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Article

Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest

1
Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Cda Fonte Lappone s.n.c., 86090 Pesche, Italy
2
Dipartimento di Agricoltura Ambiente e Alimenti, Università degli Studi del Molise, Via De Sanctis s.n.c., 86100 Campobasso, Italy
3
Department of Geodesy and Geoinformation, Technical University of Vienna, 1040 Vienna, Austria
4
Department of Innovation in Biological, Agro-Food and Forest Systems—DIBAF, University of Tuscia, 01100 Viterbo, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Lin Cao
Remote Sens. 2021, 13(21), 4265; https://doi.org/10.3390/rs13214265
Received: 31 August 2021 / Revised: 6 October 2021 / Accepted: 19 October 2021 / Published: 23 October 2021
Timber assortments are some of the most important goods provided by forests worldwide. To quantify the amount and type of timber assortment is strongly important for socio-economic purposes, but also for accurate assessment of the carbon stored in the forest ecosystems, regardless of their main function. Terrestrial laser scanning (TLS) became a promising tool for timber assortment assessment compared to the traditional surveys, allowing reconstructing the tree architecture directly and rapidly. This study aims to introduce an approach for timber assortment assessment using TLS data in a mixed and multi-layered Mediterranean forest. It consists of five steps: (1) pre-processing, (2) timber-leaf discrimination, (3) stem detection, (4) stem reconstruction, and (5) timber assortment assessment. We assume that stem form drives the stem reconstruction, and therefore, it influences the timber assortment assessment. Results reveal that the timber-leaf discrimination accuracy is 0.98 through the Random Forests algorithm. The overall detection rate for all trees is 84.4%, and all trees with a diameter at breast height larger than 0.30 m are correctly identified. Results highlight that the main factors hindering stem reconstruction are the presence of defects outside the trunk, trees poorly covered by points, and the stem form. We expect that the proposed approach is a starting point for valorising the timber resources from unmanaged/managed forests, e.g., abandoned forests. Further studies to calibrate its performance under different forest stand conditions are furtherly required. View Full-Text
Keywords: timber assortment; roundwood; mixed-species; point cloud; stem modelling timber assortment; roundwood; mixed-species; point cloud; stem modelling
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MDPI and ACS Style

Alvites, C.; Santopuoli, G.; Hollaus, M.; Pfeifer, N.; Maesano, M.; Moresi, F.V.; Marchetti, M.; Lasserre, B. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest. Remote Sens. 2021, 13, 4265. https://doi.org/10.3390/rs13214265

AMA Style

Alvites C, Santopuoli G, Hollaus M, Pfeifer N, Maesano M, Moresi FV, Marchetti M, Lasserre B. Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest. Remote Sensing. 2021; 13(21):4265. https://doi.org/10.3390/rs13214265

Chicago/Turabian Style

Alvites, Cesar, Giovanni Santopuoli, Markus Hollaus, Norbert Pfeifer, Mauro Maesano, Federico V. Moresi, Marco Marchetti, and Bruno Lasserre. 2021. "Terrestrial Laser Scanning for Quantifying Timber Assortments from Standing Trees in a Mixed and Multi-Layered Mediterranean Forest" Remote Sensing 13, no. 21: 4265. https://doi.org/10.3390/rs13214265

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