Hot spot tracking of flood remote sensing research over the past 22 years:abibliometric analysis using CiteSpace

Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.

Use of GIS and Remote Sensing Technology as a Decision Support Tool in Flood Disaster Management: The Case of Southeast Louisiana, USA

The primary objective of this paper was to identify flood-prone areas in Southeast of Louisiana to help decision-makers to develop appropriate adaptation strategies and flood prediction, and mitigation of the effects on the community. In doing so, the paper uses satellite remote sensing and Geographic Information System (GIS) data for this purpose. Elevation data was obtained from the National Elevation Dataset (NED) produced by the United States Geological Survey (USGS) seamless data warehouse. Satellite data was also acquired from USGS Earth explorer website. Topographical information on runoff characteristics such as slope, aspect and the digital elevation model was generated. Grid interpolation TIN (triangulated irregular network) was carried from the digital elevation model (DEM) to create slope map. Image Drape was performed using ERDAS IMAGINE Virtual GIS. The output image was then draped over the NED elevation data for visualization purposes with vertical exaggeration of 16 feet. Results of the study revealed that majority of the study area lies in low-lying and very low-lying terrain below sea level. Policy recommendation in the form of the need to design and build a comprehensive Regional Information Systems (RIS) in the form of periodic inventorying, monitoring and evaluation with full support of the governments was made for the study area.

Implementation of Remote Sensing and GIS Techniques to Study the Flash Flood Risk at NEOM Mega-City, Saudi Arabia

Southern Red Sea flooding is common. Assessing flood-prone development risks helps decrease life and property threats. It tries to improve flood awareness and advocate property owner steps to lessen risk. DEMs and topography data were analyzed by RS and GIS. Fifth-through seventh-order rivers were studied. Morphometric analysis assessed the area’s flash flood danger. NEOM has 14 catchments. We determined each catchment’s area, perimeter, maximum length, total stream length, minimum and maximum elevations. It also uses remote sensing. It classifies Landsat 8 photos for land use and cover maps. Image categorization involves high-quality Landsat satellite images and secondary data, plus user experience and knowledge. This study used the wetness index, elevation, slope, stream power index, topographic roughness index, normalized difference vegetation index, sediment transport index, stream order, flow accumulation, and geological formation. Analytic hierarchy considered all earlier criteria (AHP). The geometric consistency index GCI (0.15) and the consistency ratio CR (4.3%) are calculated. The study showed five degrees of flooding risk for Wadi Zawhi and four for Wadi Surr, from very high to very low. 9.16% of Wadi Surr is vulnerable to very high flooding, 50% to high flooding, 40% to low flooding, and 0.3% to very low flooding. Wadi Zawhi’s flood risk is 0.23% high, moderate, low, or extremely low. They’re in Wadi Surr and Wadi Zawhi. Flood mapping helps prepare for emergencies. Flood-prone areas should prioritize resilience.

Flood disaster monitoring based on Sentinel-1 data:A case study of Sihu Basin and Huaibei Plain,China

Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.

Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index(DVDI)

Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing,food policy,and agricultural trade.The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index(DVDI).By incorporating the DVDI along with information on crop types and flood inundation extents,this research assessed crop damage for three case-study events:Iowa Severe Storms and Flooding(DR 4386),Nebraska Severe Storms and Flooding(DR 4387),and Texas Severe Storms and Flooding(DR 4272).Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa,Nebraska,and Texas.More than half of flooded corn has experienced no damage,whereas 60%of affected soybean has a higher degree of loss in most of the selected counties in Iowa.Similarly,a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming,which is the most affected county in Nebraska.A total of 454 ha of corn are severely damaged in Anderson County,Texas.More than 200 ha of alfalfa have moderate to severe damage in Navarro County,Texas.The results of damage assessment are validated through the NDVI profile and yield loss in percentage.A linear relation is found between DVDI values and crop yield loss.An R2 value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation.The results also indicate the association between DVDI class and crop yield loss.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

北斗导航卫星系统在水利行业的应用与展望

北斗导航卫星系统是我国重要的时空基础设施,建设以来已经广泛应用于社会经济多个领域。智慧水利是新阶段水利高质量发展的最显著标志和6条实施路径之一,北斗导航卫星系统为智慧水利提供了强大的技术支撑,为数字孪生水利建设提供了重要的时空基准和高精度的位置及通信服务。从北斗导航卫星系统的功能视角,选择水利工程变形监测、水利巡检、水文监测数据传输等典型应用场景,分析了北斗导航卫星系统的应用现状;同时,还讨论了北斗遥感技术在水位监测、土壤水分反演和大气水汽反演等方面的应用情况;最后,展望了北斗系统在水利领域的发展趋势,指出其应用场景和领域将持续拓展、应用规模将不断增长,以及与新技术融合应用将不断增强,随着北斗导航卫星系统技术的持续创新和应用范围的进一步扩大,北斗导航卫星系统在未来水利行业中将发挥越来越重要的作用,它将成为加快水利新质生产力、推动水利高质量发展的重要推力。

基于SAR卫星遥感技术的农田洪涝灾害信息提取技术

为提高农田洪涝灾害信息提取能力,探索了SAR卫星遥感影像水体及农田边界信息的自动提取方法。以江西丰城某次强降雨过程为例,采用阈值分割法、雷达及光学影像融合法,利用Sentinel-1卫星影像对灾前水体信息进行提取,巢湖一号卫星影像对灾中的水体信息进行提取,将二者提取信息进行叠加,得到本次强降水新增水体范围;利用Sentinel-2卫星影像,叠加天地图影像提取出研究区域的农田边界范围,将该边界与新增水体范围叠加,得到受本次强降雨影响农田洪涝灾害区域的范围。经评价,该方法可有效提高地物散射特征的分类精度,提取的11处受淹农田验证地块完整率均在80%以上。SAR遥感影像不受云雨天气影响,能够在洪涝灾害应急监测中提供有力的数据支撑,该分析方法有利于相关部门全面掌握农田灾情数据,迅速做出应急响应,提高洪灾的应急救助管理能力。

降雨监测与预报技术在防洪减灾中的应用进展

洪水灾害突发性强,成灾速度快,对人民生命和财产安全造成较大的威胁。降雨作为洪水灾害致灾因子,数据的精确度对防洪减灾具有重要意义。以降雨监测与预报技术为切入点,对雨量站点观测、天气雷达降雨估计及预报、降雨数值预报、卫星遥感反演的现状进行了总结,通过分析时空降尺度方法及多源数据融合技术在降雨监测与预报中的应用,揭示了其在提升降雨数据“量”与“型”准确度方面的效果。研究表明:降雨监测与预报技术在当前取得了显著进展,但在山丘区和城市环境空间的复杂地形方面仍面临分辨率受到限制及精确性、时效性不足的问题。多源数据融合能提高降雨数据精度、时空覆盖能力和预测准确性,优化算法模型、融合“空-天-地”多源数据形成高分辨率预报是未来的研究方向。

遥感技术在洪涝灾害防御中的前沿应用与面临挑战

及时全面掌握洪涝发生的关键信息是洪涝灾害防御的基础,将遥感技术与传统监测技术相结合,形成“天空地”一体化的立体监测体系,可以实现宏观观测与重点监测的互补与协同,有利于洪涝灾害的监测、预报和预警,显著提高洪涝灾害防御能力。遥感技术可应用于水体提取、水位测算、降水测量、流量估算、洪涝模拟、灾害损失估计等,但在监测数据可用性、监测方法有效性等方面还有很多问题需要解决。

遥感和地面调查相结合的洪涝农作物灾情评估研究

农作物种植结构和空间分布情况是洪涝灾害农作物损失估算的基础,为满足洪涝灾害灾情农作物损失快速评估的精度要求,亟须构建一种将遥感技术与地面调查相结合的农作物实时调查和灾情评估方法。以2021年河南省暴雨洪涝灾害为例,综合利用卫星遥感数据与实地调研获取的真实地物样本数据,开展河南新乡卫辉市农作物精细分类研究,结合基于遥感数据得到的洪水淹没历时演算结果,对卫辉市此次洪涝灾害事件农作物受灾情况进行评估。结果表明:卫辉市洪涝灾害类型主要为河流高水位入侵洪水和地势低洼地区内涝滞水,洪水淹没历时集中在30 d以内,最大淹没面积98.62 km~2,出现在7月31日;受灾地物类型为主要农作物玉米、道路周边的绿化林地以及卫辉市城区;提出的基于多源遥感数据和真实地物样本的洪涝农作物灾情评估方法具有较高的精度和时效性。

基于CiteSpace的洪涝灾害遥感监测评估研究进展

卫星遥感技术由于具有覆盖范围广、周期短、时效性强等优势,在洪涝灾害风险分析、损失评估以及灾后恢复与重建等方面发挥了巨大作用,受到国内外学者的广泛关注与研究。为深入了解当前洪涝灾害遥感监测评估研究热点及变化趋势,对2001~2021年国内外该领域相关文献进行定量分析与归纳总结。利用CiteSpace等可视化分析工具,从发文量、作者、主要发文机构及研究热点关键词等多方面,对洪涝灾害遥感监测与评估研究的特征与趋势进行分析。结果表明:(1)遥感技术应用于洪涝灾害监测评估研究的成果数量随时间变化呈增加趋势,外文文献数量上升态势明显高于中文文献数量;2018年以后国内学者在外文期刊论文发表数已超过中文期刊论文发表数。(2)在洪涝灾害遥感监测评估研究领域,中国学者发文量占全球总发文量的17.72%,中国科学院以54篇占据研究机构发文数量首位;中国学者国际合作比率为31.9%,国际合作积极性高,但学术影响力及国际合作比例还有提升空间。(3)欧美及南亚等国家和地区学者的研究成果得到较高关注,高被引论文主题从风险分析与评估逐步向机器学习、城市洪涝等内容转变。(4)地表分类、水体提取、动态监测、风险评估等主题是国内外学者长期关注的重点研究内容,城市洪涝、承洪韧性、机器学习等正成为洪涝灾害遥感监测领域新的研究热点。基于云计算的机器学习与深度学习技术发展将会给洪涝灾害遥感监测、评估与风险分析带来新机遇。

卡霍夫卡溃坝造成的下游灾情评估

2023年6月6日凌晨乌克兰赫尔松州卡霍夫卡水电站大坝被炸毁。该大坝坐落于第聂伯河下游,上游约有18亿m3水库蓄水,下游有60多个村镇、数10万居民,溃坝洪水对下游产生了巨大影响。基于Sentinel-1遥感影像,利用双极化水体指数与最大类间方差法(OTSU)提取了第聂伯河下游区域受灾前后的水域分布情况;通过对比分析溃坝前后一周内的情况提取受灾区域,结合地物分类数据和交通矢量数据,综合评估了部分重要地物的受灾情况,约170.72 km2的农田、5.14 km2的建筑被淹没,16.63%的道路存在不同程度的淹没情况。

青海大通“8·18”山洪灾害特征及风险分析

2022年8月18日凌晨,青海省西宁市大通回族土族自治县发生山洪灾害,导致26人死亡、5人失联,这是典型的小溪小河发生灾害造成多人伤亡事件。降雨叠加是此次山洪灾害的直接原因,前期连续降雨使土壤含水量达到或接近饱和,2022年8月17日晚超历史记录局地短时强降雨难以通过土壤下渗或被植被截留,形成山洪灾害。综合分析遥感数据、数字高程模型、现场数据和媒体数据发现,山洪区具有汇水面大、河谷狭窄、相对高差大、河道较浅、障碍物多等特征,造成山洪势能大、运动距离长、局地雍水外溢严重,致使沟道两侧部分房屋、农田、道路等被冲毁。全球变化背景下,西北干旱区、青藏高原等山洪低风险区降水量显著增加,局地短时强降水时有发生,小溪小河发生山洪甚至特大山洪风险形势日益严峻,如再次遭遇强降雨灾区存在山洪灾害复发风险。

基于Landsat数据的新藏铁路沿线(西藏段)积雪覆盖变化分析

文章利用Landsat陆地卫星数据和DEM数据,通过GIS分析了新藏铁路沿线(西藏段)噶尔—普兰—仲巴-吉隆一线2020至2023年雪季积雪覆盖情况和气候变化情况,可对铁路选线提供一定程度参考,后续也可在此基础上对线路段冰雪灾害进行全面分析。研究结果表明:(1)整个雪季稳定积雪主要分布在海拔4200~5500m的山岭上,阿里地区噶尔县昆莎乡和普兰县马悠木山区域的积雪较多,覆盖到研究区的积雪主要是季节性积雪,受气候因素影响较大。(2)西藏西南部积雪的时空分布极不均匀,雪季历年同月分布差异极大,差异均在20%以上,极端月可达50%以上。(3)研究区内冬季平均气温在-35.5~6.9℃之间,平均风速在不同月份均处于0~5.77 m/s的范围内,达到雪颗粒启动风速,利于风吹雪的形成和发展,在铁路选线时应注意冰雪灾害对工程建设的影响。

集成光学与雷达遥感数据的鄱阳湖洪涝灾害过程监测研究

洪涝灾害一直是自然灾害研究的热点问题,具有突发性、频发性、影响范围广泛等特点。现有的洪水灾害遥感研究存在监测时相不足、时效性低等问题,难以对其过程进行有效监测。针对这一问题,基于光学与雷达影像,首先评估随机森林、支持向量机、最小距离算法对鄱阳湖洪涝灾害淹没范围提取的效果;然后采用提取精度最高的随机森林算法对鄱阳湖区2020年6月至7月的洪涝灾害过程进行监测;最后结合洪涝灾害发生前的土地利用类型图,分析洪涝灾害淹没的地物类型及面积。结果表明:1)随机森林算法对洪涝淹没范围提取效果最好,总体分类精度为95.50%,Kappa系数达到0.91;2)集成光学与雷达遥感数据能够有效地对洪峰过程进行监测,相比于传统光学遥感监测,雷达遥感传感器不受云雨天气影响,应受到更加多的重视;3)2020年6月26日至7月14日,鄱阳湖区域新增淹没面积达1367.18 km^(2),主体淹没范围呈持续扩大趋势,此后洪水逐渐退水消亡;整个过程淹没面积最大的地物类型为农田,淹没面积达到246.68 km^(2)。

基于遥感智能解译技术的围堰溃决洪水淹没分析--以旭龙水电站为例

水电站围堰溃决洪水突发性强且非常规,应急调查时效性要求高。为分析水电站围堰溃决洪水风险,以旭龙水电站为例,提出了一套基于遥感智能解译技术的溃堰洪水淹没分析方法用于实物指标应急调查,并选取与居民财产安全密切相关的建筑物为典型对象,通过构建U-Net卷积神经网络模型,对旭龙水电站下游区域进行建筑物提取。结果表明:该方法可有效识别出建筑物分布情况,F-score指标精度在93%以上,在算法效率上也明显优于人工解译。

基于MODIS和GIS的洪灾监测评估系统

在分析我国遥感技术在洪水监测方面应用的基础上,指出目前洪灾遥感监测在时效性等方面的不足.通过介绍MODIS的特点,以及与NOAA AVHRR的对比,提出建立基于MODIS和GIS的洪灾监测评估系统.利用免费、较高时间分辨率的MODIS数据,运用GIS技术,实现对不同土地利用类型的淹没情况的实时监测,为汛情的动态监测和评估提供迅速、直观、可靠的现状和变化信息.

遥感与GIS支持下的基于网络的洪涝灾害监测评估系统关键技术研究

以对中国的洪涝灾害监测评估为例 ,介绍了以遥感与GIS支持下的基于网络环境的洪涝灾害监测评估关键技术系统的工作环境、关键技术流程和技术方法。该关键技术系统以遥感和GIS为基础 ,快速将多源数据复合 ,通过网络集成了多种技术成果和数据 ,进行快速、准确、连续、动态与全天候的洪涝灾害的监测与评估 ,为有关部门快速了解灾情、制定救灾方案以及灾后规划等提供了重要的决策支持手段 ,已在抗洪救灾工作中发挥了重要作用。

1998年全国洪涝灾害遥感监测评估的主要成果──基于网络的洪涝灾情遥感速报系统的应用

介绍了基于网络的洪涝灾害遥感速报系统在1998年全国特大洪涝灾害监测评估中的主要应用成果，包括动态监测、农作物损失评估、防洪工程有效性分析、险工险段调查分析、城市洪灾监测、工业区生命线工程易损性评估、长江洪水蓄洪分洪必要性分析、防洪减灾决策建议和灾后重建家园功能分区规划等。