000126360 001__ 126360
000126360 005__ 20240731103402.0
000126360 0247_ $$2doi$$a10.1016/j.rsase.2023.100997
000126360 0248_ $$2sideral$$a133759
000126360 037__ $$aART-2023-133759
000126360 041__ $$aeng
000126360 100__ $$0(orcid)0000-0002-9123-304X$$aHoffrén, R.$$uUniversidad de Zaragoza
000126360 245__ $$aUAV-derived photogrammetric point clouds and multispectral indices for fuel estimation in Mediterranean forests
000126360 260__ $$c2023
000126360 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126360 5203_ $$aSensors attached to unmanned aerial vehicles (UAVs) allow estimating a large number of forest attributes related to forest fuels. This study assesses photogrammetric point clouds and multispectral indices obtained from a fixed-wing UAV for the classification of Prometheus fuel types in 82 forest plots in Aragón (NE Spain). Images captured by an RGB camera and a multispectral sensor allowed generating high density photogrammetric point clouds (RGB: 3000 points/m2; multispectral: 85 points/m2), which were normalized using alternatively a Digital Elevation Model (DEM) of 0.5, 1, and 2 m resolution. A set of structural and textural variables were derived from the normalized point cloud heights, and for the latter, the gray-level co-occurrence matrix (GLCM) approach was used. Multispectral images were also used to create seven spectral vegetation indices. The most relevant structural, textural, and spectral variables to introduce into the fuel types classification models were selected using Dunn's test, which included: the vegetation height at the 50th percentile, the coefficient of variation of the heights, the percentage of returns above 4 m, the mean textural dissimilarity, and the mean of the Green Chlorophyll Index. Three different data samples were introduced in the models: i) the relevant structural and textural variables from the RGB camera (RGB data sample); ii) the relevant structural, textural, and spectral variables from the multispectral sensor (MS data sample); and iii) the relevant structural and textural variables from the RGB camera plus the relevant spectral variable from the multispectral sensor (integrated data sample). After comparing three machine learning classification techniques (Random Forest, and Linear and Radial Support Vector Machine), the best results were obtained with Random Forest with k-fold cross-validation (k-10) and the integrated data sample with normalized point clouds at 0.5 m DEM resolution (overall accuracy = 71%). The variables successfully identified the Prometheus main fire carriers (i.e., shrubs or trees) and confusions were mainly located within the fuel types of the same dominant stratum, especially in fuel types 3 and 6. These results demonstrate the ability of UAV imagery to classify forest fuels in Mediterranean environments when RGB and multispectral data are combined.
000126360 536__ $$9info:eu-repo/grantAgreement/ES/MCIU/FPU18-05027$$9info:eu-repo/grantAgreement/ES/DGA/S51-20R
000126360 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126360 592__ $$a0.892$$b2023
000126360 593__ $$aGeography, Planning and Development$$c2023$$dQ1
000126360 593__ $$aComputers in Earth Sciences$$c2023$$dQ1
000126360 594__ $$a8.0$$b2023
000126360 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126360 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M. T.$$uUniversidad de Zaragoza
000126360 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva, J.$$uUniversidad de Zaragoza
000126360 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000126360 773__ $$g31 (2023), 100997 [11 pp.]$$tRemote Sensing Applications: Society and Environment$$x2352-9385
000126360 8564_ $$s5473448$$uhttps://zaguan.unizar.es/record/126360/files/texto_completo.pdf$$yVersión publicada
000126360 8564_ $$s1917274$$uhttps://zaguan.unizar.es/record/126360/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126360 909CO $$ooai:zaguan.unizar.es:126360$$particulos$$pdriver
000126360 951__ $$a2024-07-31-09:59:45
000126360 980__ $$aARTICLE