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.

翻译：植被结构制图对于理解全球碳循环以及监测基于自然的气候适应与减缓方法至关重要。对这些数据进行重复测量，可以观测到森林砍伐或现有森林退化、自然森林再生，以及农林复合等可持续农业实践的实施情况。高空间分辨率的树冠高度和冠层投影面积评估对于监测碳通量和评估基于树木的土地利用同样重要，因为森林结构可能具有高度的空间异质性，尤其是在农林复合系统中。超高分辨率卫星影像（地面采样距离小于1米）使得在单木尺度上提取信息成为可能，同时能够进行大规模监测。本文首次提出了同时为多个次国家级辖区生成的高分辨率树冠高度图。具体而言，我们为加利福尼亚州和圣保罗州生成了超高分辨率树冠高度图，相比此前基于Sentinel/GEDI的全球树冠高度图（10米分辨率），分辨率得到了显著提升。这些地图通过从2017年至2020年Maxar影像上训练的自监督模型中提取特征，并针对航空激光雷达地图训练密集预测解码器生成。我们还引入了一个后处理步骤，使用基于GEDI观测数据训练的卷积网络。我们通过保留的验证激光雷达数据以及其他遥感地图和实地采集数据对提出的地图进行评估，发现我们的模型平均绝对误差（MAE）为2.8米，平均误差（ME）为0.6米。