Information on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing processes involved in the carbon cycle and supporting international policies for climate change mitigation and adaption. Furthermore, these products provide important baseline data for the development of sustainable management strategies to local stakeholders. The use of remote sensing data can provide spatially explicit information of AGB from local to global scales. In this study, we mapped national Mexican forest AGB using satellite remote sensing data and a machine learning approach. We modelled AGB using two scenarios: (1) extensive national forest inventory (NFI), and (2) airborne Light Detection and Ranging (LiDAR) as reference data. Finally, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB map. The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics (R2, Root Mean Square Error (RMSE)) at three different scales compared to the independent validation data set. We observed different spatial patterns of AGB in tropical dense forests, where no or limited number of NFI data were available, with higher AGB values in the LiDAR-calibrated map. We estimated much higher uncertainties in the AGB maps based on two-stage up-scaling method (i.e., from field measurements to LiDAR and from LiDAR-based estimates to satellite imagery) compared to the traditional field to satellite up-scaling. By removing LiDAR-based AGB pixels with high uncertainties, it was possible to estimate national forest AGB with similar uncertainties as calibrated with NFI data only. Since LiDAR data can be acquired much faster and for much larger areas compared to field inventory data, LiDAR is attractive for repetitive large scale AGB mapping. In this study, we showed that two-stage up-scaling methods for AGB estimation over large areas need to be analyzed and validated with great care. The uncertainties in the LiDAR-estimated AGB propagate further in the wall-to-wall map and can be up to 150%. Thus, when a two-stage up-scaling method is applied, it is crucial to characterize the uncertainties at all stages in order to generate robust results. Considering the findings mentioned above LiDAR can be used as an extension to NFI for example for areas that are difficult or not possible to access.

中文翻译：

---

通过整合实地测量，机载LiDAR，SAR和墨西哥的光学卫星数据，估算森林地上生物量和不确定性。

需要有关大面积地上生物量（AGB）的空间分布的信息，以了解和管理碳循环所涉及的过程，并支持缓解和适应气候变化的国际政策。此外，这些产品为当地利益相关者提供了制定可持续管理策略的重要基准数据。遥感数据的使用可以提供从本地到全球尺度的AGB的空间明确信息。在这项研究中，我们使用卫星遥感数据和机器学习方法绘制了墨西哥国家森林AGB的地图。我们使用两种情况对AGB进行建模：（1）广泛的国家森林清单（NFI），以及（2）机载光检测和测距（LiDAR）作为参考数据。最后，我们将不确定性从野外测量传播到了LiDAR衍生的AGB以及全国壁垒森林AGB地图。与独立验证数据集相比，估计的AGB图（经NFI和LiDAR校准）在三个不同的尺度上显示出相似的拟合优度统计数据（R2，均方根误差（RMSE））。我们在热带茂密森林中观察到了不同的AGB空间格局，在这些森林中，没有可用的NFI数据或数量有限，在LiDAR校准的地图中AGB值较高。与传统的野外卫星放大方法相比，我们基于两阶段放大方法（即，从野外测量到LiDAR以及从基于LiDAR的估计到卫星图像）估计了AGB地图的不确定性更高。通过删除不确定性较高的基于LiDAR的AGB像素，可以估计具有与仅使用NFI数据校准的相似不确定性的国家森林AGB。与现场库存数据相比，由于LiDAR数据的获取速度更快，面积更大，因此LiDAR对于重复性大规模AGB映射具有吸引力。在这项研究中，我们表明需要认真分析和验证大面积AGB估算的两步放大方法。LiDAR估算的AGB中的不确定性会在墙到墙地图中进一步传播，最高可达150％。因此，当应用两阶段放大方法时，至关重要的是表征所有阶段的不确定性以产生可靠的结果。考虑到上述发现，LiDAR可以用作NFI的扩展，例如用于难以访问或无法访问的区域。