<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Horizontal visibility <italic>v</italic> in hemiboreal forest transects was measured in the field and then predicted, both from forest inventory (FI) data and from airborne laser scanning (ALS) data. Stand density <italic>N</italic> and mean diameter at breast height <italic>D</italic> were used as arguments in an FI predictive model assuming Poisson distribution of trees on a horizontal plane. It was found that a lack of FI data on forest regrowth and understorey trees caused <italic>v</italic> to be overestimated. Point cloud metrics of sparse ALS data from summer 2017 and spring 2019 were used as predictive variables for v in regression models. The best models were based on three variables: the 10th percentile of the point cloud height distribution, relative density of returns in a horizontal layer ranging 0.7–2.2 m above the ground, and canopy cover. The models had a coefficient of determination of up to 67% and a residual standard error of less than 25 m. In forests in which fertile soil produces rapid height growth of understorey woody vegetation after recent thinning, visibility was found to be substantially overestimated because the understorey was not detected by the lidar measurements.</p></abstract>

Horizontal visibility v in hemiboreal forest transects was measured in the field and then predicted, both from forest inventory (FI) data and from airborne laser scanning (ALS) data. Stand density N and mean diameter at breast height D were used as arguments in an FI predictive model assuming Poisson distribution of trees on a horizontal plane. It was found that a lack of FI data on forest regrowth and understorey trees caused v to be overestimated. Point cloud metrics of sparse ALS data from summer 2017 and spring 2019 were used as predictive variables for v in regression models. The best models were based on three variables: the 10th percentile of the point cloud height distribution, relative density of returns in a horizontal layer ranging 0.7–2.2 m above the ground, and canopy cover. The models had a coefficient of determination of up to 67% and a residual standard error of less than 25 m. In forests in which fertile soil produces rapid height growth of understorey woody vegetation after recent thinning, visibility was found to be substantially overestimated because the understorey was not detected by the lidar measurements.

Options for estimating horizontal visibility in hemiboreal forests using sparse airborne laser scanning data and forest inventory data

Forestry Studies

This work is licensed under the Creative Commons Attribution 4.0 International License.

Horisontaalse nähtavuse hindamine poolboreaalses metsas hõredate laserskaneerimise andmete põhjal

Horizontal visibility v in hemiboreal forest transects was measured in the field and then predicted, both from forest inventory (FI) data and from airborne...

Options for estimating horizontal visibility in hemiboreal forests using sparse airborne laser scanning data and forest inventory data

Article Category: Research paper

Publicado en línea: 11 mar 2021

Páginas: 125 - 135

Recibido: 11 nov 2020

Aceptado: 17 dic 2020

DOI: https://doi.org/10.2478/fsmu-2020-0019

Palabras clave
forest, horizontal visibility, airborne lidar, structural diversity, sparse point clouds.

© 2020 Mait Lang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Options for estimating horizontal visibility in hemiboreal forests using sparse airborne laser scanning data and forest inventory data

Article Category: Research paper

Publicado en línea: 11 mar 2021

Páginas: 125 - 135

Recibido: 11 nov 2020

Aceptado: 17 dic 2020

DOI: https://doi.org/10.2478/fsmu-2020-0019

Palabras claveforest, horizontal visibility, airborne lidar, structural diversity, sparse point clouds.

© 2020 Mait Lang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
forest, horizontal visibility, airborne lidar, structural diversity, sparse point clouds.