Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 136-701, South Korea.
J Plant Res. 2010 Jul;123(4):421-32. doi: 10.1007/s10265-010-0310-0. Epub 2010 Feb 25.
The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R (2) = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R (2) = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R (2) = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm(3)), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI).
本研究的目的是利用从小光斑激光雷达（LiDAR）数据中提取的冠层几何体积（CGV）来估算单株树木的树干体积和生物量。尝试分析三种树木密度（低：240 N/ha、中：370 N/ha、高：1340 N/ha）的红松（Pinus koraiensis Sieb. et Zucc.）林分的树干体积和生物量。为了描绘单株树木，应用了图像处理方法的扩展极大值变换和分水岭分割，如我们之前的一项研究中所述。下一步，需要确定单株树木的冠底高度（CBH）；这方面的信息是在 LiDAR 点云数据中使用 K-均值聚类找到的。可以基于 CGV 与树干体积之间的比例关系来估算 LiDAR 衍生的 CGV 和树干体积。结果表明，低树木密度样地的 LiDAR 衍生 CBH、CGV 和树干体积的表现最佳（R2 分别为 0.67、0.57 和 0.68），而高树木密度样地的精度最低（R2 分别为 0.48、0.36 和 0.44）。中等树木密度样地的精度分别为 R2 = 0.51、0.52 和 0.62。可以根据针叶树木材的基本密度（0.48 g/cm3）从树干体积预测 LiDAR 衍生的树干生物量，然后可以使用韩国林业研究所（KFRI）提出的生物量转换和扩展因子（BCEF，1.29）从树干体积估算 LiDAR 衍生的地上生物量。
