Country-wide high-resolution vegetation height mapping with Sentinel-2
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Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon, respectively Switzerland. A deep convolutional network was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7m in Switzerland, respectively 4.3m in Gabon, and correctly reproduce vegetation heights up to >50m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data, dense vegetation height maps with 10m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.
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