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Evaluation and Mapping of Rice Flood Damage Using Domestic Remotely Sensed Data in China

机译:中国国内远程传感数据的稻米洪水损伤评价与绘图

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It has great significance to study quick monitoring of rice flood disaster and applying timely remedial measures in the disaster area. The purpose of this research paper was to evaluate the rice flood damage, which happened on July to August 2009 in Shouxian, Huoqiu, Bengbu and Huainan are mainly rice producing areas in Anhui Province in China, using two images from domestic remotely sensing data from China that named Huan Jing satellite (HJ-CCD images). One was pre-flood disaster and the other was post-flood disaster. According to the change characteristics of NDVI (Normalized Difference Vegetation Index) of forty field-sampling points of post-flood in study area, the flood damage degrees (light, moderate and serious) were been classified also. The verification of the classification accuracy calculated by confusion matrix that based on 40 field-sampling verification points. Accuracy results showed that Kappa coefficient (k) was 0.6907 and the overall accuracy was 80.0%. At the same time, extract nine vegetation indices calculated from HJ-CCDs data were to build the model to inverted LAI of rice, and analyze the growth of rice after flood disaster stress. Hence, this study descriptive that multi-temporal and multispectral imagery domestic remotely sensing data from China (HJ-1 CCD images) are sufficient to assess rice flood disaster areas, specify relative damage degrees and growth analysis after flood stress.
机译:研究快速监测水稻洪水灾害并在灾区施加及时补救措施具有重要意义。本研究论文的目的是评估稻洪损伤,这些洪水损坏于2009年7月在寿县,霍丘,蚌埠和淮南主要是中国安徽省的稻米生产区,采用来自中国的国内远程传感数据的两种形象那个名为Huan Jing卫星(HJ-CCD图像)。一个是预洪水灾难,另一个是洪水后灾害。根据研究区后洪水洪水的第四十几个场采样点的NDVI(归一化差异植被指数)的变化特征,洪水损伤程度(轻盈,中等和严重)也被分类。基于40个现场采样验证点的混淆矩阵计算的分类精度验证。准确度结果表明,Kappa系数(K)为0.6907,总体精度为80.0%。与此同时,从HJ-CCD的数据计算提取9植被指数是对模型建立水稻倒LAI,并分析大米的洪涝灾害胁迫后的生长。因此,本研究描述了国内远程传感数据的多时间和多光谱图像来自中国(HJ-1 CCD图像)的数据足以评估水稻洪水灾区,在洪水应力后指定相对损伤程度和生长分析。

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