Evaluation of Key Remote Sensing Features for Bushfire Analysis
This study evaluates remote sensing features to resolve problems associated with feature redundancy, low efficiency, and insufficient input feature analysis in bushfire detection. It calculates spectral features, remote sensing indices, and texture features from Sentinel-2 data for the Blue Mountain...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1823 |
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| author | Ziyi Yang Husam Al-Najjar Ghassan Beydoun Bahareh Kalantar Mohsen Zand Naonori Ueda |
| author_facet | Ziyi Yang Husam Al-Najjar Ghassan Beydoun Bahareh Kalantar Mohsen Zand Naonori Ueda |
| author_sort | Ziyi Yang |
| collection | DOAJ |
| description | This study evaluates remote sensing features to resolve problems associated with feature redundancy, low efficiency, and insufficient input feature analysis in bushfire detection. It calculates spectral features, remote sensing indices, and texture features from Sentinel-2 data for the Blue Mountains region of New South Wales, Australia. Feature separability was evaluated with three measures: J-M distance, discriminant index, and mutual information, leading to an assessment of the best remote sensing features. The results show that for post-fire smoke detection, the best features are the normalized difference vegetation index (NDVI), the B1 band, and the angular second moment (ASM) in the B1 band, with respective scores of 0.900, 0.900, and 0.838. For burned land detection, the best features are NDVI, the B2 band, and correlation (Corr) in the B5 band, with corresponding scores of 1.000, 0.9436, and 0.9173. These results demonstrate the effectiveness of NDVI, the B1 and B2 bands, and specific texture features in the post-fire analysis of remote sensing data. These findings provide valuable insights for the monitoring and analysis of bushfires and offer a solid foundation for future model construction, fire mapping, and feature interpretation tasks. |
| format | Article |
| id | doaj-art-7b2d28d4e4814af0a28a19726c82e2bf |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7b2d28d4e4814af0a28a19726c82e2bf2025-08-20T03:11:22ZengMDPI AGRemote Sensing2072-42922025-05-011711182310.3390/rs17111823Evaluation of Key Remote Sensing Features for Bushfire AnalysisZiyi Yang0Husam Al-Najjar1Ghassan Beydoun2Bahareh Kalantar3Mohsen Zand4Naonori Ueda5School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, AustraliaRIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team, Tokyo 103-0027, JapanResearch Computing Center, University of Chicago, Chicago, IL 60637, USARIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team, Tokyo 103-0027, JapanThis study evaluates remote sensing features to resolve problems associated with feature redundancy, low efficiency, and insufficient input feature analysis in bushfire detection. It calculates spectral features, remote sensing indices, and texture features from Sentinel-2 data for the Blue Mountains region of New South Wales, Australia. Feature separability was evaluated with three measures: J-M distance, discriminant index, and mutual information, leading to an assessment of the best remote sensing features. The results show that for post-fire smoke detection, the best features are the normalized difference vegetation index (NDVI), the B1 band, and the angular second moment (ASM) in the B1 band, with respective scores of 0.900, 0.900, and 0.838. For burned land detection, the best features are NDVI, the B2 band, and correlation (Corr) in the B5 band, with corresponding scores of 1.000, 0.9436, and 0.9173. These results demonstrate the effectiveness of NDVI, the B1 and B2 bands, and specific texture features in the post-fire analysis of remote sensing data. These findings provide valuable insights for the monitoring and analysis of bushfires and offer a solid foundation for future model construction, fire mapping, and feature interpretation tasks.https://www.mdpi.com/2072-4292/17/11/1823bushfireremote sensing indicestexture featurefeature separabilityJ-M distancediscriminant index |
| spellingShingle | Ziyi Yang Husam Al-Najjar Ghassan Beydoun Bahareh Kalantar Mohsen Zand Naonori Ueda Evaluation of Key Remote Sensing Features for Bushfire Analysis Remote Sensing bushfire remote sensing indices texture feature feature separability J-M distance discriminant index |
| title | Evaluation of Key Remote Sensing Features for Bushfire Analysis |
| title_full | Evaluation of Key Remote Sensing Features for Bushfire Analysis |
| title_fullStr | Evaluation of Key Remote Sensing Features for Bushfire Analysis |
| title_full_unstemmed | Evaluation of Key Remote Sensing Features for Bushfire Analysis |
| title_short | Evaluation of Key Remote Sensing Features for Bushfire Analysis |
| title_sort | evaluation of key remote sensing features for bushfire analysis |
| topic | bushfire remote sensing indices texture feature feature separability J-M distance discriminant index |
| url | https://www.mdpi.com/2072-4292/17/11/1823 |
| work_keys_str_mv | AT ziyiyang evaluationofkeyremotesensingfeaturesforbushfireanalysis AT husamalnajjar evaluationofkeyremotesensingfeaturesforbushfireanalysis AT ghassanbeydoun evaluationofkeyremotesensingfeaturesforbushfireanalysis AT baharehkalantar evaluationofkeyremotesensingfeaturesforbushfireanalysis AT mohsenzand evaluationofkeyremotesensingfeaturesforbushfireanalysis AT naonoriueda evaluationofkeyremotesensingfeaturesforbushfireanalysis |