Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preparation
2.2.1. Field Measurement of Tree Heights
2.2.2. Remote Sensing Data
2.3. Data Analysis
2.3.1. Analysis of Field Measurements
2.3.2. Extraction of Tree Heights from National ALS and DAP Data
2.3.3. Canopy Height Diversity Analysis
2.3.4. Detection and Analysis of Changes in the Dominant Heights of Trees
3. Results
3.1. Analyses of Dominant and Maximum Tree Heights and Canopy Height Diversity Derived from Field Measurements, Lidar Data, and Cyclic Aerial Survey Data
3.2. Periodic Annual Increment Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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hdom (m) | SD1 (m) | Statistical Test and p Value (hdom) | hmax (m) | SD2 (m) | Statistical Test and p Value (hmax) | ||
---|---|---|---|---|---|---|---|
Field 2013 | Even-aged | 28.7 | 6.3 | Two-Sample t-test | 31.6 | 6.7 | Two-Sample t-test |
Uneven-aged | 31.6 | 4.6 | p < 0.05 | 35.8 | 5.6 | p < 0.01 | |
LSS 2014 | Even-aged | 28.6 | 6.0 | Two-Sample t-test | 32.5 | 5.7 | Two-Sample t-test |
Uneven-aged | 31.4 | 5.5 | p < 0.05 | 35.8 | 6.4 | p < 0.05 | |
CAS 2019 | Even-aged | 27.0 | 6.0 | Two-Sample t-test | 29.8 | 5.7 | Two-Sample t-test |
Uneven-aged | 29.9 | 5.8 | p < 0.05 | 33.1 | 6.4 | p < 0.05 | |
Field 2023 | Even-aged | 30.2 | 5.3 | Two-Sample t-test | 33.0 | 5.1 | Two-Sample t-test |
Uneven-aged | 32.2 | 4.5 | p < 0.05 | 36.3 | 5.2 | p < 0.01 | |
LSS 2023 | Even-aged | 29.0 | 5.4 | Two-Sample t-test | 32.8 | 5.4 | Two-Sample t-test |
Uneven-aged | 32.0 | 6.2 | p < 0.05 | 35.9 | 6.5 | p < 0.05 | |
CAS 2022 | Even-aged | 26.1 | 6.3 | Wilcoxon rank sum test | 28.9 | 6.2 | Wilcoxon rank sum test |
Uneven-aged | 28.8 | 7.3 | p < 0.05 | 32.4 | 7.5 | p < 0.01 |
Mean | SD | Statistical Test and p Value | ||
---|---|---|---|---|
LSS 2014 | Even-aged CHD | 1.46 | 0.23 | Two-Sample t-test |
Uneven-aged CHD | 1.66 | 0.26 | p < 0.001 | |
CAS 2019 | Even-aged CHD | 1.16 | 0.32 | Two-Sample t-test |
Uneven-aged CHD | 1.30 | 0.31 | p < 0.05 | |
LSS 2023 | Even-aged CHD | 1.47 | 0.24 | Wilcoxon rank sum test |
Uneven-aged CHD | 1.64 | 0.25 | p < 0.01 | |
CAS 2022 | Even-aged CHD | 1.20 | 0.28 | Wilcoxon rank sum test |
Uneven-aged CHD | 1.38 | 0.36 | p < 0.01 |
PAI | Mean (m/year) | Median (m/year) | Min (m/year) | Max (m/year) | SD (m/year) | Statistical Test and p Value |
---|---|---|---|---|---|---|
Even-aged PAI(hdom(field)) | 0.16 | 0.11 | −0.86 | 0.98 | 0.34 | Wilcoxon rank sum test |
Uneven-aged PAI(hdom(field)) | 0.06 | 0.05 | −0.59 | 0.57 | 0.23 | p > 0.05 |
Even-aged PAI(hdom(LSS)) | 0.04 | 0.14 | −1.64 | 0.53 | 0.45 | Wilcoxon rank sum test |
Uneven-aged PAI(hdom(LSS)) | 0.08 | 0.11 | −1.95 | 1.15 | 0.45 | p > 0.05 |
Even-aged PAI(hdom(CAS)) | −0.42 | 0.17 | −9.32 | 1.05 | 1.84 | Wilcoxon rank sum test |
Uneven-aged PAI(hdom(CAS)) | −0.42 | −0.04 | −5.27 | 2.56 | 1.22 | p > 0.05 |
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Pintar, A.M.; Skudnik, M. Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data. Forests 2025, 16, 35. https://doi.org/10.3390/f16010035
Pintar AM, Skudnik M. Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data. Forests. 2025; 16(1):35. https://doi.org/10.3390/f16010035
Chicago/Turabian StylePintar, Anže Martin, and Mitja Skudnik. 2025. "Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data" Forests 16, no. 1: 35. https://doi.org/10.3390/f16010035
APA StylePintar, A. M., & Skudnik, M. (2025). Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data. Forests, 16(1), 35. https://doi.org/10.3390/f16010035