Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level. However, ALS data has limited spatial coverage, while SLS data has relatively lower estimation accuracy. To this end, we proposed a two-step FCH mapping framework by combining ALS, SLS and auxiliary data. Firstly, using the ALS-derived FCH as reference, the SLS-derived relative height metrics were calibrated at the footprint-level using a regression method. Secondly, to further address the spatial discontinuities in SLS-derived FCH maps, a site-level FCH model was built using a weighted ensemble multi-machine learning model incorporating spatial effects (WEML_SE). The calibrated footprint-level calibration FCH model was used as a reference, and multiple remote sensing data metrics were selected and subjected to important variable selection. Specifically, a spatial adjacency matrix was established based on the spatial locations of SLS footprints, and spatial feature vectors were extracted. The result indicated that the correlation coefficient between the SLS-derived FCH and the ALS-derived FCH (r = 0.39–0.73, MRE=10.6–25.9 %, and RMSE=2.58–9.37 m) improved at footprint-level (r = 0.71–0.84, MRE=7.7–18.7 %, RMSE=1.96–7.68 m). Moreover, the WEML_SE exhibited better performance (r = 0.59–0.75, MRE=8.8–14.8 %, RMSE=2.12–5.4 m) compared to the model without incorporating spatial effects (r = 0.45–0.71, MRE=9.4–15.8 %, RMSE=2.28–5.89 m). This study emphasizes the advantages of integrating spaceborne and airborne LiDAR data to construct footprint-level estimation of FCH. The proposed WEML_SE model provides new possibilities for accurately generating wall-to-wall estimates of forest biomass.

中文翻译：

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使用机载、星载激光雷达和空间连续遥感数据将空间效应纳入森林冠层高度测绘


森林冠层高度（FCH）对于监测森林结构和地上生物量至关重要。光探测和测距（LiDAR）作为一种很有前途的遥感技术，为测量和绘制 FCH 提供了多种形式的数据。机载激光扫描 (ALS) 可以在绘图级别准确测量 FCH。星载激光雷达系统 (SLS) 允许在足迹级别对 FCH 进行全局采样。然而，ALS数据的空间覆盖范围有限，而SLS数据的估计精度相对较低。为此，我们结合ALS、SLS和辅助数据提出了一个两步FCH映射框架。首先，使用 ALS 导出的 FCH 作为参考，使用回归方法在足迹级别校准 SLS 导出的相对高度度量。其次，为了进一步解决 SLS 派生的 FCH 地图中的空间不连续性，使用结合空间效应的加权集成多机器学习模型 (WEML_SE) 构建了站点级 FCH 模型。以已校准的足迹级校准FCH模型为参考，选择多个遥感数据指标并进行重要变量选择。具体来说，根据SLS足迹的空间位置建立空间邻接矩阵，并提取空间特征向量。结果表明，SLS 衍生的 FCH 和 ALS 衍生的 FCH 之间的相关系数（r = 0.39–0.73，MRE=10.6–25.9 %，RMSE=2.58–9.37 m）在足迹水平上有所改善（r = 0.71 –0.84，MRE=7.7–18.7%，RMSE=1.96–7.68 m）。此外，与未考虑空间效应的模型（r = 0.45–0.71，MRE=9.4–15.8 %， RMSE=2.28–5.89 m）。 本研究强调了集成星载和机载 LiDAR 数据来构建 FCH 足迹级估计的优势。所提出的 WEML_SE 模型为准确生成森林生物量的全面估计提供了新的可能性。