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.

翻译：植被结构制图对于理解全球碳循环、监测基于自然的气候适应与减缓方案至关重要。重复测量这类数据可观测森林砍伐、退化、自然再生以及农林业等可持续农业实践的实施情况。高空间分辨率的树冠高度与冠层投影面积评估对于监测碳通量及评估基于树木的土地利用同样重要，因为森林结构（尤其在农林复合系统中）存在高度空间异质性。超高分辨率卫星影像（地面采样距离小于1米）使得在树级提取信息的同时实现大规模监测成为可能。本文首次提出了同时覆盖多个次国家级行政区域的高分辨率冠层高度图。具体而言，我们为加利福尼亚州和圣保罗州生成了亚米级分辨率的冠层高度图，相较此前基于Sentinel/GEDI的全球冠层高度图（10米分辨率）实现了显著提升。该地图通过将视觉Transformer应用于2017至2020年Maxar影像的自监督模型提取特征，并利用航空激光雷达和GEDI观测数据进行训练生成。我们通过预留验证激光雷达数据、与其他遥感地图及实地采集数据对比进行评估，发现模型在预留验证区域的平均绝对误差（MAE）为3.0米。