[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125988-en":3,"doc-seo-125988-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125988,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria","Forest fires present a serious and growing risk worldwide, and Algeria has experienced severe outbreaks, including large burned areas in 2020. This study targets the Akfadou forest region and develops a robust workflow to map fire severity. Satellite imagery analysis, machine learning, and GIS are combined to evaluate remote-sensing attributes from Sentinel-2 and Planetscope, comparing reflectance-index metrics (RBR and dBIAS2) with classifiers such as SVM and CNN, achieving accuracy above 0.84.","remote sensing  \nArticle  \nRemote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria  \nNadia Zikiou 1,2,*, Holly Rushmeier 2, Manuel I. Capel 3, Tarek Kandakji 4, Nelson Rios 5 and Mourad Lahdir 6  \nCitation: Zikiou, N.; Rushmeier, H.; Capel, M.I.; Kandakji, T.; Rios, N.; Lahdir, M. Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria. Remote Sens. 2024, 16, 1517 . [https://doi.org/10.3390/rs16091517](https://doi.org/10.3390/rs16091517)  \nAcademic Editor: Melanie Vanderhoof  \nReceived: 22 February 2024  \nRevised: 12 April 2024  \nAccepted: 22 April 2024  \nPublished: 25 April 2024  \nCopyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Yale Institute for Biospheric Studies, Yale University, 170 Whitney Avenue, RM 213A, New Haven, CT 06511, USA  \n2 Computer Science Department, Yale University, A.K. Watson Hall, 51 Prospect Street, New Haven, CT 06511, USA; [holly.rushmeier@yale.edu](holly.rushmeier@yale.edu)  \n3 Software Engineering Department, ETSI Informatics and Telecommunication, University of Granada, 18071 Granada, Spain; [manuelcapel@ugr.es](manuelcapel@ugr.es)  \n4 Yale Center for Earth Observation, Yale School of the Environment, Yale University, New Haven, CT 06520, USA; [tarek.kandakji@yale.edu](tarek.kandakji@yale.edu)  \n5 Yale Peabody Museum, 170 Whitney Ave., New Haven, CT 06511, USA; [nelson.rios@yale.edu](nelson.rios@yale.edu)  \n6 Laboratory of Analysis and Modeling of Random Phenomena (LAMPA), Mouloud Maameri Tizi Ouzou University, BP 17 RP, Tizi-Ouzou 15000, Algeria; [mourad.lahdir@ummto.dz](mourad.lahdir@ummto.dz)  \n* [Correspondence: nadia.zikiou@yale.edu or zikiounadia@gmail.com](Correspondence: nadia.zikiou@yale.edu or zikiounadia@gmail.com)  \nAbstract: Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0 . Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84 . An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately m","cbCairHl2onf7Oj5","https://ap.wps.com/l/cbCairHl2onf7Oj5","pdf",22832686,5,1,30,"English","en",105,"# Introduction\n## Fire and burn severity concepts\n## Need for accurate remote-sensing mapping\n## Study focus and data comparison","[{\"question\":\"What region and objective does the study focus on?\",\"answer\":\"The study focuses on the Akfadou forest and surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity.\"},{\"question\":\"Which satellites and key metrics are evaluated for fire-severity mapping?\",\"answer\":\"The study evaluates remote-sensing attributes from Sentinel-2 and Planetscope, including reflectance-index metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2).\"},{\"question\":\"Which machine learning models are used, and how accurate are the results?\",\"answer\":\"Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are compared, with both achieving accuracy scores exceeding 0.84 against ground truth data.\"}]","Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria | 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