Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

中文翻译：

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使用 GEDI 和分层模型绘制印度尼西亚低地森林的地上生物量图


全球生态系统动力学调查 (GEDI) 等星载激光雷达（光探测和测距）仪器通过生成对植物物质垂直排列敏感的大范围测量值作为测量值的补充，为全球森林清查提供了独特的机会。激光雷达可测量值与大多数感兴趣的物理属性（包括生物量）并不直接相关，因此必须通过统计模型进行关联。此外，GEDI 观测在空间上并不完整，因此需要将不完整的样本转换为面积平均值/总数的预测的方法。这些方法在赤道和持续多云的地区可能会面临挑战，例如印度尼西亚，那里的高质量观​​测密度下降。我们开发并实施了一个分层模型，以在印度尼西亚占碑省低地范围内生成各种分辨率的无间隙地上生物量密度 (AGBD) 地图。在局部田间图和使用一致机载激光扫描 (ALS) 数据模拟的 GEDI 波形指标之间训练生物量模型。选择适合本地的探地算法设置后，我们训练了一个误差模型，该模型描述了模拟波形与 GEDI 观测波形之间的差异。最后，使用地质统计模型对在轨 GEDI 观测的空间分布进行建模。这三个模型嵌套在一个层次模型中，将 GEDI 观测的空间分布与现场测量的 AGBD 相关联。该模型允许以任意分辨率进行空间完整的预测，同时考虑关系每个阶段的不确定性。 相对于预测的生物量，模型的不确定性较低，1 km 分辨率下的中值相对标准偏差为 8%，100 m 分辨率下的中值相对标准偏差为 26%。我们的模型提供的 AGBD 空间一致信息有利于支持可持续森林管理、碳封存举措和减缓气候变化。这对于像印度尼西亚占碑这样充满活力的热带景观尤其重要，以便了解土地利用从森林转变为油棕和橡胶等经济作物的影响。更一般地说，我们主张使用分层模型作为框架来解释现场和传感器数据之间关系的多个阶段，并为最终预测提供可靠的不确定性审核。