利用谷歌地球引擎平台对冲积和冲积洪水进行灾害风险评估:菲尤斯河流域案例研究

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
{"title":"利用谷歌地球引擎平台对冲积和冲积洪水进行灾害风险评估:菲尤斯河流域案例研究","authors":"","doi":"10.1007/s41064-024-00277-z","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The aim of this study is to conduct a risk analysis of fluvial and pluvial flood disasters, focusing on the vulnerability of those residing in the river basin in coastal regions. However, there are numerous factors and indicators that need to be considered for this type of analysis. Swift and precise acquisition and evaluation of such data is an arduous task, necessitating significant public investment. Remote sensing offers unique data and information flow solutions in areas where access to information is restricted. The Google Earth Engine (GEE), a remote sensing platform, offers strong support to users and researchers in this context. A data-based and informative case study has been conducted to evaluate the disaster risk analysis capacity of the platform. Data on three factors and 17 indicators for assessing disaster risk were determined using coding techniques and web geographic information system (web GIS) applications. The study focused on the Filyos River basin in Turkey. Various satellite images and datasets were utilized to identify indicators, while land use was determined using classification studies employing machine learning algorithms on the GEE platform. Using various applications, we obtained information on ecological vulnerability, fluvial and pluvial flooding analyses, and the value of indicators related to construction and population density. Within the scope of the analysis, it has been determined that the disaster risk index (DRI) value for the basin is 4. This DRI value indicates that an unacceptable risk level exists for the 807,889 individuals residing in the basin.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disaster Risk Assessment of Fluvial and Pluvial Flood Using the Google Earth Engine Platform: a Case Study for the Filyos River Basin\",\"authors\":\"\",\"doi\":\"10.1007/s41064-024-00277-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Abstract</h3> <p>The aim of this study is to conduct a risk analysis of fluvial and pluvial flood disasters, focusing on the vulnerability of those residing in the river basin in coastal regions. However, there are numerous factors and indicators that need to be considered for this type of analysis. Swift and precise acquisition and evaluation of such data is an arduous task, necessitating significant public investment. Remote sensing offers unique data and information flow solutions in areas where access to information is restricted. The Google Earth Engine (GEE), a remote sensing platform, offers strong support to users and researchers in this context. A data-based and informative case study has been conducted to evaluate the disaster risk analysis capacity of the platform. Data on three factors and 17 indicators for assessing disaster risk were determined using coding techniques and web geographic information system (web GIS) applications. The study focused on the Filyos River basin in Turkey. Various satellite images and datasets were utilized to identify indicators, while land use was determined using classification studies employing machine learning algorithms on the GEE platform. Using various applications, we obtained information on ecological vulnerability, fluvial and pluvial flooding analyses, and the value of indicators related to construction and population density. Within the scope of the analysis, it has been determined that the disaster risk index (DRI) value for the basin is 4. This DRI value indicates that an unacceptable risk level exists for the 807,889 individuals residing in the basin.</p>\",\"PeriodicalId\":56035,\"journal\":{\"name\":\"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s41064-024-00277-z\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s41064-024-00277-z","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

摘要 本研究的目的是对河流和冲积洪水灾害进行风险分析,重点是沿海地区河流流域居民的脆弱性。然而,此类分析需要考虑众多因素和指标。迅速、准确地获取和评估这些数据是一项艰巨的任务,需要大量的公共投资。在信息获取受限的地区,遥感技术提供了独特的数据和信息流解决方案。在这方面,遥感平台谷歌地球引擎(GEE)为用户和研究人员提供了强有力的支持。为评估该平台的灾害风险分析能力,开展了一项基于数据和信息的案例研究。利用编码技术和网络地理信息系统(web GIS)应用程序确定了评估灾害风险的三个因素和 17 个指标的数据。研究的重点是土耳其的 Filyos 河流域。我们利用各种卫星图像和数据集来确定指标,同时利用 GEE 平台上的机器学习算法进行分类研究,确定土地使用情况。通过各种应用,我们获得了有关生态脆弱性、河流和冲积洪水分析以及与建筑和人口密度相关的指标值的信息。在分析范围内,确定该流域的灾害风险指数(DRI)值为 4。该 DRI 值表明,对于居住在该流域的 807 889 人而言,存在不可接受的风险水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disaster Risk Assessment of Fluvial and Pluvial Flood Using the Google Earth Engine Platform: a Case Study for the Filyos River Basin

Abstract

The aim of this study is to conduct a risk analysis of fluvial and pluvial flood disasters, focusing on the vulnerability of those residing in the river basin in coastal regions. However, there are numerous factors and indicators that need to be considered for this type of analysis. Swift and precise acquisition and evaluation of such data is an arduous task, necessitating significant public investment. Remote sensing offers unique data and information flow solutions in areas where access to information is restricted. The Google Earth Engine (GEE), a remote sensing platform, offers strong support to users and researchers in this context. A data-based and informative case study has been conducted to evaluate the disaster risk analysis capacity of the platform. Data on three factors and 17 indicators for assessing disaster risk were determined using coding techniques and web geographic information system (web GIS) applications. The study focused on the Filyos River basin in Turkey. Various satellite images and datasets were utilized to identify indicators, while land use was determined using classification studies employing machine learning algorithms on the GEE platform. Using various applications, we obtained information on ecological vulnerability, fluvial and pluvial flooding analyses, and the value of indicators related to construction and population density. Within the scope of the analysis, it has been determined that the disaster risk index (DRI) value for the basin is 4. This DRI value indicates that an unacceptable risk level exists for the 807,889 individuals residing in the basin.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信