Ferraz António, Saatchi Sassan S, Longo Marcos, Clark David B
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA.
Institute of Environment and Sustainability, University of California, Los Angeles, California, 90024, USA.
Ecol Appl. 2020 Oct;30(7):e02154. doi: 10.1002/eap.2154. Epub 2020 Jul 6.
In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size-frequency distributions over an old-growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10 m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot- (97.4%; 3% bias) and tree-height-classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar-derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot-level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot- and tree-height-classes level (-5.2% and 2.1%). Our ITC method provides a powerful tool for tree- to landscape-level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area-based approaches that require local calibration using a large number of inventory plots.
在热带雨林中，树木大小和数量密度受干扰历史、土壤、地形、气候以及生物因素的影响。如果没有详细且广泛的森林清查数据，这些因素很难预测。在此，我们通过对哥斯达黎加拉塞尔瓦生物站一片老龄湿润热带森林的机载激光雷达测量数据应用单木树冠（ITC）算法，来量化树木大小-频率分布。ITC提供了树高、树冠面积、高度大于10米的树木数量、预测的树木直径以及基于野外异速生长法得出的地上生物量。数量密度在样地水平（97.4%；偏差3%）和树高等级水平（97.4%；偏差3%）与实地观测结果高度一致。激光雷达测量的树木直径和高度的大小谱紧密遵循地面测量的分布，但与树冠面积观测结果的一致性较差。将激光雷达获取的树高和树冠面积转换为树木直径的模型对样地水平的断面积产生了无偏差（0.8%）的估计，且不确定性较低（6%）。对树高等级的断面积预测同样无偏差（1.3%），但不确定性较大（22%）。在样地和树高等级水平上，生物量估计没有显著偏差（-5.2%和2.1%）。我们的ITC方法克服了基于激光雷达面积法需要使用大量清查样地进行局部校准的局限性，为从树木到景观尺度的热带森林清查和生物量估计提供了一个强大的工具。
