Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing.
Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing.
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中南半岛疟疾流行区遥感分析.
DOI：
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发表时间：
2002
期刊：
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通讯作者：
A. Ishii
中科院分区：
文献类型：
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作者：
N. Nihei;Y. Hashida;Mutsuo Kobayashi;A. Ishii
我们应用遥感技术，利用能够跨越国界获得大范围数据的卫星图像，作为一种快速、准确和安全地增加我们对疟疾潜在分布的了解的方法。我们的目标地区是位于印度支那半岛的所谓湄公河疟疾地区。作为疟疾指数，我们使用了1997年和1998年报告的疟疾病例总数、疟疾死亡率、间日疟和恶性疟发病率等的现有分布图。我们制作的归一化差异植被指数（NDVI）的月分布图的值为0.2+，0.3+，0.35+，和0.4+使用地理信息系统/遥感软件的基础上，1997年东亚月NDVI地图。这些地图与各种疟疾指数分布图重叠，并进行交叉制表。结果得到的NDVI值为0.3+和0.4+的地图与恶性疟疾分布很好地匹配，我们特别意识到，恶性疟疾在NDVI值为0.4+持续6个月或更长时间的地区很流行，而在NDVI值为0.4+的地区，病例较少未来有必要利用高分辨率的遥感图像，研究植被指数与各种病媒蚊栖息地之间的关系。该项目还将利用高分辨率卫星图像，并通过归一化差异植被指数对疟疾流行地区进行详细预报。
We applied remote sensing using satellite images capable of obtaining data over a broad range, transcending national borders, as a method of rapidly, precisely, and safely increasing our understanding of the potential distribution of malaria. Our target region was the so-called Mekong malaria region on the Indochina Peninsula. As a malaria index, we used existing distribution maps of total reported malaria cases, malaria mortality, vivax malaria and falciparum malaria incidences, and so forth for 1997 and 1998. We produced monthly distribution maps of a normalized difference vegetation index (NDVI) with values of 0.2+, 0.3+, 0.35+, and 0.4+ using the geographical information system/remote sensing software based on the East Asia monthly NDVI maps of 1997. These maps were overlaid with various malaria index distribution maps, and cross-tabulations were carried out. The resulting maps with NDVI values of 0.3+ and 0.4+ matched the falciparum malaria distribution well, and we realized, in particular, that falciparum malaria is prevalent in regions in which NDVI values of 0.4+ continue for 6 months or more, while cases are fewer in regions with NDVI values of 0.4+ that continue for 5 months or less. It will be necessary in the future to examine the relationship between NDVI values and the habitats of the various vector mosquitoes using high-resolution satellite images and to implement detailed forecasts for malaria endemic areas by means of NDVI.