[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127493-en":3,"doc-seo-127493-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127493,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Remote Sensing Applications for Mapping Large Wildfires - Based on Machine Learning and Time Series","Mapping large wildfires is essential for environmental applications and supports understanding of the dynamics of affected areas. Remote sensing approaches integrating machine learning with time-series data have shown strong potential for this task. The study develops a methodology for mapping large wildfires in northwestern Portugal using Landsat time series and a machine learning workflow combining Fourier harmonic outlier detection with a random forest classifier for burnt-area mapping.","Article  \nRemote Sensing Applications for Mapping Large Wildfires Based on Machine Learning and Time Series in Northwestern Portugal  \nSarah Moura Batista dos Santos 1, Soltan Galano Duverger 2, António Bento-Gonçalves 1,*,  \nWashington Franca-Rocha 3, António Vieira 1 and Georgia Teixeira 4  \nCitation: Santos, S.M.B.; Duverger, S.G.; Bento-Gonçalves, A.; FrancaRocha, W.; Vieira, A.; Teixeira, G. Remote Sensing Applications for Mapping Large Wildfires Based on Machine Learning and Time Series in Northwestern Portugal. Fire 2023, 6, 43. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)fire6020043  \nAcademic Editors: Andrew T. Hudak and Grant Williamson  \nReceived: 14 December 2022  \nRevised: 5 January 2023  \nAccepted: 20 January 2023  \nPublished: 24 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://cre](https://cre)[ativecommons.org/licenses/by/4.0/](ativecommons.org/licenses/by/4.0/)).  \n1 Centro de Estudos em Comunicação e Sociedade (CECS), Departamento de Geografia, Universidade do Minho (UMinho), 4800-058 Guimarães, Portugal  \n2 Doutorado Multi-Institucional Multidisciplinar em Difusão do Conhecimento (DMMDC), Universidade Federal da Bahia (UFBA), 40110-909 Salvador, Brazil  \n3 Programa de Pós-Graduação em Ciências da Terra e do Ambiente (PPGM), Departamento de Ciências Exatas, Universidade Estadual de Feira de Santana (UEFS), 44036-900 Feira de Santana, Brazil  \n4 Instituto de Geografia (IG), Universidade Federal de Uberlândia (UFU), 38408-100 Uberlândia, Brazil  \n* [Correspondence: bento@geografia.uminho.pt](Correspondence: bento@geografia.uminho.pt)  \nAbstract: Mapping large wildfires (LW) is essential for environmental applications and enhances the understanding of the dynamics of affected areas. Remote sensing techniques supported by machine learning and time series have been increasingly used in studies addressing this issue and have shown potential for this type of analysis. The main aim of this article is to develop a methodology for mapping LW in northwestern Portugal using a machine learning algorithm and time series from Landsat images. For the burnt area classification, we initially used the Fourier harmonic model to define outliers in the time series that represented pixels of possible burnt areas and, then, we applied the random forest classifier for the LW classification. The results indicate that the harmonic analysis provided estimates with the actual observed values of the NBR index; thus, the pixels classified by random forest were only those that were masked, collaborated in the processing, and reduced possible spectral confusion between targets with similar behaviour. The burnt area maps revealed that~23.5% of the territory was burnt at least once from 2001 to 2020. The temporal variability of the burnt area indicated that, on average, 6.504 hectares were affected by LW within the 20 years. The annual burnt area varied over the years, with the minimum annual area detected in 2014 (679.5 hectares) and the maximum mapped area detected in 2005 (73,025.1 hectares) . We concluded that the process of defining the mask with the outliers considerably reduced the universe of pixels to be classified within each image, which leaves the training of the classifier focused on separating the set of pixels into two groups with very similar spectral characteristics, thus contributing so that the separation of groups with similar spectral behaviour was performed automatically and without great sampling effort. The method showed satisfactory accuracy results with little omission for burnt areas.  \nKeywords: burnt area; spectral index; Google Earth Engine; landsat time series; random forest  \n1. Introduction  \nIn recent decades, large wildfires (LW) have caused severe short-and long-term disruptions to ecosystems, bi","cbCaimJ2Ou07SLXQ","https://ap.wps.com/l/cbCaimJ2Ou07SLXQ","pdf",1953261,1,25,"English","en",105,"# Introduction\n## Wildfire impacts and changing fire regimes\n## Europe and Portugal as high-risk regions\n## Remote sensing history for wildfire detection\n# Methodology and data approach\n## Landsat time series and spectral index use\n## Fourier harmonic outlier masking\n## Random forest classification for large wildfires\n# Results and findings\n## Burnt area extent and temporal variability\n## Accuracy and reduced spectral confusion","[{\"question\":\"What is the main objective of the study on large wildfires in Portugal?\",\"answer\":\"To develop a methodology for mapping large wildfires in northwestern Portugal using machine learning and time-series data from Landsat images.\"},{\"question\":\"How does the method identify candidate burnt-area pixels before classification?\",\"answer\":\"It uses a Fourier harmonic model to find outliers in the time series that represent potential burnt-area pixels, which are then used to create a mask.\"},{\"question\":\"Which algorithm performs the large wildfire classification and what do results show?\",\"answer\":\"A random forest classifier is applied for large wildfire classification. Results indicate harmonic analysis produced estimates consistent with observed NBR index values and the burnt-area mapping shows 23.5% of the territory burned at least once from 2001 to 2020.\"}]","Remote Sensing Applications for Mapping Large Wildfires - Based on Machine Learning and Time Series | PDF",1785939460,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"remote-sensing-applications-for-mapping-large-wildfires-based-on-machine-learning-and-time-series","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/remote-sensing-applications-for-mapping-large-wildfires-based-on-machine-learning-and-time-series/127493/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study on large wildfires in Portugal?","Question",{"text":75,"@type":76},"To develop a methodology for mapping large wildfires in northwestern Portugal using machine learning and time-series data from Landsat images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method identify candidate burnt-area pixels before classification?",{"text":80,"@type":76},"It uses a Fourier harmonic model to find outliers in the time series that represent potential burnt-area pixels, which are then used to create a mask.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performs the large wildfire classification and what do results show?",{"text":84,"@type":76},"A random forest classifier is applied for large wildfire classification. 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