Vegetation structure mapping is critical for understanding the global carbon
cycle and monitoring nature-based approaches to climate adaptation and
mitigation. Repeat 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 canopy height maps for the states of California and
S\~{a}o Paolo, at sub-meter resolution, a significant improvement over the ten
meter (10m) resolution of previous Sentinel / GEDI based worldwide maps of
canopy height. The maps are generated by applying a vision transformer to
features extracted from a self-supervised model in Maxar imagery from 2017 to
2020, and are trained against aerial lidar and 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) within set-aside validation areas
of 3.0 meters.

本研究采用高分辨率卫星影像，利用视觉变换技术和自我监督模型，同时生成了加利福尼亚和圣保罗地区的林冠高度图，可以有效观测森林生长和可持续农林业管理，是全球碳循环和应对气候变化的重要研究领域之一。