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Article

Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology

Department of Forest- and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1180 Vienna, Austria
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Remote Sens. 2020, 12(9), 1509; https://doi.org/10.3390/rs12091509
Received: 12 April 2020 / Revised: 30 April 2020 / Accepted: 7 May 2020 / Published: 9 May 2020
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
The use of new and modern sensors in forest inventory has become increasingly efficient. Nevertheless, the majority of forest inventory data are still collected manually, as part of field surveys. The reason for this is the sometimes time-consuming and incomplete data acquisition with static terrestrial laser scanning (TLS). The use of personal laser scanning (PLS) can reduce these disadvantages. In this study, we assess a new personal laser scanner and compare it with a TLS approach for the estimation of tree position and diameter in a wide range of forest types and structures. Traditionally collected forest inventory data are used as reference. A new density-based algorithm for position finding and diameter estimation is developed. In addition, several methods for diameter fitting are compared. For circular sample plots with a maximum radius of 20 m and lower diameter at breast height (dbh) threshold of 5 cm, tree mapping showed a detection of 96% for PLS and 78.5% for TLS. Using plot radii of 20 m, 15 m, and 10 m, as well as a lower dbh threshold of 10 cm, the respective detection rates for PLS were 98.76%, 98.95%, and 99.48%, while those for TLS were considerably lower (86.32%, 93.81%, and 98.35%, respectively), especially for larger sample plots. The root mean square error (RMSE) of the best dbh measurement was 2.32 cm (12.01%) for PLS and 2.55 cm (13.19%) for TLS. The highest precision of PLS and TLS, in terms of bias, were 0.21 cm (1.09%) and −0.74 cm (−3.83%), respectively. The data acquisition time for PLS took approximately 10.96 min per sample plot, 4.7 times faster than that for TLS. We conclude that the proposed PLS method is capable of efficient data capture and can detect the largest number of trees with a sufficient dbh accuracy. View Full-Text
Keywords: forest inventory; point cloud; personal laser scanning; SLAM; terrestrial laser scanning; tree detection; diameter estimation forest inventory; point cloud; personal laser scanning; SLAM; terrestrial laser scanning; tree detection; diameter estimation
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MDPI and ACS Style

Gollob, C.; Ritter, T.; Nothdurft, A. Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology. Remote Sens. 2020, 12, 1509. https://doi.org/10.3390/rs12091509

AMA Style

Gollob C, Ritter T, Nothdurft A. Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology. Remote Sensing. 2020; 12(9):1509. https://doi.org/10.3390/rs12091509

Chicago/Turabian Style

Gollob, Christoph, Tim Ritter, and Arne Nothdurft. 2020. "Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology" Remote Sensing 12, no. 9: 1509. https://doi.org/10.3390/rs12091509

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