Hybrid application of AVHRR based satellite remote sensing and ENSO signals for early warning and monitoring of Malaria in Asia and South America

A better understanding of the relationship among satellite-observed vegetation health, climatic anomalies, ENSO events and malaria epidemics could help mitigate the worldwide increase in incidence of mosquito-transmitted diseases.;This thesis investigates association between Vegetation Health (condition) Index and malaria transmission over the last 15-years in eight regions of different ecosystems within four different nations in South Asia and South America. The Vegetation Health (condition) Index, derived from a combination of Advanced Very High Resolution Radiometer (AVHRR) based Normalized Difference Vegetation Index and 10-mum to 11-mum thermal radiances, was designed for monitoring moisture and thermal impacts on vegetation health.;This study attempts to identify the potential factors for malaria transmission for different climatic conditions. We demonstrate that thermal condition is more sensitive to malaria transmission with different seasonal malaria activities. The weekly VH indices were correlated with the epidemiological data. A good correlation was found between malaria cases and TCI and VCI one to two months earlier than the malaria transmission season. Two different malaria transmission seasons in some area have been also detected because of two major vectors of different seasonal behavior. Following the results of correlation analysis principal component regression (PCR) method along with leave one out cross validation was used to construct a model to predict malaria as a function of the TCI and VCI.;Furthermore, I investigate 15-year association between monthly sea surface temperature (SST) anomalies in the tropical Pacific and Southern Oscillation Index (SOI) with Vegetation Health Indices for the some study regions. I also incorporate SST, SOI and Vegetation Health data into statistical model for long term forecasting malaria transmission. The overall results show that remote sensing is a valuable tool for anticipating malaria severity well in advance so that preventive measures can be taken.