Next Article in Journal
Correction: Z. Liu and Y. Liu. Does Anthropogenic Land Use Change Play a Role in Changes of Precipitation Frequency and Intensity over the Loess Plateau of China? Remote Sens. 2018, 10, 1818
Previous Article in Journal
High-Resolution Lightning Detection and Possible Relationship with Rainfall Events over the Central Mediterranean Area
Previous Article in Special Issue
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds
Open AccessArticle

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

Department of Forest- and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1180 Vienna, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1602; https://doi.org/10.3390/rs11131602
Received: 5 June 2019 / Revised: 2 July 2019 / Accepted: 2 July 2019 / Published: 5 July 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
  |  
PDF [8557 KB, uploaded 5 July 2019]
  |  

Abstract

This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate. View Full-Text
Keywords: terrestrial laser scanning; automatic tree mapping; forest inventory; scanner positions terrestrial laser scanning; automatic tree mapping; forest inventory; scanner positions
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Gollob, C.; Ritter, T.; Wassermann, C.; Nothdurft, A. Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots. Remote Sens. 2019, 11, 1602.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top