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首页> 外文期刊>Natural Hazards >Water surface variations monitoring and flood hazard analysis inTI Water surface variations monitoring and flood hazard analysis in Dongting Lake area using long-term Terra/MODIS data time series
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Water surface variations monitoring and flood hazard analysis inTI Water surface variations monitoring and flood hazard analysis in Dongting Lake area using long-term Terra/MODIS data time series

机译:利用长期Terra / MODIS数据时间序列在洞庭湖地区进行水面变化监测和洪水灾害分析inTI

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摘要

Dongting Lake is the second largest freshwater lake in China, and its water surface area varied very significantly during last decade. Remote sensing technology has more advantages in macro monitoring of lake water surface area than the traditional methods. In the paper, an integrated threshold method of water body extraction based on MODIS data is given, which synthesizes several factors, including vegetation index-NDVI, spectrum characters of water body, cloud and shadow, and the SRTM digital elevation information. With this method and 356 scenes MODIS 8-Day composite (MOD09Q1) image, water surface area of Dongting Lake was dynamically monitored from 2000 to 2009. The result shows that during 1 year, the water area variation in Dongting Lake area had a typical seasonal (monsoon) behavior, and during last decade, the water area decreased gradually and obviously. Based on variation monitoring, yearly max-submersion time index has been suggested to analyze flood hazard in study area. With the support of ArcGIS software, authors estimated the yearly submersion time of the Dongting Lake for each year separately and average submersion time from 2000 to 2009. The result shows 67.46% of study area is being with high flood hazard.
机译:洞庭湖是中国第二大淡水湖,在过去十年中,其水表面积变化很大。与传统方法相比,遥感技术在宏观监测湖泊水表面积方面具有更多优势。提出了一种基于MODIS数据的水体综合阈值提取方法,综合了植被指数-NDVI,水体光谱特征,云影阴影,SRTM数字高程信息等因素。利用该方法和356个场景的MODIS 8天合成(MOD09Q1)图像,动态监测了洞庭湖2000年至2009年的水表面积。结果表明,在1年中,洞庭湖地区的水域变化具有典型的季节性。 (季风)行为,并且在最近十年中,水域面积逐渐减少且明显减少。基于变化监测,提出了年度最大淹没时间指标来分析研究区的洪灾危害。在ArcGIS软件的支持下,作者分别估算了洞庭湖每年的年度淹没时间和2000年至2009年的平均淹没时间。结果表明,研究区的67.46%处于高洪灾风险之中。

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