000069650 001__ 69650
000069650 005__ 20210301081638.0
000069650 0247_ $$2doi$$a10.1080/22797254.2017.1336067
000069650 0248_ $$2sideral$$a100558
000069650 037__ $$aART-2017-100558
000069650 041__ $$aeng
000069650 100__ $$0(orcid)0000-0002-8362-7559$$aDomingo, Darío$$uUniversidad de Zaragoza
000069650 245__ $$aComparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest
000069650 260__ $$c2017
000069650 5060_ $$aAccess copy available to the general public$$fUnrestricted
000069650 5203_ $$aThe 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.
000069650 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000069650 590__ $$a1.122$$b2017
000069650 591__ $$aREMOTE SENSING$$b24 / 30 = 0.8$$c2017$$dQ4$$eT3
000069650 592__ $$a0.577$$b2017
000069650 593__ $$aApplied Mathematics$$c2017$$dQ2
000069650 593__ $$aEnvironmental Science (miscellaneous)$$c2017$$dQ2
000069650 593__ $$aComputers in Earth Sciences$$c2017$$dQ2
000069650 593__ $$aAtmospheric Science$$c2017$$dQ3
000069650 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000069650 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas-Gracia, María Teresa
000069650 700__ $$0(orcid)0000-0001-6288-2780$$aMontealegre-Gracia, Antonio Luis$$uUniversidad de Zaragoza
000069650 700__ $$0(orcid)0000-0003-2615-270X$$aRiva-Fernández, Juan de la$$uUniversidad de Zaragoza
000069650 7102_ $$13006$$2435$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Geografía Humana
000069650 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000069650 773__ $$g50, 1 (2017), 384-396$$pEur. j. remote sens.$$tEuropean journal of remote sensing$$x2279-7254
000069650 8564_ $$s1944457$$uhttps://zaguan.unizar.es/record/69650/files/texto_completo.pdf$$yVersión publicada
000069650 8564_ $$s126419$$uhttps://zaguan.unizar.es/record/69650/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000069650 909CO $$ooai:zaguan.unizar.es:69650$$particulos$$pdriver
000069650 951__ $$a2021-03-01-08:01:12
000069650 980__ $$aARTICLE