Malaria remains a major health problem in French Guiana despite the decrease in the number of cases since 2010. A previous study in Camopi, an Amerindian village on the Oyapock River, revealed that Plasmodium falciparum malaria incidence was significantly related to landscape features and that its spatial distribution exhibited some obvious patterns. In the present study, we first identified the spatial patterns that exhibit a significant Moran's index of spatial auto-correlation and that significantly explain P. falciparum malaria incidence within the multiple linear regression framework. Secondly, we linked these patterns with remotely sensed environmental features. The selected model is composed of five spatial components: three patterns, representing 50.7% of the total variance, are associated with large scale variations of the incidence, and the remaining patterns are associated with local scale variations and represent 28.7% of the total variance. Different hamlet clusters that have specificities in terms of P. falciparum malaria incidence rate, environmental characteristics and mosquito control strategies have been identified. The methodology proposed in the present study can provide useful knowledge on the spatial distribution of the P. falciparum malaria incidence in Camopi at different scales and on possible explanation of such distribution, as a function of the scale.