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遥感图像在山区钉螺孳生地监测中的应用
引用本文:何明祯,彭文祥,周艺彪,依火伍力,刘刚明,姜庆五.遥感图像在山区钉螺孳生地监测中的应用[J].复旦学报(医学版),2010,37(5):510-513.
作者姓名:何明祯  彭文祥  周艺彪  依火伍力  刘刚明  姜庆五
作者单位:复旦大学公共卫生学院流行病学教研室-教育部国家公共安全重点实验室 上海200032;四川省普格县疾病预防控制中心 普格615300
基金项目:国家自然科学基金重大项目,国家科技重大专项项目 
摘    要: 目的 在山丘型血吸虫病流行区利用陆地卫星(Landsat)专题制图仪(thematic mapper,TM)遥感图像探测钉螺孳生地。方法 收集四川省普格县地形图、TM遥感图像,现场调查钉螺孳生地,选择已知地物类型的区域建立训练样本,对TM卫星图像进行监督分类,并验证分类结果。结果 所有地物被分成钉螺孳生地、河流、居住区、阴影区和其他地物等5类,钉螺孳生地主要分布在田地、坡地等有植被覆盖的环境。钉螺孳生地分类的准确性和可靠性分别为79.82%和85.58%,分类总精度达到80.22%。结论 TM遥感图像的监督分类能将山区钉螺孳生地有效地区分出来,有利于对钉螺孳生地进行监测,为山区钉螺的控制及血吸虫病的防治提供依据。

关 键 词:遥感  钉螺孳生地  监督分类  山区

Application of Landsat TM images on the snail habitats monitoring in mountainous regions
HE Ming-zhen,PENG Wen-xiang,ZHOU Yi-biao,YIHUO Wu-li,LIU Gang-ming,JIANG Qing-wu.Application of Landsat TM images on the snail habitats monitoring in mountainous regions[J].Fudan University Journal of Medical Sciences,2010,37(5):510-513.
Authors:HE Ming-zhen  PENG Wen-xiang  ZHOU Yi-biao  YIHUO Wu-li  LIU Gang-ming  JIANG Qing-wu
Institution:Key Laboratory on Public Health Safety, Ministry of Education-Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China; Center for Disease Control and Prevention of Puge County, Puge 615300, Sichuan Province, China
Abstract:Objective To monitor snail habitats in mountainous regions using thematic mapper (TM) remote sensing images of Landsat. Methods The topographic map and TM images of Puge County, Sichuan province of China were collected. Then the snail habitats were surveyed in Puge County. Training samples were built by selecting the known ground objects. Supervised classification analysis was applied to classify the TM images, and the classification results were verified via creating a confusion matrix. Results The TM images were classified into snail habitats, rivers, residential districts, shadow and other ground objects. Snail habitats were mainly located in vegetation-covered environments, such as croplands and grassed hillsides. The classification accuracy and reliability of snail habitats were 79.82% and 85.58% respectively, and the overall classification accuracy reached 80.22%. Conclusions Snail habitats can be identified by supervised classification analysis of TM remote sensing images, which is helpful for the surveillance of snail habitats and for the prevention of schistosomiasis in mountainous regions.
Keywords:remote sensing  snail habitats  supervised classification  mountainous regions
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