{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:19:08Z","timestamp":1769753948684,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"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>Mapping of fire extent and severity across broad landscapes and timeframes using remote sensing approaches is valuable to inform ecological research, biodiversity conservation and fire management. Compiling imagery from various satellite sensors can assist in long-term fire history mapping; however, inherent sensor differences need to be considered. The New South Wales Fire Extent and Severity Mapping (FESM) program uses imagery from Sentinel and Landsat satellites, along with supervised classification algorithms, to produce state-wide fire maps over recent decades. In this study, we compared FESM outputs from Sentinel 2 and Landsat 8 sensors, which have different spatial and spectral resolutions. We undertook independent accuracy assessments of both Sentinel 2 and Landsat 8 sensor algorithms using high-resolution aerial imagery from eight training fires. We also compared the FESM outputs from both sensors across 27 case study fires. We compared the mapped areas of fire severity classes between outputs and assessed the classification agreement at random sampling points. Our independent accuracy assessment demonstrated very similar levels of accuracy for both sensor algorithms. We also found that there was substantial agreement between the outputs from the two sensors. Agreement on the extent of burnt versus unburnt areas was very high, and the severity classification of burnt areas was typically either in agreement between the sensors or in disagreement by only one severity class (e.g., low and moderate severity or high and extreme severity). Differences between outputs are likely partly due to differences in sensor resolution (10 m and 30 m pixel sizes for Sentinel 2 and Landsat 8, respectively) and may be influenced by landscape complexity, such as terrain roughness and foliage cover. Overall, this study supports the combined use of both sensors in remote sensing applications for fire extent and severity mapping.<\/jats:p>","DOI":"10.3390\/rs14071661","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:28:39Z","timestamp":1648675719000},"page":"1661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparing Fire Extent and Severity Mapping between Sentinel 2 and Landsat 8 Satellite Sensors"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5790-2035","authenticated-orcid":false,"given":"Laura A.","family":"White","sequence":"first","affiliation":[{"name":"Department of Planning and Environment, Science, Economics and Insights Division, Alstonville, NSW 2477, Australia"}]},{"given":"Rebecca K.","family":"Gibson","sequence":"additional","affiliation":[{"name":"Department of Planning and Environment, Science, Economics and Insights Division, Alstonville, NSW 2477, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Teske, C., Vanderhoof, M.K., Hawbaker, T.J., Noble, J., and Hiers, J.K. (2021). Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA. Fire, 4.","DOI":"10.3390\/fire4020026"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1111\/emr.12242","article-title":"Mapping fire severity and fire extent in forest in Victoria for ecological and fuel outcomes","volume":"18","author":"McCarthy","year":"2017","journal-title":"Ecol. Manag. Restor."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"44037","DOI":"10.1088\/1748-9326\/aab791","article-title":"High-severity fire: Evaluating its key drivers and mapping its probability across western US forests","volume":"13","author":"Parks","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.scitotenv.2019.02.237","article-title":"Analysing eucalypt expansion in Portugal as a fire-regime modifier","volume":"666","author":"Fernandes","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1071\/WF16167","article-title":"Mapping prescribed fire severity in south-east Australian eucalypt forests using modelling and satellite imagery: A case study","volume":"26","author":"Loschiavo","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.14358\/PERS.71.11.1311","article-title":"Classifying and Mapping Wildfire Severity","volume":"71","author":"Brewer","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/ddi.13292","article-title":"Responding to the biodiversity impacts of a megafire: A case study from south-eastern Australia\u2019s Black Summer","volume":"28","author":"Geary","year":"2022","journal-title":"Divers. Distrib."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1002\/eap.1555","article-title":"Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR","volume":"27","author":"Gordon","year":"2017","journal-title":"Ecol. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Walker, R.B., Coop, J.D., Downing, W.M., Krawchuk, M.A., Malone, S.L., and Meigs, G.W. (2019). How Much Forest Persists Through Fire? High-Resolution Mapping of Tree Cover to Characterize the Abundance and Spatial Pattern of Fire Refugia Across Mosaics of Burn Severity. Forests, 10.","DOI":"10.3390\/f10090782"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1071\/WF08007","article-title":"Using Landsat data to assess fire and burn severity in the North American boreal forest region: An overview and summary of results","volume":"17","author":"French","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Efthimiou, N., Psomiadis, E., and Panagos, P. (2020). Fire severity and soil erosion susceptibility mapping using multi-temporal Earth Observation data: The case of Mati fatal wildfire in Eastern Attica, Greece. Catena, 187.","DOI":"10.1016\/j.catena.2019.104320"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.catena.2008.08.001","article-title":"Using SPOT images and field sampling to map burn severity and vegetation factors affecting post forest fire erosion risk","volume":"75","author":"Fox","year":"2008","journal-title":"Catena"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1890\/15-0225","article-title":"Post-fire vegetation and fuel development influences fire severity patterns in reburns","volume":"26","author":"Coppoletta","year":"2016","journal-title":"Ecol. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1177\/0309133311407654","article-title":"Pathways for climate change effects on fire: Models, data, and uncertainties","volume":"35","author":"Hessl","year":"2011","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sommers, W. (2010). Fire history, fire regimes, and climate change\u2013integrating information for management and planning. Nat. Preced.","DOI":"10.1038\/npre.2010.5238.1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1071\/WF08187","article-title":"Implications of changing climate for global wildland fire","volume":"18","author":"Flannigan","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111702","DOI":"10.1016\/j.rse.2020.111702","article-title":"A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest","volume":"240","author":"Gibson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2018.07.005","article-title":"The utility of Random Forests for wildfire severity mapping","volume":"216","author":"Collins","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112863","DOI":"10.1016\/j.rse.2021.112863","article-title":"Regional-scale fire severity mapping of Eucalyptus forests with the Landsat archive","volume":"270","author":"Dixon","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111345","DOI":"10.1016\/j.rse.2019.111345","article-title":"Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies","volume":"233","author":"Tanase","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112025","DOI":"10.1016\/j.rse.2020.112025","article-title":"Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests","volume":"249","author":"Montorio","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Department of Planning, Industry and Environment (DPIE) (2022, January 30). Fire Extent and Severity Mapping-Annual Report for the 2019\u20132020, 2018\u20132019 and 2017\u20132018 Fire Years, Available online: https:\/\/www.environment.nsw.gov.au\/-\/media\/OEH\/Corporate-Site\/Documents\/Animals-and-plants\/Native-vegetation\/fire-extent-and-severity-mapping-annual-report-2017-18-2019-20-210180.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Flood, N. (2017). Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens., 9.","DOI":"10.3390\/rs9070659"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mandanici, E., and Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens., 8.","DOI":"10.3390\/rs8121014"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vuolo, F., Zoltak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., and Atzberger, C. (2016). Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples. Remote Sens., 8.","DOI":"10.3390\/rs8110938"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Archour, H., Toujani, A., Trabelsi, H., and Jaouadi, W. (2021). Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia. Geocarto Int., 1\u201320.","DOI":"10.1080\/10106049.2021.1980118"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2017.1354803","article-title":"Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece","volume":"55","author":"Mallinis","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3390\/rs5010083","article-title":"An operational scheme for deriving standardised surface reflectance from Landsat TM\/ETM+ and SPOT HRG imagery for eastern Australia","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_31","unstructured":"Gallant, J., and Read, A. (September, January 31). Enhancing the SRTM Data for Australia. Proceedings of the Geomorphometry, Zurich, Switzerland. Available online: https:\/\/geomorphometry.org\/gallantread2009."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2015.01.021","article-title":"Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data","volume":"161","author":"Guerschman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1071\/WF05051","article-title":"Remote sensing of fire severity in the Blue Mountains: Influence of vegetation type and inferring fire intesity","volume":"15","author":"Hammill","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_34","unstructured":"Hudak, A.T., Robichaud, P.R., Evans, J.S., Clark, J., Lannom, K., Morgan, P., and Stone, C. (2004, January 5\u20139). Field validation of burned area reflectance classification (BARC) products for post fire assessment. Proceedings of the Remote Sensing for Field Users: Proceedings of the Tenth Forest Service Remote Sensing Applications Conference, Salt Lake City, UT, USA. Available online: https:\/\/www.fs.usda.gov\/treesearch\/pubs\/23530."},{"key":"ref_35","unstructured":"Kuhn, M. (2022, January 30). Classification and Regression Training (Package \u2018Caret\u2019). Available online: https:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_36","unstructured":"Breiman, L., and Cutler, A. (2022, January 30). Breiman and Cutler\u2019s Random Forest for Classification and Regression (Package \u2018RandomForest\u2019). Available online: https:\/\/CRAN.R-project.org\/package=randomForest."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s11336-014-9439-4","article-title":"A New Interpretation of the Weighted Kappa Coefficients","volume":"81","author":"Vanbelle","year":"2016","journal-title":"Psychometrika"},{"key":"ref_38","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01490410701295962","article-title":"Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope","volume":"30","author":"Wilson","year":"2007","journal-title":"Mar. Geod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"180040","DOI":"10.1038\/sdata.2018.40","article-title":"A suite of global, cross-scale topographic variables for environmental and biodiversity modeling","volume":"5","author":"Amatulli","year":"2018","journal-title":"Sci. Data"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"033540","DOI":"10.1117\/1.3216031","article-title":"Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery","volume":"3","author":"Armston","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fisher, A., Day, M., Gill, T., Roff, A., Danaher, T., and Flood, N. (2016). Large-area, highresolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8060515"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4888","DOI":"10.1080\/01431161.2017.1331057","article-title":"The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover","volume":"38","author":"Munyati","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Naegeli, K., Damm, A., Huss, M., Wulf, H., Schaepman, M., and Hoelzle, M. (2017). Cross-Comparison of Albedo Products for Glacier Surfaces Derived from Airborne and Satellite (Sentinel-2 and Landsat 8) Optical Data. Remote Sens., 9.","DOI":"10.3390\/rs9020110"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Van der Werff, H., and Van der Meer, F. (2016). Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8110883"},{"key":"ref_47","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:46:42Z","timestamp":1760136402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,30]]},"references-count":47,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071661"],"URL":"https:\/\/doi.org\/10.3390\/rs14071661","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,30]]}}}