{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T07:44:48Z","timestamp":1777189488565,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"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>Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy\u2013Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.<\/jats:p>","DOI":"10.3390\/rs12223827","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T01:28:48Z","timestamp":1606094928000},"page":"3827","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets"],"prefix":"10.3390","volume":"12","author":[{"given":"Yosio Edemir","family":"Shimabukuro","sequence":"first","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4454-7732","authenticated-orcid":false,"given":"Andeise Cerqueira","family":"Dutra","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egidio","family":"Arai","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valdete","family":"Duarte","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6728-4712","authenticated-orcid":false,"given":"Henrique Lu\u00eds Godinho","family":"Cassol","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research, S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2093-9942","authenticated-orcid":false,"given":"Gabriel","family":"Pereira","sequence":"additional","affiliation":[{"name":"Department of Geoscience, Federal University of S\u00e3o Jo\u00e3o del-Rei, S\u00e3o Jo\u00e3o del-Rei 36307-352, MG, Brazil"},{"name":"Graduate Program in Physical Geography, Faculty of Philosophy, Languages and Literature, and Human Sciences, University of S\u00e3o Paulo, S\u00e3o Paulo 05508-080, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4775-4649","authenticated-orcid":false,"given":"Francielle da Silva","family":"Cardozo","sequence":"additional","affiliation":[{"name":"Graduate Program in Geography, Federal University of S\u00e3o Jo\u00e3o del-Rei, S\u00e3o Jo\u00e3o del-Rei 36307-352, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Belcher, C.M. (2013). Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science, College of Life and Environmental Sciences, University of Exeter.","DOI":"10.1002\/9781118529539"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Goldammer, J.G. (1990). Fire in the ecology of the Brazilian cerrado. Fire in the Tropical Biota, Springer-Velarg.","DOI":"10.1007\/978-3-642-75395-4"},{"key":"ref_3","unstructured":"Alencar, A., Nepstad, D., Silva, E., Brown, F., Lefebvre, P., Mendosa, E., Almeida, D., and Carvalho, O. (1997). Uso do Fogo na Amaz\u00f4nia: Estudos de Caso ao Longo do Arco de Desmatamento, World Bank. World Bank Report."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2007GL030612","article-title":"Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion","volume":"34","author":"Sampaio","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","first-page":"251","article-title":"Regional climate change cenarios in South America in the Late XXI Century: Projections and expected impacts","volume":"112","author":"Marengo","year":"2010","journal-title":"Nova Acta Leopoldina"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1111\/gcb.13087","article-title":"Towards an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity","volume":"1","author":"Bustamante","year":"2016","journal-title":"Global Change Biology"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cochrane, M.A. (2009). Fire and fire ecology: Concepts and principles. Tropical Fire Ecology, Climate Change, Land Use and Ecosystem Dynamics, Springer.","DOI":"10.1007\/978-3-540-77381-8"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10652-005-0243-7","article-title":"Monitoring the transport of biomass burning emissions in South America","volume":"5","author":"Freitas","year":"2005","journal-title":"Environ. Fluid Mechanics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1016\/j.foreco.2009.07.042","article-title":"Biomass and greenhouse gas emissions from land-use change in Brazil\u2019s Amazonian \u2018\u2018arc of deforestation\u2019\u2019: The states of Mato Grosso and Rond\u00f4nia","volume":"258","author":"Fearnside","year":"2009","journal-title":"Forest Ecology Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Curkovic, S. (2012). Use of Remote Sensing in wildfire management. Sustainable Development\u2013Authoritative and Leading Edge Content for Environmental Management, InTech Press.","DOI":"10.5772\/2562"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1601047","DOI":"10.1126\/sciadv.1601047","article-title":"Types and rates of forest disturbance in Brazilian Legal Amazon, 2000\u20132013","volume":"3","author":"Tyukavina","year":"2017","journal-title":"Science Advances"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nature10717","article-title":"The amazon basin in transition","volume":"481","author":"Davidson","year":"2012","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8002","DOI":"10.3390\/rs6098002","article-title":"Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest","volume":"6","author":"Cardozo","year":"2014","journal-title":"Remote Sensing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/17477891.2012.703490","article-title":"Estimating the economic, social and environmental impacts of wildfires in Australia","volume":"12","author":"Stephenson","year":"2013","journal-title":"Environ. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.5194\/acp-6-3423-2006","article-title":"Interannual variability in global biomass burning emissions from 1997 to 2004","volume":"6","author":"Randerson","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Levine, J.S. (1991). Biomass burning: Its history, use, and distribution and its impact on environmental quality and global climate. Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, The MIT Press.","DOI":"10.7551\/mitpress\/3286.003.0001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1111\/1365-2664.12559","article-title":"The need for a consistent fire policy for Cerrado conservation","volume":"53","author":"Durigan","year":"2016","journal-title":"J. Appl. Ecol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125","DOI":"10.5194\/nhess-18-125-2018","article-title":"Satellite observations for describing fire patterns and climate-related fire drivers in the Brazilian savannas","volume":"18","author":"Mataveli","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosres.2012.03.007","article-title":"Satellite contributions to the quantitative characterization of biomass burning for climate modeling","volume":"111","author":"Ichoku","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0034-4257(02)00076-7","article-title":"The MODIS fire product","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6961","DOI":"10.5194\/acp-16-6961-2016","article-title":"Assessment of fire emission inventories during the South American Biomass Burning Analysis (SAMBBA) experiment","volume":"16","author":"Pereira","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0034-4257(02)00077-9","article-title":"Burned area mapping using multi-temporal moderate spatial resolution data\u2014a bi-directional reflectance model-based expectation approach","volume":"83","author":"Roy","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite Earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.jenvman.2018.10.113","article-title":"Increased fire severity alters initial vegetation regeneration across Calluna-dominated ecosystems","volume":"231","author":"Davies","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2643","DOI":"10.3390\/rs2122643","article-title":"Broad-scale environmental conditions responsible for post-fire vegetation dynamics","volume":"2","author":"Casady","year":"2010","journal-title":"Remote Sens."},{"key":"ref_27","unstructured":"Chen, Z. (2019). A literature Survey: The Effects of Forest Fire on Ecology and Regeneration. [Ph.D. Thesis, Faculty of Forestry and the Forest Environment, Lakehead University]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2012.12.004","article-title":"Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence","volume":"131","author":"Hantson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.5194\/bg-13-3717-2016","article-title":"Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite","volume":"13","author":"Andela","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.rse.2007.01.017","article-title":"Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data","volume":"109","author":"Loboda","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI120.1","article-title":"Characterizing vegetation fire dynamics in Brazil through multisatellite Data: Common trends and practical issues","volume":"9","author":"Schroeder","year":"2005","journal-title":"Earth Interact."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.08.005","article-title":"The Collection 6 MODIS Burned Area Mapping Algorithm and Product","volume":"217","author":"Giglio","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1080\/17538947.2018.1433727","article-title":"Spatial and temporal intercomparison of four global burned area products","volume":"12","author":"Humber","year":"2019","journal-title":"Int. J. Digital Earth"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.rse.2017.06.027","article-title":"Mapping burned areas using dense time-series of Landsat data","volume":"198","author":"Hawbaker","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2018.05.007","article-title":"Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees","volume":"142","author":"Cabral","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.apgeog.2017.05.013","article-title":"Mapping fire regimes in China using MODIS active fire and burned area data","volume":"85","author":"Chen","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Melchiorre, A., and Boschetti, L. (2018). Global Analysis of Burned Area Persistence Time with MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10050750"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2018.10.007","article-title":"A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States","volume":"219","author":"Urbanski","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Manso, A., and Quintano, C. (2020). A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots. Remote Sens., 12.","DOI":"10.3390\/rs12050858"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shimabukuro, Y.E., Arai, E., Duarte, V., and Dutra, A.C. (August, January 28). Assessment of Land Use Land Cover in Brazil, South America, Using Fraction Images Derived from Proba-V Datasets. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899110"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1080\/014311699213118","article-title":"Use of synthetic bands derived from mixing models in the multispectral classification of remote sensing images","volume":"20","author":"Aguiar","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","unstructured":"Shimabukuro, Y.E., and Ponzoni, F.J. (2017). Spectral Mixture for Remote Sensing. Linear Model and Applications, Springer Nature."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1590\/S0044-59672005000400009","article-title":"Detec\u00e7\u00e3o de Cicatrizes de \u00c1reas Queimadas Baseada No Modelo Linear de Mistura Espectral e Imagens \u00cdndice de Vegeta\u00e7\u00e3o Utilizando Dados Multitemporais Do Sensor MODIS\/TERRA No Estado Do Mato Grosso, Amaz\u00f4nia Brasileira","volume":"35","author":"Anderson","year":"2005","journal-title":"Acta Amaz."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1080\/01431160500212195","article-title":"Mapping Burned Areas in Mediterranean Countries Using Spectral Mixture Analysis from a Uni-Temporal Perspective","volume":"27","author":"Quintano","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5475","DOI":"10.1080\/01431161.2019.1579943","article-title":"Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon","volume":"40","author":"Shimabukuro","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","unstructured":"(2020, April 07). IBGE, Estados, Available online: Ftp:\/\/geoftp.ibge.gov.br\/cartas_e_mapas\/mapas_estaduais_e_distrito_federal\/informacoes_ambientais\/."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rossi, F.S., and Santos, G.A.A. (2020). Fire dynamics in Mato Grosso State, Brazil: The relative roles of gross primary productivity. Big Earth Data, 1\u201322.","DOI":"10.1080\/20964471.2019.1706832"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Assis, L.F.F.G., Ferreira, K.R., Vinhas, L., Maurano, L., Almeida, C., Carvalho, A., Rodrigues, J., Maciel, A., and Camargo, C. (2019). TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110513"},{"key":"ref_50","unstructured":"INPE\u2014Instituto Nacional de Pesquisas Espaciais (2020, April 15). Coordena\u00e7\u00e3o Geral de Observa\u00e7\u00e3o da Terra. Programa de Monitoramento da Amaz\u00f4nia e Demais Biomas. Desmatamento\u2014Amaz\u00f4nia Legal. Available online: http:\/\/terrabrasilis.dpi.inpe.br\/downloads\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1017\/S0266467402002237","article-title":"Fire as a large-scale edge effect in Amazonian forests","volume":"18","author":"Cochrane","year":"2002","journal-title":"J. Trop. Ecol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Silva Junior, C.H.L., Arag\u00e3o, L.E.O.C., Fonseca, M.G., Almeida, C.T., Vedovato, L.B., and Anderson, L.O. (2018). Deforestation-Induced Fragmentation Increases Forest Fire Occurrence in Central Brazilian Amazonia. Forests, 9.","DOI":"10.3390\/f9060305"},{"key":"ref_53","unstructured":"INPE\u2014Instituto Nacional de Pesquisas Espaciais (2020, April 15). Portal do Monitoramento de Queimadas e Inc\u00eandios. Available online: http:\/\/www.inpe.br\/queimadas."},{"key":"ref_54","unstructured":"Pettinari, M.L., Lizundia-Loiola, J., and Chuvieco, E. (2020, May 01). ESA CCI ECV Fire Disturbance: D4.2 Product User Guide\u2014MODIS, Version 1.0. Available online: https:\/\/www.esa-fire-cci.org\/documents."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rse.2005.04.007","article-title":"Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data","volume":"97","author":"Roy","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"Giglio, L., Justice, C., Boschetti, L., and Roy, D. (2020, March 10). MCD64A1 MODIS\/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid V006. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD64A1.006."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Boschetti, L., Eva, H.D., Brivio, P.A., and Gr\u00e9goire, J.M. (2004). Lessons to be learned from the comparison of three satellite-derived biomass burning products. Geophys. Res. Lett., 31.","DOI":"10.1029\/2004GL021229"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Arai, E., Eyji Sano, E., Dutra, A.C., Cassol, H.L.G., Hoffmann, T.B., and Shimabukuro, Y.E. (2020). Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil. Remote Sens., 12.","DOI":"10.3390\/rs12071152"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/36.103288","article-title":"The least squares mixing models to generate fraction images derived from remote sensing multispectral data","volume":"29","author":"Shimabukuro","year":"1991","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/0097-8493(96)00008-8","article-title":"SPRING: Integrating remote sensing and GIS by object-oriented data model","volume":"20","author":"Camara","year":"1996","journal-title":"Comput. Graph."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1080\/014311698216152","article-title":"Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon region","volume":"19","author":"Shimabukuro","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","unstructured":"Bins, L.S., Fonseca, L.M.G., and Earthal, G.J. (1996, January 14\u201319). Satellite imagery segmentation: A region growing approach. Proceedings of the VIII Simp\u00f3sio Brasileiro de Sensoriamento Remoto, Salvador, BA, Brazil."},{"key":"ref_63","unstructured":"Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, Academic Press. [3rd ed.]."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TGRS.2008.2009000","article-title":"Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products","volume":"47","author":"Roy","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ecolecon.2017.03.043","article-title":"Policy instruments to control Amazon fires: A simulation approach","volume":"138","author":"Morello","year":"2017","journal-title":"Ecol. Econ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.rse.2017.03.033","article-title":"Testing a Landsat-based approach for mapping disturbance causality in U.S. forests","volume":"195","author":"Schroeder","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.1016\/j.rse.2007.12.008","article-title":"Detection rates of the MODIS active fire product in the United States","volume":"112","author":"Hawbaker","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1016\/j.rse.2008.01.005","article-title":"Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data","volume":"112","author":"Schroeder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2015.01.005","article-title":"Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation","volume":"160","author":"Padilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.5194\/bg-16-3147-2019","article-title":"Theoretical uncertainties for global satellite-derived burned area estimates","volume":"16","author":"Brennan","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_71","first-page":"318","article-title":"How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections","volume":"78","author":"Rodrigues","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3827\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:35:26Z","timestamp":1760178926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,21]]},"references-count":71,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223827"],"URL":"https:\/\/doi.org\/10.3390\/rs12223827","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,21]]}}}