Modeling Mediterranean forest structure using airborne laserscanning data

The conservation of biological diversity is recognized as a fundamental component of sustainable devel-opment, and forests contribute greatly to its preservation. Structural complexity increases the potentialbiological diversity of a forest by creating multiple niches that can host a wide variety of species. Tofacilitate greater understanding of the contributions of forest structure to forest biological diversity, wemodeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data fortwo Italian study areas representing two common Mediterranean forests, conifer plantations and cop-pice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. Theobjectives were twofold: (i) to compare model prediction accuracies when using two types of ALS met-rics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferencesin 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 effectsof the two types of ALS metrics. In particular, accuracies were less for the more complex study area interms of species composition and forest structure. However, accuracies achieved using the echo-basedmetrics were only slightly greater than when using the CHM-based metrics, thus demonstrating thatboth 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 usingthe echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For theforested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple randomsampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standarderror of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m.