69650 20210301081638.0 doi 10.1080/22797254.2017.1336067 sideral 100558 ART-2017-100558 eng (orcid)0000-0002-8362-7559 Domingo, Darío Universidad de Zaragoza Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest 2017 Access copy available to the general public Unrestricted 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 ð Þ ¿NBR , (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. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 1.122 2017 REMOTE SENSING 24 / 30 = 0.8 2017 Q4 T3 0.577 2017 Applied Mathematics 2017 Q2 Environmental Science (miscellaneous) 2017 Q2 Computers in Earth Sciences 2017 Q2 Atmospheric Science 2017 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion (orcid)0000-0002-8954-7517 Lamelas-Gracia, María Teresa (orcid)0000-0001-6288-2780 Montealegre-Gracia, Antonio Luis Universidad de Zaragoza (orcid)0000-0003-2615-270X Riva-Fernández, Juan de la Universidad de Zaragoza 3006 435 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Geografía Humana 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. 50, 1 (2017), 384-396 Eur. j. remote sens. European journal of remote sensing 2279-7254 1944457 http://zaguan.unizar.es/record/69650/files/texto_completo.pdf Versión publicada 126419 http://zaguan.unizar.es/record/69650/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:69650 articulos driver 2021-03-01-08:01:12 ARTICLE