Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest
作者： Darío Domingo , María Teresa Lamelas-Gracia , Antonio Luis Montealegre-Gracia , Juan de la Riva-Fernández
DOI: 10.1080/22797254.2017.1336067
关键词: Mean squared error 、 Aleppo Pine 、 Agroforestry 、 Geography 、 Biomass 、 Bayesian multivariate linear regression 、 Greenhouse gas 、 Regression analysis 、 Percentile 、 Atmospheric sciences 、 Atmosphere
摘要: ABSTRACTThe knowledge of the forest biomass reduction produced by a wildfire can assist in estimation greenhouse gases to atmosphere. This study focuses on losses and CO2 emissions combustion Aleppo pine occurred municipality Luna (Spain). The availability low point density airborne laser scanning (ALS) data allowed pre-fire aboveground biomass. A comparison nine regression models was performed order relate biomass, estimated 46 field plots, several independent variables extracted from ALS data. multivariate linear selected model, including percentage first returns above 2 m 40th percentile return heights, validated using leave-one-out cross-validation technique (6.1 ton/ha root mean square error). Biomass were three-phase approach: (i) severity obtained difference normalized burn ratio , (ii) Aleppo...
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