{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:27:51Z","timestamp":1772026071602,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation\u2019s Leading Engineering for America\u2019s Prosperity, Health, and Infrastructure (LEAP HI)","award":["CMMI- 1953333"],"award-info":[{"award-number":["CMMI- 1953333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere\u2014wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task.<\/jats:p>","DOI":"10.3390\/rs14061447","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-6516","authenticated-orcid":false,"given":"Amy L.","family":"DeCastro","sequence":"first","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, CO 80307, USA"},{"name":"Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0417-0886","authenticated-orcid":false,"given":"Timothy W.","family":"Juliano","sequence":"additional","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, CO 80307, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1746-0746","authenticated-orcid":false,"given":"Branko","family":"Kosovi\u0107","sequence":"additional","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, CO 80307, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-6033","authenticated-orcid":false,"given":"Hamed","family":"Ebrahimian","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3983-7970","authenticated-orcid":false,"given":"Jennifer K.","family":"Balch","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"ref_1","first-page":"22","article-title":"Biophysical Settings that Influenced Plantation Survival during the 2015 Wildfires in Northern Rocky Mountain Moist Mixed-Conifer Forests","volume":"120","author":"Jain","year":"2022","journal-title":"J. For."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1073\/pnas.1718850115","article-title":"Rapid growth of the US wildland-urban interface raises wildfire risk","volume":"115","author":"Radeloff","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1073\/pnas.2011048118","article-title":"The changing risk and burden of wildfire in the United States","volume":"118","author":"Burke","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2020GL089858","DOI":"10.1029\/2020GL089858","article-title":"Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985 to 2017","volume":"47","author":"Parks","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","unstructured":"(2022, January 26). Environmental Protection Agency, Climate Change Indicators: Wildfire, Available online: https:\/\/www.epa.gov\/climate-indicators\/climate-change-indicators-wildfires."},{"key":"ref_6","unstructured":"U.S. (2022, January 21). Department of Agriculture, Biden-Harris Administration Announces Over $1 Billion in Disaster Relief Funds for Post-Wildfire and Hurricane Recovery, Available online: https:\/\/www.usda.gov\/media\/press-releases\/2022\/01\/21\/biden-harris-administration-announces-over-1-billion-disaster."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142788","DOI":"10.1016\/j.scitotenv.2020.142788","article-title":"Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern Cali-fornia","volume":"765","author":"Cardil","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF01028","article-title":"Mapping wildland fuels for fire man-agement across multiple scales: Integrating remote sensing, GIS, and biophysical modeling","volume":"10","author":"Keane","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1071\/WF08088","article-title":"LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel as-sessment","volume":"18","author":"Rollins","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Scott, J.H. (2005). Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel\u2019s Surface Fire Spread Model.","DOI":"10.2737\/RMRS-GTR-153"},{"key":"ref_11","unstructured":"(2021, June 01). Inciweb, East Troublesome Fire Information, Available online: https:\/\/inciweb.nwcg.gov\/incident\/7242\/."},{"key":"ref_12","unstructured":"(2021, June 01). Earth Engine Data Catalog, Sentinel-1. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S1_GRD?hl=en."},{"key":"ref_13","unstructured":"(2021, June 01). Earth Engine Data Catalog, Sentinel-2. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S2_SR."},{"key":"ref_14","unstructured":"(2021, June 01). Earth Engine Data Catalog, USFS Landscape Change Monitoring System v2020.5. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/USFS_GTAC_LCMS_v2020-5?hl=en."},{"key":"ref_15","unstructured":"U.S. (2021, June 10). Department of Agriculture, Forest Health. Available online: https:\/\/www.fs.fed.us\/foresthealth\/applied-sciences\/mapping-reporting\/detection-surveys.shtml."},{"key":"ref_16","unstructured":"(2022, January 13). Sentinel Online, Sentinel-1. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-1-sar\/applications\/land-monitoring."},{"key":"ref_17","unstructured":"(2022, January 13). Sentinel Online, Sentinel-2. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-2-msi\/applications."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2013.01.002","article-title":"Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery","volume":"132","author":"Meddens","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Anderson, H.E. (1982). Aids to Determining Fuel Models for Estimating Fire Behavior [Grass, Shrub, Timber, and Slash, Photographic Examples, Danger Ratings], INT-Intermountain Forest and Range Experiment Station (USA). USDA Forest Service General Technical Report."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.foreco.2009.01.020","article-title":"Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA","volume":"257","author":"Krasnow","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_23","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., and Powers, J.G. (2005). A Description of the Advanced Research WRF Version 2, National Center For Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1071\/WF03043","article-title":"Description of a coupled atmosphere\u2013fire model","volume":"13","author":"Clark","year":"2004","journal-title":"J. Wildland Fire"},{"key":"ref_25","unstructured":"Coen, J.L. (2013). Modeling Wildland Fires: A Description of the Coupled Atmosphere-Wildland Fire Environment Model (CAWFE), National Center for Atmospheric Research."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1175\/JAMC-D-12-023.1","article-title":"WRF-Fire: Coupled weather\u2013wildland fire modeling with the weather research and forecasting model","volume":"52","author":"Coen","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"591","DOI":"10.5194\/gmd-4-591-2011","article-title":"Coupled atmosphere-wildland fire mod-eling with WRF 3.3 and SFIRE 2011","volume":"4","author":"Mandel","year":"2011","journal-title":"Geosci. Model. Dev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1002\/2017MS001108","article-title":"An accurate fire-spread algo-rithm in the Weather Research and Forecasting model using the level-set method","volume":"10","author":"Coen","year":"2018","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_29","unstructured":"Rothermel, R.C. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1071\/WF9950173","article-title":"Calibration of a large fuel burn-out model","volume":"5","author":"Albini","year":"1995","journal-title":"Int. J. Wildland Fire"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/B:BOUN.0000020164.04146.98","article-title":"An improved Mellor\u2013Yamada level-3 model with con-densation physics: Its design and verification","volume":"112","author":"Nakanishi","year":"2004","journal-title":"Bound.-Layer Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"305010","DOI":"10.1088\/2632-2153\/aba480","article-title":"Enhancing wildfire spread model-ling by building a gridded fuel moisture content product with machine learning","volume":"1","author":"McCandless","year":"2020","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.foreco.2018.08.020","article-title":"Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States","volume":"430","author":"Coleman","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Keydan, G.P., and Merzlyak, M.N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL026457"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:38:21Z","timestamp":1760135901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,17]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061447"],"URL":"https:\/\/doi.org\/10.3390\/rs14061447","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,17]]}}}