Articles by Harri Kaartinen

Harvester-mounted mobile laser scanning (HarvMLS) is a novel approach to quantify forest structure in situ during harvesting operations. However, its reliability relative to established aerial and ground-based laser scanning systems remains unknown. In this study, we benchmarked HarvMLS against drone-based laser scanning (DLS) and backpack mobile laser scanning (BpMLS) for forest structure assessment. Point cloud data were acquired using these laser scanning systems in a boreal forest in southern Finland during thinning operations in 2024. HarvMLS collected data during thinning, while BpMLS and DLS collected data immediately before and after thinning. A grid-level approach was used for point cloud processing. Sixty-two 10 m × 10 m plots were analyzed, each with pre- and post-thinning data. Forest structure was characterised using estimates of structural attributes and complexity. The structural attributes included canopy height, canopy cover (CC), and filled voxel proportion (FVP). Structural complexity was quantified using the coefficient of variation (CV) of the structural attributes. Furthermore, FVP was analyzed across various canopy height classes to evaluate the scanning system’s capacity to capture vegetation density at different canopy layers. The scanning systems consistently estimated canopy height, CC, and the CV of these attributes, including the CV of FVP. Significant differences were detected for FVP and FVP at lower canopy layers (<15 m). However, post hoc tests showed no significant difference between HarvMLS and BpMLS. These findings indicate that HarvMLS provides reliable estimates of forest structural attributes and their variability during active forest operations.

• Kafle, School of Forest Sciences, University of Eastern Finland (UEF), Yliopistokatu 7, FI-80100 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), P.O. Box 84, FI-00521 Helsinki, Finland https://orcid.org/0000-0002-4796-3942 E-mail: harri.kaartinen@nls.fi
• Väätäinen, Production systems, Natural Resources Institute Finland (Luke), Yliopistokatu 6, 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), P.O. Box 84, FI-00521 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), P.O. Box 84, FI-00521 Helsinki, Finland https://orcid.org/0000-0001-5334-5537 E-mail: tamas.faitli@nls.fi
• Koivumäki, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), P.O. Box 84, FI-00521 Helsinki, Finland https://orcid.org/0000-0002-6307-1637 E-mail: niko.koivumaki@maanmittauslaitos.fi
• Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), P.O. Box 84, FI-00521 Helsinki, Finland E-mail: juha.coelasr@gmail.com
• Kukko, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), P.O. Box 84, FI-00521 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-80100 Joensuu, Finland E-mail: kalle.karha@uef.fi

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