Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods
Abstract
Very high point density laser scanning data from unmanned aerial systems (UAS) can be used to segment the trunks of individual trees. Such segmentation (e.g., individual trunk segmentation (ITS)) is useful, for example, in the estimation of diameter at breast height (DBH), which in turn is needed for the estimation of other tree and stand attributes, such as stem volume and diameter of the basal area median tree (DGM). In this paper, we assess the estimation of DBH directly from UAS LiDAR data in open and closed canopy conditions that represent the range of operational conditions encountered in boreal forests in Finland. We also compare trunk-based DBH estimates to corresponding estimates from individual tree crown segmentation (ITC) and fuse the results from the trunk- and crown-based estimates. The results showed that trunk segmentation performed slightly better than ITC in open canopy areas, whereas ITC performed better in closed canopy areas. The DBH prediction error was smaller for ITS (3.0 cm) than ITC (3.9 cm) when considering the trees that were recognized by both methods. We also conclude that a hybrid method, where both segmented tree trunks and tree crowns are fused, considerably increases the number of correctly segmented trees but does not decrease the prediction error associated with DGM compared to using either ITC or ITS individually.
Résumé
Les données obtenues par balayage par faisceau laser avec densité de points très élevée par les systèmes d’aéronefs sans pilote (UAS) peuvent être utilisées pour segmenter les troncs des arbres individuels. Une telle segmentation (p. ex., segmentation d’un tronc individuel (STI)) est utile, par exemple, dans l’estimation du diamètre à hauteur d’homme (DHH), laquelle est par ailleurs requise pour l’estimation des attributs des autres arbres et du peuplement forestier, par exemple le volume du tronc et la surface terrière moyenne des arbres (DGM). Dans la présente communication, nous évaluons l’estimation du DHH à partir directement des données de l’UAS LiDAR dans des conditions de couvert ouvert et fermé qui représentent l’éventail des conditions opérationnelles rencontrées dans les forêts boréales en Finlande. Nous comparons également les estimés du DHH fondé sur les troncs aux estimés correspondants de la segmentation de la couronne des arbres individuels (CAI) et nous fusionnons les résultats des estimés fondés sur les troncs et les couronnes. Les résultats ont indiqué que la segmentation des troncs fonctionnait un peu mieux que la CAI dans les secteurs de couverts ouverts alors que la CAI fonctionnait mieux dans des secteurs de couverts fermés. L’erreur de prévision du DHH était plus petite pour la STI (3,0 cm) que la CAI (3,9 cm) lorsqu’on tenait compte des arbres qui étaient reconnus par les deux méthodes. Nous concluons également qu’une méthode hybride où les troncs et les couronnes des arbres segmentés sont fusionnés accroît considérablement le nombre d’arbres correctement segmentés, mais ne diminue par l’erreur de prévision associée à la DGM comparativement à l’utilisation de la CAI ou de la STI individuellement. [Traduit par la Rédaction]
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Published In
Canadian Journal of Forest Research
Volume 52 • Number 5 • May 2022
Pages: 674 - 684
History
Received: 5 August 2021
Accepted: 18 January 2022
Version of record online: 14 April 2022
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© 2022 The Author(s). Permission for reuse (free in most cases) can be obtained from copyright.com.
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This work was supported by the Academy of Finland through the project Unmanned Aerial Vehicles in Forest Remote Sensing (the Research Council for Biosciences, Health and the Environment) under grant 323484, and by the Finnish Flagship Programme for the Forest-Human-Machine Interplay — Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences (UNITE) under grants 337127 and 337655.
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Mikko Kukkonen, Matti Maltamo, Lauri Korhonen, and Petteri Packalen. 2022. Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods. Canadian Journal of Forest Research.
52(5): 674-684. https://doi.org/10.1139/cjfr-2021-0217
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