J. For. Sci., 2017, 63(2):88-97 | DOI: 10.17221/86/2016-JFS

Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern SpainOriginal Paper

Rafael M. NAVARRO-CERRILLO*,1, Eduardo GONZÁLEZ-FERREIRO2, Jorge GARCÍA-GUTIÉRREZ3, Carlos J. CEACERO RUIZ4, Rocío HERNÁNDEZ-CLEMENTE5
1 Department of Forestry Engineering, School of Forest Engineering, University of Cordoba, Córdoba, Spain
2 Department of Agroforestry Engineering, School of Forest Engineering, University of Santiago de Compostela, Lugo, Spain
3 Department of Computer Science Languages and Systems, School of Industrial Engineering, University of Seville, Sevilla, Spain
4 Department of Physiology, Anatomy and Cellular Biology, Faculty of Biology, Pablo de Olavide University, Sevilla, Spain
5 Department of Geography, College of Science, Swansea University, Swansea, UK

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots - six for each species - and three different fixed-radius designs (i.e. 7, 15, and 30 m) were established within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.

Keywords: airborne laser scanning; forest inventory; regression; survey design; genetic selection methods; Pinus sp.

Published: February 28, 2017  Show citation

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NAVARRO-CERRILLO RM, GONZÁLEZ-FERREIRO E, GARCÍA-GUTIÉRREZ J, CEACERO RUIZ CJ, HERNÁNDEZ-CLEMENTE R. Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain. J. For. Sci.. 2017;63(2):88-97. doi: 10.17221/86/2016-JFS.
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