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Abstract: Even-aged forests are still predominant across Europe. However, due to the higher resilience and resistance of uneven-aged forests to disturbances and climate change, their proportion is expected to increase both in Europe and globally. The primary objective of this study is to demonstrate the feasibility of distinguishing between uneven- and even-aged forest stand structures on National Forest Inventory (NFI) permanent sample plots solely based on freely available, national airborne low-resolution laser scanning data, without the use of field-based estimates or measurements. Forest structure was described and classified based on canopy closure, dominant height, and canopy height diversity derived from the canopy height model (CHM) and voxel-based metrics calculated from the point cloud. Comparable results were obtained using both approaches for assessing forest structural diversity: canopy height diversity derived from the canopy height model (CHDCHM) and from voxel-based metrics (CHDV). However, differences in vertical diversity between uneven- and even-aged stands were more pronounced when using CHM-based metrics. Therefore, we conclude that in areas with low-density laser scanning data, CHM analysis represents a more suitable method for evaluating the vertical heterogeneity of forest stand structures. The CHDCHM values were estimated at 1.71 for uneven-aged forests, with values of 1.24 and 1.54 observed in mature even-aged forests. In comparison, CHDV values were 2.50 for uneven-aged forests, while mature even-aged forests showed values of 2.18 and 2.24.
Keywords: vertical heterogeneity, national forest inventory, canopy height model, voxels, uneven- aged forests, even-aged forests
Published in DiRROS: 26.02.2026; Views: 324; Downloads: 248
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Abstract: 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/m2) 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—CHDCHM and CHDV) 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 CHDCHM 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 CHDV 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.
Keywords: uneven-aged forest, lidar data, canopy height model, voxels, canopy height diversity
Published in DiRROS: 13.08.2024; Views: 1224; Downloads: 1494
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