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

• Main Authors: Darío Domingo, María Teresa Lamelas-Gracia, Antonio Luis Montealegre-Gracia, Juan de la Riva-Fernández
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-01-01
• Series: European Journal of Remote Sensing
• Subjects: ALS; aboveground tree biomass losses; CO2 emissions; Aleppo pine; burn severity
• Online Access: http://dx.doi.org/10.1080/22797254.2017.1336067

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 CO2 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 $$\left({\Delta {\rm{NBR}}} \right)$$, (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 CO2 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 CO2 emissions in a Mediterranean Aleppo pine forest.