Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and spatial accuracy of a SDM depend very strongly on the type of model used and the environmental data used as explanatory variables. In this paper, we study a country-wide species distribution model based on very high resolution (1m) remote sensing images processed by a convolutional neural network. We demonstrate that this model can capture landscape and habitat information at very fine spatial scales, while providing overall better predictive performance than conventional models. Moreover, to demonstrate the ecological significance of the model, we propose an original analysis based on the t-SNE dimension reduction technique. It allows visualising the relation between input data and species traits or environment learned by the model as well as conducting some statistical tests verifying them. We also analyse the spatial mapping of the t-SNE dimensions at both national and local levels, showing the model benefit to automatically learn environmental variation at multiple scales