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首页> 外文期刊>International journal of applied earth observation and geoinformation >Mapping the distribution of the main host for plague in a complex landscape in kazakhstan: An object-based approach using SPOT-5 XS, landsat 7 ETM+, SRTM and multiple random forests
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Mapping the distribution of the main host for plague in a complex landscape in kazakhstan: An object-based approach using SPOT-5 XS, landsat 7 ETM+, SRTM and multiple random forests

机译:哈萨克斯坦复杂景观中鼠疫的主要宿主分布图:使用SPOT-5 XS,landsat 7 ETM +,SRTM和多个随机森林的基于对象的方法

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Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil,the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery.In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape. The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eightlandscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derivedstandard deviation in elevation. In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit. Burrows were successfully classified in all landscape units. In the 'steppe on floodplain' areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the 'floodplain' areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively. In this study, an innovative stratification method using high- and medium resolution imagery was applied inorder to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.
机译:鼠疫是哈萨克斯坦大量沙鼠种群中的一种人畜共患传染病。传染病动态受疾病携带者(宿主)空间分布的影响。沙鼠是我们研究区域的主要寄主,它生活在洞穴中,可以在高分辨率卫星图像上对其进行识别。在这项研究中,我们使用各种空间尺度的地球观测数据,绘制了一个洞穴的空间分布图。沙漠景观。研究区域包括各种景观类型。为了评估在这些景观类型中是否可能通过分类识别洞穴,根据Landsat 7 ETM +得出的derived穗绿度和亮度,以及SRTM得出的高程标准差,将研究区域分为八个景观单元。在野外,共绘制了904个洞穴。使用两个分段的2.5 m分辨率SPOT-5 XS卫星场景,创建了参考对象集。随机森林是为两个SPOT场景构建的,用于对图像进行分类。此外,通过为每个景观单元构建单独的随机森林进行了分层分类。在所有景观单元中,洞穴均已成功分类。在“洪泛草原”地区,分类效果最好:在这些地区,生产者和用户的准确性分别达到88%和100%。在植被更不均匀的“洪泛区”地区,分类效果最差。在那里,准确度分别为86%和58%。分层分类改善了所有可以比较的景观单位的结果(四个),使kappa系数分别增加了13%,10%,9%和1%。在这项研究中,使用了高分辨率和中分辨率图像的创新分层方法,以便在较大的空间尺度上绘制主机分布。我们开发的洞穴图将有助于检测沙鼠种群的分布变化,此外,它还可以作为唯一的经验数据集,可以用作流行病鼠疫模型的输入。这是了解鼠疫动态的重要一步。

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