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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >APPLICATIONS OF SENTINEL-1 SYNTHETIC APERTURE RADAR IMAGERY FOR FLOODS DAMAGE ASSESSMENT: A CASE STUDY OF NAKHON SI THAMMARAT, THAILAND
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APPLICATIONS OF SENTINEL-1 SYNTHETIC APERTURE RADAR IMAGERY FOR FLOODS DAMAGE ASSESSMENT: A CASE STUDY OF NAKHON SI THAMMARAT, THAILAND

机译:SENTINEL-1合成孔径雷达成像技术在洪水损伤评估中的应用:以泰国那空-西-塔玛拉特为例

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Flooding is one of the major disasters occurring in various parts of the world. Estimation of economic loss due to flood often becomes necessary for flood damage mitigation. This present practice to carry out post flood survey to estimate damage, which is a laborious and time-consuming task. This paper presents a framework of rapid estimation of flood damage using SAR earth observation satellite data.In Nakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province, especially the urban area. Every year, it causes lives and damages to infrastructure, agricultural production and severely affects local economic development. In order to monitor and estimate flood damages in near-real time, numerous techniques can be used, from a simply digitizing on maps, to using detailed surveys or remote sensing techniques. However, when using the last-mentioned technique, the results are conditioned by the time of data acquisition (day or night) as well as by weather conditions. Although, these impediments can be surpassed by using RADAR satellite imagery. The aim of this study is to delineate the land surface of Chian Yai, Pak Phanang and Hua Sai districts of that was affected by floods in December 2018 and January 2019. For this case study, Sentinel-1 C-Band SAR data provided by ESA (European Space Agency) were used. The data sets were taken before and after the flood took place, all within 1 days and were processed using Sentinel Toolbox. Cropland mapping has been carried out to assess the agricultural loss in study area using Sentinel-1 SAR data. The thematic accuracy has been assessed for cropland classification for test site shows encouraging overall accuracy as 82.63 % and kappa coefficients (κ) as 0.78.
机译:洪水是世界各地发生的重大灾难之一。估算因洪水造成的经济损失通常对于减轻洪水危害变得十分必要。目前的做法是进行洪水后调查以估计破坏,这是一项费时费力的任务。本文提出了使用SAR地球观测卫星数据快速估算洪水灾害的框架。在泰国南部的那空是他玛叻(Nakhon Si Thammarat),泰国是洪水泛滥的事件,影响到整个省份,特别是城市地区。每年,它造成生命和基础设施,农业生产的损害,并严重影响当地的经济发展。为了近乎实时地监测和评估洪水灾害,可以使用多种技术,从简单地在地图上数字化到使用详细的勘测或遥感技术。但是,使用最后提到的技术时,结果取决于数据采集时间(白天或晚上)以及天气情况。虽然,使用RADAR卫星图像可以克服这些障碍。这项研究的目的是勾勒出2018年12月和2019年1月受洪水影响的Chian Yai,Pak Phanang和Hua Sai地区的土地表面。对于本案例研究,ESA提供的Sentinel-1 C波段SAR数据(欧洲航天局)被使用。数据集是在洪水发生之前和之后(均在1天内)获取的,并使用Sentinel Toolbox进行了处理。已经使用Sentinel-1 SAR数据进行了耕地制图,以评估研究区域的农业损失。测试场地的农田分类的主题准确性已得到评估,显示总体准确性令人鼓舞,为82.63%,kappa系数(κ)为0.78。

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