Modeling Mediterranean forest structure using airborne laser scanning data |
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| Institution: | 1. Department of Forest Resource Management, 2424 Main Mall, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada;2. Department of Renewable Resources, Faculty of Agricultural, Life, and Environmental Sciences, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada;3. Forest Management Branch, Forestry Division, Alberta Agriculture and Forestry, 9920-108 Street NW, Edmonton, Alberta, T5K 2M4, Canada;1. Department of Forest Sciences, University of Helsinki, PO Box 27, FI-00014 Helsinki, Finland;2. Department of Forest Resource Management, Swedish University of Agricultural Sciences, SLU Skogsmarksgränd, SE-901 83 Umeå, Sweden;3. Finnish Forest Research Institute, PO Box 18, FI-01301 Vantaa, Finland;4. Finnish Geodetic Institute, PO Box 15, FI-02431 Masala, Finland |
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| Abstract: | The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters.Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R2 = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R2 = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m. |
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| Keywords: | Forest biodiversity Forest inventory Forest monitoring Structural complexity indicators Airborne laser scanning LiDAR |
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