Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using piñon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R² = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R² = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R² = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest that Landsat may be better-suited than GEDI for estimating AGB in woodland environments where low canopy covers and short trees limit the capacity for precisely characterizing vegetation structure within large-footprint, waveform lidar data. The footprint size analysis revealed that larger simulated footprints (e.g., 30 m radius and greater) produced higher GEDI model accuracies; however, increasing footprint radii beyond 30 m does not significantly increase model accuracy. This research represents an important step forward in improving our capacity for reliably mapping woodland AGB, and provides an early test case for the application of GEDI data to woodland AGB mapping.

机载激光扫描（ALS）数据可以对地上生物量（AGB）进行精确建模和映射，但是ALS数据收集的时空范围有限，限制了进行大规模碳核算的能力。相反，虽然天基遥感仪器提供了更大的空间和时间覆盖范围，但由于较粗糙的空间分辨率和位置不确定性，可能难以将田间植被生物特征与卫星数据直接联系起来。结合使用ALS和卫星遥感数据可以为跨时空有效，准确且一致的AGB映射提供解决方案。这种机载-星载数据融合已在高生物量环境中得到了成功证明。但是，干旱的林地生态系统具有独特的结构条件，具有开放的树冠和低叶面积指数，提出制图挑战，需要进一步研究。这些挑战在大空间足迹的激光雷达中尤为严重，在这种情况下，短而宽的树木可能会限制准确进行AGB估算的能力。在这项研究中，我们提出了一个规模化的方法框架，用于将实地测得的林地AGB与ALS数据联系起来，然后，以犹他州东南部的针叶杜松林为例，将ALS模型的AGB与卫星数据联系起来。我们使用两个卫星传感器Landsat 8 OLI（多光谱）和GEDI（激光雷达）比较了这种缩放方法的有效性。由于我们将本地模型的预测输出用作区域模型的输入，因此，我们还展示了一种在整个嵌套的多尺度分析框架中传播不确定性的方法，利用随机森林决策树中的固有变异性。给定GEDI足迹的位置不确定性，我们测试了一系列不同的足迹大小对ALS-GEDI AGB模型准确性的相对影响。我们的本地规模（田间ALS）预测模型能够解决AGB中74％的方差，并估计AGB的均方根误差（RMSE）为14 Mg / ha，平均绝对误差（MAE）为11.09毫克/公顷 我们具有不确定性传播的区域规模（ALS-Landsat / GEDI）分析显示，结合使用Landsat和GEDI指标可产生最佳的预测模型（R 我们的本地规模（田间ALS）预测模型能够解决AGB中74％的方差，并估计AGB的均方根误差（RMSE）为14 Mg / ha，平均绝对误差（MAE）为11.09毫克/公顷 我们具有不确定性传播的区域规模（ALS-Landsat / GEDI）分析显示，结合使用Landsat和GEDI指标可产生最佳的预测模型（R 我们的本地规模（田间ALS）预测模型能够解决AGB中74％的方差，并估计AGB的均方根误差（RMSE）为14 Mg / ha，平均绝对误差（MAE）为11.09毫克/公顷 我们具有不确定性传播的区域规模（ALS-Landsat / GEDI）分析显示，结合使用Landsat和GEDI指标可产生最佳的预测模型（R²  = 0.68; RMSE = 12.71 Mg / ha; MAE = 9.40 Mg / ha），然后是仅Landsat的指标（R ²  = 0.66，RMSE = 13.08 Mg / ha； MAE = 9.71 Mg / ha），以及仅GEDI的指标（R ² = 0.49，RMSE = 16.01 Mg / ha；MAE = 12.14 Mg / ha）。这些结果表明，Landsat可能比GEDI更适合于在林冠覆盖率低且树木短的树状环境限制了在大面积波形激光雷达数据中精确表征植被结构的林地环境中的AGB。足迹尺寸分析表明，较大的模拟足迹（例如，半径30 m及更大）可产生更高的GEDI模型精度。但是，增加足迹半径超过30 m并不会显着提高模型精度。这项研究代表了提高我们可靠地绘制林地AGB的能力的重要一步，并为将GEDI数据应用于林地AGB映射提供了早期的测试案例。