Combining point cloud slicing and terrestrial laser scanning data to retrieve an effective leaf area index

Lu LU; Guang ZHENG; Lixia MA

doi:10.11834/jrs.20187076

Technology and Methodology | Views : 0 下载量: 525 CSCD: 7

PDF
Export
Share
Collection
Album

Combining point cloud slicing and terrestrial laser scanning data to retrieve an effective leaf area index
Vol. 22, Issue 3, Pages: 432-449(2018)
Received：05 April 2017，

Accepted：05 September 2017，

Published：2018-05
DOI： 10.11834/jrs.20187076
稿件说明：

移动端阅览

路璐, 郑光, 马利霞. 2018. 激光雷达和点云切片算法结合的森林有效叶面积指数估算. 遥感学报, 22(3): 432–449 DOI： 10.11834/jrs.20187076.

Lu L, Zheng G and Ma L X. 2018. Combining point cloud slicing and terrestrial laser scanning data to retrieve an effective leaf area index. Journal of Remote Sensing, 22(3): 432–449 DOI： 10.11834/jrs.20187076.

摘要

叶面积指数(LAI)是定量模拟和刻画植被冠层结构、生理过程以及研究森林生态系统碳水循环中物质和能量交换的重要生物物理参数之一。定量分析林冠元素的3维空间分布是准确估算森林叶面积指数的重要基础和关键步骤。本文利用地面激光雷达扫描系统(TLS)，获取了不同树种、密度、年龄和空间分布的森林3维点云数据，利用“径向半球点云切片”和“点云法向量重建”算法分别计算森林的角度孔隙率和消光系数，进而得到森林冠层的有效叶面积指数。通过与利用传统光学仪器得到的结果比较发现：单站半球式激光雷达扫描得到森林样方尺度的结果与LAI-2200和数字半球摄影观测所得结果的相关性分别为

=0.7084(

=9，

0.01)和0.7409(

=14，

0.01)。通过角度分辨率(LBA)和角度孔隙率(AGF)的敏感性分析，建议径向半球切片算法的角度分辨率应参考中心单站TLS半球式扫描所设置的采样间距。本文所采用的径向半球切片算法可以有效地利用单站半球式扫描所获取的3维点云进行森林冠层有效叶面积指数的定量估算。

Abstract

Leaf Area Index (LAI) is one of the most important forest canopy biophysical parameters and plays a key role in many forest biophysical processes and energy and matter exchange among forest–soil–atmosphere. Terrestrial laser scanning (TLS)-based Point Cloud Data (PCD) implicitly contain three-dimensional forest structural information. PCD can be used to quantitatively characterize the structural information of forests from a three-dimensional perspective and provides a high spatial resolution. Consequently

PCD provides highly detailed information about forests and enable the retrieval of forest parameters. In this study

we retrieved the canopy effective leaf area index (LAIe) by using TLS and validated the TLS-based results through field-based measurements. Three-dimensional PCD were acquired with a single- or multiple-location experimental setup in all forest plots. All PCD were clipped into a circular plot with a radius of 30 m. An algorithm called “radial hemispherical point cloud slicing (RHPCS)” was utilized to process the original PCD to retrieve the “Angular Gap Fraction (AGF)” and extinction coefficients by dividing the hemispherical volume into many “trapezoid voxels.” A “gap” voxel is an empty or a foliage voxel depending on the number of points within it. By reconstructing the normal vector of a point within a certain neighbor region

we computed the leaf mean inclination angle and obtained the canopy extinction coefficient by using Campbell’s algorithm. We calculated the LAIe of each forest plot on the basis of Beer’s law by dividing a hemisphere PCD into 9° intervals in the inclination direction. Ten annulus hemispherical regions were obtained. Each region had an azimuthal angle ranging from 0° to 360° and a 9° inclination angle range. Then

we computed the AGF

extinction coefficient

and LAIe of each hemispherical region. We obtain the LAIe of the entire plot by computing the means of the LAIe of 10 hemispherical regions. Results showed that the TLS-based LAIe captured 70.84% (

0.01) and 74.09% (

=14

0.01) of the variations in LAI-2200 and Digital Hemispherical Plotography (DHP)

respectively. We conducted a sensitivity analysis of laser beam angle in AGF estimation and discovered that the AGF of RHPCS should be determined based on a user-predefined laser sampling space and is affected by the characteristic size of foliage elements of a forest canopy. We also analyzed the effect of the number of neighboring points on LAIe calculation. The optimal number of neighboring points was 12 in this study

and we suggest that the number of neighboring points be based on leaf curvature when reconstructing a normal vector. Comparison of TLS-based LAIe with the LAIe results of LAI-2200 and DHP showed that calculating the LAIe of forest plots by combining TLS-based PCD with the RHPCS algorithm is feasible and suitable for forests with different densities and ages. The TLS-based LAIe estimation approach presents considerable importance in quantitatively characterizing forest canopy structural parameters from a 3D perspective.

关键词

Keywords

references

Alliez P, Saboret L and Guennebaud G. 2016. Surface reconstruction from point sets. CGAL User and Reference Manual, 4: 1. http://doc.cgal.org/latest/Surface_reconstruction_points_3/index.html http://doc.cgal.org/latest/Surface_reconstruction_points_3/index.html [2016-11-01]

Asner G P, Braswell B H, Schimel D S and Wessman C A. 1998. Ecological research needs from multiangle remote sensing data. Remote Sensing of Environment, 63(2): 155–165

Asner G P, Scurlock J M O and Hicke J A. 2003. Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Global Ecology and Biogeography, 12(3): 191–205

Black T A, Chen J M, Lee X H and Sagar R M. 1991. Characteristics of shortwave and longwave irradiances under a douglas-fir forest stand. Canadian Journal of Forest Research, 21(7): 1020–1028

Campbell G S. 1986. Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agricultural and Forest Meteorology, 36(4): 317–321

Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421–429

Chen J M, Black T A and Adams R S. 1991. Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand. Agricultural and Forest Meteorology, 56(1/2): 129–143

Chen J M, Chen X Y, Ju W M and Geng X Y. 2005. Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. Journal of Hydrology, 305(1/4): 15–39

Chen J M and Cihlar J. 1995. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Transactions on Geoscience and Remote Sensing, 33(3): 777–787

Chen J M and Cihlar J. 1996. Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sensing of Environment, 55(2): 153–162

Chen J M, Rich P M, Gower S T, Norman J M and Plummer S. 1997. Leaf area index of boreal forests: theory, techniques, and measurements. Journal of Geophysical Research: Atmospheres, 102(D24): 29429–29443

Cleugh H A, Leuning R, Mu Q Z and Running S W. 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3): 285–304

Danson F M, Hetherington D, Morsdorf F, Koetz B and Allgower B. 2007. Forest canopy gap fraction from terrestrial laser scanning. IEEE Geoscience and Remote Sensing Letters, 4(1): 157–160

Disney M, Lewis P and Saich P. 2006. 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sensing of Environment, 100(1): 114–132

Duchemin B, Hadria R, Erraki S, Boulet G, Maisongrande P, Chehbouni A, Escadafal R, Ezzahar J, Hoedjes J C B, Kharrou M H, Khabba S, Mougenot B, Olioso A, Rodriguez J C and Simonneaux V. 2006. Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1): 1–27

Fuchs M, Asrar G, Kanemasu E T and Hipps L E. 1984. Leaf area estimates from measurements of photosynthetically active radiation in wheat canopies. Agricultural and Forest Meteorology, 32(1): 13–22

Greaves H E, Vierling L A, Eitel J U H, Boelman N T, Magney T S, Prager C M and Griffin K L. 2015. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164: 26–35

Hancock S, Essery R, Reid T, Carle J, Baxter R, Rutter N and Huntley B. 2014. Characterising forest gap fraction with terrestrial lidar and photography: an examination of relative limitations. Agricultural and Forest Meteorology, 189-190: 105–114

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

Henning J G and Radtke P J. 2006. Detailed stem measurements of standing trees from ground-based scanning lidar. Forest Science, 52(1): 67–80

Hirose T. 2005. Development of the Monsi-Saeki theory on canopy structure and function. Annals of Botany, 95(3): 483–494

Hoppe H, DeRose T, Duchamp T, McDonald J and Stuetzle W. 1992. Surface reconstruction from unorganized points. ACM SIGGRAPH Computer Graphics, 26(2): 71–78

Hosoi F and Omasa K. 2006. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3610–3618

Hyvönen R, Ågren G I, Linder S, Persson T, Cotrufo M F, Ekblad A, Freeman M, Grelle A, Janssens I A, Jarvis P G, Kellomäki S, Lindroth A, Loustau D, Lundmark T, Norby R J, Oren R, Pilegaard K, Ryan M G, Sigurdsson B D, Strömgren M, van Oijen M and Wallin G. 2007. The likely impact of elevated [CO 2 ], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. New Phytologist, 173(3): 463–480

Ilangakoon N T, Gorsevski P V and Milas A S. 2015. Estimating leaf area index by Bayesian Linear Regression using terrestrial LiDAR, LAI-2200 plant canopy analyzer, and landsat TM spectral indices. Canadian Journal of Remote Sensing, 41(4): 315–333

Jongschaap R E E. 2006. Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. European Journal of Agronomy, 24(4): 316–324

Kankare V, Holopainen M, Vastaranta M, Puttonen E, Yu X W, Hyyppä J, Vaaja M, Hyyppä H and Alho P. 2013. Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 75: 64–75

Lang A R G and Xiang Y Q. 1986. Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agricultural and Forest Meteorology, 37(3): 229–243

Leblanc S G. 2004. Digital Hemispherical Photography Manual, Draft Version 1.0. Ottawa: Canada Centre for Remote Sensing, Natural Resources Canada: 25

Lee D K and Kwon K C. 2006. Biomass and annual net production of Quercus mongolica stands in Pyungchang and Jecheon areas. Journal of Korean Forestry Society, 95(3): 309–315

Lim K, Treitz P, Wulder M, St-Onge B and Flood M. 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography, 27(1): 88–106

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

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 (

骆社周, 程峰, 王方建, 习晓环, 王成. 2012. 基于TM遥感数据的西藏林芝地区叶面积指数反演. 遥感技术与应用, 27(5): 740–745

Luo S Z, Cheng F, Wang F J, Xi X H and Wang C. 2012. Leaf area index inversion based on TM in Linzhi, Tibet. Remote Sensing Technology and Application, 27(5): 740–745 (

Moorthy I, Miller J R, Hu B X, Chen J and Li Q M. 2008. Retrieving crown leaf area index from an individual tree using ground-based lidar data. Canadian Journal of Remote Sensing, 34(3): 320–332

Moskal L M and Zheng G. 2012. Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sensing, 4(1): 1–20

Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith G R, Lotsch A, Friedl M, Morisette J T, Votava P, Nemani R R and Running S W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1/2): 214–231

Neumann H H, Den Hartog G and Shaw R H. 1989. Leaf area measurements based on hemispheric photographs and leaf-litter collection in a deciduous forest during autumn leaf-fall. Agricultural and Forest Meteorology, 45(3/4): 325–345

Olsoy P J, Glenn N F, Clark P E and Derryberry D R. 2014. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 166–173

Olsoy P J, Mitchell J J, Levia D F, Clark P E and Glenn N F. 2016. Estimation of big sagebrush leaf area index with terrestrial laser scanning. Ecological Indicators, 61: 815–821

Rakocevic M, Sinoquet H, Christophe A and Varlet-Grancher C. 2000. Assessing the geometric structure of a white clover ( Trifolium repens L.) canopy using 3-D digitising. Annals of Botany, 86(3): 519–526

Soegaard H. 1999. Fluxes of carbon dioxide, water vapour and sensible heat in a boreal agricultural area of Sweden-scaled from canopy to landscape level. Agricultural and Forest Meteorology, 98-99: 463–478

Strahler A H, Jupp D L B, Woodcock C E, Schaaf C B, Yao T, Zhao F, Yang X Y, Lovell J, Culvenor D, Newnham G, Ni-Miester W and Boykin-Morris W. 2008. Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna ® ). Canadian Journal of Remote Sensing, 34(2): S426–S440

Takeda T, Oguma H, Sano T, Yone Y and Fujinuma Y. 2008. Estimating the plant area density of a Japanese larch ( Larix kaempferi Sarg.) plantation using a ground-based laser scanner. Agricultural and Forest Meteorology, 148(3): 428–438

Tansey K, Selmes N, Anstee A, Tate N J and Denniss A. 2009. Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data. International Journal of Remote Sensing, 30(19): 5195–5209

Wang W M, Li Z L and Su H B. 2007. Comparison of leaf angle distribution functions: effects on extinction coefficient and fraction of sunlit foliage. Agricultural and Forest Meteorology, 143(1/2): 106–122

Waring R H and Pitman G B. 1985. Modifying lodgepole pine stands to change susceptibility to mountain pine beetle attack. Ecology, 66(3): 889–897

Wilson J W. 1965. Stand structure and light penetration. I. Analysis by point quadrats. Journal of Applied Ecology, 2(2): 383–390

Zheng G, Ma L X, He W, Eitel J U H, Moskal L M and Zhang Z Y. 2016. Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1475–1487

Zheng G and Moskal L M. 2012. Leaf orientation retrieval from terrestrial laser scanning (TLS) data. IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3970–3979

Zheng G, Moskal L M and Kim S H. 2013. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 51(2): 777–786

Alert me when the article has been cited

提交

Estimation of forest leaf area index based on spectrally corrected airborne LiDAR pulse penetration index by intensity of point cloud

Time-series accuracy validation and variation characteristic analysis of MODIS leaf-area index products for crop in the middle reaches of the Heihe River

UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data

GF-1 leaf area index product across China based on three-dimensional stochastic radiation transfer model

Spectral unmixing method considering endmember variability of vegetation