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Monitoring and managing Earth's forests in an informed manner is an importantrequirement for addressing challenges like biodiversity loss and climatechange. While traditional in situ or aerial campaigns for forest assessmentsprovide accurate data for analysis at regional level, scaling them to entirecountries and beyond with high temporal resolution is hardly possible. In thiswork, we propose a method based on deep ensembles that densely estimates foreststructure variables at country-scale with 10-meter resolution, using freelyavailable satellite imagery as input. Our method jointly transforms Sentinel-2optical images and Sentinel-1 synthetic-aperture radar images into maps of fivedifferent forest structure variables: 95th height percentile, mean height,density, Gini coefficient, and fractional cover. We train and test our model onreference data from 41 airborne laser scanning missions across Norway anddemonstrate that it is able to generalize to unseen test regions, achievingnormalized mean absolute errors between 11% and 15%, depending on the variable.Our work is also the first to propose a variant of so-called Bayesian deeplearning to densely predict multiple forest structure variables withwell-calibrated uncertainty estimates from satellite imagery. The uncertaintyinformation increases the trustworthiness of the model and its suitability fordownstream tasks that require reliable confidence estimates as a basis fordecision making. We present an extensive set of experiments to validate theaccuracy of the predicted maps as well as the quality of the predicteduncertainties. To demonstrate scalability, we provide Norway-wide maps for thefive forest structure variables.

Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles