用多源卫星数据生成10米分辨率的全球动态树高图，追踪森林生长与灾害变化。
ECHOSAT: Estimating Canopy Height Over Space And Time
- 基于视觉变换器模型，融合多源卫星数据实现像素级时序回归。
- 在单年预测上优于现有方法，首次实现全球尺度树高动态量化。
- 适合碳汇监测、森林灾害评估等研究者使用。
森林监测对减缓气候变化至关重要。然而，现有全球树高地图仅提供静态快照，无法捕捉时间维度上的森林动态，而这对于精准碳核算至关重要。本文提出ECHOSAT，一个覆盖多年、分辨率达10米的全球时空一致树高地图。通过融合多传感器卫星数据，训练专用视觉变压器模型，实现像素级时序回归。引入自监督生长损失，使预测结果符合自然树木发育规律——包括随时间渐进增高，也包含火灾等扰动事件导致的突然下降。实验表明，该模型在单年预测任务上显著优于当前最佳方法。我们首次提供了可精确量化全球范围内树高增长与干扰事件的动态地图。相关成果有望推动全球碳监测与扰动评估工作。地图数据可于https://github.com/ai4forest/echosat获取。
原文摘要 · Abstract (English)
Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression. A self-supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions. We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.
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