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Detection of temporary flooded areas with potential floodwater mosquito production using imaging radar

机译:用成像雷达探测可能产生洪水蚊子的临时洪水区

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Temporarily flooded areas can produce enormous numbers of floodwater mosquitoes, causing tremendous nuisance to people living in the vicinity. The aim of this study is to develop a remote-sensing method for detecting temporary flooded areas that can produce floodwater mosquitoes. For this objective, synthetic aperture radar (SAR) imagery from the European Remote Sensing satellite (ERS-2) and the Environmental Satellite (Envisat) are chosen. The images cover both flooded and dry periods around Lake Färnebofjärden, located in the lowlands of the River Dalälven, central Sweden, during the vegetation season between 2000 and 2006. Unsupervised classification and principal component analysis (PCA) are tested as methods for detecting floodwater mosquito production sites. In the unsupervised classification experiment, four types of images are tested. The classification of a synthetic colour image gives the best result with an overall accuracy of 85.7% and a kappa value of 0.7, as well as a 46% detection rate of field-mapped flooded areas. PCA is performed on a data set of 16 time series radar images. The resulting principal component (PC) bands provide information about flooding probability as well as vegetation structures. Regular flooding increases the probability for an area to provide breeding sites for floodwater mosquitoes. Thus, this approach will be very useful in estimating the risk of floodwater mosquito establishment.View full textDownload full textRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/01431161.2011.604053
机译:临时水灾地区会产生大量的洪水蚊子,给附近的人们造成很大的麻烦。这项研究的目的是开发一种遥感方法,以检测可能产生洪水蚊子的临时洪灾区。为此,选择了来自欧洲遥感卫星(ERS-2)和环境卫星(Envisat)的合成孔径雷达(SAR)图像。这些图像涵盖了2000年至2006年植被旺季期间位于瑞典中部达勒文河低地的Färnebofjärden湖周围的淹水期和干旱期。测试了无监督分类和主成分分析(PCA)作为检测洪水蚊子产地的方法。在无监督分类实验中,测试了四种类型的图像。合成彩色图像的分类以85.7%的整体精度和0.7的kappa值以及46%的实地映射淹没区域检测率提供了最佳结果。对16个时间序列雷达图像的数据集执行PCA。所得的主成分(PC)波段提供有关洪水概率以及植被结构的信息。定期洪水增加了该地区为洪水蚊子提供繁殖地点的可能性。因此,这种方法在估计洪水蚊子形成的风险方面将非常有用。查看全文下载全文相关的var addthis_config = {ui_cobrand:“ Taylor&Francis Online”,services_compact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin, facebook,stumbleupon,digg,google,more“,发布号:” ra-4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/01431161.2011.604053

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