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

[EN] 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 mo...

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Detalhes bibliográficos
Autores: Navarro Cerrillo, Rafael M., González Ferreiro, Eduardo Manuel, García Gutiérrez, Jorge, Ceacero Ruiz, Carlos J., Hernández Clemente, Rocío
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2017
País:España
Recursos:Universidad de León
Repositório:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/22634
Acesso em linha:https://jfs.agriculturejournals.cz/artkey/jfs-201702-0005_impact-of-plot-size-and-model-selection-on-forest-biomass-estimation-using-airborne-lidar-a-case-study-of-pine.php
https://hdl.handle.net/10612/22634
Access Level:Acceso aberto
Palavra-chave:Ingeniería forestal
Topografía
Airborne laser scanning
Forest Inventory (FI)
Regression
Survey design
Genetic selection methods
Pinus sp.
Descrição
Resumo:[EN] 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