[U-03] Remote Sensing Role in Sustainable Development

[U03-04] Remote Sensing Monitoring of Mosquito-Borne Diseases: Malaria in the China-Myanmar Border Region

*Zhaoyan Liu¹ (1.Aerospace Information Research Institute,Chinese Academy of Sciences)

キーワード：Remote Sending, Malaria, mosquito-borne diseases

Malaria is one of the most common mosquito-borne diseases and a significant public health challenge worldwide. Historically, most regions in China were at risk of malaria. For instance, in the China-Myanmar border area, particularly in Tengchong County, Yunnan Province, malaria has been difficult to control due to the high incidence of imported cases. Mosquitoes act as vectors, transmitting the disease from one person to another. Therefore, this study aims to develop methods for long-term dynamic monitoring of mosquito populations and their distribution, providing early warnings to prevent imported malaria cases from triggering outbreaks.Remote sensing technology offers the advantage of rapidly acquiring up-to-date and continuous information over large geographical areas, especially in inaccessible regions. Mosquito survival is influenced by various environmental factors. Thus, remote sensing can be utilized to characterize and monitor these factors related to mosquito breeding and reproduction, serving as a powerful tool for mosquito surveillance. Given the complex relationship between mosquitoes and environmental factors, this study integrates fuzzy information theory with multi-source remote sensing data and products, quantitative remote sensing to analyze the interactions between mosquitoes and their environment. Consequently, a T-S fuzzy remote sensing monitoring and prediction model for malaria risk was established using malaria case data and the associated environmental data. The quantitative relationship between malaria and remote sensing environmental parameters is consistent with the biological characteristics of malaria. Comparative analysis has been performed to validate the predicted results against the survey data. The average accuracy of the predicted malaria incident locations is 74%. The 12-month dynamic monitoring results from Tengchong demonstrate the strong application potential of the T-S Fuzzy Remote Sensing monitoring model in predicting and providing early warnings for mosquito-borne diseases.