TY - JOUR

T1 - Modeling Mediterranean forest structure using airborne laser scanning data

AU - Bottalico, Francesca

AU - Chirici, Gherardo

AU - Giannini, Raffaello

AU - Mele, Salvatore

AU - Mura, Matteo

AU - Puxeddu, Michele

AU - McRobert, Ronald E.

AU - Valbuena, Ruben

AU - Travaglini, Davide

PY - 2017/5/31

Y1 - 2017/5/31

N2 - 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 airbdrne 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 thb 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) (R-2= 0.91; RMSE 8.2 and mean height weighted by basal area (R-2 = 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. (C) 2016 Elsevier B.V. All rights reserved.

AB - 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 airbdrne 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 thb 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) (R-2= 0.91; RMSE 8.2 and mean height weighted by basal area (R-2 = 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. (C) 2016 Elsevier B.V. All rights reserved.

U2 - 10.1016/j.jag.2016.12.013

DO - 10.1016/j.jag.2016.12.013

M3 - Article

VL - 57

SP - 145

EP - 153

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 0303-2434

ER -