论文标题
精细分辨率景观尺度的生物量映射，使用LIDAR覆盖范围的时空拼布
Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages
论文作者
论文摘要
估计大规模森林AGB和精细的空间决议对于温室气体会计，监测和验证工作以减轻气候变化的范围变得越来越重要。机载LiDAR对于在包括AGB在内的森林结构的属性建模非常有价值，但大多数LiDAR收集都发生在涵盖不规则，不连续足迹的本地或区域尺度上，导致不同景观段的各个时间点的拼布。在这里，作为纽约州（美国）的全州森林碳评估的一部分，我们解决了利用LiDAR拼布在景观尺度上进行AGB映射的共同障碍，包括选择培训数据，研究预测误差的区域或覆盖特定模式，以及与多个尺度跨多个尺度的现场库存一致的区域或覆盖范围。三种机器学习算法和一个集合模型接受了FIA场测量，空气载激元和地形，气候和心形地理的训练。使用一组严格的地块选择标准，选择了801个FIA图，并从17个叶子覆盖范围（2014- 2019年）的拼布中绘制的共同确定的点云（2014-2019）。我们的合奏模型用于在预测定义的适用性区域（占LiDAR覆盖范围的98％）内生成30 m AGB预测表面，并将所得的AGB图与FIA绘图级别和面积估计值进行比较。我们的模型总体上是准确的（％RMSE 22-45％； MAE 11.6-29.4 mg ha $^{ - 1} $; me 2.4-6.3 mg ha $^{ - 1} $）解释了73-80％的现场观察变化的变化的73-80％，并且与FIA基于设计的估计值相一致（89％的估计值（89％）估计值（89％）。我们分享了使用LIDAR时空拼布所面临的挑战的实用解决方案，以满足不断增长的AGB映射需求，以支持森林碳会计和生态系统中的应用。
Estimating forest AGB at large scales and fine spatial resolutions has become increasingly important for greenhouse gas accounting, monitoring, and verification efforts to mitigate climate change. Airborne LiDAR is highly valuable for modeling attributes of forest structure including AGB, yet most LiDAR collections take place at local or regional scales covering irregular, non-contiguous footprints, resulting in a patchwork of different landscape segments at various points in time. Here, as part of a statewide forest carbon assessment for New York State (USA), we addressed common obstacles in leveraging a LiDAR patchwork for AGB mapping at landscape scales, including selection of training data, the investigation of regional or coverage specific patterns in prediction error, and map agreement with field inventory across multiple scales. Three machine learning algorithms and an ensemble model were trained with FIA field measurements, airborne LiDAR, and topographic, climatic and cadastral geodata. Using a strict set of plot selection criteria, 801 FIA plots were selected with co-located point clouds drawn from a patchwork of 17 leaf-off LiDAR coverages (2014-2019). Our ensemble model was used to produce 30 m AGB prediction surfaces within a predictor-defined area of applicability (98% of LiDAR coverage), and the resulting AGB maps were compared with FIA plot-level and areal estimates at multiple scales of aggregation. Our model was overall accurate (% RMSE 22-45%; MAE 11.6-29.4 Mg ha$^{-1}$; ME 2.4-6.3 Mg ha$^{-1}$), explained 73-80% of field-observed variation, and yielded estimates that were consistent with FIA's design-based estimates (89% of estimates within FIA's 95% CI). We share practical solutions to challenges faced in using spatiotemporal patchworks of LiDAR to meet growing needs for AGB mapping in support of applications in forest carbon accounting and ecosystem.
