<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Airborne laser scanning (ALS) has emerged as a remote sensing technology capable of providing data suitable for deriving all types of elevation models. A canopy height model (CHM), which represents absolute height of objects above the ground in metres (e.g., trees), is the one most commonly used within the forest inventory. The aim of this study was to assess the accuracy of forest inventory performed for forest unit covered 17,583 ha (Slovakia, Central Europe) using the CHM derived from ALS data. This objective also included demonstrating the applicability of freely available data and software. Specifically, ALS data acquired during regular airborne survey, QGIS software, and packages for R environment were used for purpose of this study. A total of 180 testing plots (5.6 ha) were used for accuracy assessment. The differences between CHM-predicted and ground-observed forest stand attributes reached a relative root mean square error at 10.9%, 23.1%, and 34.5% for the mean height, mean diameter, and volume, respectively. Moreover, all predictions were unbiased (<italic>p</italic>-value &lt; 0.05) and the strength of the relationships between CHM-predicted and ground-observed forest stand attributes were relative high (R<sup>2</sup> = 0.7 – 0.8).</p>
</abstract>

Airborne laser scanning (ALS) has emerged as a remote sensing technology capable of providing data suitable for deriving all types of elevation models. A canopy height model (CHM), which represents absolute height of objects above the ground in metres (e.g., trees), is the one most commonly used within the forest inventory. The aim of this study was to assess the accuracy of forest inventory performed for forest unit covered 17,583 ha (Slovakia, Central Europe) using the CHM derived from ALS data. This objective also included demonstrating the applicability of freely available data and software. Specifically, ALS data acquired during regular airborne survey, QGIS software, and packages for R environment were used for purpose of this study. A total of 180 testing plots (5.6 ha) were used for accuracy assessment. The differences between CHM-predicted and ground-observed forest stand attributes reached a relative root mean square error at 10.9%, 23.1%, and 34.5% for the mean height, mean diameter, and volume, respectively. Moreover, all predictions were unbiased (p-value < 0.05) and the strength of the relationships between CHM-predicted and ground-observed forest stand attributes were relative high (R2 = 0.7 – 0.8).

Forest inventory based on canopy height model derived from airborne laser scanning data

Central European Forestry Journal

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

Airborne laser scanning (ALS) has emerged as a remote sensing technology capable of providing data suitable for deriving all types of elevation models. A...

Forest inventory based on canopy height model derived from airborne laser scanning data

Article Category: Original Paper

Publié en ligne: 21 oct. 2022

Pages: 224 - 231

DOI: https://doi.org/10.2478/forj-2022-0013

Mots clés
airborne LiDAR, natural resources, area-based approach, freeware

© 2022 Ivan Sačkov, published by Sciendo

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

Forest inventory based on canopy height model derived from airborne laser scanning data

Article Category: Original Paper

Publié en ligne: 21 oct. 2022

Pages: 224 - 231

DOI: https://doi.org/10.2478/forj-2022-0013

Mots clésairborne LiDAR, natural resources, area-based approach, freeware

© 2022 Ivan Sačkov, published by Sciendo

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

Mots clés
airborne LiDAR, natural resources, area-based approach, freeware