Snail density prediction for schistosomiasis control using ikonos and ASTER images
Snail density prediction for schistosomiasis control using ikonos and ASTER images
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DOI：
10.14358/pers.70.11.1285
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发表时间：
2004-11-01
影响因子：
1.3
通讯作者：
Spear, R
中科院分区：
文献类型：
--
作者：
Xu, B;Gong, P;Spear, R
血吸虫病是热带和亚热带地区流行的一种水传播寄生虫病。它的传播依赖于作为寄生虫中间宿主的蜗牛的存在。在利用遥感数据对钉螺栖息地进行分类方面已经做了一些努力，但没有对血吸虫病传播模型中的一个重要参数--钉螺丰度进行估计。本研究结合实地调查和不同空间分辨率的卫星影像，对钉螺密度进行了预测。西昌市附近的山区环境。位于中国西南部四川省的一个实验地点被选定。从4分辨率的Ikonos数据中提取的土地覆盖和土地利用信息以及从高级星载热发射和反射辐射计数据中提取的高程数据被用作扩大空间范围的参考。因此，我们估计土地覆盖和土地利用分数数据在30分辨率水平的分类结果的基础上从Ikonos数据。蜗牛丰度为每30英寸的分辨率网格，然后预测回归实地调查数据与土地覆盖和土地利用分数。随后，以200多个居民区为单位，绘制了蜗牛密度图。19个组的平均钉螺密度预测值与调查值之间的相关系数为0.87。有了这样一个模型，我们能够推断分散的蜗牛丰度调查在有限的网站到整个地区。钉螺分布的空间自相关性被认为是预测钉螺密度的可能因素之一，并为进一步的模型校正进行了检验。
Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission depends upon the presence of snails, which serve as intermediate hosts for the parasite. Some efforts have been made to classify snail habitats with remotely sensed data, but not to estimate snail abundance that is an important parameter in schistosomiasis transmission modeling. In this research, snail density was predicted by integrating the field survey and satellite images of different spatial resolution. A mountainous environment near Xichang city. in southwest Sichuan province, China, was chosen as the test site. Land-cover and land-use information extracted from 4 in resolution Ikonos data and elevation data derived from ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) data were used as reference for scaling up to greater spatial extents. Therefore, we estimated land-cover and land-use fraction data at the 30 in resolution level based on classification results from the Ikonos data. Snail abundance for each 30 in resolution grid was then predicted by regressing field Survey data with land-cover and land-use fractions. Subsequently, a snail density map was generated using the territory of each of the over 200 residential groups as a mapping unit. An R-2 of 0.87 was obtained between the average snail density predicted and that surveyed for 19 groups. With such a model, we were able to extrapolate scattered snail abundance surveyed at a limited number of sites to the entire area. Spatial autocorrelation of snail distribution was considered as one of the possible factors in predicting snail density and tested for further model calibration.