An Earth Observation Framework in Service of the Sendai Framework for Disaster Risk Reduction 2015-2030

被引:3
|
作者
Li, Boyi [1 ,2 ,3 ,4 ]
Gong, Adu [1 ,2 ,3 ,4 ]
Liu, Longfei [5 ]
Li, Jing [1 ,2 ,3 ,4 ]
Li, Jinglin [6 ]
Li, Lingling [6 ]
Pan, Xiang [6 ]
Chen, Zikun [6 ]
机构
[1] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing Pr, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Key Lab Environm Change & Nat Disasters, Minist Educ, Beijing 100875, Peoples R China
[4] Beijing Normal Univ, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China
[5] Minist Emergency Management Peoples Republ China, Natl Disaster Reduct Ctr China, Beijing 100124, Peoples R China
[6] Beijing Normal Univ Zhuhai, Fac Arts & Sci, Zhuhai 519087, Peoples R China
基金
中国国家自然科学基金;
关键词
Sendai Framework for Disaster Risk Reduction (SFDRR); Sustainable Development Goals (SDGs); disaster; earth observation (EO); essential variable (EV); tropical cyclone (TC); Google Earth Engine (GEE);
D O I
10.3390/ijgi12060232
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.
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页数:20
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