Optimizing line-plot size for personal laser scanning: modeling distance-dependent tree detection probability along transects

doi:10.3832/ifor4588-017

Optimizing line-plot size for personal laser scanning: modeling distance-dependent tree detection probability along transects

Tim Ritter , Andreas Tockner, Ralf Krassnitzer, Sarah Witzmann, Christoph Gollob, Arne Nothdurft

iForest - Biogeosciences and Forestry, Volume 17, Issue 5, Pages 269-276 (2024)
doi: https://doi.org/10.3832/ifor4588-017
Published: Sep 07, 2024 - Copyright © 2024 SISEF

Research Articles

PDF Version

Full Text

Citation

Geo-mapping

Personal laser scanning (PLS) systems are gaining popularity in forest inventory research and practice. They are primarily utilized on circular or compact rectangular sample plots to mitigate potential instrument drift and enhance tree detection rates, and a closed-loop scan path is usually implemented to achieve these objectives, ensuring thorough coverage of the plot. This study introduced a novel approach by applying the distance-sampling framework to PLS data collected during walks along line transects. Modeling the distance-dependent probability of tree detection using PLS coupled with automatic routines for point cloud processing aimed to ascertain the optimal width of line-plots to maximize tree detection rates. The optimized plots exhibited tree detection rates exceeding 99%, which facilitated accurate estimates of tree density, basal area, and growing stock volumes. This proposed method demonstrated considerable potential for data collection while walking along line transects in forests. For instance, the otherwise unproductive working time of field crews moving between systematically arranged sample plots can be utilized for additional data collection without generating additional costs. This innovative approach not only enhances operational efficiency but also establishes a foundation for further advancements to explore PLS applications in forest management practices.

Keywords

Personal Laser Scanning, Lidar, Forest Inventory, Distance Sampling, Line Transect Sampling, Tree Detection

Authors’ address

(1)

0000-0001-7520-628X
0000-0001-6833-6713

0000-0002-4882-5920
0000-0002-7036-5115
0000-0002-7065-7601
University of Natural Resources and Life Sciences, Vienna - BOKU, Department of Forest and Soil Science, Institute of Forest Growth, Vienna (Austria)

Corresponding author

Tim Ritter
tim.ritter@boku.ac.at

Citation

Ritter T, Tockner A, Krassnitzer R, Witzmann S, Gollob C, Nothdurft A (2024). Optimizing line-plot size for personal laser scanning: modeling distance-dependent tree detection probability along transects. iForest 17: 269-276. - doi: 10.3832/ifor4588-017

Academic Editor

Marco Borghetti

Paper history

Received: Feb 15, 2024
Accepted: Jul 26, 2024

First online: Sep 07, 2024
Publication Date: Oct 31, 2024
Publication Time: 1.43 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 1041
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 262
Abstract Page Views: 268
PDF Downloads: 473
Citation/Reference Downloads: 0
XML Downloads: 38

Web Metrics
Days since publication: 75
Overall contacts: 1041
Avg. contacts per week: 97.16

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

(No citations were found up to date. Please come back later)

(no Plumx Metrics available for this paper)

Publication Metrics

by Dimensions ^©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

(1)

Astrup R, Ducey MJ, Granhus A, Ritter T, Von Lüpke N (2014)
Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. Canadian Journal of Forest Research 44 (6): 666-676.
CrossRef | Gscholar

(2)

Bäuerle H, Nothdurft A (2011)
Spatial modeling of habitat trees based on line transect sampling and point pattern reconstruction. Canadian Journal of Forest Research 41: 715-727.
CrossRef | Gscholar

(3)

Bauwens S, Bartholomeus H, Calders K, Lejeune P (2016)
Forest inventory with terrestrial LiDAR: a comparison of static and hand-held mobile laser scanning. Forests 7 (6): 127.
CrossRef | Gscholar

(4)

Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001)
Introduction to distance sampling: estimating abundance of biological populations. Oxford Univ. Press, Oxford, UK, pp. 432.
CrossRef | Gscholar

(5)

Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2004)
Advanced distance sampling: estimating abundance of biological populations. Oxford Univ. Press, Oxford, UK, pp. 416.
Online | Gscholar

(6)

Chen S, Liu H, Feng Z, Shen C, Chen P (2019)
Applicability of personal laser scanning in forestry inventory. PLoS One 14: e0211392.
CrossRef | Gscholar

(7)

Cochran WG (1977)
Sampling techniques. John Wiley and Sons, New York, USA, pp. 428.
Gscholar

(8)

Dassot M, Constant T, Fournier M (2011)
The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Annals of Forest Science 68: 959-974.
CrossRef | Gscholar

(9)

De Vries PG (1986)
Sampling theory for forest inventory: a teach-yourself course. Springer, Berlin and Heidelberg, Germany, pp. 399.
Gscholar

(10)

Del Perugia B, Giannetti F, Chirici G, Travaglini D (2019)
Influence of scan density on the estimation of single-tree attributes by hand-held mobile laser scanning. Forests 10: 277.
CrossRef | Gscholar

(11)

Didas CM (2009)
Sampling and classification of tree holes within a Northeast temperate forest system. Master’s Thesis, University of New Hampshire, Durham, USA, pp. 78.
Online | Gscholar

(12)

Ducey MJ, Astrup R (2013)
Adjusting for nondetection in forest inventories derived from terrestrial laser scanning. Canadian Journal of Remote Sensing 39: 410-425.
Online | Gscholar

(13)

GeoSLAM (2023a)
GeoSLAM Hub. Web site.
Online | Gscholar

(14)

GeoSLAM (2023b)
GeoSLAM ZEB-HORIZON 3D. Web site.
Online | Gscholar

(15)

Gollob C, Ritter T, Nothdurft A (2020)
Forest inventory with long range and high-speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) technology. Remote Sensing 12 (9): 1509.
CrossRef | Gscholar

(16)

Gollob C, Ritter T, Wassermann C, Nothdurft A (2019)
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 Sensing 11 (13): 1602.
CrossRef | Gscholar

(17)

Hyyppä E, Muhojoki J, Yu X, Kukko A, Kaartinen H, Hyyppä J (2021)
Efficient coarse registration method using translation- and rotation-invariant local descriptors towards fully automated forest inventory. ISPRS Open Journal of Photogrammetry and Remote Sensing 2 (Part 3/W52): 100007.
CrossRef | Gscholar

(18)

Kändler G (2009)
The design of the second German National forest inventory. In: Proceedings of the “8th Annual Forest Inventory and Analysis Symposium” (McRoberts RE, Reams GA, Van PC D, McWilliams WH eds). Monterey (CA, USA) 16-19 Oct 2006. USDA Forest Service, Washington, DC, USA, pp. 408.
Online | Gscholar

(19)

Kangas A, Gove JH, Scott CT (2006)
Introduction. In: “Forest Inventory - Methodology and Applications” (Kangas A, Maltamo M eds). Springer, Dordrecht, Netherlands, pp. 362.
CrossRef | Gscholar

(20)

Kauffman JB, Arifanti VB, Basuki I, Kurnianto S, Novita N, Murdiyarso D, Donato DC, Warren MW (2017)
Protocols for the measurement, monitoring, and reporting of structure, biomass, carbon stocks and greenhouse gas emissions in tropical peat swamp forests. Center for International Forestry Research - CIFOR, Bogor, Indonesia, pp. 44.
CrossRef | Gscholar

(21)

Kershaw JA, Ducey MJ, Beers TW, Husch B (2016)
Forest mensuration (5th edn). John Wiley and Sons, New York, USA, pp. 613.
CrossRef | Gscholar

(22)

Kissa DO, Sheil D (2012)
Visual detection based distance sampling offers efficient density estimation for distinctive low abundance tropical forest tree species in complex terrain. Forest Ecology and Management 263: 114-121.
CrossRef | Gscholar

(23)

Köhl M, Magnussen S (2016)
Sampling in forest inventories. In: “Tropical Forestry Handbook (2nd edn)” (K Pancel, M Köhl eds). Springer, Berlin and Heidelberg, Germany, pp. 3633.
CrossRef | Gscholar

(24)

Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F, Holopainen M, Vastaranta M (2016)
Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 115: 63-77.
CrossRef | Gscholar

(25)

Liang X, Hyyppä J, Kaartinen H, Lehtomäki M, Pyörälä J, Pfeifer N, Holopainen M, Brolly G, Francesco P, Hackenberg J, Huang H, Jo HW, Katoh M, Liu L, Mokroš M, Morel J, Olofsson K, Poveda-Lopez J, Trochta J, Wang D, Wang J, Xi Z, Yang B, Zheng G, Kankare V, Luoma V, Yu X, Chen L, Vastaranta M, Saarinen N, Wang Y (2018)
International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 144: 137-179.
CrossRef | Gscholar

(26)

McRoberts RE, Bechtold WA, Patterson PL, Scott CT, Reams GA (2005)
The enhanced forest inventory and analysis program of the USDA Forest Service: historical perspective and announcement of statistical documentation. Journal of Forestry 103 (6): 304-308.
CrossRef | Gscholar

(27)

Miller DL, Rexstad E, Thomas L, Marshall L, Laake JL (2019)
Distance sampling in R. Journal of Statistical Software 89 (1): 1-28.
CrossRef | Gscholar

(28)

Mokroš M, Mikita T, Singh A, Tomaštík J, Chudá J, Wezyk P, Kuelka K, Surovy P, Klimánek M, Zieba-Kulawik K, Bobrowski R, Liang X (2021)
Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives. International Journal of Applied Earth Observation and Geoinformation 104: 102512.
CrossRef | Gscholar

(29)

Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D (2003)
Good practice guidance for land use, land-use change and forestry. Institute for Global Environmental Strategies - IGES, Hayama, Japan, pp. 590.
Online | Gscholar

(30)

Pollanschütz J (1965)
Eine neue Methode der Formzahl- und Massenbestimmung stehender Stämme - Neue Form- bzw. Kubierungsfunkionen und ihre Anwendung [A new method for determining the shape, number and mass of standing trunks - New shape or cubic functions and their application]. Technical Report 68, Forstliche Bundesversuchsanstalt, Mariabrunn, Germany, pp. 186. [in German]
Gscholar

(31)

Prieto Gonzalez R, Thomas L, Marques TA (2017)
Estimation bias under model selection for distance sampling detection functions. Environmental and Ecological Statistics 24: 399-414.
CrossRef | Gscholar

(32)

Quang PX (1993)
Nonparametric estimators for variable circular plot surveys. Biometrics 49: 837-852.
CrossRef | Gscholar

(33)

R Core Team (2022)
R: a language and environment for statistical computing, v. 4.2.2. The R Foundation for Statistical Computing, Vienna, Austria.
Online | Gscholar

(34)

Ritter T, Saborowski J (2012)
Point transect sampling of deadwood: a comparison with well-established sampling techniques for the estimation of volume and carbon storage in managed forests. European Journal of Forest Research 131 (6): 1845-1856.
CrossRef | Gscholar

(35)

Ritter T, Nothdurft A, Saborowski J (2013)
Correcting the nondetection bias of angle count sampling. Canadian Journal of Forest Research 43 (4): 344-354.
CrossRef | Gscholar

(36)

Ritter T, Saborowski J (2014)
Efficient integration of a deadwood inventory into an existing forest inventory carried out as two-phase sampling for stratification. Forestry 87 (4): 571-581.
CrossRef | Gscholar

(37)

Ritter T, Schwarz M, Tockner A, Leisch F, Nothdurft A (2017)
Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning. Forests 8 (8): 265.
CrossRef | Gscholar

(38)

Ritter T, Gollob C, Nothdurft A (2020)
Towards an optimization of sample plot size and scanner position layout for terrestrial laser scanning in multi-scan mode. Forests 11: 1-23.
CrossRef | Gscholar

(39)

Ritter T, Gollob C, Nothdurft A (2021)
Modelling the detection rate of terrestrial laser scanning in multi scan mode. In: Proceedings of the “SilviLaser Conference 2021”. Vienna (Austria) 28-30 Sep 2021, pp. 53-55.
CrossRef | Gscholar

(40)

Schadauer K, Gschwantner T, Gabler K (2007)
Austrian national forest inventory: caught in the past and heading toward the future. In: Proceedings of the “7th Annual Forest Inventory and Analysis Symposium” (McRoberts R, Reams G, Van P D, McWilliams W eds). Portland (OR, USA) 3-6 Oct 2005. USDA Forest Service, Washington, DC, USA, pp. 319.
Gscholar

(41)

Seber GAF (1982)
The estimation of animal abundance and related parameters (2nd edn). Macmillian, New York, USA, pp. 654.
Gscholar

(42)

Thomas L, Buckland ST, Burnham KP, Anderson DR, Laake JL, Borchers DL, Strindberg S (2012)
Distance sampling. In: “Encyclopedia of Environmetrics” (El-Shaarawi AH, Piegorsch WW eds). John Wiley and Sons, New York, USA.
CrossRef | Gscholar

(43)

Tockner A, Gollob C, Kranitzer R, Ritter T, Nothdurft A (2022)
Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS). International Journal of Applied Earth Observations and Geoinformation 114: 103025.
CrossRef | Gscholar

(44)

Tockner A (2024)
treeX - Package for individual tree detection and tree segmentation from terrestrial LiDAR data. Web site.
Online | Gscholar

(45)

Tokola T (2006)
Europe. In: “Forest Inventory - Methodology and Applications” (Kangas A, Maltamo M eds). Springer, Dordrecht, Netherlands, pp. 362.
CrossRef | Gscholar

(46)

Tomppo E (2006)
The Finnish national forest inventory. In: “Forest Inventory - Methodology and Applications” (A Kangas, M Maltamo eds). Springer, Dordrecht, Netherlands, pp. 362.
Online | Gscholar

(47)

Tomppo E (2009)
The Finnish National Forest Inventory. In: Proceedings of the “8th Annual Forest Inventory and Analysis Symposium” (McRoberts RE, Reams GA, Van PC D, McWilliams WH eds). Monterey (CA, USA) 16-19 Oct 2006. USDA Forest Service, Washington, DC, USA, pp. 408.
Gscholar

(48)

Tupinambá-Simões F, Pascual A, Guerra-Hernández J, Ordóñez C, De Conto T, Bravo F (2023)
Assessing the performance of a handheld laser scanning system for individual tree mapping a mixed forests showcase in Spain. Remote Sensing 15 (5): 1169.
CrossRef | Gscholar

(49)

Witzmann S (2022)
Development and evaluation of algorithms for the automatic marker-free registration of forest point clouds obtained from Personal Laser Scanning. Master / Diploma Thesis, University of Natural Resources and Life Sciences, Vienna, pp. 29.
Online | Gscholar

(50)

Witzmann S, Matitz L, Gollob C, Ritter T, Kranitzer R, Tockner A, Stampfer K, Nothdurft A (2022)
Accuracy and precision of stem cross-section modeling in 3D point clouds from TLS and caliper measurements for basal area estimation. Remote Sensing 14 (8): 1923.
CrossRef | Gscholar

iForest - Biogeosciences and Forestry

Contents

Search

Journal Info

Journal Subjects and Fields

For Authors

For Reviewers

For Readers

SISEF Publishing

Search iForest Contents