内电力恢复的时空动态。然后利用时间序列NTL数据聚类来识别像素级恢复模式。此外,我们还测量了城市形态变量,以探索它们在街区水平上与城市恢复的关系。我们的研究结果揭示了三种不同的动态模式,其特征是上升、下降和稳定的NTL变化轨迹。值得注意的是,NTL上升趋势的电网与灾后重建工作(如新建住宿区)密切相关。此外,低密度建筑、丰富的开放空间和统一布局的街区具有更强的弹性和更快的恢复速度。总体而言,本研究强调,SDGSAT-1 NTL数据在捕捉灾后异质性城市恢复过程方面具有强大的功能,从而显示了其对推进可持续发展目标11的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Urban recovery patterns after the 2023 Turkey-Syria earthquake revealed by SDGSAT-1 nighttime light data
The Turkey-Syria earthquake, which struck in February 2023, was the worst earthquake the region had suffered in the last two decades. The post-earthquake reconstruction process remains underway, with local communities continuing to grapple with challenges. In this study, four cities in Turkey (i.e., Antakya, Kirikhan, Samandag, and Nurdagi) were investigated to track the post-disaster recovery process. We employed the power recovery percentage metric, derived from glimmer imagery acquired by the Sustainable Development Science Satellite 1 (SDGSAT-1), which records nighttime light (NTL), to track the spatial-temporal dynamics of power restoration over the year following the earthquake. Clustering on time series NTL data was then utilized to identify pixel-scale recovery patterns. Furthermore, urban morphological variables were measured to explore their relationship with urban recovery at the block level. Our findings reveal three diverse dynamic patterns, characterized by ascending, descending, and stable NTL change trajectories. Notably, grids exhibiting rising NTL trends align closely with post-disaster reconstruction efforts (e.g., newly-built accommodation areas). In addition, blocks with low-density buildings, abundant open spaces, and uniform layouts demonstrate greater resilience and faster recovery pace. Overall, this study highlights that the SDGSAT-1 NTL data is powerful in capturing heterogeneous urban recovery processes following the disaster, thereby showing its contribution to advancing Sustainable Development Goal 11.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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