  Landscapes are meaningful ecological units that strongly depend on the
environmental conditions. Such dependencies between landscapes and the
environment have been noted since the beginning of Earth sciences and cast into
conceptual models describing the interdependencies of climate, geology,
vegetation and geomorphology. Here, we ask whether landscapes, as seen from
space, can be statistically predicted from pertinent environmental conditions.
To this end we adapted a deep learning generative model in order to establish
the relationship between the environmental conditions and the view of
landscapes from the Sentinel-2 satellite. We trained a conditional generative
adversarial network to generate multispectral imagery given a set of climatic,
terrain and anthropogenic predictors. The generated imagery of the landscapes
share many characteristics with the real one. Results based on landscape patch
metrics, indicative of landscape composition and structure, show that the
proposed generative model creates landscapes that are more similar to the
targets than the baseline models while overall reflectance and vegetation cover
are predicted better. We demonstrate that for many purposes the generated
landscapes behave as real with immediate application for global change studies.
We envision the application of machine learning as a tool to forecast the
effects of climate change on the spatial features of landscapes, while we
assess its limitations and breaking points.
