{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:39:57Z","timestamp":1768549197615,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T00:00:00Z","timestamp":1593129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["CO2-2014-3-252620"],"award-info":[{"award-number":["CO2-2014-3-252620"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CONAFOR\/CONACYT","award":["CO-2018-2-A3-S-131553"],"award-info":[{"award-number":["CO-2018-2-A3-S-131553"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012\u20132108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.<\/jats:p>","DOI":"10.3390\/rs12122061","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"2061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico"],"prefix":"10.3390","volume":"12","author":[{"given":"Carlos Ivan","family":"Briones-Herrera","sequence":"first","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Jos\u00e9","family":"Vega-Nieva","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norma Ang\u00e9lica","family":"Monjar\u00e1s-Vega","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaime","family":"Brise\u00f1o-Reyes","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablito Marcelo","family":"L\u00f3pez-Serrano","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-7517","authenticated-orcid":false,"given":"Jos\u00e9 Javier","family":"Corral-Rivas","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Forestales, Universidad Ju\u00e1rez del Estado de Durango, R\u00edo Papaloapan y Blvd, Durango S\/N Col. Valle del Sur, 34120 Durango, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9606-9963","authenticated-orcid":false,"given":"Ernesto","family":"Alvarado-Celestino","sequence":"additional","affiliation":[{"name":"School of Environmental and Forest Sciences, University of Washington, Mailbox 352100, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-8618","authenticated-orcid":false,"given":"St\u00e9fano","family":"Arellano-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Agroforestal, Universidad de Santiago de Compostela, Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-9128","authenticated-orcid":false,"given":"Juan Gabriel","family":"\u00c1lvarez-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Agroforestal, Universidad de Santiago de Compostela, Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana Dar\u00eda","family":"Ruiz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Agroforestal, Universidad de Santiago de Compostela, Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Campus Universitario s\/n, 27002 Lugo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0457-6563","authenticated-orcid":false,"given":"William Mathew","family":"Jolly","sequence":"additional","affiliation":[{"name":"USDA Forest Service, Missoula Fire Sciences Laboratory, Missoula, MT 59808, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2982-5255","authenticated-orcid":false,"given":"Sean A.","family":"Parks","sequence":"additional","affiliation":[{"name":"USDA Forest Service, Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, Missoula, MT 59801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/essd-9-697-2017","article-title":"Global fire emissions estimates during 1997\u20132016","volume":"9","author":"Randerson","year":"2017","journal-title":"Earth Syst. Sci. Data."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.5194\/bg-7-1171-2010","article-title":"Assessing variability and long-term trends in burned area by merging multiple satellite fire products","volume":"7","author":"Giglio","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_3","first-page":"D23112","article-title":"Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in Central Kalimantan, Indonesia","volume":"113","author":"Tansey","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2012.12.004","article-title":"Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence","volume":"131","author":"Hantson","year":"2013","journal-title":"Rem. Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite Earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1016\/j.rse.2008.05.013","article-title":"The Collection 5 MODIS Burned Area Product\u2013Global evaluation by comparison with the MODIS active fire product","volume":"112","author":"Roy","year":"2008","journal-title":"Rem. Sens. Environm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2008.10.006","article-title":"An active-fire based burned area mapping algorithm for the MODIS sensor","volume":"113","author":"Giglio","year":"2009","journal-title":"Rem. Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.08.005","article-title":"The Collection 6 MODIS burned area mapping algorithm and product","volume":"217","author":"Giglio","year":"2018","journal-title":"Rem. Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111493","DOI":"10.1016\/j.rse.2019.111493","article-title":"A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data","volume":"236","author":"Ramo","year":"2020","journal-title":"Rem. Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2016.02.054","article-title":"The collection 6 MODIS active fire detection algorithm and fire products","volume":"178","author":"Giglio","year":"2016","journal-title":"Rem. Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2013.12.008","article-title":"The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment","volume":"143","author":"Schroeder","year":"2014","journal-title":"Rem. Sens. Environm."},{"key":"ref_12","first-page":"G04012","article-title":"Global burned area and biomass burning emissions from small fires","volume":"117","author":"Randerson","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1071\/WF13138","article-title":"Mapping day-of-burning with coarse resolution satellite fire-detection data","volume":"23","author":"Parks","year":"2014","journal-title":"Int. J. Wildland Fire."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1071\/WF13015","article-title":"Mapping the daily progression of large wildland fires using MODIS active fire data","volume":"23","author":"Veraverbeke","year":"2014","journal-title":"Int. J. Wildland Fire."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Art\u00e9s, T., Boca, R., Liberta, G., and San-Miguel-Ayanz, J. (2017, January 20\u201323). Non-supervised method for early forest fire detection and rapid mapping, Proc. SPIE 10444. Proceedings of the Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104440R, Paphos, Cyprus.","DOI":"10.1117\/12.2280714"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"957","DOI":"10.5194\/acp-6-957-2006","article-title":"Global estimation of burned area using MODIS active fire observations","volume":"6","author":"Giglio","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Giglio, L., Csiszar, I., and Justice, C.O. Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. J. Geophys. Res., 111, G02016.","DOI":"10.1029\/2005JG000142"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1046\/j.1365-2486.2003.00604.x","article-title":"Carbon emissions from fires in tropical and subtropical ecosystems","volume":"9","author":"Randerson","year":"2003","journal-title":"Glob. Change Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.rse.2004.08.011","article-title":"AVHRR-based mapping of fires in Russia: New products for fire management and carbon cycle studies","volume":"93","author":"Sukhinin","year":"2004","journal-title":"Rem. Sens. Environm."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jgrg.20042","article-title":"Analysis of daily, monthly, and annual burned area using the fourth generation global fire emissions database (GFED4)","volume":"118","author":"Giglio","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"511","DOI":"10.5194\/acp-7-511-2007","article-title":"Arctic smoke-record high air pollution levels in the European Arctic due to agricultural fires in Eastern Europe in spring 2006","volume":"7","author":"Stohl","year":"2007","journal-title":"Atmos. Chem. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2006.12.011","article-title":"Estimating the area of stubble burning from the number of active fires detected by satellite","volume":"109","author":"Smith","year":"2007","journal-title":"Rem. Sens. Environm."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/S0034-4257(98)00006-6","article-title":"Remote sensing of biomass burning in tropical regions: Sampling issues and multisensor approach","volume":"64","author":"Eva","year":"1998","journal-title":"Rem. Sens. Environ."},{"key":"ref_24","first-page":"3175","article-title":"Interannual variability of global biomass burning emissions from 1997 to 2004","volume":"6","author":"Randerson","year":"2006","journal-title":"Atmos. Chem. Phys. Discuss. Eur. Geosci. Union"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1080\/01431160050144956","article-title":"Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm","volume":"21","author":"Li","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1080\/01431160050144965","article-title":"Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms","volume":"21","author":"Li","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1080\/01431160110078449","article-title":"A statistical methodology for burned area estimation using multitemporal AVHRR data","volume":"23","author":"Nielsen","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1029\/96JD01623","article-title":"The quantity of biomass burned in southern Africa","volume":"101","author":"Scholes","year":"1996","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1029\/2003GL017859","article-title":"The use of ATSR active fire counts for estimating relative patterns of biomass burning a study from the boreal forest region","volume":"30","author":"Kasischke","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1071\/WF09027","article-title":"The validity and utility of MODIS data for simple estimation of area burned and aerosols emitted by wildfire events","volume":"19","author":"Henderson","year":"2010","journal-title":"Int. J. Wildland Fire."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2015.01.010","article-title":"Assessment of VIIRS 375m active fire detection product for direct burned area mapping","volume":"160","author":"Oliva","year":"2015","journal-title":"Rem. Sens. Environm."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/eScience.2016.7870928","article-title":"Automatic fire perimeter determination using MODIS hotspots information","volume":"2016","author":"Chiaraviglio","year":"2016","journal-title":"IEEE 12th Int. Conf. e-Sci. (e-Science)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.gloplacha.2006.07.015","article-title":"Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product","volume":"56","author":"Loboda","year":"2007","journal-title":"Glob. Planet. Change."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/01431160903439858","article-title":"Large wildfire in Iceland in 2006: Size and intensity estimates from satellite data","volume":"32","author":"Thorsteinsson","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1111\/j.1365-2486.2011.02573.x","article-title":"Controls on carbon consumption during Alaskan wildland fires","volume":"18","author":"Kasischke","year":"2012","journal-title":"Glob. Change Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1071\/WF06069","article-title":"Fire growth modeling using meteorological data with random and systematic perturbations","volume":"16","author":"Anderson","year":"2007","journal-title":"Int. J. Wildland. Fire."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1071\/WF08046","article-title":"An approach to operational forest fire growth predictions for Canada","volume":"18","author":"Anderson","year":"2009","journal-title":"Int. J. Wildland. Fire."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5536","DOI":"10.1002\/2013GL057868","article-title":"Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations","volume":"40","author":"Coen","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1186\/s40064-016-2842-9","article-title":"Probabilistic fire spread forecast as a management tool in an operational setting","volume":"5","author":"Pinto","year":"2016","journal-title":"Springerplus"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2016.12.023","article-title":"Evaluating fire growth simulations using satellite active fire data","volume":"190","author":"Benali","year":"2017","journal-title":"Rem. Sens. Environm."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.scitotenv.2017.03.106","article-title":"Fire spread predictions: Sweeping uncertainty under the rug","volume":"592","author":"Benali","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Duff, T.J., Cawson, J.G., Cirulis, B., Nyman, P., Sheridan, G.J., and Tolhurst, K.G. (2018). Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development. Forests., 9.","DOI":"10.3390\/f9040189"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.jenvman.2018.10.115","article-title":"Assessing and reinitializing wildland fire simulations through satellite active fire data","volume":"231","author":"Cardil","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_44","first-page":"103","article-title":"Predicting fire spread and behaviour on the fireline","volume":"392","author":"Monedero","year":"2019","journal-title":"Wildfire analyst pocket: A mobile app for wildland fire prediction. Ecol. Model."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/2013JD020453","article-title":"Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results","volume":"119","author":"Csiszar","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.rse.2017.07.003","article-title":"Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes","volume":"199","author":"Waigl","year":"2017","journal-title":"Rem. Sens. Environm."},{"key":"ref_47","unstructured":"Salmon, J.M., Hao, W.M., Miller, M.E., Nordgren, B., Kaufman, Y., and Li, R. (2003, January 5\u20137). Validation of two MODIS single-scene fire products for mapping burned area: Hot spots and NIR spectral test burn scars. Proceedings of the 4th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Innovative Concepts and Methods in Fire Danger Estimation. Emilio Chuvieco, Pilar Mart\u00edn and Chris Justice (Editors), Ghent, Belgium."},{"key":"ref_48","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD\u201996), Portland, OR, USA."},{"key":"ref_49","unstructured":"INEGI (Instituto Nacional de Estad\u00edstica y Geograf\u00eda-M\u00e9xico) (2020, June 24). Guide for the interpretation of land use and vegetation type map, Series VI, Scale 1, 250, 000). [In Spanish: Gu\u00eda Para la Interpretaci\u00f3n de Cartograf\u00eda: Uso del suelo y Vegetaci\u00f3n. Escala 1, 250, 000: Serie VI]; 2014, Ed. Instituto Nacional de Estad\u00edstica y Geograf\u00eda, Mexico City, Mexico. Available online: http:\/\/internet.contenidos.inegi.org.mx\/contenidos\/Productos\/prod_serv\/contenidos\/espanol\/bvinegi\/productos\/nueva_estruc\/702825092030.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Briones-Herrera, C.I., Vega-Nieva, D.J., Monjar\u00e1s-Vega, N.A., Flores-Medina, F., Lopez-Serrano, P.M., Corral-Rivas, J.J., Carrillo-Parra, A., Pulgarin-G\u00e1miz, M.A., Alvarado-Celestino, E., and Gonz\u00e1lez-Cab\u00e1n, A. (2019). Modeling and mapping forest fire occurrence from aboveground carbon density in Mexico. Forests, 10.","DOI":"10.3390\/f10050402"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Vega-Nieva, D.J., Nava-Miranda, M.G., L\u00f3pez Serrano, P.M., Brise\u00f1o-Reyes, J., L\u00f3pez-S\u00e1nchez, C., Corral-Rivas, J.J., Cruz-Lopez, M., Ressl, R., Cuahtle, M., and Alvarado, E. (2018). Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico. Forests, 9.","DOI":"10.3390\/f9040190"},{"key":"ref_52","first-page":"1","article-title":"Temporal patterns of active fire density and its relationship with a satellite fuel greenness index by vegetation type and region in Mexico during 2003\u20132014","volume":"15","author":"Ressl","year":"2019","journal-title":"Fire Ecol."},{"key":"ref_53","unstructured":"ESRI (2011). ArcGIS Desktop 10.1, Environmental Systems Research Institute."},{"key":"ref_54","unstructured":"JetBrains (2019, April 11). Pycharm. Available online: https:\/\/www.jetbrains.com\/pycharm\/."},{"key":"ref_55","unstructured":"R Core Team (2017, March 20). Available online: https:\/\/www.R-project.org\/."},{"key":"ref_56","unstructured":"Ryan, T.P. (1997). Modern Regression Methods. Wiley Series in Probability and Statistics, John Wile and Sons."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"817","DOI":"10.2307\/1912934","article-title":"A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity","volume":"48","author":"White","year":"1980","journal-title":"Econometrica"},{"key":"ref_58","unstructured":"Kutner, M.H., Nachtsheim, C.J., Neter, J., and William, L. (2005). Applied Linear Statistical Models, McGraw-Hill. [5th ed.]."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4996\/fireecology.0301003","article-title":"A project for monitoring trends in burn severity","volume":"3","author":"Eidenshink","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.rse.2017.06.027","article-title":"Mapping burned areas using dense time-series of Landsat data","volume":"198","author":"Hawbaker","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_62","unstructured":"Vega-Nieva, D.J. (2019, January 19\u201321). New Developments for the Forest Fire Danger Prediction System of Mexico. Oral Presentation. Proceedings of the 8th International Association of Fire Ecology Congress, Tucson, Arizona."},{"key":"ref_63","unstructured":"Silva Cardoza, A.I. (2019, January 6\u2013). Evaluation and mapping of forest fires severity in the Western Sierra Madre, Mexico. Proceedings of the XIV Congreso Mexicano de Recursos Forestales, Durango, Mexico."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"133505","DOI":"10.1016\/j.scitotenv.2019.07.311","article-title":"Stochastic decision trigger modelling to assess the probability of wildland fire impact","volume":"694","author":"Ramirez","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ecolmodel.2019.01.017","article-title":"Adjusting the rate of spread of fire simulations in real-time","volume":"395","author":"Cardil","year":"2019","journal-title":"Ecol. Model."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1016\/j.procs.2015.05.294","article-title":"Forest Fire Propagation Prediction Based on Overlapping DDDAS Forecasts","volume":"51","author":"Cardil","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2005.09.012","article-title":"Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments","volume":"99","author":"Laris","year":"2005","journal-title":"Rem. Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2005.02.004","article-title":"Comparison of burned area estimates derived from SPOT-VEGETATION and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type","volume":"96","author":"Silva","year":"2005","journal-title":"Rem. Sens. Environm."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4181","DOI":"10.1038\/s41598-017-03739-0","article-title":"Size-dependent validation of MODIS MCD64A1 burned area over six vegetation types in boreal Eurasia: Large underestimation in croplands","volume":"7","author":"Zhu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Rem. Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1080\/01431160903369592","article-title":"Mapping sub-pixel burnt percentage using AVHRR data: Application to the Alcalaten area in Spain","volume":"31","author":"Ruescas","year":"2010","journal-title":"Int. J. Rem. Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1038\/s41597-019-0312-2","article-title":"A global wildfire dataset for the analysis of fire regimes and fire behavior","volume":"6","author":"Oom","year":"2019","journal-title":"Sci. Data"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/essd-11-529-2019","article-title":"The Global Fire Atlas of individual fire size, duration, speed and direction","volume":"11","author":"Andela","year":"2019","journal-title":"Earth. Syst. Sci. Data."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Philipp, M.B., and Levick, S.R. (2020). Exploring the Potential of C-Band SAR in Contributing to Burn Severity Mapping in Tropical Savanna. Remote Sens., 12.","DOI":"10.3390\/rs12010049"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1023\/A:1020375721520","article-title":"Fire Danger Monitoring Using ERS-1 SAR Images in the Case of Northern Boreal Forests","volume":"27","author":"Leblon","year":"2002","journal-title":"Nat. Hazards"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1038\/s41598-019-56967-x","article-title":"Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning","volume":"10","author":"Ban","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Lapini, A., Pettinato, S., Santi, E., Paloscia, S., Fontanelli, G., and Garzelli, A. (2020). Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas. Remote Sens., 12.","DOI":"10.3390\/rs12030369"},{"key":"ref_78","unstructured":"Vega-Nieva, D.J., Nava-Miranda, M.G., Briones-Herrera, C.I., Vega-Nieva, D.J., Monjar\u00e1s-Vega, N.A., Flores-Medina, F., L\u00f3pez Serrano, P.M., Brise\u00f1o-Reyes, J., L\u00f3pez-S\u00e1nchez, C., and Corral-Rivas, J.J. (May, January 29). The Forest Fire Danger Prediction System of Mexico. Proceedings of the 6th International Fire Behavior and Fuels Conference, Albuquerque, NM, USA. Available online: http:\/\/albuquerque.firebehaviorandfuelsconference.com\/wp-content\/uploads\/sites\/13\/2019\/04\/DANIEL-JOSE-VEGA-NIEVA-Albuquerque.pdf."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3832\/ifor1939-009","article-title":"Wildland fire typologies and extreme temperatures in NE Spain","volume":"10","author":"Cardil","year":"2016","journal-title":"iForest Biogeosci. For."},{"key":"ref_80","first-page":"107789","article-title":"Identifying large fire weather typologies in the Iberian Peninsula. Agric","volume":"280","author":"Rodrigues","year":"2020","journal-title":"For. Meteorol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"137313","DOI":"10.1016\/j.scitotenv.2020.137313","article-title":"Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico","volume":"718","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Holsinger, L.M., Koontz, M.J., Collins, L., Whitman, E., Parisien, M.-A., Loehman, R.A., Barnes, J.L., Bourdon, J.-F., and Boucher, J. (2019). Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests. Remote Sens., 11.","DOI":"10.3390\/rs11141735"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires. Remote Sens., 11.","DOI":"10.3390\/rs11060622"},{"key":"ref_84","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_85","doi-asserted-by":"crossref","unstructured":"Sobrino, J.A., Llorens, R., Fern\u00e1ndez, C., Fern\u00e1ndez-Alonso, J.M., and Vega, J.A. (2019). Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection. Forests, 10.","DOI":"10.3390\/f10050457"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2061\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:43:12Z","timestamp":1760175792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,26]]},"references-count":85,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12122061"],"URL":"https:\/\/doi.org\/10.3390\/rs12122061","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,26]]}}}