Satellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation. Assessing such variability is not possible with spatially-sparse vegetation plot networks. In the current study, our objective was to determine whether high-resolution lidar-based and moderate-resolution Landsat-base aboveground live forest biomass maps converged on similar predictions at stand- to landscape-levels (10 s to 100 s ha) and whether such differences depended on biophysical setting. Specifically, we examined deviations between lidar- and Landsat-based biomass mapping methods across scales and ecoregions using a measure of error (normalized root mean square deviation), a measure of the unsystematic deviations, or noise (Pearson correlation coefficient), and two measures related to systematic deviations, or biases (intercept and slope of a regression between the two sets of predictions). Compared to forest inventory data (0.81-ha aggregate-level), lidar and Landsat-based mean biomass predictions exhibited similar performance, though lidar predictions exhibited less normalized root mean square deviation than Landsat when compared with the reference plot data. Across aggregate-levels, the intercepts and slopes of regression equations describing the relationships between lidar- and Landsat-based biomass predictions stabilized (i.e., little additional change with increasing area of aggregates) at aggregate-levels between 10 and 100 ha, suggesting a consistent relationship between the two maps at landscape-scales. Differences between lidar- and Landsat-based biomass maps varied as a function of forest canopy heterogeneity and composition, with systematic deviations (regression intercepts) increasing with mean canopy cover and hardwood proportion within forests and correlations decreasing with hardwood proportion. Deviations between lidar- and Landsat-based maps indicated that satellite-based approaches may represent general gradients in forest biomass. Ecoregion impacted deviations between lidar and Landsat biomass maps, highlighting the importance of biophysical setting in determining biomass map performance across aggregate scales. Therefore, regardless of the source of remote sensing (e.g., Landsat vs. lidar), factors affecting the measurement and prediction of forest biomass, such as species composition, need to be taken into account whether one is estimating biomass at the plot, stand, or landscape scale.

中文翻译：

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基于陆地卫星和激光雷达的生物量图之间的多尺度差异与冠层覆盖和组成的区域变化有关。

基于卫星的地上森林生物量图通常构成森林生物量和碳库测绘和监测的基础，但是生物量图的性能可能会因地区而异，并随聚集的空间规模而变化。对于空间稀疏的植被图网络，无法评估这种可变性。在当前的研究中，我们的目标是确定基于高分辨率激光雷达和中分辨率Landsat的地上活动森林生物量图是否在相似的预测水平（从10 s到100 s ha）上收敛。这种差异取决于生物物理环境。具体来说，我们使用误差（标准化的均方根偏差）测量了基于激光雷达和Landsat的生物量制图方法在尺度和生态区域之间的偏差，非系统偏差或噪声（皮尔逊相关系数）的度量，以及与系统偏差或偏差有关的两个度量（两组预测之间的回归的截距和斜率）。与森林清查数据（总量为0.81公顷）相比，基于激光雷达和Landsat的平均生物量预测表现出相似的性能，尽管与参考地块数据相比，激光雷达预测的标准化均方根偏差小于Landsat。在整个骨料水平上，描述基于激光雷达和Landsat的生物量预测之间关系的回归方程的截距和斜率在10到100公顷之间的骨料水平上稳定（即，随着骨料面积的增加几乎没有其他变化），这表明存在一致性两幅地图在景观比例上的关系。基于激光雷达和Landsat的生物量图之间的差异随森林冠层异质性和组成的变化而变化，系统偏差（回归截距）随着平均冠层覆盖率和森林中硬木比例的增加而增加，相关性随硬木比例的减小而减小。基于激光雷达和Landsat的地图之间的偏差表明，基于卫星的方法可能代表森林生物量的一般梯度。生态区影响了激光雷达和Landsat生物量图之间的偏差，突出了生物物理环境在确定总体规模上生物量图性能方面的重要性。因此，无论遥感的来源（例如，Landsat与激光雷达）如何，影响森林生物量测量和预测的因素（例如物种组成，