利用多传感器数据和谷歌地球引擎绘制尼日尔河和贝努埃河交汇地区十年洪水淹没图和损害评估图

Caleb Odiji, Godstime James, Ademuyiwa Oyewumi, Shomboro Karau, Belinda Odia, Halima Idris, Olaide Aderoju, Abubakar Taminu
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Flooding is the most frequent and serious natural hazard in the confluence region of Rivers Niger and Benue, impacting lives, agriculture, and socio-economic activities significantly. Advancements in satellite technology and computational capabilities have enhanced rapid information about flood extent for monitoring, mitigation, and planning. However, there is a dearth of information based on time series analysis of flood inundation and monitoring in the confluence region. In this study, Sentinel-1 Synthetic Aperture Radar, Sentinel-2, and Landsat-7 and Landsat-8 data were used to extract flood inundation for 10 years (2012–2022) in the confluence region of Rivers Niger and Benue. Flood extent/surface waterbodies were extracted using the Google Earth Engine platform, modified normalized difference water index, and normalized difference water index methods. The findings revealed that within 10 years, four significant flooding incidents occurred in 2012, 2018, 2020, and 2022, inundating 60.57, 48.24, 39.98, and 84.39 km<sup>2</sup> of the area, respectively. The study underscores the need for the establishment of a decision support system for monitoring flood inundation and providing decision-makers necessary information for flood disaster preparedness, mitigation, and adaptation.</p>","PeriodicalId":510893,"journal":{"name":"Journal of Water & Climate Change","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine\",\"authors\":\"Caleb Odiji, Godstime James, Ademuyiwa Oyewumi, Shomboro Karau, Belinda Odia, Halima Idris, Olaide Aderoju, Abubakar Taminu\",\"doi\":\"10.2166/wcc.2024.166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div data- reveal-group-><div><img alt=\\\"graphic\\\" data-src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jwcc/15/2/10.2166_wcc.2024.166/1/m_jwc-d-23-00166gf01.png?Expires=1712302570&amp;Signature=NT3lucFfXCkTayJRKX5qBKwJJ67J~ItCwSFnnKKSP6Q6KZIDhrfKT8IGNo12aQ1DUm9cEFfq6W3LSsVQPBERsyZa3vyoZKIYgsvWrEf6HMb9LXQZuQQ5kK4iOYkSg3PKc-pyLzb~lme3nr7MLBiEuCt19WwexZD4U6InWhb8NWbuM2EmbKjAsddoT5Au0nDU5eM3dFPO~jVmHA2PnWTrgc2PMpPT1NkOjs2xVWHmp8NUMIsSKrFlwInN~v1c9Z3rroIEErRPtSYFWd68auDVNqHDAvT3Xm7-1XTmcf~Ds8VsNKUyouW9JN2QTe~ukgwiHRgPepGwhS0aq5Yd~BJXPg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\" path-from-xml=\\\"jwc-d-23-00166gf01.tif\\\" src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jwcc/15/2/10.2166_wcc.2024.166/1/m_jwc-d-23-00166gf01.png?Expires=1712302570&amp;Signature=NT3lucFfXCkTayJRKX5qBKwJJ67J~ItCwSFnnKKSP6Q6KZIDhrfKT8IGNo12aQ1DUm9cEFfq6W3LSsVQPBERsyZa3vyoZKIYgsvWrEf6HMb9LXQZuQQ5kK4iOYkSg3PKc-pyLzb~lme3nr7MLBiEuCt19WwexZD4U6InWhb8NWbuM2EmbKjAsddoT5Au0nDU5eM3dFPO~jVmHA2PnWTrgc2PMpPT1NkOjs2xVWHmp8NUMIsSKrFlwInN~v1c9Z3rroIEErRPtSYFWd68auDVNqHDAvT3Xm7-1XTmcf~Ds8VsNKUyouW9JN2QTe~ukgwiHRgPepGwhS0aq5Yd~BJXPg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\"/><div>View largeDownload slide</div></div></div><div content- data-reveal=\\\"data-reveal\\\"><div><img alt=\\\"graphic\\\" data-src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jwcc/15/2/10.2166_wcc.2024.166/1/m_jwc-d-23-00166gf01.png?Expires=1712302570&amp;Signature=NT3lucFfXCkTayJRKX5qBKwJJ67J~ItCwSFnnKKSP6Q6KZIDhrfKT8IGNo12aQ1DUm9cEFfq6W3LSsVQPBERsyZa3vyoZKIYgsvWrEf6HMb9LXQZuQQ5kK4iOYkSg3PKc-pyLzb~lme3nr7MLBiEuCt19WwexZD4U6InWhb8NWbuM2EmbKjAsddoT5Au0nDU5eM3dFPO~jVmHA2PnWTrgc2PMpPT1NkOjs2xVWHmp8NUMIsSKrFlwInN~v1c9Z3rroIEErRPtSYFWd68auDVNqHDAvT3Xm7-1XTmcf~Ds8VsNKUyouW9JN2QTe~ukgwiHRgPepGwhS0aq5Yd~BJXPg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\" path-from-xml=\\\"jwc-d-23-00166gf01.tif\\\" src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jwcc/15/2/10.2166_wcc.2024.166/1/m_jwc-d-23-00166gf01.png?Expires=1712302570&amp;Signature=NT3lucFfXCkTayJRKX5qBKwJJ67J~ItCwSFnnKKSP6Q6KZIDhrfKT8IGNo12aQ1DUm9cEFfq6W3LSsVQPBERsyZa3vyoZKIYgsvWrEf6HMb9LXQZuQQ5kK4iOYkSg3PKc-pyLzb~lme3nr7MLBiEuCt19WwexZD4U6InWhb8NWbuM2EmbKjAsddoT5Au0nDU5eM3dFPO~jVmHA2PnWTrgc2PMpPT1NkOjs2xVWHmp8NUMIsSKrFlwInN~v1c9Z3rroIEErRPtSYFWd68auDVNqHDAvT3Xm7-1XTmcf~Ds8VsNKUyouW9JN2QTe~ukgwiHRgPepGwhS0aq5Yd~BJXPg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Climate change has made weather patterns more extreme, causing floods in Nigeria. 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引用次数: 0

摘要

查看大幅下载幻灯片查看大幅下载幻灯片 关闭模版气候变化使天气模式变得更加极端,导致尼日利亚洪水泛滥。洪水是尼日尔河和贝努埃河交汇地区最频繁、最严重的自然灾害,对生命、农业和社会经济活动造成了重大影响。卫星技术和计算能力的进步提高了有关洪水范围的快速信息,可用于监测、减灾和规划。然而,基于汇流区域洪水淹没和监测时间序列分析的信息却十分匮乏。本研究利用哨兵-1 合成孔径雷达、哨兵-2 以及 Landsat-7 和 Landsat-8 数据提取了尼日尔河和贝努埃河汇流区 10 年(2012-2022 年)的洪水淹没情况。使用谷歌地球引擎平台、修正的归一化差异水指数和归一化差异水指数方法提取了洪水范围/地表水体。研究结果表明,在 10 年内,分别于 2012 年、2018 年、2020 年和 2022 年发生了四次重大洪灾,淹没面积分别为 60.57 平方公里、48.24 平方公里、39.98 平方公里和 84.39 平方公里。这项研究强调,有必要建立一个决策支持系统来监测洪水淹没情况,并为决策者提供必要的信息,以防备、减轻和适应洪水灾害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine
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Climate change has made weather patterns more extreme, causing floods in Nigeria. Flooding is the most frequent and serious natural hazard in the confluence region of Rivers Niger and Benue, impacting lives, agriculture, and socio-economic activities significantly. Advancements in satellite technology and computational capabilities have enhanced rapid information about flood extent for monitoring, mitigation, and planning. However, there is a dearth of information based on time series analysis of flood inundation and monitoring in the confluence region. In this study, Sentinel-1 Synthetic Aperture Radar, Sentinel-2, and Landsat-7 and Landsat-8 data were used to extract flood inundation for 10 years (2012–2022) in the confluence region of Rivers Niger and Benue. Flood extent/surface waterbodies were extracted using the Google Earth Engine platform, modified normalized difference water index, and normalized difference water index methods. The findings revealed that within 10 years, four significant flooding incidents occurred in 2012, 2018, 2020, and 2022, inundating 60.57, 48.24, 39.98, and 84.39 km2 of the area, respectively. The study underscores the need for the establishment of a decision support system for monitoring flood inundation and providing decision-makers necessary information for flood disaster preparedness, mitigation, and adaptation.

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