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Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height.
Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data
We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics.
