Individual Stem Volume Modeling Based on Tree Height and Crown Characteristics

PU Ying; ZENG Wei-sheng

[Objective]Based on the data of tree height and crown characteristics obtained through LiDAR technology, forest volume and biomass can be derived. However, the reliable individual stem volume models based on tree height and crown characteristics are still not available. [Method]Using the mensuration data of 3 010 sample trees of four species groups, i.e., Picea, Abies, Quercus and Betula, the relationships between stem volume and tree size factors such as diameter at breast height (dbh), tree height and crown characteristics were analyzed; and applying logarithmic regression, the individual stem volume models based on tree height and crown characteristics were developed, which were evaluated with six statistics such as coefficient of determination (R2) and mean prediction error (MPE). [Result]The results showed that dbh was the most efficient explanatory variable for volume model, followed by the tree height, and then the crown length and the crown width. The two-variable volume model based on tree height and crown width was highly efficient, and the three-variable model including crown length improved only a little. The R2 values of four two-variable volume models based on tree height and crown width for Picea, Abies, Quercus and Betula were 0.81, 0.80, 0.76 and 0.77, respectively, and MPE's were 4.7%, 5.3%, 5.4% and 5.3%, respectively, indicating that the prediction precisions of the volume models were about 95%. [Conclusion]The quantitative analysis results in this study about relationship between stem volume and tree size factors could provide technical support for applying LiDAR technology to measure forest parameters; and the developed volume models would provide a quantitative basis for estimating individual volume through measurements of tree height and crown characteristics.

1. Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China

Received Date: 2015-08-20

Abstract: [Objective]Based on the data of tree height and crown characteristics obtained through LiDAR technology, forest volume and biomass can be derived. However, the reliable individual stem volume models based on tree height and crown characteristics are still not available. [Method]Using the mensuration data of 3 010 sample trees of four species groups, i.e., Picea, Abies, Quercus and Betula, the relationships between stem volume and tree size factors such as diameter at breast height (dbh), tree height and crown characteristics were analyzed; and applying logarithmic regression, the individual stem volume models based on tree height and crown characteristics were developed, which were evaluated with six statistics such as coefficient of determination (R2) and mean prediction error (MPE). [Result]The results showed that dbh was the most efficient explanatory variable for volume model, followed by the tree height, and then the crown length and the crown width. The two-variable volume model based on tree height and crown width was highly efficient, and the three-variable model including crown length improved only a little. The R2 values of four two-variable volume models based on tree height and crown width for Picea, Abies, Quercus and Betula were 0.81, 0.80, 0.76 and 0.77, respectively, and MPE's were 4.7%, 5.3%, 5.4% and 5.3%, respectively, indicating that the prediction precisions of the volume models were about 95%. [Conclusion]The quantitative analysis results in this study about relationship between stem volume and tree size factors could provide technical support for applying LiDAR technology to measure forest parameters; and the developed volume models would provide a quantitative basis for estimating individual volume through measurements of tree height and crown characteristics.

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Reference (15)

[1]	庞勇,李增元,陈尔学,等. 激光雷达技术及其在林业上的应用[J]. 林业科学, 2005,41(3):129-136.
[2]	赵峰,李增元,王韵晟,等. 机载激光雷达(LiDAR)数据在森林资源调查中的应用综述[J]. 遥感信息, 2008 (1):106-110.
[3]	何祺胜,陈尔学,曹春香,等. 基于Lidar数据的森林参数反演方法研究[J]. 地球科学进展, 2009 (7):748-755.
[4]	赵丽琼,张晓丽,孙红梅. 激光雷达数据在森林参数获取中的应用[J]. 世界林业研究, 2010,23(2):61-64.
[5]	马利群,李爱农. 激光雷达在森林垂直结构参数估算中的应用[J]. 世界林业研究, 2011,24(1):41-45.
[6]	邢艳秋,尤号田,霍达,等. 小光斑激光雷达数据估测森林树高研究进展[J]. 世界林业研究, 2014,27(2):29-34.
[7]	庞勇,李增元. 基于机载激光雷达的小兴安岭温带森林组分生物量反演[J]. 植物生态学报, 2012,36(10):1095-1105.
[8]	Li W K, Guo Q H, Jakubowski M K, et al. A new method for segmenting individual trees from the lidar point cloud[J]. Photogrammetric Engineering & Remote Sensing, 2012, 78(1): 75-84.
[9]	Jakubowski M K, Guo Q H, Kelly M. Tradeoffs between lidar pulse density and forest measurement accuracy[J]. Remote Sensing of Environment, 2013, 130: 245-253.
[10]	尤号田,邢艳秋,王铮,等. 点云密度对激光雷达估计森林样方平均树高的影响[J]. 东北林业大学学报, 2014,42(5):143-148.
[11]	郭庆华,刘瑾,陶胜利,等. 激光雷达在森林生态系统监测模拟中的应用现状与展望[J]. 科学通报, 2014,59(6):459-478.
[12]	曾伟生. 杉木相容性立木材积表系列模型研建[J]. 林业科学研究, 2014,27(1):6-10.
[13]	曾伟生,唐守正. 立木生物量模型的优度评价和精度分析[J]. 林业科学, 2011, 47(11): 106-113.
[14]	曾伟生,唐守正. 非线性模型对数回归的偏差校正及与加权回归的对比分析[J]. 林业科学研究, 2011,24(2):137-143.
[15]	Snowdon P. A ratio estimator for bias correction in logarithmic regressions[J]. Can J For Res, 1991, 21(5): 720-724.

Individual Stem Volume Modeling Based on Tree Height and Crown Characteristics

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通讯作者: 陈斌, bchen63@163.com

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