Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm

Abstract

Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates.

Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data

“…Development of a method for automatically determining the stem cross-section; (2) Development of a novel method for deriving the stem diameter by simulating the path of the diameter tape using a curve fitting method with computer graphics from TLS points; and (3)…”

“…Circle fitting [8] Hough transformation for points between 1.25 m and 1.35 m AGL c and circle fitting (10-cm thickness d ) 23 Multiple scans (four positions) SD e = 2.8 cm [31] Hough transformation and circle fitting at 1.3 m AGL 11 Single scan and multiple scans (four positions) NA f [2] RANSAC algorithm for circle fitting NA Single scan RMSE g from 2.0 cm-4.2 cm [9] Circle fitting at 1.3 m AGL (5-cm thickness) 82 Multiple scans RMSE from 1.8 cm-3.25 cm [13] Circle fitting at 1.2 m, 1.3 m and 1.4 m AGL NA Single scan and multiple scans NA [12] Hough transformation for points between 1.27 m and 1.33 m AGL, circle and cylinder fitting (6-cm thickness) 146 R 2 = 0.97 RMSE = 1.85 cm [14] Cylinder fitting between 1.28 and 1.32 m AGL and pixel method (4- According to this description, these methods have three limitations in retrieving the stem diameter: (1) the procedure for the selection of points used to extract the stem diameter was not constructed based on the stem cross-section; (2) the thickness values of the points were non-uniform and typically too large, and the points of concave bark were included in the calculation; and (3) the fitting graphics conform neither to the real shape of the stem, nor to the path of the diameter tape in the field work. Considering the irregularity of the stem and the tightness of the path of the diameter tape, some differences are observed between the stem diameters obtained using the existing TLS methods and the field-measured diameter obtained using a diameter tape.…”

“…In recent years, retrieving forestry parameters from terrestrial laser scanning (TLS) data has become an active area of research [1,2]. As an active remote sensing technology, TLS can provide a fast and efficient tool for obtaining a dense 3D point cloud dataset containing an immense number of three-dimensional points reflected from scanned objects.…”

“…Compared with airborne laser scanning and field measurements, TLS can obtain accurate understory information, including a digital record of the three-dimensional structure of forests [3]. Many studies have demonstrated the high potential of TLS in forest inventory, including tree locations [1,[4][5][6], heights [2,3,7], diameters [2,3,6,[8][9][10][11][12][13][14][15], volumes [16][17][18], biomass [17,[19][20][21][22][23] and others forestry parameters [24][25][26][27]. In addition, the influence of scanner parameters on forestry parameter derivation has been studied [18,28].…”

Detection of Single Tree Stems in Forested Areas From High Density Als Point Clouds Using 3d Shape Descriptors

“…They reported detection rates of 84% to 97% for the number and location of stems depending on the tree DBH (diameter at breast height). Olofsson et al (2014) by voxelizing the point cloud and analyzing the influence of different height layers could estimate tree stem positions with an average proportion of 87%. Wang et al (2016) performed in the first step, RANSAC based circle fitting of projected stem points, which is later followed by RANSAC cylinder fitting in 3D space.…”