Category :
Research article
article id 25013,
category
Research article
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
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.
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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
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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
article id 25003,
category
Research article
Lennart Noordermeer,
Terje Gobakken,
Johannes Breidenbach,
Rune Eriksen,
Erik Næsset,
Hans Ole Ørka,
Ole M. Bollandsås.
(2025).
Effects of sample tree selection and calculation methods on the accuracy of field plot values in area-based forest inventories.
Silva Fennica
vol.
59
no.
2
article id 25003.
https://doi.org/10.14214/sf.25003
Highlights:
Retaining field-measured heights of sample trees improved accuracies of plot values; Selecting sample trees with probability proportional to basal area was recommended; The number of sample trees and sample tree selection method impacted accuracies; The choice of calculation method had a strong influence on accuracies of plot values.
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Accurate field plot data on forest attributes are crucial in area-based forest inventories assisted by airborne laser scanning, providing an essential reference for calibrating predictive models. This study assessed how sample tree selection methods and plot data calculation methods affect the accuracy of field plot values of timber volume, Lorey’s mean height, and dominant height. We used data obtained from 12 420 circular sample plots of 250 m2, measured as part of the Norwegian national forest inventory and 45 local forest management inventories. We applied Monte Carlo simulations by which we tested various numbers of sample trees, methods to select sample trees, and methods to calculate plot-level values from tree-level measurements. Accuracies of plot values were statistically significantly affected by the number of sample trees, sample tree selection method, and calculation method. Obtained values of root mean square error ranged from 5% to 16% relative to the mean observed values, across the factors studied. Accuracy improved with increasing numbers of sample trees for all forest attributes. We obtained greatest accuracies by selecting sample trees with a probability proportional to basal area, and by retaining field-measured heights for sample trees and using heights predicted with a height-diameter model for non-sample trees. This study highlights the importance of appropriate sample tree selection methods and calculation methods in obtaining accurate field plot data in area-based forest inventories.
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Noordermeer,
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway
https://orcid.org/0000-0002-8840-0345
E-mail:
lennart.noordermeer@nmbu.no
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Gobakken,
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway
https://orcid.org/0000-0001-5534-049X
E-mail:
terje.gobakken@nmbu.no
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Breidenbach,
Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway
E-mail:
johannes.breidenbach@nibio.no
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Eriksen,
Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway
E-mail:
rune.eriksen@nibio.no
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Næsset,
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway
E-mail:
erik.naesset@nmbu.no
-
Ørka,
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway
E-mail:
hans-ole.orka@nmbu.no
-
Bollandsås,
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway
https://orcid.org/0000-0002-1231-7692
E-mail:
ole.martin.bollandsas@nmbu.no
Category :
Complex remote sensing-assisted forest surveys – Discussion article
article id 24063,
category
Complex remote sensing-assisted forest surveys – Discussion article
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This paper examines forest resource mapping from a statistical perspective, highlighting the opportunity to use a design-based approach to ensure inferential congruency with the estimation of averages and totals of forest attributes. Traditionally, in forest surveys estimates of averages and totals are obtained using design-unbiased estimators, with known variance expressions that can be easily estimated using standard sampling methodologies. The paper emphasizes the prominent role of kNN and Random Forest techniques in forest mapping while addressing the methodological limitations identified over more than thirty years of forest literature in efforts to estimate map precision. The critical importance of design-based map consistency, often overlooked in forest literature, is discussed and clarified, demonstrating that it allows for the development of design-based estimators of map precision through bootstrap resampling from the estimated maps.
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Franceschi,
Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy
https://orcid.org/0000-0001-6675-4540
E-mail:
franceschi2@unisi.it
-
Pisani,
Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy
E-mail:
caterin.pisani@unisi.it
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Fattorini,
Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy
E-mail:
lorenzo.fattorini@unisi.it
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Corona,
CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, 52100 Arezzo, Italy
E-mail:
piermaria.corona@crea.gov.it