{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:28:29Z","timestamp":1780486109790,"version":"3.54.1"},"reference-count":82,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T00:00:00Z","timestamp":1539648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union  H2020","award":["DIABOLO (H2020 GA 633464)"],"award-info":[{"award-number":["DIABOLO (H2020 GA 633464)"]}]},{"name":"Plan Estatal de Investigaci\u00f3n Cient\u00edfica y T\u00e9cnica y de Innovaci\u00f3n 2013-2016","award":["GEPRIF (RTA 2014-00011-c06-04)."],"award-info":[{"award-number":["GEPRIF (RTA 2014-00011-c06-04)."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.<\/jats:p>","DOI":"10.3390\/rs10101645","type":"journal-article","created":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T11:07:51Z","timestamp":1539688071000},"page":"1645","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard"],"prefix":"10.3390","volume":"10","author":[{"given":"St\u00e9fano","family":"Arellano-P\u00e9rez","sequence":"first","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1656-5255","authenticated-orcid":false,"given":"Fernando","family":"Castedo-Dorado","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Agraria y Forestal, Universidad de Le\u00f3n, Avda. Astorga s\/n, 24401 Ponferrada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-5739","authenticated-orcid":false,"given":"Carlos Antonio","family":"L\u00f3pez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica de Mieres, Universidad de Oviedo, C\/Gonzalo Guti\u00e9rrez de Quir\u00f3s s\/n, 33600 Mieres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4565-2155","authenticated-orcid":false,"given":"Eduardo","family":"Gonz\u00e1lez-Ferreiro","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, Campus Universitario s\/n, 27002 Lugo, Spain"},{"name":"Escuela Superior y T\u00e9cnica de Ingenieros de Minas, Universidad de Le\u00f3n, Avda. Astorga s\/n, 24401 Ponferrada, Spain"},{"name":"Department of Forest Ecosystems and Society (FES), Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Rocky Mountain Research Station, USFS, 507 25th Street, Ogden, UT 84401, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram\u00f3n Alberto","family":"D\u00edaz-Varela","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-9128","authenticated-orcid":false,"given":"Juan Gabriel","family":"\u00c1lvarez-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Antonio","family":"Vega","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Forestal de Louriz\u00e1n, P.O. Box 127, 36080 Pontevedra, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Dar\u00eda","family":"Ruiz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,16]]},"reference":[{"key":"ref_1","first-page":"24","article-title":"Conditions for the start and spread of crown fire","volume":"7","year":"1977","journal-title":"Can. J. For. Res."},{"key":"ref_2","first-page":"640","article-title":"Modelling the likelihood of crown fire occurrence in conifer forest stands","volume":"50","author":"Cruz","year":"2004","journal-title":"For. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1139\/x05-085","article-title":"Development and testing of models for predicting crown fire rate of spread in conifer forest stands","volume":"35","author":"Cruz","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Werth, P.A., Potter, B.E., Clements, C.B., Finney, M.A., Goodrick, S.L., Alexander, M.E., Cruz, M.G., Forthofer, J.A., and McAllister, S.S. (2011). Crown fire dynamics in conifer forests, Synthesis of Knowledge of Extreme Fire Behavior: Volume I for Fire Managers.","DOI":"10.2737\/PNW-GTR-854"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Scott, J.H., and Reinhardt, E.D. (2001). Assessing Crown Fire Potential by Linking Models of Surface and Crown Fire Behavior.","DOI":"10.2737\/RMRS-RP-29"},{"key":"ref_6","first-page":"156","article-title":"Influence of crown biomassestimators and distribution on canopy fuel characteristics in ponderosa pine stands of the Black Hills","volume":"56","author":"Keyser","year":"2010","journal-title":"For. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.foreco.2012.06.056","article-title":"Mapping fire risk in the Model Forest of Urbi\u00f3n (Spain) based on airborne LiDAR measurements","volume":"282","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1071\/WF13054","article-title":"Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low density LiDAR data","volume":"23","author":"Miranda","year":"2014","journal-title":"Int. J. Wild. Fire"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Ferreiro, E., Arellano-P\u00e9rez, S., Castedo-Dorado, F., Hevia, A., Vega, J.A., Vega-Nieva, D., \u00c1lvarez-Gonz\u00e1lez, J.G., and Ruiz-Gonz\u00e1lez, A.D. (2017). Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176114"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, M., Saatchi, S., Casas, A., Koltunov, A., Ustin, S.L., Ramirez, C., and Balzter, H. (2017). Extrapolating forest canopy fuel properties in the California Rim Fire by combining airborne LiDAR and Landsat OLI data. Remote Sens., 9.","DOI":"10.3390\/rs9040394"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF01028","article-title":"Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling","volume":"10","author":"Keane","year":"2001","journal-title":"Int. J. Wild. Fire"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Keane, R.E., Mincemoyer, S.A., Schmidt, K.M., Long, D.G., and Garner, J. (2000). Mapping Vegetation and Fuels for Fire Management on the Gila National Forest Complex, New Mexico, Rocky Mountain Research Station. USDA Forest Service General Technical Report GTR-RMS-046.","DOI":"10.2737\/RMRS-GTR-46"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rollins, M.G., and Frame, C.K. (2006). The LANDFIRE Prototype Project: Nationally Consistent and Locally Relevant Geospatial Data for Wildland Fire Management, Rocky Mountain Research Station. USDA Forest Service General Technical Report RMRS-GTR-175.","DOI":"10.2737\/RMRS-GTR-175"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.foreco.2012.05.010","article-title":"Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA","volume":"279","author":"Pierce","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1080\/01431161.2013.779399","article-title":"Forest mapping by geoinformatics for landscape fire behavior modelling in coastal forests, Greece","volume":"34","author":"Palaiologou","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2005.06.013","article-title":"Characterizing and Mapping Forest Fire Fuels Using ASTER Imagery and Gradient Modeling","volume":"217","author":"Falkowski","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1071\/WF02049","article-title":"Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA","volume":"13","author":"Reich","year":"2004","journal-title":"Int. J. Wild. Fire"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1071\/WF03032","article-title":"Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia","volume":"12","author":"Brandis","year":"2003","journal-title":"Int. J. Wild. Fire"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1071\/WF11018","article-title":"Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China","volume":"21","author":"Jin","year":"2012","journal-title":"Int. J. Wild. Fire"},{"key":"ref_20","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_22","first-page":"32","article-title":"Use of Sentinel-2 for forest classification in Mediterranean environments","volume":"42","author":"Puletti","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2017.03.021","article-title":"Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index","volume":"195","author":"Korhonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/2150704X.2017.1295479","article-title":"Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem","volume":"8","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation trough hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"016008","DOI":"10.1117\/1.JRS.12.016008","article-title":"Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data","volume":"12","author":"Laurin","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","unstructured":"Di\u00e9guez-Aranda, U., Rojo Alboreca, A., Castedo-Dorado, F., \u00c1lvarez Gonz\u00e1lez, J.G., Barrio-Anta, M., Crecente-Campo, F., Gonz\u00e1lez Gonz\u00e1lez, J.M., P\u00e9rez-Cruzado, C., Rodr\u00edguez Soalleiro, R., and L\u00f3pez-S\u00e1nchez, C.A. (2009). Herramientas Selv\u00edcolas para la Gesti\u00f3n Forestal Sostenible en Galicia, Conseller\u00eda do Medio Rural, Xunta de Galicia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1093\/forestry\/cpt019","article-title":"Development of crown profile models for Pinus pinaster Ait. and Pinus sylvestris L. in northwestern Spain","volume":"86","year":"2013","journal-title":"Forestry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1016\/j.foreco.2009.03.038","article-title":"A crown profile model for Pinus radiata D. Don in northwestern Spain","volume":"257","author":"Marshall","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_30","unstructured":"Arellano-P\u00e9rez, S. (2011). Modelos de Combustibles Forestales de Galicia. [Master\u2019s Thesis, University of Santiago de Compostela]."},{"key":"ref_31","first-page":"96","article-title":"A planar intersect method for sampling fuel volume and surface area","volume":"17","author":"Brown","year":"1971","journal-title":"For. Sci."},{"key":"ref_32","unstructured":"Brown, J.K. (1974). Handbook for Inventorying Downed Woody Material."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Brown, J.K., Oberheu, R.D., and Johnston, C.M. (1982). Handbook for Inventorying Surface Fuels and Biomass in the Interior West.","DOI":"10.2737\/INT-GTR-129"},{"key":"ref_34","unstructured":"Busing, R., Rimar, K., Stolte, K.W., and Stohlgren, T.J. (1999). Forest Health Monitoring Vegetation Pilot Field Methods Guide: Vegetation Diversity and Structure, Down Woody Debris, Fuel Loading."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S1470-160X(01)00012-7","article-title":"Sampling coarse woody debris for multiple attributes in extensive resource inventories","volume":"1","author":"Waddell","year":"2002","journal-title":"Ecol. Indic."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., and Gangi, L.J. (2006). FIREMON: Fire Effects Monitoring and Inventory System.","DOI":"10.2737\/RMRS-GTR-164"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1071\/WF9980029","article-title":"Reduction of fire hazard through thinning\/residue disposal in the urban interface","volume":"8","author":"Kalabokidis","year":"1998","journal-title":"Int. J. Wild. Fire"},{"key":"ref_38","first-page":"329","article-title":"Does the lack of reference ecosystems limit our science? A case study in non-native invasive plants as forest fuels","volume":"103","author":"Dibble","year":"2005","journal-title":"J. For."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1071\/WF07003","article-title":"A comparison of five sampling techniques to estimate surface fuel loading in montane forests","volume":"17","author":"Sikkink","year":"2008","journal-title":"Int. J. Wild. Fire"},{"key":"ref_40","first-page":"57","article-title":"Drying rates of heartwood below fiber saturation","volume":"16","author":"Fosberg","year":"1970","journal-title":"For. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Burgan, R.E., and Rothermel, R.C. (1984). BEHAVE: Fire Behavior Prediction and Fuel Modeling System-FUEL Subsystem, Intermountain Forest and Range Experiment Station. USDA Forest Service, Gen. Tech. Rep. INT-167.","DOI":"10.2737\/INT-GTR-167"},{"key":"ref_42","unstructured":"Andrews, P.L., Bevins, C.D., and Seli, R.C. (2008). BehavePlus Fire Modeling System, Version 4.0: User\u2019s Guide, Intermountain Forest and Range Experiment Station. USDA Forest Service, Gen. Tech. Rep. RMRS-GTR-106WWW Revised."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Finney, M.A. (1998). FARSITE: Fire Area Simulator\u2014Model Development and Evaluation, Intermountain Forest and Range Experiment Station. USDA Forest Service, Res. Pap. RMRSRP-4.","DOI":"10.2737\/RMRS-RP-4"},{"key":"ref_44","unstructured":"Finney, M.A. (2006). An overview of FlamMap fire modeling capabilities, Fuels Management\u2014How to Measure Success: Conference Proceedings."},{"key":"ref_45","unstructured":"Viegas, D.X. (2006, January 27\u201330). CFIS: A software tool for simulating crown fire initiation and spread. Proceedings of V International Conference on Forest Fire Research, Figueira da Foz, Portugal."},{"key":"ref_46","unstructured":"GmbH TVD (2015, December 21). Sentinel-2 MSI\u2014Level-2A Prototype Processor Installation and User Manual. Available online: http:\/\/step.esa.int\/thirdparties\/sen2cor\/2.2.1\/S2PAD-VEGA-SUM-0001-2.2.pdf."},{"key":"ref_47","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, W.D. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington, DC, USA. NASA SP-351."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","first-page":"71","article-title":"Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan\u2019s Kenting National Park","volume":"48","author":"Chen","year":"2007","journal-title":"Bot. Stud."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_55","first-page":"18","article-title":"Classification and Regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_56","unstructured":"R Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org."},{"key":"ref_57","first-page":"1","article-title":"Multivariate adaptive regression splines (with discussion)","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_58","unstructured":"Milborrow, S. (2017, April 21). Derived from mda:mars by Hastie T and Tibshirani, R. Uses Alan Miller\u2019s Fortran Utilities with Thomas Lumley\u2019s Leaps Wrapper. Available online: https:\/\/CRAN.R-project.org\/package=earth."},{"key":"ref_59","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and Benesty, M. (2017, April 21). Caret: Classification and Regression Training. Available online: https:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_60","unstructured":"Cronan, J., and Jandt, R. (2008). How Succession Affects Fire Behavior in Boreal Black Spruce Forest of Interior Alaska, U.S. Department of the Interior. Bureau of Land Management. BLM Alaska Technical Report 59."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e02S","DOI":"10.5424\/fs\/2017262-10652","article-title":"Assessment of crown fire initiation and spread models in Mediterranean conifer forests by using data from field and laboratory experiments","volume":"26","author":"Guijarro","year":"2017","journal-title":"For. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1051\/forest:2007006","article-title":"Canopy fuel characteristics and potential crown FIRE behavior in Aleppo pine (Pinus halepensis Mill.) forests","volume":"64","author":"Mitsopoulos","year":"2007","journal-title":"Ann. For. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s10342-012-0680-z","article-title":"Canopy fuel characteristics in relation to crown fire potential in pine stands: Analysis, modelling and classification","volume":"132","author":"Alberdi","year":"2013","journal-title":"Eur. J. For. Res."},{"key":"ref_64","first-page":"G00K05","article-title":"Model comparisons for estimating carbon emissions from North American wildland fire","volume":"116","author":"French","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Keane, R.E., Gray, K., and Bacciu, V. (2012). Spatial Variability of Wildland Fuel Characteristics in Northern Rocky Mountain Ecosystems, USDA Forest Service, Rocky Mountain Research Station. Research Paper RMRS-RP-98.","DOI":"10.2737\/RMRS-RP-98"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1016\/j.foreco.2008.09.016","article-title":"Objectives and considerations for wildland fuel treatment in forested ecosystems of the interior western United States","volume":"256","author":"Reinhardt","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0301-4797(03)00062-8","article-title":"Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem","volume":"68","author":"Miller","year":"2003","journal-title":"J. Environ. Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1080\/01431160210144679","article-title":"The USE of multitemporal Landsat normalized difference vegetation index (NDVI) data for mapping fuels models in Yosemite National Park, USA","volume":"24","author":"Root","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"S259","DOI":"10.1016\/j.foreco.2006.08.288","article-title":"Fuel type mapping with Landsat TM images and ancillary data in the Prealpine region of Italy","volume":"234S","author":"Francesetti","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolmodel.2006.12.022","article-title":"On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape","volume":"204","author":"Lasaponara","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_71","first-page":"225","article-title":"Remotely sensed characterization of forest fuel types by using satellite ASTER data","volume":"9","author":"Lasaponara","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1139\/cjfr-2012-0213","article-title":"Mapping fuels in Yosemite National Park","volume":"43","author":"Peterson","year":"2013","journal-title":"Can. J. For. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/S0034-4257(99)00055-3","article-title":"Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products","volume":"70","author":"Thomlinson","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/rs6054515","article-title":"Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality","volume":"6","author":"Waser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"094096","DOI":"10.1117\/1.JRS.9.094096","article-title":"Potential of Sentinel-2 spectral configuration to assess rangeland quality","volume":"9","author":"Ramoelo","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Bright, B.C., Hudak, A.T., Meddens, A.J.H., Hawbaker, T.J., Briggs, J.S., and Kennedy, R.E. (2017). Prediction of forest canopy and surface fuels from lidar and satellite time series data in a bark beetle-affected forest. Forests, 8.","DOI":"10.3390\/f8090322"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1071\/WF01044","article-title":"Fuel loading prediction models developed from aerial photographs of the Sangre de Cristo and Jemez mountains of New Mexico, USA","volume":"11","author":"Scott","year":"2002","journal-title":"Int. J. Wild. Fire"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2006.09.032","article-title":"Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey","volume":"108","author":"Skowronski","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10342-016-0963-x","article-title":"Midterm fuel structure recovery and potential fire behaviour in a Pinus pinaster Ait. forest in northern central Spain after thinning and mastication","volume":"135","author":"Rey","year":"2016","journal-title":"Eur. J. For. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.foreco.2008.06.048","article-title":"Fire models and methods to map fuel types: The role of remote sensing","volume":"256","author":"Arroyo","year":"2008","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1645\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:26:04Z","timestamp":1760196364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,16]]},"references-count":82,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101645"],"URL":"https:\/\/doi.org\/10.3390\/rs10101645","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,16]]}}}