Jpn. J. Infect. Dis., 55, 160-166, 2002

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Original Article

Analysis of Malaria Endemic Areas on the Indochina Peninsula Using Remote Sensing

Naoko Nihei*, Yoshihiko Hashida¹, Mutsuo Kobayashi and Akira Ishii²

Department of Medical Entomology, National Institute of Infectious Diseases, Toyama 1-23-1, Shinjuku-ku, Tokyo 162-8640, ¹College of Science and Technology, Nihon University, Sakurajosui 3-25-40, Setagaya-ku, Tokyo 156-8550 and ²Department of Medical Zoology, Jichi Medical School, Yakushiji 3311-1, Minami-kawachi, Tochigi 329-0498, Japan

(Received August 14, 2002. Accepted October 24, 2002)

SUMMARY: 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.

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