{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:48:00Z","timestamp":1779925680159,"version":"3.53.1"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T00:00:00Z","timestamp":1519948800000},"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>Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolution WorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classified WorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-off between accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer.<\/jats:p>","DOI":"10.3390\/rs10030389","type":"journal-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T11:53:40Z","timestamp":1519991620000},"page":"389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy"],"prefix":"10.3390","volume":"10","author":[{"given":"Zachary","family":"Tane","sequence":"first","affiliation":[{"name":"Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA"},{"name":"United States Department of Agriculture, Forest Service, Pacific Southwest Region, Remote Sensing Lab, McClellan, CA 95652, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-4842","authenticated-orcid":false,"given":"Dar","family":"Roberts","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-5125","authenticated-orcid":false,"given":"Sander","family":"Veraverbeke","sequence":"additional","affiliation":[{"name":"Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands"},{"name":"Department of Earth System Science, University of California Irvine, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ngeles","family":"Casas","sequence":"additional","affiliation":[{"name":"Independent Researcher"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Ramirez","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Forest Service, Pacific Southwest Region, Remote Sensing Lab, McClellan, CA 95652, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0461","authenticated-orcid":false,"given":"Susan","family":"Ustin","sequence":"additional","affiliation":[{"name":"Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California Davis, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1002\/2014GL059576","article-title":"Large Wildfire Trend in the Western United States, 1984\u20132011","volume":"41","author":"Dennison","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s10021-008-9201-9","article-title":"Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA","volume":"12","author":"Miller","year":"2009","journal-title":"Ecosystems"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1890\/ES14-00224.1","article-title":"The fire frequency-severity relationship and the legacy of fire suppression in California forests","volume":"6","author":"Steel","year":"2015","journal-title":"Ecosphere"},{"key":"ref_4","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment: Sampling and Analysis Methods, USDA Forest Service. General Technical Report RMRS-GTR-164-CD."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.rse.2003.12.015","article-title":"Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity","volume":"92","author":"Root","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1071\/WF09057","article-title":"Spectral analysis of charcoal on soils: Implications for wildland fire severity mapping methods","volume":"19","author":"Smith","year":"2010","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2005.03.002","article-title":"Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+","volume":"96","author":"Epting","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1071\/WF07091","article-title":"Remote sensing for prediction of 1-year post-fire ecosystem condition","volume":"18","author":"Lentile","year":"2009","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8098","DOI":"10.1029\/JB091iB08p08098","article-title":"Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site","volume":"91","author":"Adams","year":"1986","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0034-4257(93)90020-X","article-title":"Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data","volume":"44","author":"Roberts","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1080\/10106040108542218","article-title":"Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery","volume":"16","author":"Rogan","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2013.04.017","article-title":"Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries","volume":"136","author":"Quintano","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.4996\/fireecology.0301064","article-title":"The Relationship of Multispectral Satellite Imagery","volume":"3","author":"Hudak","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.rse.2006.08.006","article-title":"Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing","volume":"106","author":"Kokaly","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2006.11.027","article-title":"Postfire soil burn severity mapping with hyperspectral image unmixing","volume":"108","author":"Robichaud","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1071\/WF12168","article-title":"Evaluating spectral indices and spectral mixture analysis for assessing fire severity, combustion completeness and carbon emissions","volume":"22","author":"Veraverbeke","year":"2013","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1071\/WF05097","article-title":"Remote sensing techniques to assess active fire characteristics and post-fire effects","volume":"15","author":"Lentile","year":"2006","journal-title":"Int. J. Wildl. Fire"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2548","DOI":"10.1016\/j.rse.2010.05.029","article-title":"The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire\/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece","volume":"114","author":"Veraverbeke","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in Spectral Mixture Analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2006.02.025","article-title":"Assessing spatial patterns of forest fuel using AVIRIS data","volume":"102","author":"Jia","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP spaceborne imaging spectroscopy mission for earth observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","first-page":"4558","article-title":"The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring","volume":"2567","author":"Stefano","year":"2013","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.rse.2015.06.012","article-title":"An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities","volume":"167","author":"Lee","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Boardman, J., Kruse, F., and Green, R.O. (1995, January 23\u201326). Mapping Target Signatures via Partial Unmixing of AVIRIS Data. Proceedings of the Fifth Annual JPL Airborne Earth Science Workshop, Volume 1: AVIRIS Workshop, Pasadena, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/S0034-4257(96)00122-8","article-title":"Optimization of endmembers for spectral mixture analysis","volume":"59","author":"Tompkins","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2003.08.015","article-title":"Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index","volume":"88","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"324","DOI":"10.2747\/1548-1603.48.3.324","article-title":"Mapping Plant Functional Types at Multiple Spatial Resolutions Using Imaging Spectrometer Data","volume":"48","author":"Schaaf","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2012.08.030","article-title":"Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data","volume":"127","author":"Roth","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(03)00135-4","article-title":"Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.07.021","article-title":"Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission","volume":"117","author":"Roberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_37","unstructured":"Veganzones, M.A., and Grana, M. (2008, January 3\u20135). Endmember Extraction Methods: A Short Review. Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Zagreb, Croatia."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Parente, M., and Plaza, A. (2010, January 14\u201316). Survey of geometric and statistical unmixing algorithms for hyperspectral images. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594929"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature Selection for Classification of Hyperspectral Data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.rse.2006.01.006","article-title":"Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear spectral mixture models","volume":"101","author":"Miao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TGRS.2003.822750","article-title":"Wavelet-based feature extraction for improved endmember abundance estimation in linear unmixing of hyperspectral signals","volume":"42","author":"Li","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0034-4257(00)00126-7","article-title":"A biogeophysical approach for automated SWIR unmixing of soils and vegetation","volume":"74","author":"Asner","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1080\/01431160903311305","article-title":"An automated waveband selection technique for optimized hyperspectral mixture analysis","volume":"31","author":"Somers","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, C.H., Cui, S.L., and Qi, B. (2014, January 19\u201323). A sparse multiple endmember spectral mixture analysis algorithm of hyperspectral image. Proceedings of the International Conference on Signal Processing, Hangzhou, China.","DOI":"10.1109\/ICOSP.2014.7015091"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.rse.2014.12.009","article-title":"Oil detection in the coastal marshes of Louisiana using MESMA applied to band subsets of AVIRIS data","volume":"159","author":"Peterson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.04.006","article-title":"Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests","volume":"136","author":"Somers","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2015.02.010","article-title":"Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign","volume":"167","author":"Thompson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/36.655326","article-title":"On the errors of two estimators of sub-pixel fractional cover when mixing is linear","volume":"36","author":"Settle","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009GL040069","article-title":"Contemporaneous deposition of phyllosilicates and sulfates: Using Australian acidic saline lake deposits to describe geochemical variability on Mars","volume":"36","author":"Baldridge","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s10021-004-0144-5","article-title":"Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales","volume":"7","author":"Roberts","year":"2004","journal-title":"Ecosystems"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/TGRS.2003.812904","article-title":"Evaluation of the potential of Hyperion for fire danger assessment by comparison to the airborne visible\/infrared imaging spectrometer","volume":"41","author":"Roberts","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficent of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Roberts, D.A., Alonzo, M., Wetherley, E.B., Dudley, K.L., and Dennison, P.E. (2017). Multiscale Analysis of Urban Areas Using Mixing Models. Integrating Scale in Remote Sensing and GIS, CRC Press.","DOI":"10.1201\/9781315373720-10"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Adams, J.B., and Gillespie, A.R. (2006). Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach, Cambridge University Press.","DOI":"10.1017\/CBO9780511617195"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2014.08.019","article-title":"Remote Sensing of Environment Assessing fire severity using imaging spectroscopy data from the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS) and comparison with multispectral capabilities","volume":"154","author":"Veraverbeke","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1175\/BAMS-D-14-00060.1","article-title":"The 2013 Rim Fire: Implications for predicting extreme fire spread, pyroconvection, smoke emissions","volume":"96","author":"Peterson","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"125","DOI":"10.2134\/agronj2001.931125x","article-title":"Discriminating Crop Residues from Soil by Shortwave Infrared Reflectance","volume":"93","author":"Daughtry","year":"2001","journal-title":"Agron. J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.05.004","article-title":"A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients","volume":"167","author":"Dudley","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1016\/j.rse.2009.03.018","article-title":"Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments","volume":"113","author":"Franke","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TGRS.2003.815238","article-title":"Spectral resolution requirements for mapping urban areas","volume":"41","author":"Herold","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hamlin, L., Green, R.O., Mouroulis, P., Eastwood, M., Wilson, D., Dudik, M., and Paine, C. (2011, January 5\u201312). Imaging Spectrometer Science Measurements for Terrestrial Ecology: AVIRIS and New Developments. Proceedings of the 2011 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2011.5747395"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"456","DOI":"10.3390\/rs4020456","article-title":"How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods","volume":"4","author":"Cansler","year":"2012","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1071\/WF13058","article-title":"Challenges of assessing fire and burn severity using field measures, remote sensing and modelling","volume":"23","author":"Morgan","year":"2014","journal-title":"Int. J. Wildl. Fire"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/389\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:21Z","timestamp":1760194641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,2]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030389"],"URL":"https:\/\/doi.org\/10.3390\/rs10030389","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,2]]}}}