用卫星数据生成2019-2022年欧洲树高动态图，精度达10米。
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
- 融合哨兵1号、2号时序数据与GEDI激光雷达真值训练模型
- 首次产出10米分辨率的欧洲树高时间序列地图（2019-2022）
- 适合森林监测、碳储量估算及生态变化研究者使用
随着全球温室气体排放上升，精准的大规模树冠高度图对理解森林结构、估算地上生物量和监测生态扰动至关重要。为此，我们提出一种新方法，利用哨兵1号合成数据与哨兵2号时序卫星数据，实现多年份大范围高分辨率树冠高度预测。以GEDI激光雷达数据作为训练真值，首次生成2019-2022年欧洲大陆10米分辨率的时序树冠高度图。作为该成果的一部分，我们还提供2020年更精细的树冠高度图，精度优于以往研究。该流程与生成的高度图均公开可用，可支持大规模森林监测，促进未来生态研究与分析。
原文摘要 · Abstract (English)
With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel-1 composite and Sentinel~2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10m resolution temporal canopy height map of the European continent for the period 2019-2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses.
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