{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:05:42Z","timestamp":1783119942500,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Despite being a natural ecological process, wildfires are dramatic events that, accelerated by global change, could negatively affect ecosystem services depending on their severity level. However, because of data processing constraints, fire severity has been mostly neglected in risk analysis (especially at regional levels). Indeed, previous studies addressing fire severity focused mainly on analyzing single fire events, preventing the projection of the results over large areas. Although, building and projecting robust models of fire severity to integrate into risk analysis is of main importance to best anticipate decisions. Here, taking advantage of free data-processing platforms, such as Google Earth Engine, we use more than 1000 fire records from Western Italy and Southern France in the years 2004\u20132017, to assess the performance of random forest models predicting the relativized delta normalized burn ratio (rdNBR) used as proxy of fire severity. Furthermore, we explore the explanatory capacity and meaning of several variables related to topography, vegetation, and burning conditions. To show the potentialities of this approach for operational purposes, we projected the model for one of the regions (Sardinia) within the study area. Results showed that machine learning algorithms explain up to 75% of the variability in rdNBR, with variables related to vegetation amount and topography being the most important. These results highlight the potential usefulness of these tools for mapping fire severity in risk assessments.<\/jats:p>","DOI":"10.3390\/rs14194812","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"4812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Predicting and Mapping Potential Fire Severity for Risk Analysis at Regional Level Using Google Earth Engine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-6111","authenticated-orcid":false,"given":"Jose Maria","family":"Costa-Saura","sequence":"first","affiliation":[{"name":"Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy"},{"name":"Euro-Mediterranean Center on Climate Change Foundation, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Bacciu","sequence":"additional","affiliation":[{"name":"Euro-Mediterranean Center on Climate Change Foundation, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy"},{"name":"National Research Council of Italy, Institute of Bioeconomy, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio","family":"Ribotta","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5661-0241","authenticated-orcid":false,"given":"Donatella","family":"Spano","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy"},{"name":"Euro-Mediterranean Center on Climate Change Foundation, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Massaiu","sequence":"additional","affiliation":[{"name":"French National Forest Office, 20090 Ajaccio, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7605-5700","authenticated-orcid":false,"given":"Costantino","family":"Sirca","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy"},{"name":"Euro-Mediterranean Center on Climate Change Foundation, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1111\/brv.12544","article-title":"Fire as a key driver of Earth\u2019s biodiversity","volume":"94","author":"He","year":"2019","journal-title":"Biol. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1071\/WF07151","article-title":"Are wildfires a disaster in the Mediterranean basin?\u2014A review","volume":"17","author":"Pausas","year":"2008","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"847","DOI":"10.5194\/nhess-18-847-2018","article-title":"Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean","volume":"18","author":"Ruffault","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135841","DOI":"10.1016\/j.scitotenv.2019.135841","article-title":"Fire regime dynamics in mainland Spain. Part 1: Drivers of change","volume":"721","author":"Rodrigues","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_5","unstructured":"Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M.I. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pausas, J., and Vallejo, R. (1999). The role of fire in European Mediterranean Ecosystems. Remote Sensing of Large Wildfires, Springer.","DOI":"10.1007\/978-3-642-60164-4_2"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1111\/risa.12739","article-title":"Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas","volume":"37","author":"Lozano","year":"2017","journal-title":"Risk Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1007\/s10661-014-4175-x","article-title":"Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy","volume":"187","author":"Salis","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106474","DOI":"10.1016\/j.eiar.2020.106474","article-title":"Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks","volume":"85","author":"Elia","year":"2020","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.envsoft.2017.12.019","article-title":"Wildfire susceptibility mapping: Deterministic vs. stochastic approaches","volume":"101","author":"Leuenberger","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.cliser.2017.04.001","article-title":"Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe","volume":"9","author":"Bedia","year":"2018","journal-title":"Clim. Serv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1038\/s41597-020-0554-z","article-title":"ERA5-based global meteorological wildfire danger maps","volume":"7","author":"Vitolo","year":"2020","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1007\/s004420050198","article-title":"Alternative fire resistance strategies in savanna trees","volume":"110","author":"Gignoux","year":"1997","journal-title":"Oecologia"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.foreco.2008.04.032","article-title":"Fire resistance of European pines","volume":"256","author":"Fernandes","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sugihara, N., van Wagtendonk, J., and Fites-Kaufman, J. (2006). Fire as an ecological process. Fire California\u2019s Ecosystems, University of California Press.","DOI":"10.1525\/california\/9780520246058.003.0004"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1071\/WF07049","article-title":"Fire intensity, fire severity and burn severity: A brief review and suggested usage","volume":"18","author":"Keeley","year":"2009","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_17","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment (LA) Sampling and Analysis Methods, General Technical Report RMRS-GTR."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.rse.2008.10.011","article-title":"GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data","volume":"113","author":"Chuvieco","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1071\/WF04010","article-title":"Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data","volume":"14","author":"Cocke","year":"2005","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.rse.2008.11.009","article-title":"Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA","volume":"113","author":"Miller","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.jenvman.2019.01.077","article-title":"Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data","volume":"235","author":"Cardil","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1016\/j.rse.2010.03.013","article-title":"Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada","volume":"114","author":"Soverel","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10021-014-9824-y","article-title":"Fire Severity in a Large Fire in a Pinus pinaster Forest is Highly Predictable from Burning Conditions. Stand Structure, and Topography","volume":"18","author":"Viedma","year":"2015","journal-title":"Ecosystems"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.jenvman.2019.01.056","article-title":"Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem","volume":"235","author":"Mitsopoulos","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1890\/11-1930.1","article-title":"Uses and misuses of bioclimatic envelope modelling","volume":"93","author":"Araujo","year":"2012","journal-title":"Ecology"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Holsinger, L.M., Voss, M.A., Loehman, R.A., and Robinson, N.P. (2018). Mean composite fire severity metrics computed with google earth engine offer improved accuracy and expanded mapping potential. Remote Sens., 10.","DOI":"10.3390\/rs10060879"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1139\/cjfr-2020-0353","article-title":"Trends in wildfire burn severity across Canada, 1985 to 2015","volume":"51","author":"Guidon","year":"2020","journal-title":"Can. J. For. Res."},{"key":"ref_29","first-page":"137","article-title":"Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems","volume":"80","author":"Quintano","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Parisien, M.A., Miller, C., and Dobrowski, S.Z. (2014). Fire activity and severity in the western US vary along proxy gradients representing fuel amount and fuel moisture. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0099699"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111891","DOI":"10.1016\/j.rse.2020.111891","article-title":"Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain","volume":"247","author":"Viedma","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.foreco.2018.10.051","article-title":"Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems","volume":"433","author":"Taboada","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Costa-Saura, J.M., Balaguer-Beser, \u00c1., Ruiz, L.A., Pardo-Pascual, J.E., and Soriano-Sancho, J.L. (2021). Empirical models for spatio-temporal live fuel moisture content estimation in mixed mediterranean vegetation areas using sentinel-2 indices and meteorological data. Remote Sens., 13.","DOI":"10.3390\/rs13183726"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/01431160110069818","article-title":"Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment","volume":"23","author":"Chuvieco","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Swetnam, T.L., Yool, S.R., Roy, S., and Falk, D.A. (2021). On the use of standardized multi-temporal indices for monitoring disturbance and ecosystem moisture stress across multiple earth observation systems in the google earth engine. Remote Sens., 13.","DOI":"10.3390\/rs13081448"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s10694-014-0405-6","article-title":"An Implementation of the Rothermel Fire Spread Model in the R Programming Language","volume":"51","author":"Vacchiano","year":"2015","journal-title":"Fire Technol."},{"key":"ref_39","unstructured":"Rothermel, R.C. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels, Research Paper INT-115."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wagenbrenner, N.S., Forthofer, J.M., Page, W.G., and Butler, B.W. (2019). Development and evaluation of a reynolds-averaged navier-stokes solver in windninja for operational wildland fire applications. Atmosphere, 10.","DOI":"10.20944\/preprints201909.0315.v1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1017\/S0376892997000088","article-title":"A review of methods for the assessment of prediction errors in conservation presence\/absence models","volume":"24","author":"Fielding","year":"1997","journal-title":"Environ. Conserv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"421","DOI":"10.32614\/RJ-2017-016","article-title":"pdp: An R package for constructing partial dependence plots","volume":"9","author":"Greenwell","year":"2017","journal-title":"R J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Viana-Soto, A., Aguado, I., Salas, J., and Garc\u00eda, M. (2020). Identifying post-fire recovery trajectories and driving factors using landsat time series in fire-prone mediterranean pine forests. Remote Sens., 12.","DOI":"10.3390\/rs12091499"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Marcos, B., Gon\u00e7alves, J., Alcaraz-Segura, D., Cunha, M., and Honrado, J.P. (2021). A framework for multi-dimensional assessment of wildfire disturbance severity from remotely sensed ecosystem functioning attributes. Remote Sens., 13.","DOI":"10.3390\/rs13040780"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Miquelajauregui, Y., Cumming, S.G., and Gauthier, S. (2016). Modelling variable fire severity in boreal forests: Effects of fire intensity and stand structure. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150073"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"D\u2019este, M., Elia, M., Giannico, V., Spano, G., Lafortezza, R., and Sanesi, G. (2021). Machine learning techniques for fine dead fuel load estimation using multi-source remote sensing data. Remote Sens., 13.","DOI":"10.3390\/rs13091658"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13595-016-0599-5","article-title":"Characterizing potential wildland fire fuel in live vegetation in the Mediterranean region","volume":"74","author":"Fares","year":"2017","journal-title":"Ann. For. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1071\/WF12077","article-title":"The influence of fuel moisture content on the combustion of Eucalyptus foliage","volume":"22","author":"Possell","year":"2012","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Andrews, P.L. (2018). The Rothermel Surface Fire Spread Model and Associated Developments: A Comprehensive Explanation, General Technical Report RMRS-GTR-371.","DOI":"10.2737\/RMRS-GTR-371"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1038\/s41467-022-30030-2","article-title":"Human-ignited fires result in more extreme fire behavior and ecosystem impacts","volume":"13","author":"Hantson","year":"2022","journal-title":"Nat. Commun."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4812\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:40:16Z","timestamp":1760143216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":52,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194812"],"URL":"https:\/\/doi.org\/10.3390\/rs14194812","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,27]]}}}