基于地基激光雷达的亚热带森林单木胸径与树高提取

doi:10.11707/j.1001-7488.20160204

林业科学 ›› 2016, Vol. 52 ›› Issue (2): 26-37.doi: 10.11707/j.1001-7488.20160204

基于地基激光雷达的亚热带森林单木胸径与树高提取

刘鲁霞, 庞勇, 李增元

中国林业科学研究院资源信息研究所北京 100091

收稿日期:2015-03-19 修回日期:2015-10-29 出版日期:2016-02-25 发布日期:2016-03-25
通讯作者: 庞勇
基金资助:
国家高技术研究发展计划(863计划)项目(2012AA12A306);国家重点基础研究发展计划(973计划)项目(2013CB733404,2013CB733406)。

Individual Tree DBH and Height Estimation Using Terrestrial Laser Scanning (TLS) in A Subtropical Forest

Liu Luxia, Pang Yong, Li Zengyuan

Research Institute of Forest Resource Information Techniques, CAF Beijing 100091

Received:2015-03-19 Revised:2015-10-29 Online:2016-02-25 Published:2016-03-25

摘要/Abstract

摘要： [目的] 以云南省普洱市天然林与杉木人工林为研究对象,针对云南省山区森林树种繁多、林下灌木草本茂密的林分环境,根据森林中树木的形态特征,利用地基激光雷达(TLS)扫描数据提取样地尺度单木胸径与树高,为森林调查工作提供参考。[方法] 将获取的多站地基激光雷达扫描数据分为多站拼接及单站2种分析方式,采用Hough变换算法及树干的形态特征对样地内单木进行识别与胸径提取,根据树干生长方向及单木在垂直方向上的分布提取树高。[结果] 1)对于多站拼接数据,即使在林分条件最为复杂的原始林,单木识别率仍可达到81%;对于单站数据,随着扫描距离增加,单木识别率降低,实际操作时单站布设比多站拼接简单; 2)多站拼接胸径及胸高断面积估测结果更接近于样地真实值,多个单站平均结果比只使用一站扫描数据提取的结果更加适合估测样地胸径及胸高断面积,半径10 m比半径5 m及15 m范围内数据更加适合估测样地胸径及胸高断面积; 3)天然林单木树高估测结果为R²=0.77,RMSE=1.46 m;人工林单木树高估测结果为R²=0.94,RMSE=0.96 m。[结论] 本研究根据树干垂直向特征,设置的一系列参数可以剔除Hough变换算法在非树干处的识别圆,可提高单木识别及胸径、树高的估测精度。受扫描站布设及林分条件影响,人工林的估测结果好于天然林。多站拼接相比单站扫描更加接近于样地实测结果,多个单站平均更能代表样地实际情况,只用一站数据具有一定的偶然性。

关键词: 地基激光雷达, 亚热带森林, 胸径, 树高, 胸高断面积

Abstract: [Objective] Based on the trunk's shape and terrestrial laser scanning (TLS) data, the DBH, height and location of individual tree were estimated in the mountainous forests including natural forest stands and Cunninghamia plantation stands of Yunnan Province.[Method] DBH and height of individual tree were extracted by merged and single station TLS data. Location and DBH of individual tree were detected and extracted by applying a Hough transform algorithm coupled with trunk's shape. Then, tree height was estimated via trunk's direction and vertical distribution of canopy.[Result] 1) Based on multiple stations TLS data, the accuracy of tree identification was about 81% in the natural forest stands, with complicated stand structures and compositions. For the single station TLS data, the accuracy of tree identification decreased with the increasing TLS data area. The acquisition of single station TLS data was much easier than that of multiple stations. 2) Mosaicked multiple stations data provided higher accuracies of DBH and basal area of breast-height estimation, as compared to single station data. It was better to use the averages of multiple stations data to derive DBH and basal area of breast-height than single station data. It was more suitable to use data collected within a radius of 10 m to estimate DBH and basal area of breast-height than those collected within 5 m and 15 m radii. 3) The tree height estimation (R²=0.94,RMSE=0.96 m) of the plantations was more accurate than that (R²=0.77,RMSE=1.46 m) of the natural forests.[Conclusion] According to the feature along trunk, the most detected circle out of trunk could be deleted. This improved the estimating accuracies of tree detection, DBH and tree height. Locating single trunks and estimating DBH and height of individual trees were greatly dependent upon stand structural conditions and distributions of scanning stations. The merged multi scan TLS data extracted the most accurate result compared with averaged multi scan and single scan TLS data. The single scan TLS data showed occasional result.

Key words: terrestrial laser scanning, subtropical forest, DBH, tree height, basal area of breast-height

中图分类号:

S757

刘鲁霞, 庞勇, 李增元. 基于地基激光雷达的亚热带森林单木胸径与树高提取[J]. 林业科学, 2016, 52(2): 26-37.

Liu Luxia, Pang Yong, Li Zengyuan. Individual Tree DBH and Height Estimation Using Terrestrial Laser Scanning (TLS) in A Subtropical Forest[J]. Scientia Silvae Sinicae, 2016, 52(2): 26-37.

参考文献

李丹, 庞勇, 岳彩荣,等. 2012. 基于TLS数据的单木胸径和树高提取研究. 北京林业大学学报, 34(4):79-86.
(Li D, Pang Y, Yue C R, et al. 2012. Extraction of individual tree DBH and height based on terrestrial laser scanner data. Journal of Beijing Forestry University, 34(4):79-86.[in Chinese])
Aschoff T, Thies M, Spiecker H. 2004. Describing forest stands using terrestrial laser-scanning. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(Part B):237-241.
Bienert A, Scheller S, Keane E, et al. 2007. Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds. ISPRS Workshop on Laser Scanning, 50-55.
Chmielewski L J, Bator M, Zasada M, et al. 2010. Fuzzy Hough transform-based methods for extraction and measurements of single trees in large-volume 3D terrestrial Lidar data. Computer Vision and Graphics, Springer, 265-274.
Hopkinson C, Chasmer L, Young-Pow C, et al. 2004. Assessing forest metrics with a ground-based scanning Lidar. Canadian Journal of Forest Research, 34:573-583.
Király G, Brolly G. 2007. Tree height estimation methods for terrestrial laser scanning in a forest reserve. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W52):211-215.
Liang X, Hyyppa J. 2013. Automatic stem mapping by merging several terrestrial laser scans at the feature and decision level. Sensors, 13(2):1614-1634.
Litkey P, Liang X, Kaartinen H, et al. 2008. Single-scan TLS methods for forest parameter retrieval. Proceedings of SilviLaser 2008, 8th.
Maas H G, Bienert A, Scheller S, et al. 2008. Automatic forest inventory parameter determination from terrestrial laser scanner data. International Journal of Remote Sensing, 29(5):1579-1593.
Moskal L M, Zheng G. 2011. Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sensing, 4(1):1-20.
Pfeifer N, Winterhalder D. 2004. Modelling of tree cross sections from terrestrial laser scanning data with free-form curves. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(Part 8):76-81.
Van Leeuwen M, Coops N C, Wulder M A. 2010. Canopy surface reconstruction from a Lidar point cloud using Hough transform. Remote Sensing Letters, 1(3):125-132.
Wezyk P, Koziol K, Glista M, et al. 2007. Terrestrial laser scanning versus traditional forest inventory:First results from the Polish forests. ISPRS Workshop on Laser Scanning, 12-14.

[1]	毛学刚, 姚瑶, 范文义. 基于Landsat长时间序列的森林扰动参数提取与树高估算[J]. 林业科学, 2019, 55(3): 79-87.
[2]	耿林, 李明泽, 范文义, 王斌. 基于机载LiDAR的单木结构参数及林分有效冠的提取[J]. 林业科学, 2018, 54(7): 62-72.
[3]	尤号田, 邢艳秋, 彭涛, 丁建华. 机载LiDAR航带旁向重叠对针叶林结构参数估测的影响[J]. 林业科学, 2018, 54(6): 109-118.
[4]	娄明华, 张会儒, 雷相东, 李春明, 臧颢. 基于空间自相关的天然蒙古栎阔叶混交林林木胸径-树高模型[J]. 林业科学, 2017, 53(6): 67-76.
[5]	王冬至, 张冬燕, 张志东, 黄选瑞. 基于非线性混合模型的针阔混交林树高与胸径关系[J]. 林业科学, 2016, 52(1): 30-36.
[6]	曹庆杰, 迟德富. 杨树品系抗杨干象水平及其与木质部和韧皮部硬度等关系[J]. 林业科学, 2015, 51(5): 56-67.
[7]	应雨骐, 项婷婷, 林维雷, 吴家森, 杨杰, 姜培坤. 中国亚热带5种林分凋落物层植硅体碳的封存特性[J]. 林业科学, 2015, 51(3): 1-7.
[8]	祖笑锋, 倪成才, Gorden Nigh, 覃先林. 基于混合效应模型及EBLUP预测美国黄松林分优势木树高生长过程[J]. 林业科学, 2015, 51(3): 25-33.
[9]	张雄清, 张建国, 段爱国. 基于贝叶斯法估计杉木人工林树高生长模型[J]. 林业科学, 2014, 50(3): 69-75.
[10]	王长委, 胡月明, 沈德才, 黄胜利, 朱剑云, 王璐. 基于CBERS数据的亚热带森林地上碳储量估算[J]. 林业科学, 2014, 50(1): 88-96.
[11]	周宇峰, 顾蕾, 刘红征, 周国模, 李翠琴, 施拥军, 韩笑, 林海. 基于竹展开技术的毛竹竹板材碳转移分析[J]. 林业科学, 2013, 49(8): 96-102.
[12]	贺鹏;张会儒;雷相东;徐广;高祥. 基于地统计学的森林地上生物量估计[J]. , 2013, 49(5): 101-109.
[13]	白超;张文辉;雷亚芳. 秦岭北坡2种类型栓皮栎软木生长及特性[J]. 林业科学, 2013, 49(4): 62-69.
[14]	符利勇;李永慈;李春明;唐守正. 利用2种非线性混合效应模型(2水平) 对杉木林胸径生长量的分析[J]. 林业科学, 2012, 48(5): 36-43.
[15]	李春明. 基于两层次线性混合效应模型的杉木林单木胸径生长量模型[J]. 林业科学, 2012, 48(3): 66-73.

基于地基激光雷达的亚热带森林单木胸径与树高提取

Individual Tree DBH and Height Estimation Using Terrestrial Laser Scanning (TLS) in A Subtropical Forest

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

作者中心

期刊获奖

引证指标

联系方式