Binod Kafle, Ville Kankare, Harri Kaartinen, Kari Väätäinen, Heikki Hyyti, Tamas Faitli, Juha Hyyppä, Antero Kukko, Kalle Kärhä. (2025). Assessing the consistency of low vegetation characteristics estimated using harvester, handheld, and drone light detection and ranging (LiDAR) systems. Silva Fennica vol. 59 no. 2 article id 25013. https://doi.org/10.14214/sf.25013

Keywords: biodiversity; harvester; wood harvesting; dense area for games; drone laser scanning (DLS); handheld mobile laser scanning (HMLS); point cloud

Highlights: Harvester-mounted LiDAR consistently estimated low vegetation height and volume comparable to handheld and drone LiDAR; Enhancing LiDAR range could improve harvester LiDAR efficiency, reducing processing time and increasing accuracy beyond 20 m.

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Evaluating the potential of a harvester-mounted LiDAR system in monitoring biodiversity indicators such as low vegetation during forest harvesting could enhance sustainable forest management and habitat conservation including dense forest areas for game. However, there is a lack of understanding on the capabilities and limitations of these systems to detect low vegetation characteristics. To address this knowledge gap, this study investigated the performance of a harvester-mounted LiDAR system for measuring low vegetation (height <5 m) attributes in a boreal forest in Finland, by comparing it with handheld mobile laser scanning (HMLS) and drone laser scanning (DLS) systems. LiDAR point cloud data was collected in September 2023 to quantify the low vegetation height (maximum, mean, and percentiles), volume (voxel-based and mean height-based) and cover (grid method). Depending on the system, LiDAR point cloud data was collected either before (HMLS and DLS), during (harvester LiDAR) or after (HMLS and DLS) harvesting operations. A total of 46 fixed-sized (5 m × 5 m) grid cells were studied and analyzed. Results showed harvester-mounted LiDAR provided consistent estimates with HMLS and DLS for maximum height, 99th height percentile, and volume across various grids (5 cm, 10 cm, 20 cm) and voxel (20 cm) sizes. High correlation was observed between the systems used for these attributes. This study demonstrated that harvester-mounted LiDAR is comparable to HMLS and DLS for assessing low vegetation height and volume. The findings could assist forest harvester operators in identifying potential low vegetation and dense areas for conservation and game management.

Kafle, School of Forest Sciences, University of Eastern Finland (UEF), Yliopistokatu 7, FI-80101 Joensuu, Finland https://orcid.org/0000-0003-0744-3480 E-mail: binod.kafle@uef.fi
Kankare, Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Finland https://orcid.org/0000-0001-6038-1579 E-mail: viveka@utu.fi
Kaartinen, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), Opastinsilta 12 C, FI-00520 Helsinki, Finland https://orcid.org/0000-0002-4796-3942 E-mail: harri.kaartinen@nls.fi
Väätäinen, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-6886-0432 E-mail: kari.vaatainen@luke.fi
Hyyti, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), Opastinsilta 12 C, FI-00520 Helsinki, Finland https://orcid.org/0000-0003-4664-6221 E-mail: heikki.hyyti@nls.fi
Faitli, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), Opastinsilta 12 C, FI-00520 Helsinki, Finland https://orcid.org/0000-0001-5334-5537 E-mail: tamas.faitli@nls.fi
Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), Opastinsilta 12 C, FI-00520 Helsinki, Finland E-mail: juha.coelasr@gmail.com
Kukko, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), Opastinsilta 12 C, FI-00520 Helsinki, Finland https://orcid.org/0000-0002-3841-6533 E-mail: antero.kukko@nls.fi
Kärhä, School of Forest Sciences, University of Eastern Finland (UEF), Yliopistokatu 7, FI-80101 Joensuu, Finland E-mail: kalle.karha@uef.fi

Category : Data note

article id 24066, category Data note

Tuomas Yrttimaa, Lauri Liikonen, Aapo Erkkilä, Mikko Vastaranta. (2025). Terrestrial laser scanning point clouds and tree attributes from 55 sample plots at the Evo test site (spring 2024). Silva Fennica vol. 59 no. 1 article id 24066. https://doi.org/10.14214/sf.24066

Keywords: boreal forest; ground-based LiDAR; LIS TreeAnalyzer; point cloud processing; Riegl VZ-400i; tree characterization; tree reconstruction

Highlights: Terrestrial laser scanning dataset from 55 sample plots (32 × 32 m) representing boreal forests of Southern Finland; Data was acquired in April–May 2024 using a Riegl VZ-400i scanner, providing multiple returns per pulse; Each point is annotated with reflectance, return properties, and tree linkage; Crown and stem diameters were derived for further analysis, advancing tree and forest research applications.

Abstract | Full text in HTML | Full text in PDF | Author Info

Terrestrial laser scanning (TLS) is an active remote sensing technique that digitizes trees and forest stands by capturing range measurements, resulting in detailed point clouds. To support the development of computational methods for tree and forest stand characterization, as well as to facilitate the exploration of tree structures, we collected TLS data from 55 sample plots (32 m × 32 m) and 6320 trees in Evo, Southern Finland. Data acquisition was conducted during April–May 2024 using a Riegl VZ-400i terrestrial laser scanner (Riegl Laser Measurement Systems GmbH, Austria), capable of recording up to eight returns per laser pulse. This dataset includes TLS point clouds in the projected coordinate reference system commonly used in Finland (ETRS-TM35FIN). Each point is annotated with reflectance, return number, return count, and height above ground. Additionally, information linking each return to its originating tree is provided. For individual trees, the point clouds were further processed to derive key attributes such as crown projection area, crown diameter, and stem diameter. In addition, tree species was derived by linking the TLS-based tree measurements with field inventory data. Here, we describe and share these curated TLS data files and related tree measurements, which offer a valuable resource for advancing tree- and forest-related research and applications.

Yrttimaa, School of Forest Sciences, University of Eastern Finland, FI-80101 Joensuu, Finland https://orcid.org/0000-0003-2648-523X E-mail: tuomas.yrttimaa@uef.fi
Liikonen, School of Forest Sciences, University of Eastern Finland, FI-80101 Joensuu, Finland E-mail: lauriliik@uef.fi
Erkkilä, School of Forest Sciences, University of Eastern Finland, FI-80101 Joensuu, Finland E-mail: aapo.erkkila@uef.fi
Vastaranta, School of Forest Sciences, University of Eastern Finland, FI-80101 Joensuu, Finland https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

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