{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:09:53Z","timestamp":1776128993890,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development (CNPQ)\u2013PELD Jalap\u00e3o","award":["443100\/2020-9"],"award-info":[{"award-number":["443100\/2020-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 \u00d7 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R2 values ranged from 0.63 to 0.78, while the R2 values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R2 values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations.<\/jats:p>","DOI":"10.3390\/rs15235481","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T11:31:25Z","timestamp":1700739085000},"page":"5481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2745-7337","authenticated-orcid":false,"given":"Micael Moreira","family":"Santos","sequence":"first","affiliation":[{"name":"Department of Forest Sciences, Federal University of Paran\u00e1, Curitiba 80210-170, Brazil"}]},{"given":"Antonio Carlos","family":"Batista","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, Federal University of Paran\u00e1, Curitiba 80210-170, Brazil"}]},{"given":"Eduardo Henrique","family":"Rezende","sequence":"additional","affiliation":[{"name":"Environmental Monitoring and Fire Management Center\u2014CeMAF, Federal University of Tocantins, Gurupi 77410-530, Brazil"}]},{"given":"Allan Deyvid Pereira","family":"Da Silva","sequence":"additional","affiliation":[{"name":"Environmental Monitoring and Fire Management Center\u2014CeMAF, Federal University of Tocantins, Gurupi 77410-530, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9150-5431","authenticated-orcid":false,"given":"Jader Nunes","family":"Cachoeira","sequence":"additional","affiliation":[{"name":"Environmental Monitoring and Fire Management Center\u2014CeMAF, Federal University of Tocantins, Gurupi 77410-530, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3830-9463","authenticated-orcid":false,"given":"Gil Rodrigues","family":"Dos Santos","sequence":"additional","affiliation":[{"name":"Department of Phytopathology, Federal University of Tocantins, Gurupi 77410-530, Brazil"}]},{"given":"Daniela","family":"Biondi","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, Federal University of Paran\u00e1, Curitiba 80210-170, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-6167","authenticated-orcid":false,"given":"Marcos","family":"Giongo","sequence":"additional","affiliation":[{"name":"Environmental Monitoring and Fire Management Center\u2014CeMAF, Federal University of Tocantins, Gurupi 77410-530, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Miranda, H.S., Sato, M.N., Neto, W.N., and Aires, F.S. (2009). Fires in the Cerrado, the Brazilian Savanna. Trop. Fire Ecol., 427\u2013450.","DOI":"10.1007\/978-3-540-77381-8_15"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Brown, J.K., Oberheu, R.D., and Johnston, C.M. (1982). Handbook for Inventorying Surface Fuels and Biomass in the Interior West, Gen. Tech. Rep. INT-129.","DOI":"10.2737\/INT-GTR-129"},{"key":"ref_3","unstructured":"Rothermel, R.C. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.foreco.2013.06.001","article-title":"Evaluating the Performance and Mapping of Three Fuel Classification Systems Using Forest Inventory and Analysis Surface Fuel Measurements","volume":"305","author":"Keane","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kanmegne Tamga, D., Latifi, H., Ullmann, T., Baumhauer, R., Bayala, J., and Thiel, M. (2023). Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data. Sensors, 23.","DOI":"10.3390\/s23010349"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, Z., Angerer, J.P., Jaime, X., Yang, C., and Wu, X. (2022). Ben Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14174360"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Kukavskaya, E.A., Shvetsov, E.G., Buryak, L.V., Tretyakov, P.D., and Groisman, P.Y. (2023). Increasing Fuel Loads, Fire Hazard, and Carbon Emissions from Fires in Central Siberia. Fire, 6.","DOI":"10.3390\/fire6020063"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Szpakowski, D.M., and Jensen, J.L.R. (2019). A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sens., 11.","DOI":"10.3390\/rs11222638"},{"key":"ref_10","first-page":"1639","article-title":"The Use of Multi-Temporal Landsat Normalized Difference Vegetation Index (NDVI) Data for Mapping Fuel Models in Yosemite National Park, USA","volume":"24","author":"Root","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","first-page":"451","article-title":"Estimating Aboveground Biomass in Interior Alaska with Landsat Data and Field Measurements","volume":"18","author":"Ji","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2014.03.003","article-title":"Characterization of Aboveground Biomass in an Unmanaged Boreal Forest Using Landsat Temporal Segmentation Metrics","volume":"92","author":"Frazier","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2014.01.025","article-title":"Estimation of Forest Aboveground Biomass in California Using Canopy Height and Leaf Area Index Estimated from Satellite Data","volume":"151","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.nhres.2022.11.004","article-title":"Estimation of Fuel Load Using Remote Sensing Data in Hulunbuir Grassland","volume":"2","author":"Bao","year":"2022","journal-title":"Nat. Hazards Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Y., Quan, X., Liao, Z., and He, B. (2021). Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13061189"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106114","DOI":"10.1016\/j.ecolind.2020.106114","article-title":"Using the Random Forest Model and Validated MODIS with the Field Spectrometer Measurement Promote the Accuracy of Estimating Aboveground Biomass and Coverage of Alpine Grasslands on the Qinghai-Tibetan Plateau","volume":"112","author":"Gao","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cui, L., Jiao, Z., Dong, Y., Sun, M., Zhang, X., Yin, S., Ding, A., Chang, Y., Guo, J., and Xie, R. (2019). Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances. Remote Sens., 11.","DOI":"10.3390\/rs11192239"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2017.11.010","article-title":"Hierarchical Integration of Individual Tree and Area-Based Approaches for Savanna Biomass Uncertainty Estimation from Airborne LiDAR","volume":"205","author":"Goldbergs","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.rse.2018.07.022","article-title":"Quantification of Uncertainty in Aboveground Biomass Estimates Derived from Small-Footprint Airborne LiDAR","volume":"216","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0034-4257(02)00197-9","article-title":"Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods","volume":"85","author":"Rueda","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/0143116042000273998","article-title":"Use of Normalized Difference Water Index for Monitoring Live Fuel Moisture","volume":"26","author":"Dennison","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2017.01.033","article-title":"Global Cross-Calibration of Landsat Spectral Mixture Models","volume":"192","author":"Sousa","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Routh, D., Seegmiller, L., Bettigole, C., Kuhn, C., Oliver, C.D., and Glick, H.B. (2018). Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation. Remote Sens., 10.","DOI":"10.3390\/rs10111675"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2018.08.018","article-title":"Fuel Load Mapping in the Brazilian Cerrado in Support of Integrated Fire Management","volume":"217","author":"Franke","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Secretaria do Planejamento e da Moderniza\u00e7\u00e3o da Gest\u00e3o P\u00fablica (Seplan) (2012). Atlas of Tocantins: Subsidies for Land Management Planning [Atlas Do Tocantins: Subs\u00eddios Ao Planejamento Da Gest\u00e3o Territorial]."},{"key":"ref_26","unstructured":"Instituto Chico Mendes De Conserva\u00e7\u00e3o Da Biodiversidade (ICMBio) (2014). Management Plan for Serra Geral Do Tocantins Ecological Station [Plano de Manejo Para Esta\u00e7\u00e3o Ecol\u00f3gica Serra Geral Do Tocantins (EESGT)]."},{"key":"ref_27","unstructured":"Arruda, M.B., and von Behr, M. (2002). Jalap\u00e3o: Scientific and Conservation Expedition [Jalap\u00e3o: Expedi\u00e7\u00e3o Cient\u00edfica e Conservacionista]."},{"key":"ref_28","unstructured":"Sano, S.M., Almeida, S.P., and Ribeiro, J.F. (2008). The Main Phytophysiognomies of the Cerrado Biome [As Principais Fitofisionomias Do Bioma Cerrado]."},{"key":"ref_29","unstructured":"Santos, R.P., Crema, A., Szmuchrowski, M.A., Possapp, J.J., Nogueira, C.C., Asano, K., Kawaguchi, M., and Dino, K. (2013). Atlas of the Ecological Corridor of the Jalap\u00e3o Region [Atlas Do Corredor Ecol\u00f3gico Da Regi\u00e3o Do Jalap\u00e3o]."},{"key":"ref_30","unstructured":"Schroeder, M.J., and Buck, C.C. (1970). Fire Weather: A Guide for Application of Meteorological Information to Forest Fire Control Operations."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Vermote, E., Roger, J.C., Franch, B., and Skakun, S. (2018, January 22\u201327). LASRC (Land Surface Reflectance Code): Overview, Application and Validation Using MODIS, VIIRS, LANDSAT and Sentinel 2 Data\u2019s. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517622"},{"key":"ref_33","first-page":"225","article-title":"Remote Sensing of Grassland\u2013Shrubland Vegetation Water Content in the Shortwave Domain","volume":"8","author":"Davidson","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","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_35","unstructured":"Rouse, J.W.J., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel Algorithms for Remote Estimation of Vegetation Fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1469-8137.1995.tb03064.x","article-title":"Assessment of Photosynthetic Radiation-Use Efficiency with Spectral Reflectance","volume":"131","author":"Filella","year":"1995","journal-title":"New Phytol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of Changes in Leaf Water Content Using Near- and Middle-Infrared Reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/0034-4257(88)90110-1","article-title":"Monitoring Grassland Dryness and Fire Potential in Australia with NOAA\/AVHRR Data","volume":"25","author":"Paltridge","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"439","DOI":"10.2307\/1310339","article-title":"Remote Detection of Forest DamagePlant Responses to Stress May Have Spectral \u201cSignatures\u201d That Could Be Used to Map, Monitor, and Measure Forest Damage","volume":"36","author":"Rock","year":"1986","journal-title":"Bioscience"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160210153129","article-title":"Assessment of Different Spectral Indices in the Red-near-Infrared Spectral Domain for Burned Land Discrimination","volume":"23","author":"Chuvieco","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/01431169208904049","article-title":"High-Spectral Resolution Data for Determining Leaf Water Content","volume":"13","author":"Danson","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ozkan, S., and Bozdagi Akar, G. (2018). Improved Deep Spectral Convolution Network for Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty. arXiv.","DOI":"10.1109\/ICIP.2018.8451420"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2015.08.009","article-title":"Mapping Tropical Dry Forest Succession Using Multiple Criteria Spectral Mixture Analysis","volume":"109","author":"Cao","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2015.05.006","article-title":"Forest Cover Change in Miombo Woodlands: Modeling Land Cover of African Dry Tropical Forests with Linear Spectral Mixture Analysis","volume":"165","author":"Mayes","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/01431169608949077","article-title":"A Spatially Adaptive Fast Atmospheric Correction Algorithm","volume":"17","author":"Richter","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tane, Z., Roberts, D., Veraverbeke, S., Casas, \u00c1., Ramirez, C., and Ustin, S. (2018). Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis Using Post-Fire Imaging Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030389"},{"key":"ref_54","unstructured":"Crabb\u00e9, A.H., Somers, B., Roberts, D.A., Halligan, K., Dennison, P., and Dudley, K. (2023, September 21). MESMA QGIS Plugin (Version 1.0.8). Available online: https:\/\/mesma.readthedocs.io\/."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/01431160110069818","article-title":"Estimation of Fuel Moisture Content from Multitemporal Analysis of Landsat Thematic Mapper Reflectance Data: Applications in Fire Danger Assessment","volume":"23","author":"Chuvieco","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1016\/j.rse.2010.05.021","article-title":"Mapping Tropical Dry Forest Height, Foliage Height Profiles and Disturbance Type and Age with a Time Series of Cloud-Cleared Landsat and ALI Image Mosaics to Characterize Avian Habitat","volume":"114","author":"Helmer","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2013.08.010","article-title":"Monitoring Coniferous Forest Biomass Change Using a Landsat Trajectory-Based Approach","volume":"139","author":"Cohen","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2017.07.018","article-title":"Estimating Mediterranean Forest Parameters Using Multi Seasonal Landsat 8 OLI Imagery and an Ensemble Learning Method","volume":"199","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.rse.2018.07.023","article-title":"Distinguishing Vegetation Types with Airborne Waveform Lidar Data in a Tropical Forest-Savanna Mosaic: A Case Study in Lop\u00e9 National Park, Gabon","volume":"216","author":"Marselis","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.ecolind.2019.02.023","article-title":"Estimating Grassland Aboveground Biomass on the Tibetan Plateau Using a Random Forest Algorithm","volume":"102","author":"Zeng","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1590\/s0100-204x2016000900041","article-title":"Multiple Linear Regression and Random Forest Model to Estimate Soil Bulk Density in Mountainous Regions [Regress\u00e3o Linear M\u00faltipla e Modelo Random Forest Para Estimar a Densidade Do Solo Em \u00c1reas Montanhosas]","volume":"51","author":"Bhering","year":"2016","journal-title":"Pesqui Agropecu Bras"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S2095-3119(15)61303-X","article-title":"Estimating Grassland LAI Using the Random Forests Approach and Landsat Imagery in the Meadow Steppe of Hulunber, China","volume":"16","author":"Li","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ou, Q., Lei, X., and Shen, C. (2019). Individual Tree Diameter Growth Models of Larch\u2013Spruce\u2013Fir Mixed Forests Based on Machine Learning Algorithms. Forests, 10.","DOI":"10.3390\/f10020187"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of Biomass in Wheat Using Random Forest Regression Algorithm and Remote Sensing Data","volume":"4","author":"Wang","year":"2016","journal-title":"Crop J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.enconman.2016.04.051","article-title":"Assessing the Potential of Random Forest Method for Estimating Solar Radiation Using Air Pollution Index","volume":"119","author":"Sun","year":"2016","journal-title":"Energy Convers. Manag."},{"key":"ref_68","first-page":"18","article-title":"Classification and Regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2013.08.048","article-title":"Scale Considerations for Integrating Forest Inventory Plot Data and Satellite Image Data for Regional Forest Mapping","volume":"151","author":"Ohmann","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhao, L., Li, Y., Shi, J., Yan, M., and Ji, Y. (2022). Forest Above-Ground Biomass Inversion Using Optical and SAR Images Based on a Multi-Step Feature Optimized Inversion Model. Remote Sens., 14.","DOI":"10.3390\/rs14071608"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Bozchaloei, S.K., and Blaschke, T. (2019). A Comparative Assessment of Random Forest and K-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping. Water, 11.","DOI":"10.3390\/w11102076"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2011.09.025","article-title":"Using Landsat-Derived Disturbance History (1972\u20132010) to Predict Current Forest Structure","volume":"122","author":"Pflugmacher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sun, X., Li, G., Wang, M., and Fan, Z. (2019). Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11060722"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"035010","DOI":"10.1117\/1.JRS.10.035010","article-title":"Comparison of Machine-Learning Methods for above-Ground Biomass Estimation Based on Landsat Imagery","volume":"10","author":"Wu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1016\/j.rse.2008.07.012","article-title":"Mapping Live Fuel Moisture with MODIS Data: A Multiple Regression Approach","volume":"112","author":"Peterson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_77","first-page":"1","article-title":"Relationships between Moisture Content and Flammabilityof Campestral Cerrado Species in Jalap\u00e3o","volume":"13","author":"Santos","year":"2018","journal-title":"Rev. Bras. Ci\u00eancias Agr\u00e1rias"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5380\/rf.v51i1.67440","article-title":"Characterization and Dynamics of Surface Fuel of Cerrado Grassland in Jalap\u00e3o Region\u2014Tocantins, Brazil","volume":"51","author":"Santos","year":"2020","journal-title":"Floresta"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1071\/WF19061","article-title":"Estimation of Surface Dead Fine Fuel Moisture Using Automated Fuel Moisture Sticks across a Range of Forests Worldwide","volume":"29","author":"Cawson","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.foreco.2014.09.040","article-title":"Evaluating Models to Predict Daily Fine Fuel Moisture Content in Eucalypt Forest","volume":"335","author":"Slijepcevic","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_81","unstructured":"Soares, R.V., Batista, A.C., and Tetto, A.F. (2017). Forest Fires: Control, Effects and Use of Fire [Inc\u00eandios Florestais: Controle, Efeitos e Uso Do Fogo]."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0034-4257(77)90016-5","article-title":"Spectral Estimation of Grass Canopy Variables","volume":"6","author":"Tucker","year":"1977","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the Utility of the Medium-Spatial Resolution Landsat 8 Multispectral Sensor in Quantifying Aboveground Biomass in UMgeni Catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.foreco.2012.05.010","article-title":"Use of Random Forests for Modeling and Mapping Forest Canopy Fuels for Fire Behavior Analysis in Lassen Volcanic National Park, California, USA","volume":"279","author":"Pierce","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"D\u2019este, M., Elia, M., Giannico, V., Spano, G., Lafortezza, R., and Sanesi, G. (2021). Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13091658"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.11.010","article-title":"Comparing Echo-Based and Canopy Height Model-Based Metrics for Enhancing Estimation of Forest Aboveground Biomass in a Model-Assisted Framework","volume":"174","author":"Chirici","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.rse.2016.09.010","article-title":"Statistical Inference for Forest Structural Diversity Indices Using Airborne Laser Scanning Data and the K-Nearest Neighbors Technique","volume":"186","author":"Mura","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5481\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:28:45Z","timestamp":1760131725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":87,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235481"],"URL":"https:\/\/doi.org\/10.3390\/rs15235481","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]}}}