{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:32:35Z","timestamp":1786080755252,"version":"3.56.0"},"reference-count":92,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Scientific Research Starting Foundation","award":["U03210022"],"award-info":[{"award-number":["U03210022"]}]},{"name":"the Sichuan Science and Technology Program","award":["2020YFG0048"],"award-info":[{"award-number":["2020YFG0048"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["ZYGX2019J070"],"award-info":[{"award-number":["ZYGX2019J070"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Burn severity is a key component of fire regimes and is critical for quantifying fires\u2019 impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China\u2019s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., \u201cGreen firebreaks\u201d) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.<\/jats:p>","DOI":"10.3390\/rs13245127","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"5127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China"],"prefix":"10.3390","volume":"13","author":[{"given":"Changming","family":"Yin","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5369-4638","authenticated-orcid":false,"given":"Minfeng","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-9315","authenticated-orcid":false,"given":"Marta","family":"Yebra","sequence":"additional","affiliation":[{"name":"Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia"},{"name":"School of Engineering, The Australian National University, Canberra, ACT 2601, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-7083","authenticated-orcid":false,"given":"Xiangzhuo","family":"Liu","sequence":"additional","affiliation":[{"name":"Interactions Sol Plante Atmosph\u00e8re (UMR 1391), Universit\u00e9 de Bordeaux\/French National Institute for Agriculture, Food, and Environment (INRAE), 33140 Villenave d\u2019Ornon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1126\/science.aac6759","article-title":"Forest health and global change","volume":"349","author":"Trumbore","year":"2015","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/s41467-018-08237-z","article-title":"Biophysical feedback of global forest fires on surface temperature","volume":"10","author":"Liu","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e02128","DOI":"10.1002\/ecs2.2128","article-title":"Variability and drivers of burn severity in the northwestern Canadian boreal forest","volume":"9","author":"Whitman","year":"2018","journal-title":"Ecosphere"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.4996\/fireecology.0301091","article-title":"Post-Fire Burn Severity and Vegetation Response Following Eight Large Wildfires across the Western United States","volume":"3","author":"Lentile","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"04437","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":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1890\/13-1077.1","article-title":"Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA","volume":"24","author":"Cansler","year":"2014","journal-title":"Ecol. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1007\/s10980-016-0408-4","article-title":"Drivers and trends in landscape patterns of stand-replacing fire in forests of the US Northern Rocky Mountains (1984\u20132010)","volume":"31","author":"Harvey","year":"2016","journal-title":"Landsc. Ecol."},{"key":"ref_9","unstructured":"Rothermel, R.C. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels, Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture."},{"key":"ref_10","unstructured":"Lotan, J.E., Kilgore, B.M., and Fischer, W.C. (1985). Evaluating Prescribed Fires, Utah State University."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"G4","DOI":"10.1029\/2005JG000143","article-title":"Use of a radiative transfer model to simulate the postfire spectral response to burn severity","volume":"111","author":"Chuvieco","year":"2006","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e01794","DOI":"10.1002\/ecs2.1794","article-title":"Factors influencing fire severity under moderate burning conditions in the Klamath Mountains, northern California, USA","volume":"8","author":"Estes","year":"2017","journal-title":"Ecosphere"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1139\/x05-028","article-title":"How resilient are southwestern ponderosa pine forests after crown fires?","volume":"35","author":"Savage","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.earscirev.2013.03.004","article-title":"Current research issues related to post-wildfire runoff and erosion processes","volume":"122","author":"Moody","year":"2013","journal-title":"Earth-Sci. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1073\/pnas.1315088111","article-title":"How risk management can prevent future wildfire disasters in the wildland-urban interface","volume":"111","author":"Calkin","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Lecina-Diaz, J., Alvarez, A., and Retana, J. (2014). Extreme Fire Severity Patterns in Topographic, Convective and Wind-Driven Historical Wildfires of Mediterranean Pine Forests. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085127"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"art17","DOI":"10.1890\/ES14-00213.1","article-title":"Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests","volume":"6","author":"Birch","year":"2015","journal-title":"Ecosphere"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1890\/ES11-00271.1","article-title":"Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006","volume":"2","author":"Dillon","year":"2011","journal-title":"Ecosphere"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fang, L., Yang, J., White, M., and Liu, Z. (2018). Predicting potential fire severity using vegetation, topography and surface moisture availability in a Eurasian Boreal Forest Landscape. Forests, 9.","DOI":"10.3390\/f9030130"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e02019","DOI":"10.1002\/ecs2.2019","article-title":"Previous burns and topography limit and reinforce fire severity in a large wildfire","volume":"8","author":"Harris","year":"2017","journal-title":"Ecosphere"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2014.10.038","article-title":"Water balance and topography predict fire and forest structure patterns","volume":"338","author":"Kane","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1016\/j.foreco.2009.08.017","article-title":"A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area","volume":"258","author":"Holden","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"065003","DOI":"10.1088\/1748-9326\/aa6b10","article-title":"Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States","volume":"12","author":"Keyser","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.foreco.2013.03.015","article-title":"Relationships between tree stand density and burn severity as measured by the Composite Burn Index following a ponderosa pine forest wildfire in the American Southwest","volume":"302","author":"Amato","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3390\/f9040165","article-title":"What Drives Low-Severity Fire in the Southwestern USA?","volume":"9","author":"Sean","year":"2018","journal-title":"Forests"},{"key":"ref_27","unstructured":"Barkley, Y.C. (2002). After the Burn: Assessing and Managing Your Forestland after a Wildfire, Idaho Forest, Wildlife, and Range Experiment Station, University of Idaho."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.agrformet.2007.12.005","article-title":"Estimation of live fuel moisture content from MODIS images for fire risk assessment","volume":"148","author":"Yebra","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/s41597-019-0164-9","article-title":"Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications","volume":"6","author":"Yebra","year":"2019","journal-title":"Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.envsoft.2017.06.006","article-title":"Retrieval of forest fuel moisture content using a coupled radiative transfer model","volume":"95","author":"Quan","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Luo, K., Quan, X., He, B., and Yebra, M. (2019). Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests, 10.","DOI":"10.3390\/f10100887"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, L., Quan, X., He, B., Yebra, M., Xing, M., and Liu, X. (2019). Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131568"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2018.04.053","article-title":"A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing","volume":"212","author":"Yebra","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5100","DOI":"10.1109\/JSTARS.2021.3062073","article-title":"Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method","volume":"14","author":"Quan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.foreco.2019.01.015","article-title":"Interactions between wind and fire disturbance in forests: Competing amplifying and buffering effects","volume":"436","author":"Cannon","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.foreco.2015.09.001","article-title":"Mixed severity fire effects within the Rim fire: Relative importance of local climate, fire weather, topography, and forest structure","volume":"358","author":"Kane","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1071\/WF03036","article-title":"Topography and forest composition affecting the variability in fire severity and post-fire regeneration occurring after a large fire in the Mediterranean basin","volume":"13","author":"Broncano","year":"2004","journal-title":"Int. J. Wildland Fire"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"e03721","DOI":"10.1002\/ecs2.3721","article-title":"Empirical analyses of the factors influencing fire severity in southeastern Australia","volume":"12","author":"Lindenmayer","year":"2021","journal-title":"Ecosphere"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s10980-009-9443-8","article-title":"Effects of weather, fuel and terrain on fire severity in topographically diverse landscapes of south-eastern Australia","volume":"25","author":"Bradstock","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1071\/WF15171","article-title":"The role of weather, past fire and topography in crown fire occurrence in eastern Australia","volume":"25","author":"Storey","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ndalila, M.N., Williamson, G.J., and Bowman, D.M.J.S. (2018). Geographic patterns of fire severity following an extreme eucalyptus forest fire in Southern Australia: 2013 Forcett-Dunalley Fire. Fire, 1.","DOI":"10.3390\/fire1030040"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Levin, N., Yebra, M., and Phinn, S. (2021). Unveiling the Factors Responsible for Australia\u2019s Black Summer Fires of 2019\/2020. Fire, 4.","DOI":"10.3390\/fire4030058"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.foreco.2017.05.026","article-title":"Ecological drivers of post-fire regeneration in a recently managed boreal forest landscape of eastern Canada","volume":"399","author":"Boucher","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Whitman, E., Parisien, M.-A., Thompson, D.K., and Flannigan, M.D. (2018). Topoedaphic and forest controls on post-fire vegetation assemblies are modified by fire history and burn severity in the Northwestern Canadian boreal forest. Forest, 9.","DOI":"10.3390\/f9030151"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1007\/s10021-015-9890-9","article-title":"Topography, Fuels, and Fire Exclusion Drive Fire Severity of the Rim Fire in an Old-Growth Mixed-Conifer Forest, Yosemite National Park, USA","volume":"18","author":"Harris","year":"2015","journal-title":"Ecosystems"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yao, Y., Zhao, C., Wang, J., and Yu, F. (2018). Conifers in Mountains of China. Conifers, IntechOpen.","DOI":"10.5772\/intechopen.79684"},{"key":"ref_48","first-page":"19","article-title":"Analysis on annual variation characteristics and disaster causes of forest fires in Shanxi Province","volume":"2","author":"Di","year":"2007","journal-title":"For. Fire Prev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1525\/bio.2012.62.6.6","article-title":"The Effects of Forest Fuel-Reduction Treatments in the United States","volume":"62","author":"Stephens","year":"2012","journal-title":"Bioscience"},{"key":"ref_50","unstructured":"Qian, L., Zheng, Y., and Guo, M. (1991). Shanxi Climate, China Meteorol Press."},{"key":"ref_51","unstructured":"Lutes, D.C. (2006). Landscape assessment: Ground measure of severity, the Composite Burn Index. FIREMON: Fire Effects Monitoring and Inventory System, USDA Forest Service, Rocky Mountain Research Station."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yin, C., He, B., Quan, X., Yebra, M., and Lai, G. (2020). Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires. Remote Sens., 12.","DOI":"10.3390\/rs12213590"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111454","DOI":"10.1016\/j.rse.2019.111454","article-title":"Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation","volume":"236","author":"Yin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2018.04.056","article-title":"Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar","volume":"213","author":"Minh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","first-page":"102209","article-title":"Woody vegetation cover, height and biomass at 25-m resolution across Australia derived from multiple site, airborne and satellite observations","volume":"93","author":"Liao","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"37572","DOI":"10.1038\/srep37572","article-title":"Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China","volume":"6","author":"Liu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2004.10.006","article-title":"On the relationship of NDVI with leaf area index in a deciduous forest site","volume":"94","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259409532220","article-title":"A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover","volume":"9","author":"Carlson","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/JSTARS.2009.2014008","article-title":"Generation of a Species-Specific Look-Up Table for Fuel Moisture Content Assessment","volume":"2","author":"Yebra","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","first-page":"102354","article-title":"Global fuel moisture content mapping from MODIS","volume":"101","author":"Quan","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1071\/WF20077_CO","article-title":"Integrating remotely sensed fuel variables into wildfire danger assessment for China","volume":"30","author":"Quan","year":"2021","journal-title":"Int. J. Wildland Fire"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.rse.2004.11.001","article-title":"A hybrid inversion method for mapping leaf area index from MODIS data: Experiments and application to broadleaf and needleleaf canopies","volume":"94","author":"Fang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2003.812910","article-title":"Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data","volume":"41","author":"Gong","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.5194\/acp-5-1855-2005","article-title":"The libRadtran software package for radiative transfer calculations-description and examples of use","volume":"5","author":"Mayer","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017, January 4). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_70","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A Processor for Users. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_71","first-page":"77","article-title":"The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of. Spartina Alterniflora","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.rse.2004.05.020","article-title":"Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level","volume":"92","author":"Bowyer","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_73","unstructured":"Roberts, D.W., and Cooper, S.V. (1989). Concepts and techniques of vegetation mapping. Land Classifications Based on Vegetation: Applications for Resource Management, USDA, Forest Service, Intermountain Research Station."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s00704-012-0716-9","article-title":"Spatial interpolation of monthly climate data for Finland: Comparing the performance of kriging and generalized additive models","volume":"112","author":"Aalto","year":"2013","journal-title":"Theor. Appl. Clim."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1198\/tast.2009.08199","article-title":"Variable importance assessment in regression: Linear regression versus random forest","volume":"63","year":"2009","journal-title":"Am. Stat."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2014.889863","article-title":"Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data","volume":"5","author":"Sonobe","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: Bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.foreco.2007.07.023","article-title":"Estimating potential habitat for 134 eastern US tree species under six climate scenarios","volume":"254","author":"Iverson","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1139\/cjfr-2012-0213","article-title":"Mapping fuels in Yosemite National Park","volume":"43","author":"Peterson","year":"2013","journal-title":"Can. J. For. Res."},{"key":"ref_83","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_84","doi-asserted-by":"crossref","first-page":"7575","DOI":"10.1073\/pnas.1817561116","article-title":"Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer","volume":"116","author":"Ziter","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1002\/2016GL068614","article-title":"Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia","volume":"43","author":"Nolan","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.rse.2013.05.029","article-title":"A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products","volume":"136","author":"Yebra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ecolmodel.2008.10.022","article-title":"A method for mapping fire hazard and risk across multiple scales and its application in fire management","volume":"221","author":"Keane","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1890\/07-1755.1","article-title":"Fire treatment effects on vegetation structure, fuels, and potential fire severity in western US forests","volume":"19","author":"Stephens","year":"2009","journal-title":"Ecol. Appl."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1111\/ele.12151","article-title":"Climatic stress increases forest fire severity across the western United States","volume":"16","author":"Nesmith","year":"2013","journal-title":"Ecol. Lett."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"119674","DOI":"10.1016\/j.foreco.2021.119674","article-title":"Mechanical thinning without prescribed fire moderates wildfire behavior in an Eastern Oregon, USA ponderosa pine forest","volume":"501","author":"Johnston","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4996\/fireecology.0202003","article-title":"Thinning and prescribed fire effects on fuels and potential fire behavior in an eastern Cascades forest, Washington, USA","volume":"2","author":"Agee","year":"2006","journal-title":"Fire Ecol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2981","DOI":"10.1139\/x05-206","article-title":"Fuel treatments alter the effects of wildfire in a mixed-evergreen forest, Oregon, USA","volume":"35","author":"Raymond","year":"2005","journal-title":"Can. J. For. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:35Z","timestamp":1760169035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,17]]},"references-count":92,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245127"],"URL":"https:\/\/doi.org\/10.3390\/rs13245127","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,17]]}}}