A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications

被引:4
作者
Murtiyoso, Arnadi [1 ]
Cabo, Carlos [2 ]
Singh, Arunima [3 ]
Obaya, Dimas Pereira [4 ]
Cherlet, Wout [5 ]
Stoddart, Jaz [6 ]
Fol, Cyprien Raymi [1 ]
Schwenke, Mirela Beloiu [1 ]
Rehush, Nataliia [7 ]
Sterenczak, Krzysztof [8 ,9 ]
Calders, Kim [5 ]
Griess, Verena Christiane [1 ]
Mokros, Martin [3 ,10 ,11 ]
机构
[1] Swiss Fed Inst Technol, Inst Terr Ecosyst, Dept Environm Syst Sci, Forest Resources Management, Zurich, Switzerland
[2] Univ Oviedo, Dept Min Exploitat & Prospecting, Campus Mieres, Mieres, Spain
[3] Czech Univ Life Sci, Fac Forestry & Wood Sci, Prague, Czech Republic
[4] Univ Leon, Grp Invest Geomat Ingn Cartog GEOINCA, Leon, Spain
[5] Univ Ghent, Dept Environm, Q ForestLab, Ghent, Belgium
[6] Jodrell Lab, Royal Bot Gardens, Richmond, England
[7] Swiss Fed Inst Forest Snow & Landscape Res WSL, Swiss Natl Forest Inventory, Birmensdorf, Switzerland
[8] Forest Res Inst, Dept Geomat, Raszyn, Poland
[9] IDEAS NCBR Sp Zoo, Warsaw, Poland
[10] UCL, Dept Geog, London, England
[11] Tech Univ Zvolen, Zvolen, Slovakia
来源
CURRENT FORESTRY REPORTS | 2024年 / 10卷 / 06期
基金
瑞士国家科学基金会; 欧洲研究理事会;
关键词
Forest; Ground-based; Point Cloud; Review; Software; Web Platform; LASER-SCANNING DATA; TREE SPECIES CLASSIFICATION; LEAF-AREA DISTRIBUTION; PHOTOGRAMMETRY; ORIENTATION; ALGORITHMS; FRAMEWORK; BIOMASS; SYSTEMS; HEIGHT;
D O I
10.1007/s40725-024-00228-2
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Purpose of ReviewIn recent years, the use of 3D point clouds in silviculture and forest ecology has seen a large increase in interest. With the development of novel 3D capture technologies, such as laser scanning, an increasing number of algorithms have been developed in parallel to process 3D point cloud data into more tangible results for forestry applications. From this variety of available algorithms, it can be challenging for users to decide which to apply to fulfil their goals best. Here, we present an extensive overview of point cloud acquisition and processing tools as well as their outputs for precision forestry. We then provide a comprehensive database of 24 algorithms for processing forest point clouds obtained using close-range techniques, specifically ground-based platforms.Recent FindingsOf the 24 solutions identified, 20 are open-source, two are free software, and the remaining two are commercial products. The compiled database of solutions, along with the corresponding technical guides on installation and general use, is accessible on a web-based platform as part of the COST Action 3DForEcoTech. The database may serve the community as a single source of information to select a specific software/algorithm that works for their requirements.SummaryWe conclude that the development of various algorithms for processing point clouds offers powerful tools that can considerably impact forest inventories in the future, although we note the necessity of creating a standardisation paradigm.
引用
收藏
页码:401 / 419
页数:19
相关论文
共 114 条
[1]   Automatic tree species recognition with quantitative structure models [J].
Akerblom, Markku ;
Raumonen, Pasi ;
Makipaa, Raisa ;
Kaasalainen, Mikko .
REMOTE SENSING OF ENVIRONMENT, 2017, 191 :1-12
[2]   Operationalizing the use of TLS in forest inventories: The R package FORTLS [J].
Alberto Molina-Valero, Juan ;
Martinez-Calvo, Adela ;
Ginzo Villamayor, Maria Jose ;
Novo Perez, Manuel Antonio ;
Gabriel Alvarez-Gonzalez, Juan ;
Montes, Fernando ;
Perez-Cruzado, Cesar .
ENVIRONMENTAL MODELLING & SOFTWARE, 2022, 150
[3]   Tree species classification from complex laser scanning data in Mediterranean forests using deep learning [J].
Allen, Matthew J. ;
Grieve, Stuart W. D. ;
Owen, Harry J. F. ;
Lines, Emily R. .
METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (07) :1657-1667
[4]  
[Anonymous], 2011, Photogrammetric Week
[5]  
[Anonymous], 2013, ISPRS Ann. Photogrammetry, Remote Sens. Spatial Inf. Sci., DOI [10.5194/isprsannals-II-5-W1-203-2013, DOI 10.5194/ISPRSANNALS-II-5-W1-203-2013]
[6]  
Antonio Guzman QJ, 2021, RTLS TOOLS PROCESS P
[7]   Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric [J].
Asner, Gregory P. ;
Mascaro, Joseph .
REMOTE SENSING OF ENVIRONMENT, 2014, 140 :614-624
[8]   Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the forestr R package [J].
Atkins, Jeff W. ;
Bohrer, Gil ;
Fahey, Robert T. ;
Hardiman, Brady S. ;
Morin, Timothy H. ;
Stovall, Atticus E. L. ;
Zimmerman, Naupaka ;
Gough, Christopher M. .
METHODS IN ECOLOGY AND EVOLUTION, 2018, 9 (10) :2057-2066
[9]   Evaluating Data Inter-Operability of Multiple UAV-LiDAR Systems for Measuring the 3D Structure of Savanna Woodland [J].
Bartholomeus, Harm ;
Calders, Kim ;
Whiteside, Tim ;
Terryn, Louise ;
Krishna Moorthy, Sruthi M. ;
Levick, Shaun R. ;
Bartolo, Renee ;
Verbeeck, Hans .
REMOTE SENSING, 2022, 14 (23)
[10]   On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR [J].
Beland, Martin ;
Baldocchi, Dennis D. ;
Widlowski, Jean-Luc ;
Fournier, Richard A. ;
Verstraete, Michel M. .
AGRICULTURAL AND FOREST METEOROLOGY, 2014, 184 :82-97