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Assessment of Biomass and Carbon Content in a Mediterranean Aleppo Pine Forest Using ALS Data
Antonio Luis Montealegre * 1 , María Teresa Lamelas 2 , Juan de la Riva 1 , Alberto García-Martín 2 , Francisco Escribano 3
1  Departamento de Geografía y O.T., Grupo GEOFOREST-IUCA, Universidad de Zaragoza, Pedro Cerbuna 12, 50009, Zaragoza, Spain
2  Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, 50090, Zaragoza, Spain; Grupo GEOFOREST-IUCA, Universidad de Zaragoza, Pedro Cerbuna 12, 50009, Zaragoza, Spain.
3  Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, 50090, Zaragoza, Spain

Published: 22 June 2015 by MDPI AG in 1st International Electronic Conference on Remote Sensing session Applications
10.3390/ecrs-1-d004
Abstract:

Tree biomass estimate is essential for carbon accounting, bioenergy feasibility studies, and forest sustainable management. This fact, added to the availability of airborne laser scanning (ALS) information, provided by the Spanish National Plan for Aerial Orthophotography (PNOA), and the existence of little research focusing on the use of ALS technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest, determined the main objective of this research. Thus, this study aims to test the suitability of the low point density (0.5 points/m2), discrete, multiple-return, PNOA-ALS data, to estimate and map the total biomass (TB) and its carbon content in Pinus halepensis Mill. forest stands, located in Aragón (north-eastern Spain). TB was calculated in 45 field plots, using allometric equations, and related through a multivariate linear regression analysis with a collection of independent variables extracted from the ALS data. The predictive model was validated using a leave-one-out cross-validation (LOOCV) technique. Then, a regular grid with cell size 25 x 25 m corresponding to the sample plot size was generated by means of GIS, in order to compute TB at stand level and convert biomass to carbon by using the 0.5 conversion factor. The maximum height, kurtosis and the percentage of returns above 1 meter, were the ALS metrics included in the fitted model, which presented a R2 value of 0.89. The implementation of the model in a GIS showed an average of 68633 kg/ha of TB and 34247.95 kg/ha of carbon fixed. The results indicate that despite the low point density of the ALS data, the final model is accurate enough to be used in forestry applications.

Keywords: Airborne laser scanning, LiDAR, total biomass, carbon stock, forest inventory, multivariate linear regression model, Mediterranean forest
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