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Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017).The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-h latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders. 相似文献
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《International Journal of Digital Earth》2013,6(7):781-801
ABSTRACTAlthough Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness. 相似文献
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Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making. 相似文献
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随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。 相似文献
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C. M. Bhatt G. Srinivasa Rao P. Manjushree V. Bhanumurthy 《Journal of the Indian Society of Remote Sensing》2010,38(1):99-108
One of the most important elements in flood disaster management is the availability of timely information for taking decisions
and actions by the authorities. During the August 18, 2008 Kosi floods which impacted India and Nepal and affected more than
three million people, aero-space technology proved to be a critical input for providing vital information on flood inundation.
The satellite based flood inundation maps were extensively used for identifying marooned villages, submerged roads and railway
tracks and carrying out the relief and rescue operations by the state agencies. Decision Support Centre (DSC) at National
Remote Sensing Centre (NRSC) kept a constant watch on the flood situation. More than 200 flood inundation maps, using about
30 satellite datasets were generated and provided in near real time mode to the state agencies during August to October, 2008.
DSC efforts were primarily focused in providing an overall picture of the flood situation in a short span of time to the state
agencies. The present paper discusses about the operational use of remote sensing technology for near real time flood mapping,
monitoring of Kosi floods and the satellite based observations made for the Kosi river breach. 相似文献
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洪涝过程探测与准实时服务对于保障人民生命和财产安全意义重大。通常洪涝探测仅关注洪涝断面/平均状态,缺乏对洪涝从发生、发展乃至最后结束的全过程的整体认知,且洪涝探测和服务被动滞后。本文制定洪涝过程探测规则,改进水位预测模型,并将其作为理论基础,与传感网信息模型和服务接口相结合,提出了洪涝过程动态探测与准实时服务(process-based flood detection and service,PFD&S)方法。以PFD&S方法为基础,设计并开发了PFD&S原型系统,该系统由传感器层、数据接入层、洪涝探测层和用户交互层4层组成,且具备传感器数据发布和洪涝事件订阅两种使用模式。文中以2016年夏季发生在黄汉流域的洪涝事件为例,对PFD&S方法的可行性和有效性进行验证。结果表明,PFD&S方法和系统能够精准判断洪涝事件所处阶段,并根据不同阶段的需求提供水位预测、洪涝预警或洪涝信息统计等服务,且方法具备准实时性和可扩展性特征。 相似文献
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本文首先采用基于多准则决策的层次分析评价法,根据自然灾害风险理论,将洪涝风险影响因子分为危险性和脆弱性两类,子准则层包括平均降雨量、汇流累积量、坡度、海拔和土地覆盖度、道路级别、地表产流能力7个因子,构建了道路洪涝灾害风险评价模型。然后以福建省武夷山地区为研究区,利用地形数据、气象数据及遥感影像提取土地覆盖类型数据,通过道路洪涝灾害风险评价模型,绘制了道路风险分区图。结果表明,中、高风险积水道路占比较高,主要集中在东部、西部和中南部地区。本文对道路洪涝灾害风险所进行的评价,可服务于洪涝灾害风险预警和应急救援规划。 相似文献
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针对目前"数字城市"向"智慧城市"迈进中物联网技术蓬勃发展,而原有数字化城市管理的方法和技术手段在许多方面都有待进一步完善的现状,该文探索利用物联网等高新科技手段,以全新的管理理念和管理模式来提升城市精细化管理水平。该文重点论述了如何综合运用物联网智能感知、无线传输、数据挖掘等技术,在车载感知定位技术及应用、城市安全传感器应用、水雨雪情实时监测及防汛应急、交通运行多源监测数据分析以及基于二维码的公众参与式城市管理等的典型应用实例。这些典型应用有效地解决城市运行管理中的实际问题,满足了物联网理念在城市管理中的拓展以及管理方式继续深入的需要,提升了城市运行管理的科学化、智能化水平。 相似文献
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Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models 总被引：1，自引：0，他引：1
Modelling the flood in watersheds and reducing the damages caused by this natural disaster is one of the primary objectives of watershed management. This study aims to investigate the application of the frequency ratio and maximum entropy models for flood susceptibility mapping in the Madarsoo watershed, Golestan Province, Iran. Based on the maximum entropy and frequency ratio methods as well as analysis of the relationship between the flood events belonging to training group and the factors affecting on the risk of flooding, the weight of classes of each factor was determined in a GIS environment. Finally, prediction map of flooding potential was validated using receiver operating characteristic (ROC) curve method. ROC curve estimated the area under the curve for frequency ratio and the maximum entropy models as 74.3% and 92.6%, respectively, indicating that the maximum entropy model led to better results for evaluating flooding potential in the study area. 相似文献
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With the rapid growth and popularity of mobile devices and location‐aware technologies, online social networks such as Twitter have become an important data source for scientists to conduct geo‐social network research. Non‐personal accounts, spam users and junk tweets, however, pose severe problems to the extraction of meaningful information and the validation of any research findings on tweets or twitter users. Therefore, the detection of such users is a critical and fundamental step for twitter‐related geographic research. In this study, we develop a methodological framework to: (1) extract user characteristics based on geographic, graph‐based and content‐based features of tweets; (2) construct a training dataset by manually inspecting and labeling a large sample of twitter users; and (3) derive reliable rules and knowledge for detecting non‐personal users with supervised classification methods. The extracted geographic characteristics of a user include maximum speed, mean speed, the number of different counties that the user has been to, and others. Content‐based characteristics for a user include the number of tweets per month, the percentage of tweets with URLs or Hashtags, and the percentage of tweets with emotions, detected with sentiment analysis. The extracted rules are theoretically interesting and practically useful. Specifically, the results show that geographic features, such as the average speed and frequency of county changes, can serve as important indicators of non‐personal users. For non‐spatial characteristics, the percentage of tweets with a high human factor index, the percentage of tweets with URLs, and the percentage of tweets with mentioned/replied users are the top three features in detecting non‐personal users. 相似文献
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研究城市雨洪风险问题,对提高城市洪涝灾害监测、预报的准确性,以及促进城市防洪决策制定具有重要的意义。鉴于高精度的城市三维模型可以提供丰富地理信息,便于准确分析淹没情况,本文针对当前城市洪涝模型对地形数据的高敏感性,且雨洪风险评估研究的准确性受限于地形数据精度的问题,提出利用无人机倾斜摄影测量技术重建高精度实景三维模型,并结合GIS的空间分析功能,以淹没深度为关键指标进行研究区的雨洪风险评估。通过提取不同重现期下研究区的淹没深度信息,进行可视化渲染实现三维淹没分析,可以直观地看到研究区的淹没情况,作为暴雨内涝风险管理依据,同时对城市规划布局有一定的参考价值。 相似文献
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合成孔径雷达（SAR）因其对地观测全天候、全天时优势,成为多云多雨天气限制下洪水动态监测中不可或缺的数据来源之一。由于GEE（Google Earth Engine）云计算平台的兴起和短重访Sentinel-1数据的可获取性,洪水监测与灾害评估目前正面向动态化、广域化快速发展。顾及洪水淹没区土地覆盖变化的复杂性和发生时间的不确定性,基于时序Sentinel-1A卫星数据提出了针对大尺度范围、连续长期的汛情自动检测及动态监测方法。该方法首先,利用图像二值化分割时序SAR数据实现水体时空分布粗制图,逐像素计算时间序列中被识别为水体候选点的频率。然后,利用Sentinel-2光学影像对精度较粗的初期SAR水体提取结果进行校正,得到精细的水体分布图。最后,针对不同频率区间的淹没特点,采用差异化的时序异常检测策略识别淹没范围：对低频覆水区利用欧氏距离检测时序断点,以提取扰动强度大、淹没时间短的洪涝灾害区;对高频覆水区利用标准分数（Z-Score）检测时序断点,以提取季节性水体覆盖区。在GEE平台上利用该方法,实现了2020-05—10长江中下游地区全域洪水淹没范围时空信息的自动、快速、有效监测,揭示了不同区域汛情发展模式的差异性。本文提出的洪水快速监测方法对大尺度下的汛情动态监测、灾害定量评估和快速预警响应具有重要的现实意义。 相似文献
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针对严重污染的城市水体与道路、建筑物、阴影等易于混分,以及遥感水体提取结果不连续、存在斑点问题,本文以广州市流溪河与东江水系为研究对象,基于2016年与2017年OLI遥感影像,采用本文新提出的城市水体指数法（CWI）,同时结合分形几何算法,通过设置形状面积等特征,实现城市复杂环境下的水体信息的自动提取。并与单通道算法、改进的归一化差异水体指数（MNDWI）算法、支持向量机法（SVM）与光谱角度法的水体提取结果进行对比分析。结果表明：SVM算法出现大量斑点,其次为MNDWI水体指数算法,光谱角度算法与单通道算法斑点较少,但水体提取结果不连续,部分河道漏分。本文提出的算法能够克服山体阴影、道路、建筑物等影响,实现城市污染水体以及一般水体连续、准确提取。本文的提取结果可为水资源调查、洪水灾害预测评估、水利规划、环境监测等工作提供基础数据支撑。 相似文献
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ABSTRACTNatural disasters, such as wildfires, earthquakes, landslides, or floods, lead to an increase in topical information shared on social media and in increased mapping activities in volunteered geographic information (VGI) platforms. Using earthquakes in Nepal and Central Italy as case studies, this research analyzes the effects of natural disasters on short-term (weeks) and longer-term (half year) changes in OpenStreetMap (OSM) mapping behavior and tweet activities in the affected regions. An increase of activities in OSM during the events can be partially attributed to those focused OSM mapping campaigns, for example, through the Humanitarian OSM Team (HOT). Using source tags in OSM change-sets, it was found that only a small portion of external mappers actually travels to the affected regions, whereas the majority of external mappers relies on desktop mapping instead. Furthermore, the study analyzes the spatio-temporal sequence of posted tweets together with keyword filters to identify a subset of users who most likely traveled to the affected regions for support and rescue operations. It also explores where, geographically, earthquake information spreads within social networks. 相似文献
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Ahmed Mahmood 《国际地球制图》2013,28(2):91-101
In the case of a major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the damage caused by the disaster to ground infrastructure. The International Charter ‘Space and Major Disasters’ (‘the Charter’) has been one of the primary sources of satellite data for the past 11 years to cover events like floods, fires, tsunamis, ocean storms, earthquakes, volcanic eruptions and oil spills. With the growing membership of the Charter, an increasingly large number of sensors are now available, which can be planned with the required temporal frequency and spectral range to cover a disaster event. Some of the type Charter activation cases are reported in this article to demonstrate the innovative use of multi-satellite imagery for disaster response. 相似文献