Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Abstract

The knowledge of the forest biomass reduction produced by a wildfire can assist in the estimation of greenhouse gases to the atmosphere. This study focuses on the estimation of biomass losses and CO 2 emissions by combustion of Aleppo pine forest in a wildfire occurred in the municipality of Luna (Spain). The availability of low point density airborne laser scanning (ALS) data allowed the estimation of pre-fire aboveground forest biomass. A comparison of nine regression models was performed in order to relate the biomass, estimated in 46 field plots, to several independent variables extracted from the ALS data. The multivariate linear regression selected model, including the percentage of first returns above 2 m and 40th percentile of the return heights, was validated using a leave-one-out cross-validation technique (6.1 ton/ha root mean square error). Biomass losses were estimated in a three-phase approach: (i) wildfire severity was obtained using the difference normalized burn ratio ΔNBR ð Þ, (ii) Aleppo pine forest was delimited using the National Forest Map and ALS data and (iii) burning efficiency factors were applied considering severity levels. Post-fire biomass was then transformed into CO 2 emissions (426,754.8 ton). This study evidences the usefulness of low-density ALS data to accurately estimate pre-fire biomass, in order to assess CO 2 emissions in a Mediterranean Aleppo pine forest. ARTICLE HISTORY

Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

“…The comparison between regression models shows that the MLR model had the lowest RMSE (15.14 tons/ha) and bias (0.01), matching with the values obtained by other authors for aboveground biomass estimation [73,85]. Thus, according to Görgens et al [52] and Domingo et al [38], MLR slightly outperforms other nonparametric methods. Although MLR was the best model, no statistically significant differences were found with other methods.…”

“…A maximum number of three predictor variables were selected using the different selection methods to avoid overfitting [70] and to generate more understandable models for forest management [38].…”

“…MLR is one of the most broadly applied methods for the estimation of forest structure variables using ALS data [38,57,[71][72][73][74][75]. MLR is a linear approach for modeling the relationship between a scalar dependent variable and two or more explanatory variables.…”

“…SVM is a supervised learning model which has associated learning algorithms that analyze and recognize patterns. This method tries to discover among multidimensional hyperplanes the optimal separation between classes, where the separability is a maximum, assuming that input data are separable in space [38,77]. Data located in the hyperplane are called support vectors, being the most difficult to classify since they have a lower separability.…”

“…However, few studies have focused on shrub biomass characterization with ALS data [30][31][32][33]. Some studies have used low density ALS data to estimate forest biomass [25,[34][35][36][37][38], but little research has been performed including shrub vegetation because of the inherent difficulty in the estimation related to its low height and uniform surface [30]. Several studies state that ALS data tends to underestimate shrub vegetation [39][40][41][42].…”