  Vegetation structure mapping is critical for understanding the global carbon
cycle and monitoring nature-based approaches to climate adaptation and
mitigation. Repeated measurements of these data allow for the observation of
deforestation or degradation of existing forests, natural forest regeneration,
and the implementation of sustainable agricultural practices like agroforestry.
Assessments of tree canopy height and crown projected area at a high spatial
resolution are also important for monitoring carbon fluxes and assessing
tree-based land uses, since forest structures can be highly spatially
heterogeneous, especially in agroforestry systems. Very high resolution
satellite imagery (less than one meter (1m) Ground Sample Distance) makes it
possible to extract information at the tree level while allowing monitoring at
a very large scale. This paper presents the first high-resolution canopy height
map concurrently produced for multiple sub-national jurisdictions.
Specifically, we produce very high resolution canopy height maps for the states
of California and Sao Paulo, a significant improvement in resolution over the
ten meter (10m) resolution of previous Sentinel / GEDI based worldwide maps of
canopy height. The maps are generated by the extraction of features from a
self-supervised model trained on Maxar imagery from 2017 to 2020, and the
training of a dense prediction decoder against aerial lidar maps. We also
introduce a post-processing step using a convolutional network trained on GEDI
observations. We evaluate the proposed maps with set-aside validation lidar
data as well as by comparing with other remotely sensed maps and
field-collected data, and find our model produces an average Mean Absolute
Error (MAE) of 2.8 meters and Mean Error (ME) of 0.6 meters.
