In 2008, there were 247 million cases of malaria and nearly one million deaths – mostly among children living in Africa (WHO, 2010). Despite national and international efforts, malaria remains a major public health issue and the fight to control the disease is confronted by numerous hurdles.

Study of space and time dynamics of malaria is necessary as a basis for making appropriate decision and prioritizing intervention including in areas where field data are rare and sanitary information systems are inadequate. Malarial risk evaluation should also help to anticipate epidemics risk as a basis for early warning systems (Machault et al, 2009).

In the challenging perspective of assessing, understanding and spatially focusing malaria control activities (Carter et al., 2000), providing risk maps can be useful for decision-makers. In this context, Remote Sensing and Geographic Information Systems became, from several decades, tools to evaluate the environmental factors leading to malaria risk geographical and temporal distribution (Thomson et al., 1996; Beck et al., 2000; Rogers et al., 2002; Ceccato et al., 2005; Machault et al., 2009 [1]).

Statistical models have been used to examine the associations between weather, vectors and malaria (Randolph and Rogers, 2000; Kleinschmidt et al., 2001a; Hay et al. 2002; Rogers et al., 2002).

Spatial modelling is a good way to develop a better understanding of the interactions between environmental factors and their overall impact on malaria risk.

In-situ observations in the studied area centred over the populated city of Nouna in Burkina Faso has shown that abundance of Anopheles, vector of malaria, outside of the rainy season is very rare since number of breeding sites is very low. During the dry season, there is not a lot of breeding sites excepted in area where water is permanent. The malarial situation in this region depends on precipitations more than temperature variability, water being the limiting factor to the production of Anopheles. Thus, the presence of water in the environment, linked to different types of land cover, could be used as an indicator for malarial risk. More over, some land cover are more to suitable to favour the emergence of Anopheles and their breeding sites. So land cover type play an important role to evaluate malarial risk in those rural regions.

A high-resolution satellite image from the SPOT 5 satellite during 2008 was used to generate a land cover classification in the malaria endemic lowland of North-Western Burkina Faso, centred around the Nouna city. A supervised land cover classification was carried out based on 45 training zones obtained during the 2008 ground campaign.

The applied method was the maximum likelihood. Ten land cover classes were built and correlated to land cover types known from literature for acting as Anopheles mosquito breeding sites (Dambach et al., 2009). Since for this studied region, there are no existing studies that deal with absolute numbers of mosquito larvae per habitat per time, a relative risk classification was constructed. Four classes of relative mosquito larvae presence in environmental habitats from low to very high were incremented (see Fig.1 ).

According to known correlations of Anopheles larvae presence and surface water-related land cover, cultivated areas in the riverine vicinity of Kossi River were shown to be one of the most favourable sites for Anopheles production. Similar conditions prevail in the South of the study region, where clayey soils and higher precipitations benefit the occurrence of surface water. Besides pools, which are often directly detectable, rice fields and occasionally flooded crops represent most appropriate habitats. On the other hand, forests, elevated regions on porous soils, grasslands and the dryer, sandy soils in the north-western part turned out to deliver fewer mosquito breeding opportunities (Dambach et al., 2009).

Buffers zones of 500m radius were constructed around 30 villages. These buffer zones represent the assumed Anopheles mosquito flying range (Costantini et al., 1996; Ejercito and Urbino, 1951). The surface of each class within the radius around each village was calculated. For each village, the risk of potential presence of breeding sites and then of Anopheles has been calculated by taking into account the land cover type found in the buffer zone (Minakawa et al., 1999; Munga et al., 2005; Mutuku et al., 2009). For the 30 villages included in the local demographic surveillance system, the area of potential habitats with very high risk (submerged and irrigated rice fields, water covered with vegetation and submerged vegetation) and high risk (field crops with clayey soil and turbid water) was calculated for the 500 m buffer zone.

In this study, it has been shown and validated with ground data that high resolution satellite images can indeed identify small-scale habitats with sizes of only few metres diameter conducive to Anopheles larvae development. Potential high and low risks for malaria at the village level can be differentiated from satellite data.

It is a potentially useful approach, which could lead to more focused disease control programmes. PALUCLIM, the continuing project intends to validate those first results to a important ground survey that aims to better understanding all mechanisms at stake. First objective is to update the risk level associated to the different land cover type in presence.

Then, by combining mechanisms linking rainfall variability, presence and aggressiveness of the vectors, and hosts vulnerability, the project aims to produce risks maps for being exposed to vectors bites