首页> 外文会议>Asian conference on remote sensing;ACRS 2007 >APPLICATION OF C-BAND SYNTHETIC APERTURE RADAR DATA AND DIGITAL ELEVATION MODEL TO EVALUATE THE CONDITIONS OF FLOOD-AFFECTED PADDIES: CHI RIVER BASIN, THAILAND
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APPLICATION OF C-BAND SYNTHETIC APERTURE RADAR DATA AND DIGITAL ELEVATION MODEL TO EVALUATE THE CONDITIONS OF FLOOD-AFFECTED PADDIES: CHI RIVER BASIN, THAILAND

机译:C波段合成孔径雷达数据和数字高程模型在洪灾患病状况评估中的应用:泰国河川盆地

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With the world's population growing rapidly, the global demand for rice exceeds production. A substantial amount of rice in South and Southeast Asia, where the world's leading rice-exporting countries such as Thailand are located, is damaged by monsoon flooding annually. In order to reduce the loss of rice, an accurate assessment of flood-affected paddies is essential. Taking the 2001 flooding that hit the Chi River Basin, Thailand as an example; the objective is to develop a method for accurate assessment of flood damages to rice paddies. A RADARSAT-1 image acquired during the flood was combined with a 30-m digital elevation model to estimate the floodwater depths. The water depth of 80 cm, which is based on the elongation capability of rice varieties, was used to separate non-damaged from damaged paddies. We evaluated the effectiveness of our method by comparing with the previous method, which utilized multi-temporal (3) RADARSAT-1 scenes of pre-, peak and post-flooding for the supervised classification.Our new method for assessing flood-affected paddies achieved the overall classification accuracy of 87% with a Kappa index of agreement of 0.73, while with the supervised classification method the overall accuracy was 77% with a Kappa index of 0.53. A test of significance for the Kappa indexes shows that our method was more accurate than the previous method, indicating that a single-date SAR scene of the peak flooding can be effectively used for assessing flood damages when it is integrated with a good DEM.
机译:随着世界人口的迅速增长,全球对大米的需求超过了产量。世界最大的稻米出口国如泰国所在的南亚和东南亚,每年有大量稻米受到季风洪水的破坏。为了减少稻米的损失,对受洪灾影响的稻米进行准确评估是必不可少的。以2001年袭击泰国池河流域的洪水为例;目的是开发一种准确评估洪水对稻田损害的方法。洪水期间获取的RADARSAT-1图像与30米数字高程模型相结合,以估算洪水深度。基于水稻品种的伸长能力,水深为80 cm,用于将未损坏的稻田与受损的稻田分开。通过与之前的方法进行比较,我们评估了该方法的有效性,该方法利用了洪水前,洪峰和洪灾后的多时相(3)RADARSAT-1场景进行监督分类。 我们用新方法评估受洪灾影响的稻田,总体分类准确度达到87%,Kappa一致性指数为0.73,而监督分类方法的总体分类准确度为77%,Kappa指数为0.53。对Kappa指数进行的显着性检验表明,我们的方法比以前的方法更准确,这表明与良好的DEM集成时,峰值洪水的单日SAR场景可以有效地用于评估洪水的破坏。

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