Stem and branch volume estimation using terrestrial laser scanning data

JIN Shuangna; ZHANG Wuming; CAI Shangshu; SHAO Jie; CHENG Shun; XIE Donghui; YAN Guangjian

doi:10.11834/jrs.20210537

Models and Methods | Views : 0 下载量: 124 CSCD: 0

R-PDF
PDF
Export
Share
Collection
Album

Stem and branch volume estimation using terrestrial laser scanning data
Vol. 27, Issue 7, Pages: 1653-1666(2023)
DOI： 10.11834/jrs.20210537

扫描看全文

Quote
靳双娜，张吴明，蔡尚书，邵杰，程顺，谢东辉，阎广建.2023.地基LiDAR林木点云估算枝干材积.遥感学报，27（7）： 1653-1666

Jin S N，Zhang W M，Cai S S，Shao J，Cheng S，Xie D H and Yan G J. 2023. Stem and branch volume estimation using terrestrial laser scanning data. National Remote Sensing Bulletin， 27（7）：1653-1666
靳双娜，张吴明，蔡尚书，邵杰，程顺，谢东辉，阎广建.2023.地基LiDAR林木点云估算枝干材积.遥感学报，27（7）： 1653-1666 DOI： 10.11834/jrs.20210537.

Jin S N，Zhang W M，Cai S S，Shao J，Cheng S，Xie D H and Yan G J. 2023. Stem and branch volume estimation using terrestrial laser scanning data. National Remote Sensing Bulletin， 27（7）：1653-1666 DOI： 10.11834/jrs.20210537.

摘要

材积是森林清查工作的一个重要参数，基于地基激光雷达点云的树木定量结构模型（QSM）重建方法能够实现林木材积的非破坏性获取，解决了传统森林原位调查方式耗时耗力的问题。但由于伐木材积真值的获取较难实现，使得量化结构模型方法的材积获取能力在树干及各级树枝水平上尚未开展研究，且仅应用于单木尺度地基激光雷达点云中，缺乏基于样方尺度扫描点云进行材积获取的探究。因此本文分别在单木及样方尺度完成QSM重建方法在树干及各级枝材积估算结果评估。实验结果表明，基于单木及样方尺度地基激光雷达点云均能有效地获取树干和一级枝的材积，而次级枝的材积估算存在明显的偏差：样方扫描尺度点云的树干及全树材积估算精度与单木尺度相当，估算偏差均为5%及10%左右，而一级枝材积估算偏差略大，其中单木尺度一级枝估算偏差在10%左右，样方尺度一级枝材积估算偏差在15%左右；此外，林分密度与样方尺度枝干材积估算精度呈负相关关系，在较低林分密度（425株/ha、625株/ha和925株/ha）的样方中树干材积估算误差均在5%以内，一级枝材积估算误差在15%左右，另外受树干及一级枝材积低估与各次级枝材积高估的部分中和效应影响，样方内总蓄积量估算偏差均在10%左右，因此在较低林分密度的森林中，样方尺度扫描数据能够很好地估算树干、一级枝及全树材积。

Abstract

Tree volume is an important parameter in forest inventory. The reconstruction of the Quantitative Structure Model of Trees (TreeQSM) method based on ground-based LiDAR point clouds can achieve nondestructive acquisition of forest volume. It can also solve the time-consuming and labor-intensive problem of traditional forest in situ investigation. However, the reference volume of the felled timber is difficult to obtain. Thus, the ability of the TreeQSM volume estimation has not been studied at the stem and different branch orders. Moreover, TreeQSM is only applied to the ground-based LiDAR point cloud collected at the tree level but not at the plot level. Therefore, this study proposes to assess the stem and branch volume estimation of TreeQSM from the point cloud collected from the tree and plot levels.,In this study, we evaluate the stem and branch volume estimated by TreeQSM using TLS point cloud at the tree and plot levels:(1) Estimating the volume of stem and branch at different orders based on TLS scanning at the tree-level.(2) Estimating and comparing the volume of stem and branch at different orders based on TLS point cloud at the tree and plot levels.(3) Exploring the influence of stand density on the estimation of stem and branch volumes using the TLS point cloud at the plot level.,The experimental results showed that the stem and first-order branch volume can be effectively estimated from the point cloud collected from the tree and plot levels. However, the volume estimation of the secondary branch has obvious deviations. At the plot level, the accuracy of the stem and whole tree volume is equivalent to that of the tree level. The deviations are approximately 5% and 10%. However, the first-order branch volume estimation deviation is slightly large, approximately 10% and 15% at the tree and plot levels, respectively. In addition, the stand density is negatively correlated with the accuracy of volume estimation at the plot level. In the low forest density (425, 625, and 925 plants/ha), the stem volume estimation error is within 5%, and the first-order branch volume estimation error is approximately 15%. In addition, the estimation deviations of the total volume in the plot are affected by the partial neutralization effect of the underestimation of the stem and the first-order branch volume and the overestimation of the secondary branch volume. These deviations are all approximately 10%. Thus, it can well estimate the tree stem, first-order branch, and whole tree volumes at the plot level in forests with a low stand density.

关键词

蓄积量枝干材积定量结构模型（QSM）地基激光雷达样方尺度

Keywords

volumestem and branch volumeQuantitative Structure Model (QSM)TLSplot level

references

Aguilar F J, Nemmaoui A, Peñalver A, Rivas J R and Aguilar M A. 2019. Developing allometric equations for teak plantations located in the coastal region of Ecuador from terrestrial laser scanning data. Forests, 10(12): 1050 [DOI: 10.3390/f10121050http://dx.doi.org/10.3390/f10121050]

Bechtold S and Höfle B. 2016. HELIOS: a multi-purpose LiDAR simulation framework for research, planning and training of laser scanning operations with airborne, ground-based mobile and stationary platforms. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-3: 161-168 [DOI: 10.5194/isprsannals-III-3-161-2016http://dx.doi.org/10.5194/isprsannals-III-3-161-2016]

Bienert A, Hess C, Maas H G and Von Oheimb G. 2014. A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5: 101-106 [DOI: 10.5194/isprsarchives-XL-5-101-2014http://dx.doi.org/10.5194/isprsarchives-XL-5-101-2014]

Brede B, Calders K, Lau A, Raumonen P, Bartholomeus H M, Herold M and Kooistra L. 2019. Non-destructive tree volume estimation through quantitative structure modelling: comparing UAV laser scanning with terrestrial LIDAR. Remote Sensing of Environment, 233: 111355 [DOI: 10.1016/j.rse.2019.111355http://dx.doi.org/10.1016/j.rse.2019.111355.]

Bredin Y K, Peres C A and Haugaasen T. 2020. Forest type affects the capacity of Amazonian tree species to store carbon as woody biomass. Forest Ecology and Management, 473: 118297 [DOI: 10.1016/j.foreco.2020.118297http://dx.doi.org/10.1016/j.foreco.2020.118297]

Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile V, Disney M, Armston J and Kaasalainen M. 2015. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2): 198-208 [DOI: 10.1111/2041-210X.12301http://dx.doi.org/10.1111/2041-210X.12301.]

Cao W, Chen D, Shi Y F, Cao Z and Xia S B. 2021. Progress and prospect of LiDAR point clouds to 3D tree models. Geomatics and Information Science of Wuhan University, 46(2): 203-220

曹伟, 陈动, 史玉峰, 曹震, 夏少波. 2021. 激光雷达点云树木建模研究进展与展望. 武汉大学学报(信息科学版), 46(2): 203-220 [DOI: 10.13203/j.whugis20190275http://dx.doi.org/10.13203/j.whugis20190275]

Damesin C, Ceschia E, Le Goff N, Ottorini J M and Dufrêne E. 2002. Stem and branch respiration of beech: from tree measurements to estimations at the stand level. New Phytologist, 153(1): 159-172 [DOI: 10.1046/j.0028-646X.2001.00296.xhttp://dx.doi.org/10.1046/j.0028-646X.2001.00296.x]

Dassot M, Colin A, Santenoise P, Fournier M and Constant T. 2012. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Computers and Electronics in Agriculture, 89: 86-93 [DOI: 10.1016/j.compag.2012.08.005http://dx.doi.org/10.1016/j.compag.2012.08.005]

Delagrange S., Jauvin C., & Rochon P. (2014). PypeTree: a tool for reconstructing tree perennial tissues from point clouds. Sensors, 14(3), 4271-4289 [DOI: 10.3390/s140304271http://dx.doi.org/10.3390/s140304271]

De Tanago J G, Lau A, Bartholomeus H, Herold M, Avitabile V, Raumonen P, Martius C, Goodman R C, Disney M, Manuri S, Burt A and Calders K. 2018. Estimation of above-ground biomass of large tropical trees with Terrestrial LiDAR. Methods in Ecology and Evolution, 9(2): 223-234 [DOI: 10.1111/2041-210X.12904http://dx.doi.org/10.1111/2041-210X.12904]

Delagrange S, Jauvin C and Rochon P. 2014. PypeTree: a tool for reconstructing tree perennial tissues from point clouds. Sensors, 14(3): 4271-4289 [DOI: 10.3390/s140304271http://dx.doi.org/10.3390/s140304271.]

Du S L, Lindenbergh R, Ledoux H, Stoter J and Nan L L. 2019. AdTree: accurate, detailed, and automatic modelling of laser-scanned trees. Remote Sensing, 11(18): 2074 [DOI: 10.3390/rs11182074http://dx.doi.org/10.3390/rs11182074]

Fan G P, Nan L L, Dong Y Q, Su X H and Chen F X. 2020. AdQSM: a new method for estimating above-ground biomass from TLS point clouds. Remote Sensing, 12(18): 3089 [DOI: 10.3390/rs12183089http://dx.doi.org/10.3390/rs12183089]

Hackenberg J, Morhart C, Sheppard J, Spiecker H and Disney M. 2014. Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests, 5(5): 1069-1105 [DOI: 10.3390/f5051069http://dx.doi.org/10.3390/f5051069]

Hackenberg J, Spiecker H, Calders K, Disney M and Raumonen P. 2015. SimpleTree—an efficient open source tool to build tree models from TLS clouds. Forests, 6(11): 4245-4294 [DOI: 10.3390/f6114245http://dx.doi.org/10.3390/f6114245]

Han F F and Jiang L C. 2017. Tree volume function based on diameter at different relative heights of Dahurian larch. Journal of Northeast Forestry University, 45(4): 65-69

韩斐斐, 姜立春. 2017. 基于树干不同高度直径的落叶松立木材积方程. 东北林业大学学报, 45(4): 65-69 [DOI: 10.13759/j.cnki.dlxb.2017.04.013http://dx.doi.org/10.13759/j.cnki.dlxb.2017.04.013]

Hauglin M, Astrup R, Gobakken T and Næsset E. 2013. Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features. Scandinavian journal of forest research, 28(5), 456-469 [DOI: 10.1016/j.isprsjprs.2013.02.013http://dx.doi.org/10.1016/j.isprsjprs.2013.02.013]

Hauglin M, Gobakken T, Astrup R, Ene L and Næsset E. 2014. Estimating single-tree crown biomass of Norway spruce by airborne laser scanning: A comparison of methods with and without the use of terrestrial laser scanning to obtain the ground reference data. Forests, 5(3)： 384-403 [DOI: 10.3390/f5030384http://dx.doi.org/10.3390/f5030384]

Kükenbrink D, Schneider F D, Leiterer R, Schaepman M E and Morsdorf F. 2017. Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm. Remote Sensing of Environment, 194: 424-436 [DOI: 10.1016/j.rse.2016.10.023http://dx.doi.org/10.1016/j.rse.2016.10.023]

Ma Z Y, Pang Y, Li Z Y, Lu H, Liu L X and Chen B W. 2019. Fine classification of near-ground point cloud based on terrestrial laser scanning and detection of forest fallen wood. Journal of Remote Sensing, 23(4): 743-755.

马振宇, 庞勇, 李增元, 卢昊, 刘鲁霞, 陈博伟. 2019. 地基激光雷达森林近地面点云精细分类与倒木提取. 遥感学报, 23(4): 743-755 [DOI: 10.11834/jrs.20197383http://dx.doi.org/10.11834/jrs.20197383]

Kunz M, Hess C, Raumonen P, Bienert A, Hackenberg J, Maas H G, Härdtle W, Fichtner A and Von Oheimb G. 2017. Comparison of wood volume estimates of young trees from terrestrial laser scan data. iForest, 10(2): 451-458 [DOI: 10.3832/ifor2151-010http://dx.doi.org/10.3832/ifor2151-010]

Li C G, Zhao X W and Li C G. 2006. Theory and Realization of Estimating Forest Stock Volume By Remote Sensing. Beijing: Science Press

李崇贵, 赵宪文, 李春干. 2006. 森林蓄积量遥感估测理论与实现. 北京: 科学出版社: 1-5

Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 20(5): 1138-1150

李增元, 刘清旺, 庞勇. 2016. 激光雷达森林参数反演研究进展. 遥感学报, 20(5): 1138-1150 [DOI: 10.11834/jrs.20165130http://dx.doi.org/10.11834/jrs.20165130]

Liang X L, Kankare V, Yu X W, Hyyppä J and Holopainen M. 2014. Automated stem curve measurement using terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 52(3): 1739-1748 [DOI: 10.1109/TGRS.2013.2253783http://dx.doi.org/10.1109/TGRS.2013.2253783]

Liu G J, Wang J L, Dong P L, Chen Y and Liu Z Y. 2018. Estimating individual tree height and diameter at breast height (DBH) from terrestrial laser scanning (TLS) data at plot level. Forests, 9(7): 398 [DOI: 10.3390/f9070398http://dx.doi.org/10.3390/f9070398]

Liu J Y, Wang S Q, Chen J M, Liu M L and Zhuang D F. 2004. Storages of soil organic carbon and nitrogen and land use changes in China: 1990-2000. Acta Geographica Sinica, 59(4): 483-496

刘纪远, 王绍强, 陈镜明, 刘明亮, 庄大方. 2004. 1990～2000年中国土壤碳氮蓄积量与土地利用变化. 地理学报, 59(4): 483-496 [DOI: 10.11821/xb200404001http://dx.doi.org/10.11821/xb200404001]

Liu L X, Pang Y, Li Z Y, Xu G C, Li D and Zheng G. 2014. Retrieving structural parameters of individual tree through terrestrial laser scanning data. Journal of Remote Sensing, 18(2): 365-377

刘鲁霞, 庞勇, 李增元, 徐光彩, 李丹, 郑光. 2014. 用地基激光雷达提取单木结构参数——以白皮松为例. 遥感学报, 18(2): 365-377 [DOI: 10.11834/jrs.20143091http://dx.doi.org/10.11834/jrs.20143091]

Malhi Y, Jackson T, Patrick Bentley L, Lau A, Shenkin A, Herold M, Calders K, Bartholomeus H and Disney M I. 2018. New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning. Interface Focus, 8(2): 20170052 [DOI: 10.1098/rsfs.2017.0052http://dx.doi.org/10.1098/rsfs.2017.0052]

Markku Å, Raumonen P, Kaasalainen M and Casella E. 2015. Analysis of geometric primitives in quantitative structure models of tree stems. Remote Sensing, 7(4): 4581-4603 [DOI: 10.3390/rs70404581http://dx.doi.org/10.3390/rs70404581]

Moorthy S M K, Raumonen P, Van den Bulcke J, Calders K and Verbeeck H. 2020. Terrestrial laser scanning for non-destructive estimates of liana stem biomass. Forest Ecology and Management, 456: 117751 [DOI: 10.1016/j.foreco.2019.117751http://dx.doi.org/10.1016/j.foreco.2019.117751]

Putman E B, Popescu S C, Eriksson M, Zhou T, Klockow P, Vogel J and Moore G W. 2018. Detecting and quantifying standing dead tree structural loss with reconstructed tree models using voxelized terrestrial lidar data. Remote Sensing of Environment, 209: 52-65 [DOI: 10.1016/j.rse.2018.02.028http://dx.doi.org/10.1016/j.rse.2018.02.028]

Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019. LESS: LargE-scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221: 695-706 [DOI: 10.1016/j.rse.2018.11.036http://dx.doi.org/10.1016/j.rse.2018.11.036]

Raumonen P, Kaasalainen M, Åkerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, Holopainen M, Disney M and Lewis P. 2013. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 5(2): 491-520 [DOI: 10.3390/rs5020491http://dx.doi.org/10.3390/rs5020491]

Saarinen N, Kankare V, Vastaranta M, Luoma V, Pyörälä J, Tanhuanpää T, Liang X L, Kaartinen H, Kukko A, Jaakkola A, Yu X W, Holopainen M and Hyyppä J. 2017. Feasibility of terrestrial laser scanning for collecting stem volume information from single trees. ISPRS Journal of Photogrammetry and Remote Sensing, 123: 140-158 [DOI: 10.1016/j.isprsjprs.2016.11.012http://dx.doi.org/10.1016/j.isprsjprs.2016.11.012]

Shao Y N, Liu Y K, Liu Y H, Chen Y and Tian S Y. 2017. Soil nutrient characteristics in Larix olgensis plantation with different stand densities. Journal of Central South University of Forestry and Technology, 37(9): 27-31

邵英男, 刘延坤, 李云红, 陈瑶, 田松岩. 2017. 不同林分密度长白落叶松人工林土壤养分特征. 中南林业科技大学学报, 37(9): 27-31 [DOI: 10.14067/j.cnki.1673-923x.2017.09.00http://dx.doi.org/10.14067/j.cnki.1673-923x.2017.09.00]

Stovall A E L and Shugart H H. 2018. Improved biomass calibration and validation with terrestrial LiDAR: implications for future LiDAR and SAR missions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10): 3527-3537 [DOI: 10.1109/JSTARS.2018.2803110http://dx.doi.org/10.1109/JSTARS.2018.2803110]

Stovall A E L, Vorster A G, Anderson R S, Evangelista P H and Shugart H H. 2017. Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment, 200: 31-42 [DOI: 10.1016/j.rse.2017.08.013http://dx.doi.org/10.1016/j.rse.2017.08.013]

Ver Planck N R and MacFarlane D W. 2014. Modelling vertical allocation of tree stem and branch volume for hardwoods. Forestry: An International Journal of Forest Research, 87(3): 459-469 [DOI: 10.1093/forestry/cpu007http://dx.doi.org/10.1093/forestry/cpu007]

Verity P G and Lagdon C. 1984. Relationships between lorica volume, carbon, nitrogen, and ATP content of tintinnids in Narragansett Bay. Journal of Plankton Research, 6(5): 859-868 [DOI: 10.1093/plankt/6.5.859http://dx.doi.org/10.1093/plankt/6.5.859]

Zhang F and Zhang Y. 2016. Compilation of binary standing volume tables of Larix principis-rupprechtii forest in Saihanba area. Hebei Journal of Forestry and Orchard Research, 31(2): 128-131

张菲, 张岩. 2016. 塞罕坝地区华北落叶松人工林二元立木材积表编制研究. 河北林果研究, 31(2): 128-131 [DOI: 10.13320/j.cnki.hjfor.2016.0025http://dx.doi.org/10.13320/j.cnki.hjfor.2016.0025]

Zhang W M, Wan P, Wang T J, Cai S S, Chen Y M, Jin X L and Yan G J. 2019. A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data. Remote Sensing, 11(2): 211 [DOI: 10.3390/rs11020211http://dx.doi.org/10.3390/rs11020211.]

Zhou J J. 2019 Forest parameter extraction from terrestrial laser scanning data. Chengdu: University of Electronic Science and Technology of China

周珺婕. 2019. 基于地面三维激光扫描数据的林木参数提取方法研究. 成都: 电子科技大学 [DOI: 10.27005/d.cnki.gdzku.2019.000091http://dx.doi.org/10.27005/d.cnki.gdzku.2019.000091]

Alert me when the article has been cited

提交

暂无数据