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Research article2022Peer reviewedOpen access

Optimizing the airborne laser scanning estimation of basal area larger than mean (BALM): An indicator of cohort balance in forests

Adnan, Syed; Valbuena, Ruben; Kauranne, Tuomo; Gopalakrishnan, Ranjith; Maltamo, Matti


Airborne laser scanning (ALS) assisted basal area larger than mean (BALM) estimation measures the cohort balance in forests and provides adequate opportunities to describe forest structure. However, a problem still exists that how the plot size, sample size (number of trees), and ALS point density affect the BALM estimation. We tackled this question by using both field and ALS data from a typical managed boreal forest area in Finland. Various concentric circular plots (1-15 m radii) were simulated within the actual field plots (squared) and the optimal plot size and sample size were selected by observing changes in the absolute correlation between BALM estimates and various ALS metrics. Instability in the correlation was found at the smaller concentric circular plots (1-5 m radii) and sample sizes (less than 6 trees) but as the plot size and sample size increased, the correlation followed a convex curve. The maximum correlation was found between a concentric circular plot size 11-14 m radii (380-615 m2 area) and sample size 50-80 trees which could be the optimal plot size and sample size for a reliable BALM estimation. With regards to the ALS point density, no major effects were observed on the relationship between BALM estimates and various ALS metrics unless the point density is less than at least 5 points m 2. The point density of the current nationwide ALS survey is matching the minimum point density requirement obtained in this study and thus it is suitable for a reliable forest structural assessment.


Forest structure; LiDAR; Plot size effect; Sample size effect; Point density effect; Airborne laser scanning

Published in

Ecological Indicators
2022, Volume: 142, article number: 109162Publisher: ELSEVIER

    UKÄ Subject classification

    Forest Science
    Remote Sensing

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