Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Guerra-Hernández, Juan; Görgens, Eric Bastos; García-Gutiérrez, Jorge; Rodriguez, Luiz Carlos Estraviz; Tomé, Margarida; González-Ferreiro, Eduardo
• Is Part Of: European journal of remote sensing. Volume 49:Issue 1(2016)
• Page Start: 185
• Page End: 204
• Publication Date: 2016-01-01
• Subjects: Biomass components -- remote sensing -- airborne laser scanning -- mediterranean forest -- feature selection approaches; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.5721/EuJRS20164911
• Languages: English
• ISSNs: 2279-7254

Abstract

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.