Estimation of total biomass in Aleppo pine forest stands applying parametric and nonparametric methods to low-density airborne laser scanning data

Domingo, D. (Universidad de Zaragoza) ; Lamelas, M.T. ; Montealegre, A.L. (Universidad de Zaragoza) ; García-Martín, A. ; de la Riva, J. (Universidad de Zaragoza)

Resumen: The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.

Idioma: Inglés

DOI: 10.3390/f9040158

Año: 2018

Publicado en: FORESTS 9, 4 (2018), 158 [17 pp]
ISSN: 1999-4907

Factor impacto JCR: 2.116 (2018)

Categ. JCR: FORESTRY rank: 17 / 67 = 0.254 (2018) - Q2 - T1

Factor impacto SCIMAGO: 0.734 - Forestry (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MEC/FPU14-06250
Financiación: info:eu-repo/grantAgreement/ES/MINECO/CGL2014-57013-C2-2-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)