Kristensen Terje, Næsset Erik, Ohlson Mikael, Bolstad Paul V, Kolka Randall
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway.
Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, United States of America.
PLoS One. 2015 Oct 1;10(10):e0138450. doi: 10.1371/journal.pone.0138450. eCollection 2015.
A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.
大量且不断增加的证据表明，机载扫描光探测与测距（激光雷达）系统可成为测量和监测地上林木生物量的有效工具。然而，激光雷达作为协助评估森林生态系统土壤和非树木植被成分中碳（C）储量的全方位工具的潜力却很少受到关注。在此，我们将机载小光斑扫描激光雷达的使用与与植被和土壤表面相关的精细尺度空间碳数据相结合，以描述和对比多层挪威云杉（Picea abies）林分内及林分间碳库的大小和空间分布。来自激光雷达衍生指标的预测变量提供了精确的地上和地下树木碳模型，在我们的研究林分中，树木碳构成了最大的碳库。我们还发现有证据表明，激光雷达冠层数据与主要由欧石南矮灌木和草本植物组成的地被层碳储量变化密切相关。然而，直接从林下回波得出的激光雷达指标并未产生显著模型。此外，我们的结果表明，苔藓和土壤有机层碳储量样地的变化似乎受林分结构特性差异的影响小于地形梯度。通过使用来自激光雷达地面回波的地形模型，我们能够在林分水平上建立激光雷达数据与有机层碳储量之间的强相关性。将地形分辨率从样地平均值（约2000平方米）提高到单个网格单元（1平方米）并未产生一致的模型。我们的研究证明了不同森林碳库的大小和分布与从机载激光雷达数据得出的模型之间的联系，为未来利用激光雷达评估和监测北方森林碳的研究奠定了基础。
