Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales

The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.

[1]  Boris Jutzi,et al.  Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features , 2014 .

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  Babak Taati,et al.  Difference of Normals as a Multi-scale Operator in Unorganized Point Clouds , 2012, 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission.

[4]  M. Verstraete,et al.  Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements , 2011 .

[5]  Philippe Santenoise,et al.  Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment , 2012 .

[6]  Xi Zhu,et al.  Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Steffen Urban,et al.  Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas , 2015, Comput. Graph..

[8]  Heinrich Spiecker,et al.  SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds , 2015 .

[9]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[10]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[11]  D. Baldocchi,et al.  On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR , 2014 .

[12]  J. Demantké,et al.  DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS , 2012 .

[13]  Josep Peñuelas,et al.  Age-Related Modulation of the Nitrogen Resorption Efficiency Response to Growth Requirements and Soil Nitrogen Availability in a Temperate Pine Plantation , 2016, Ecosystems.

[14]  Tao Wang,et al.  Stand ages regulate the response of soil respiration to temperature in a Larix principis-rupprechtii plantation , 2014 .

[15]  M. Vastaranta,et al.  Terrestrial laser scanning in forest inventories , 2016 .

[16]  M. Fournier,et al.  The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges , 2011, Annals of Forest Science.

[17]  David Belton,et al.  PROCESSING TREE POINT CLOUDS USING GAUSSIAN MIXTURE MODELS , 2013 .

[18]  Zhen Wang,et al.  A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Anttoni Jaakkola,et al.  Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods , 2011, Remote. Sens..

[20]  Harri Kaartinen,et al.  Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modelling , 2014, Remote. Sens..

[21]  Q. Guo,et al.  A geometric method for wood-leaf separation using terrestrial and simulated Lidar data , 2015 .

[22]  Guang Zheng,et al.  Retrieval of Effective Leaf Area Index in Heterogeneous Forests With Terrestrial Laser Scanning , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Jasmine Muir,et al.  Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Ze He,et al.  Estimating Leaf Area Density of Individual Trees Using the Point Cloud Segmentation of Terrestrial LiDAR Data and a Voxel-Based Model , 2017, Remote. Sens..

[25]  Junjie Zhou,et al.  Comparison of Single and Multi-Scale Method for Leaf and Wood Points Classification from Terrestrial Laser Scanning Data , 2018 .

[26]  H. Spiecker,et al.  Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density , 2015 .

[27]  J. Suomalainen,et al.  Full waveform hyperspectral LiDAR for terrestrial laser scanning. , 2012, Optics express.

[28]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[29]  Martial Hebert,et al.  Scale selection for classification of point-sampled 3D surfaces , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[30]  Di Wang,et al.  FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD ANDLEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA , 2017 .

[31]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[32]  Martial Hebert,et al.  Natural terrain classification using three‐dimensional ladar data for ground robot mobility , 2006, J. Field Robotics.

[33]  Lin Cao,et al.  A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR , 2016, Remote. Sens..

[34]  Tiziano Ghisu,et al.  An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN , 2018, Agricultural and Forest Meteorology.

[35]  Christian Jauvin,et al.  PypeTree: A Tool for Reconstructing Tree Perennial Tissues from Point Clouds , 2014, Sensors.

[36]  Ning Ye,et al.  Automatic extraction of street trees' nonphotosynthetic components from MLS data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[37]  N. Pfeifer,et al.  Separating Tree Photosynthetic and Non-Photosynthetic Components from Point Cloud Data Using Dynamic Segment Merging , 2018 .

[38]  Michael Weinmann,et al.  A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas , 2017, Remote. Sens..

[39]  Guang Zheng,et al.  Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[40]  A. Strahler,et al.  On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements , 2018, Interface Focus.

[41]  Sorin C. Popescu,et al.  Multi-temporal terrestrial laser scanning for modeling tree biomass change , 2014 .