Identifying even- and uneven-aged forest stands using low-resolution nationwide lidar data

Pintar, Anže Martin (author), Skudnik, Mitja (author)

In uneven-aged forests, trees of different diameters, heights, and ages are located in a small area, which is due to the felling of individual trees or groups of trees, as well as small-scale natural disturbances. In this article, we present an objective method for classifying forest stands into even- and uneven-aged stands based on freely available low-resolution (with an average recording density of 5 points/m$^2$) national lidar data. The canopy closure, dominant height, and canopy height diversity from the canopy height model and the voxels derived from lidar data were used to classify the forest stands. Both approaches for determining forest structural diversity (canopy height diversity—CHD$_{CHM}$ and CHD$_V$) yielded similar results, namely two clusters of even- and uneven-aged stands, although the differences in vertical diversity between even- and uneven-aged stands were greater when using CHM. The first analysis, using CHM for the CHD assessment, estimated the uneven-aged forest area as 49.3%, whereas the second analysis using voxels estimated it as 34.3%. We concluded that in areas with low laser scanner density, CHM analysis is a more appropriate method for assessing forest stand height heterogeneity. The advantage of detecting uneven-aged structures with voxels is that we were able to detect shade-tolerant species of varying age classes beneath a dense canopy of mature, dominant trees. The CHD$_{CHM}$ values were estimated to be 1.83 and 1.86 for uneven-aged forests, whereas they were 1.57 and 1.58 for mature even-aged forests. The CHD$_V$ values were estimated as 1.50 and 1.62 for uneven-aged forests, while they were 1.33 and 1.48 for mature even-aged forests. The classification of stands based on lidar data was validated with data from measurements on permanent sample plots. Statistically significantly lower average values of the homogeneity index and higher values of the Shannon–Wiener index from field measurements confirm the success of the classification of stands based on lidar data as uneven-aged forests.

• Language: English
• Keywords: uneven-aged forest, lidar data, canopy height model, voxels, canopy height diversity
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 16 str.
• Numbering: Vol. 15, iss. 8, art. 1407
• PID: 20.500.12556/RUL-166795
• UDC: 630*53
• ISSN on article: 1999-4907
• DOI: 10.3390/f15081407
• COBISS.SI-ID: 204230147
• Publication date in RUL: 24.01.2025

Record is a part of a journal

• Title: Forests
• Shortened title: Forests
• Publisher: MDPI
• ISSN: 1999-4907
• COBISS.SI-ID: 3872166

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: lidar, višina krošnje, drevesne krošnje