Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation.

[1]  Juha Hyyppä,et al.  The effect of TLS point cloud sampling on tree detection and diameter measurement accuracy , 2016 .

[2]  C. Hopkinson,et al.  Automating Plot-Level Stem Analysis from Terrestrial Laser Scanning , 2016 .

[3]  Sorin C. Popescu,et al.  Terrestrial Laser Scanning as an Effective Tool to Retrieve Tree Level Height, Crown Width, and Stem Diameter , 2015, Remote. Sens..

[4]  N. J. Tate,et al.  Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data , 2009 .

[5]  P. Gong,et al.  Automated methods for measuring DBH and tree heights with a commercial scanning lidar , 2011 .

[6]  P. Pueschel The influence of scanner parameters on the extraction of tree metrics from FARO Photon 120 terrestrial laser scans , 2013 .

[7]  Yiu-Tong Chan,et al.  A simple approach for the estimation of circular arc center and its radius , 1989, Comput. Vis. Graph. Image Process..

[8]  P. Axelsson DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[9]  Juha Hyyppä,et al.  Automated Stem Curve Measurement Using Terrestrial Laser Scanning , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Ville Kankare,et al.  Diameter distribution estimation with laser scanning based multisource single tree inventory , 2015 .

[11]  Hans-Gerd Maas,et al.  Automatic forest inventory parameter determination from terrestrial laser scanner data , 2008 .

[12]  Markus Hollaus,et al.  FAST AND ROBUST STEM RECONSTRUCTION IN COMPLEX ENVIRONMENTS USING TERRESTRIAL LASER SCANNING , 2016 .

[13]  Johan Holmgren,et al.  Single Tree Stem Profile Detection Using Terrestrial Laser Scanner Data, Flatness Saliency Features and Curvature Properties , 2016 .

[14]  Johan Holmgren,et al.  Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm , 2014, Remote. Sens..

[15]  Philip Lewis,et al.  Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data , 2013, Remote. Sens..

[16]  Xinyu Song,et al.  Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data , 2016, Remote. Sens..

[17]  N. Pfeifer,et al.  Tree Stem Shapes Derived from TLS Data as an Indicator for Shallow Landslides , 2016 .

[18]  Pete Watt,et al.  Measuring forest structure with terrestrial laser scanning , 2005 .

[19]  Thomas Udelhoven,et al.  The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans , 2013 .

[20]  J. Trochta,et al.  3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR , 2017, PloS one.

[21]  C. Hopkinson,et al.  Assessing forest metrics with a ground-based scanning lidar , 2004 .

[22]  H. Spiecker,et al.  DESCRIBING FOREST STANDS USING TERRESTRIAL LASER-SCANNING , 2004 .

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

[24]  Juha Hyyppä,et al.  Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees , 2017 .

[25]  Sébastien Bauwens,et al.  Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning , 2016 .

[26]  A. Al-Sharadqah,et al.  Error analysis for circle fitting algorithms , 2009, 0907.0421.

[27]  Juha Hyyppä,et al.  Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[28]  M. Herold,et al.  Nondestructive estimates of above‐ground biomass using terrestrial laser scanning , 2015 .

[29]  Stefan Norra,et al.  Terrestrial laser scanning for estimating urban tree volume and carbon content , 2012 .

[30]  Di Wang,et al.  Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests , 2016, Remote. Sens..

[31]  Hui Lin,et al.  Retrieval and Accuracy Assessment of Tree and Stand Parameters for Chinese Fir Plantation Using Terrestrial Laser Scanning , 2015, IEEE Geoscience and Remote Sensing Letters.

[32]  Di Wang,et al.  Reconstructing Stem Cross Section Shapes From Terrestrial Laser Scanning , 2017, IEEE Geoscience and Remote Sensing Letters.

[33]  Johannes Heinzel,et al.  Tree Stem Diameter Estimation From Volumetric TLS Image Data , 2017, Remote. Sens..

[34]  H. Spiecker,et al.  AUTOMATIC DETERMINATION OF FOREST INVENTORY PARAMETERS USING TERRESTRIAL LASER SCANNING , 2003 .

[35]  Hossein Khodabakhshi Rafsanjani,et al.  An automatic algorithm for determination of the nanoparticles from TEM images using circular hough transform. , 2017, Micron.

[36]  P. Radtke,et al.  Detailed Stem Measurements of Standing Trees from Ground-Based Scanning Lidar , 2006, Forest Science.