Field calibration of merchantable and sawlog volumes in forest inventories based on airborne laser scanning

Publication: Canadian Journal of Forest Research
12 August 2020


In many countries, airborne laser scanning (ALS) inventories are implemented to produce predictions for stand-level forest attributes. Nevertheless, mature stands are usually field-visited prior to clear-cutting, so some measurements can be conducted on these stands to calibrate the ALS-based predictions. In this paper, we developed a seemingly unrelated multivariate mixed-effects model system that includes component models for basal area, merchantable volume, and sawlog volume for 225 m2 cells. We used ALS data and accurately positioned cut-to-length harvester observations from clear-cut stands dominated by Norway spruce (Picea abies (L.) Karst.). Our aim was to study the effect of 1–10 local angle-gauge basal area measurements on the accuracy of predicted merchantable and sawlog volumes. A seemingly unrelated mixed-effect model system was fitted to estimate cross-model correlations in residuals and random effects, which were then utilized to predict all the random effects of the system for calibrated stand-level predictions. The 10 angle-gauge plots decreased the relative root mean square error (RMSE%) of the basal area and merchantable volume predictions from 16.8% to 10.5% and from 15.8% to 11.9%, respectively. Cross-model correlations of the stand effects of sawlog volume with the other responses were low; therefore, the initial RMSE% of ∼22% was decreased only marginally by the calibration.


Dans de nombreux pays, des inventaires par balayage laser aéroporté (BLA) sont mis en œuvre afin de produire des prévisions pour les attributs forestiers à l’échelle des peuplements. Néanmoins, les peuplements matures sont généralement visités sur le terrain avant d’être coupés à blanc, ainsi certaines mesures peuvent être effectuées sur ces peuplements pour calibrer les prévisions basées sur le BLA. Dans cet article, nous avons développé un système de modèles à effets mixtes multivariés sans corrélation apparente qui inclut des composantes pour la surface terrière, le volume marchand et le volume en bois de sciage, pour des cellules de 225 m2. Nous avons utilisé les données de BLA et positionné avec précision les observations des abatteuses-tronçonneuses dans des peuplements coupés à blanc dominés par l’épicéa commun (Picea abies (L.) Karst.). Notre objectif consistait à étudier l’effet de 1 à 10 mesures locales de la surface terrière avec une jauge d’angle sur la précision des volumes marchand et de bois d’œuvre prévus. Un système de modèles à effets mixtes sans corrélation apparente a été ajusté pour estimer les corrélations croisées des modèles dans les résidus et les effets aléatoires, qui ont ensuite été utilisés pour prédire tous les effets aléatoires du système pour les prévisions calibrées à l’échelle des peuplements. L’utilisation des données des 10 placettes à la jauge d’angle ont diminué l’erreur quadratique moyenne relative (EQM %) des prévisions de la surface terrière et du volume marchand de respectivement 16,8 à 10,5 % et de 15,8 à 11,9 %. Les corrélations croisées entre les modèles d’effets du peuplement pour le volume de bois de sciage et pour les autres réponses étaient faibles : l’EQM % initial d’environ 22 % n’a par conséquent été diminué que marginalement par l’étalonnage. [Traduit par la Rédaction]

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Published In

cover image Canadian Journal of Forest Research
Canadian Journal of Forest Research
Volume 50Number 12December 2020
Pages: 1352 - 1364


Received: 27 January 2020
Accepted: 11 August 2020
Published online: 12 August 2020


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Key Words

  1. LiDAR
  2. area-based approach
  3. quality
  4. estimated best linear unbiased predictor
  5. harvester data


  1. balayage laser aéroporté
  2. approche par surface
  3. qualité
  4. meilleur prédicteur linéaire sans biais estimé
  5. données de l’abatteuse-tronçonneuse



Tomi Karjalainen
School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland.
Lauri Mehtätalo
School of Computing, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland.
Petteri Packalen
School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland.
Terje Gobakken
Faculty of Environmental Science and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway.
Erik Næsset
Faculty of Environmental Science and Natural Resource Management, Norwegian University of Life Sciences, Box 5003, 1430 Ås, Norway.
Matti Maltamo
School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland.


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