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。重复测量这些数据可观测森林砍伐或现有森林退化、天然林更新以及农林复合等可持续农业实践的实施。高空间分辨率的树冠高度与冠层投影面积评估对监测碳通量和评估树木土地利用同样重要，因为森林结构（尤其是农林复合系统）可能具有高度空间异质性。甚高分辨率卫星影像（地面采样距离小于1米）可实现单木层级信息提取，同时支持超大规模监测。本文首次提出针对多个次国家级行政区并行生成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冠层高度图，这是对基于Sentinel/GEDI的全球冠层高度图（10米分辨率）的重大改进。该地图通过将视觉Transformer应用于Maxar影像（2017-2020年）中自监督模型提取的特征生成，并以航空激光雷达和GEDI观测数据为训练目标。我们使用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等方式评估所提地图，发现模型在预留验证区域的平均绝对误差(MAE)为3.0米。