000099731 001__ 99731
000099731 005__ 20230712100014.0
000099731 0247_ $$2doi$$a10.3390/rs13030342
000099731 0248_ $$2sideral$$a123230
000099731 037__ $$aART-2021-123230
000099731 041__ $$aeng
000099731 100__ $$0(orcid)0000-0001-8481-8712$$aRevilla, S.
000099731 245__ $$aAssessing the potential of the dart model to discrete return lidar simulation—application to fuel type mapping
000099731 260__ $$c2021
000099731 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099731 5203_ $$aFuel type is one of the key factors for analyzing the potential of fire ignition and propaga-tion in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.
000099731 536__ $$9info:eu-repo/grantAgreement/ES/UZ/CUD2018-HUM-01
000099731 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099731 590__ $$a5.349$$b2021
000099731 592__ $$a1.283$$b2021
000099731 594__ $$a7.4$$b2021
000099731 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b30 / 202 = 0.149$$c2021$$dQ1$$eT1
000099731 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2021$$dQ1
000099731 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b6 / 28 = 0.214$$c2021$$dQ1$$eT1
000099731 591__ $$aREMOTE SENSING$$b11 / 34 = 0.324$$c2021$$dQ2$$eT1
000099731 591__ $$aENVIRONMENTAL SCIENCES$$b83 / 279 = 0.297$$c2021$$dQ2$$eT1
000099731 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099731 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M.T.$$uUniversidad de Zaragoza
000099731 700__ $$0(orcid)0000-0002-8362-7559$$aDomingo, D.
000099731 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva, J.$$uUniversidad de Zaragoza
000099731 700__ $$0(orcid)0000-0001-7403-1764$$aMontorio, R.$$uUniversidad de Zaragoza
000099731 700__ $$0(orcid)0000-0001-6288-2780$$aMontealegre, A.L.$$uUniversidad de Zaragoza
000099731 700__ $$0(orcid)0000-0003-2610-7749$$aGarcía-Martín, A.$$uUniversidad de Zaragoza
000099731 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000099731 773__ $$g13, 3 (2021), 342 [20 pp.]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000099731 8564_ $$s670268$$uhttps://zaguan.unizar.es/record/99731/files/texto_completo.pdf$$yVersión publicada
000099731 8564_ $$s2732357$$uhttps://zaguan.unizar.es/record/99731/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099731 909CO $$ooai:zaguan.unizar.es:99731$$particulos$$pdriver
000099731 951__ $$a2023-07-12-09:33:46
000099731 980__ $$aARTICLE