Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest
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Abstract
This study aimed to develop ALS-based models for estimating stem, crown and aboveground biomass in three types of Mediterranean forest, based on low density ALS data. Two different modelling approaches were used: (i) linear models with different variable selection methods (Stepwise Selection [SS], Clustering/Exhaustive search [CE] and Genetic Algorithm [GA]), and (ii) previously Published Models (PM) applicable to diverse types of forest. Results indicated more accurate estimations of biomass components for pure Pinus pinea L. (rRMSE = 25.90-26.16%) than for the mixed (30.86-36.34%) and Quercus pyrenaica Willd. forests (32.78-34.84%). All the tested approaches were valuable, but SS and GA performed better than CE and PM in most cases.
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Juan Guerra-Hernández, Eric Bastos Görgens, Jorge García-Gutiérrez, Luiz Carlos Estraviz Rodriguez, Margarida Tomé & Eduardo González-Ferreiro (2016) Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest, European Journal of Remote Sensing, 49:1, 185-204
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http://dx.doi.org/10.5721/EuJRS20164911Sponsors
The study was supported by the ForEadapt project ‘Knowledge exchange between
Europe and America on forest growth models and optimization for adaptive forestry’
(PIRSES-GA-2010-269257). The authors thank (i) the foresters of the Extremadura
Forest Service for assistance with data collection, (ii) the Portuguese Science Foundation
(SFRH/BD/52408/2013) for funding the research activities of Juan Guerra and (iii) the
Galician Government and European Social Fund (Official Journal of Galicia – DOG nº 52,
17/03/2014 p. 11343, exp: POS-A/2013/049) for funding the postdoctoral research stays
of Eduardo González-Ferreiro and iv) the anonymous Reviewers of the European Journal
of Remote Sensing for their helpful feedback. The research was carried out in the Centro
de Estudos Florestais: a research unit funded by Fundação para a Ciência e a Tecnologia
(Portugal) within UID/AGR/00239/2013.
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© 2016 by the authors; licensee Italian Society of Remote Sensing (AIT). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/)