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

被引:9
作者
Alvites, Cesar [1 ]
Santopuoli, Giovanni [2 ]
Hollaus, Markus [3 ]
Pfeifer, Norbert [3 ]
Maesano, Mauro [4 ]
Moresi, Federico Valerio [4 ]
Marchetti, Marco [1 ]
Lasserre, Bruno [1 ]
机构
[1] Univ Molise, Dipartimento Biosci & Territorio, Cda Fonte Lappone Snc, I-86090 Pesche, Italy
[2] Univ Molise, Dipartimento Agricoltura Ambiente & Alimenti, Cda Fonte Lappone Snc, I-86100 Campobasso, Italy
[3] Vienna Univ Technol, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria
[4] Univ Tuscia, Dept Innovat Biol, Agro Food & Forest Syst DIBAF, I-01100 Viterbo, Italy
关键词
timber assortment; roundwood; mixed-species; point cloud; stem modelling; STEM; MODELS; ACCURACY; AIRBORNE; LEAF;
D O I
10.3390/rs13214265
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:25
相关论文
共 60 条
[1]   Unsupervised algorithms to detect single trees in a mixed-species and multilayered Mediterranean forest using LiDAR data [J].
Alvites, Cesar ;
Santopuoli, Giovanni ;
Maesano, Mauro ;
Chirici, Gherardo ;
Moresi, Federico Valerio ;
Tognetti, Roberto ;
Marchetti, Marco ;
Lasserre, Bruno .
CANADIAN JOURNAL OF FOREST RESEARCH, 2021, 51 (12) :1766-1780
[2]  
[Anonymous], 2020, DPLYR GRAMMAR DATA M
[3]   European Forest Types and Forest Europe SFM indicators: Tools for monitoring progress on forest biodiversity conservation [J].
Barbati, A. ;
Marchetti, M. ;
Chirici, G. ;
Corona, P. .
FOREST ECOLOGY AND MANAGEMENT, 2014, 321 :145-157
[4]   Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning [J].
Bauwens, Sebastien ;
Bartholomeus, Harm ;
Calders, Kim ;
Lejeune, Philippe .
FORESTS, 2016, 7 (06)
[5]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]   Nondestructive Tree Stem and Crown Volume Allometry in Hybrid Poplar Plantations Derived from Terrestrial Laser Scanning [J].
Chianucci, Francesco ;
Puletti, Nicola ;
Grotti, Mirko ;
Ferrara, Carlotta ;
Giorcelli, Achille ;
Coaloa, Domenico ;
Tattoni, Clara .
FOREST SCIENCE, 2020, 66 (06) :737-746
[7]   INFLUENTIAL OBSERVATIONS IN LINEAR-REGRESSION [J].
COOK, RD .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1979, 74 (365) :169-174
[8]  
Cowell A.M., 2004, THESIS MONTFORT U LE
[9]   The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges [J].
Dassot, Mathieu ;
Constant, Thiery ;
Fournier, Meriem .
ANNALS OF FOREST SCIENCE, 2011, 68 (05) :959-974
[10]  
Fox J., 2019, An {R} Companion to Applied Regression