Abstract
Space Technology, Geographic Information Systems (GIS) and Remote Sensing (RS) have already been widely used in environmental sectors such as the monitoring and the management of natural resources, agriculture, rural and urban planning. Further, this technology can be used to evaluate and model the relationships between environmental factors/indicators and the incidences of viral diseases. Remote sensing by its ability to uncover in a localized way the types of environment: nature, state and spatial organization; and GIS by the links created between spatial data and their related descriptive information (Non-spatial data): socio-economic, cultural and medical. In 1985, the National Aeronautics and Space Administration (NASA) initiated the Biospheric Monitoring and Disease Prediction Project, the aim of which was to determine if remotely sensed data could be used to identify and monitor environmental factors that influence malaria vector populations (Wood et al., 1991). In this paper, we propose a method to extract the maximum amount of information from any given remotely sensed data, to get insights in the relationships between the environment and incidences of viral diseases. The aim being to demonstrate that RS, GIS, RDBMS and Statistical Analyses provide significant contributions to the spatial definition and prediction of vector-borne diseases.

Introduction
Vector-borne diseases have been the most important worldwide health problem for many years and still represent a constant and serious risk to a large part of the world’s population.

Recently, GIS and RS started to be used to evaluate and model the relationships between environmental factors/indicators and the incidences of viral diseases.

GIS is particularly well suited for Epidemiologists in the study of associations between location, environment, and disease (Gesler, 1986). It has been used, for instance, in the surveillance and monitoring of Vector-borne disease (Glass et al., 1995; Beck et al., 1994).

Remotely sensed data have been used in many vector disease studies (Beck et al., 1994; Ahearn et al., 1996). Remote sensing and GIS were used to identify villages at high risk for malaria transmission in the southern area of Chiapas, Mexico (Beck et al., 1994). In Kwara State, Nigeria, a temporal analysis of Landsat Thematic Mapper(TM) Satellite data was used to test the significance of the guinea worm eradication program based on changes in agricultural production (Ahearn et al., 1996).

This paper is part of a current research program on the vector born disease Dengue in Thailand. The data used are Thematic Mapper of 10 th March 1998, in the province of Nakhon Pathom in the Central Plain of the country.

One limiting factor in the use of remote sensing, is that the nomenclatures usually and routinely used are not meeting the requirements of epidemiological and medical researches. They appear to be lacking of resolution and precision, in regards to the levels of details: spatial and taxonomical scales. This is caused by the current state of the art in the problematic linking the vector born diseases to the environment, indeed what nomenclatures, what spatial categories should be used? No actual a priori precise definitions exist as it is still research. Our approach is to solve this lack by the introduction of remote sensing in the very discovery of detailed spatial entities that serve as support of the epidemiological and medical researches in relation to the environment. Remote sensing creates and proposes the different spatial categories existing at the date and location of the studied area. These categories can be used intrinsically, but ultimately have to be labeled environmentally, medico-epidemiologically and/or socio-economically, in a qualitative and/or quantitative way.