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Multispectral Indices for Wildfire Management

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Title: Multispectral Indices for Wildfire Management
Authors: Oliveira, Afonso, Matos-Carvalho, João P., Moutinho, Filipe, Fachada, Nuno
Publication Year: 2023
Collection: Computer Science
Physics (Other)
Subject Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Geophysics, I.2.10, I.4, I.5, J.2
More Details: The increasing frequency and severity of wildfires requires advanced methods for effective surveillance and management. Traditional ground-based observation techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This paper examines the application of multispectral aerial and satellite imagery in wildfire management, emphasizing the identification and analysis of key factors influencing wildfire behavior, such as combustible vegetation and water features. Through a comprehensive review of current literature and the presentation of two practical case studies, we assess various multispectral indices and evaluate their effectiveness in extracting critical environmental attributes essential for wildfire prevention and management. Our case studies highlight several indices as particularly effective for segmentation and extraction: NVDI for vegetation, MNDWI for water features, and MSR for artificial structures. These indices significantly enhance wildfire data processing, thereby supporting improved monitoring and response strategies.
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2309.01751
Accession Number: edsarx.2309.01751
Database: arXiv
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  Data: Multispectral Indices for Wildfire Management
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  Data: <searchLink fieldCode="AR" term="%22Oliveira%2C+Afonso%22">Oliveira, Afonso</searchLink><br /><searchLink fieldCode="AR" term="%22Matos-Carvalho%2C+João+P%2E%22">Matos-Carvalho, João P.</searchLink><br /><searchLink fieldCode="AR" term="%22Moutinho%2C+Filipe%22">Moutinho, Filipe</searchLink><br /><searchLink fieldCode="AR" term="%22Fachada%2C+Nuno%22">Fachada, Nuno</searchLink>
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  Data: 2023
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  Data: Computer Science<br />Physics (Other)
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  Data: <searchLink fieldCode="DE" term="%22Electrical+Engineering+and+Systems+Science+-+Image+and+Video+Processing%22">Electrical Engineering and Systems Science - Image and Video Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Computer+Vision+and+Pattern+Recognition%22">Computer Science - Computer Vision and Pattern Recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Physics+-+Geophysics%22">Physics - Geophysics</searchLink><br /><searchLink fieldCode="DE" term="%22I%2E2%2E10%22">I.2.10</searchLink><br /><searchLink fieldCode="DE" term="%22I%2E4%22">I.4</searchLink><br /><searchLink fieldCode="DE" term="%22I%2E5%22">I.5</searchLink><br /><searchLink fieldCode="DE" term="%22J%2E2%22">J.2</searchLink>
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  Data: The increasing frequency and severity of wildfires requires advanced methods for effective surveillance and management. Traditional ground-based observation techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This paper examines the application of multispectral aerial and satellite imagery in wildfire management, emphasizing the identification and analysis of key factors influencing wildfire behavior, such as combustible vegetation and water features. Through a comprehensive review of current literature and the presentation of two practical case studies, we assess various multispectral indices and evaluate their effectiveness in extracting critical environmental attributes essential for wildfire prevention and management. Our case studies highlight several indices as particularly effective for segmentation and extraction: NVDI for vegetation, MNDWI for water features, and MSR for artificial structures. These indices significantly enhance wildfire data processing, thereby supporting improved monitoring and response strategies.
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      – SubjectFull: Electrical Engineering and Systems Science - Image and Video Processing
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      – SubjectFull: Computer Science - Computer Vision and Pattern Recognition
        Type: general
      – SubjectFull: Physics - Geophysics
        Type: general
      – SubjectFull: I.2.10
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      – TitleFull: Multispectral Indices for Wildfire Management
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            NameFull: Oliveira, Afonso
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            NameFull: Fachada, Nuno
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              Y: 2023
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