用深度学习融合多源遥感数据，提升北方森林冠层高度估算精度与不确定性分析。
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
- 融合季节性光学、SAR和有限GEDI激光雷达数据，构建深度学习回归模型。
- 在安大略省测试中达R² 0.72，RMSE 3.43米，比单季数据误差低0.45米。
- 生成冠层高度与不确定性图，适合林业碳汇与生态监测应用。
准确估算森林冠层高度对评估地上生物量和碳储量动态至关重要，支持木材供给、气候变化缓解和生物多样性保护等生态系统监测服务。然而，尽管星载激光雷达技术不断进步，由于轨道和采样限制，高纬度地区数据仍十分有限。本研究提出一种深度学习回归方法，整合哨兵1号、陆地卫星和ALOS-PALSAR-2的多源多季节卫星数据，并以空间激光雷达GEDI为参考，生成高分辨率、空间连续的冠层高度及不确定性估计。模型在加拿大安大略省进行测试，并通过机载激光雷达验证，表现良好。最佳结果结合了季节性哨兵1号与陆地卫星特征及PALSAR数据，达到R² 0.72，RMSE 3.43米，偏差2.44米。相比仅使用夏季数据，采用季节数据使变异性提升10%，误差降低0.45米，偏差减少1米。深度学习权重策略显著降低了高冠层高度的误差，但对低冠层高度存在过估计。不确定性图显示，林缘区域不确定性更高，因GEDI易受误差影响，而SAR数据可能存在后向散射问题如斜距压缩、叠掩和阴影。该研究提升了缺乏星载激光雷达覆盖区域的冠层高度估算能力，为林业管理、环境监测和碳储量评估提供重要工具。
原文摘要 · Abstract (English)
Accurate forest canopy height estimation is essential for evaluating aboveground biomass and carbon stock dynamics, supporting ecosystem monitoring services like timber provisioning, climate change mitigation, and biodiversity conservation. However, despite advancements in spaceborne LiDAR technology, data for northern high latitudes remain limited due to orbital and sampling constraints. This study introduces a methodology for generating spatially continuous, high-resolution canopy height and uncertainty estimates using Deep Learning Regression models. We integrate multi-source, multi-seasonal satellite data from Sentinel-1, Landsat, and ALOS-PALSAR-2, with spaceborne GEDI LiDAR as reference data. Our approach was tested in Ontario, Canada, and validated with airborne LiDAR, demonstrating strong performance. The best results were achieved by incorporating seasonal Sentinel-1 and Landsat features alongside PALSAR data, yielding an R-square of 0.72, RMSE of 3.43 m, and bias of 2.44 m. Using seasonal data instead of summer-only data improved variability by 10%, reduced error by 0.45 m, and decreased bias by 1 m. The deep learning model's weighting strategy notably reduced errors in tall canopy height estimates compared to a recent global model, though it overestimated lower canopy heights. Uncertainty maps highlighted greater uncertainty near forest edges, where GEDI measurements are prone to errors and SAR data may encounter backscatter issues like foreshortening, layover, and shadow. This study enhances canopy height estimation techniques in areas lacking spaceborne LiDAR coverage, providing essential tools for forestry, environmental monitoring, and carbon stock estimation.
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