Using low-density discrete Airborne Laser Scanning data to assess the potential carbon dioxide emission in case of a fire event in a Mediterranean pine forest

Abstract

The aim of study is to map the carbon dioxide (CO2) emission of the aboveground tree biomass (AGB) in case of a fire event. The suitability of low point density, discrete, multiple-return, Airborne Laser Scanning (ALS) data and the influence of several characteristics of these data and the study area on the results obtained have been evaluated. A sample of 45 circular plots representative of Pinus halepensis Miller stands were used to fit and validate the model of AGB. The ALS point clouds were processed to obtain the independent variables and a multivariate linear regression analysis between field data and ALS-derived variables allowed estimation of AGB. Then, the influence of several characteristics on the residuals of the model was analyzed. Finally, conversion factors were applied to obtain the CO2 values. The AGB model presented a R2 value of 0.84 with a relative root-mean-square error of 27.35%. This model included ALS variables related to vegetation height variability and to canopy density. Terrain slope, aspect, canopy cover, scan angle and the number of laser returns did not influence AGB estimations at plot level.

Keywords

• lidar
• area-based approach
• biomass
• pinus halepensis miller