24.4.13.8 Forest Fire Evaluation, Wildfire Analysis, Brushfire Analysis, Fire Detection

Chapter Contents (Back)
Forest. Forest Fires. Fire Detection.
See also Forest Fire Prediction, Fire Hazard, Mitigation, Risk, Susceptibility.
See also Smoke from Forest Fires, Smoke from Wildfires.
See also Burned Area Detection, Fire Damage Assessment, Post-Fire Analysis. Mostly for non-fire changes:
See also Forest Change Evaluation, Change Detection, Temporal Analysis. More the surveillance systems:
See also Surveillance Systems, Applied to Fire and Flame Detection. General Infrared analysis.
See also ATR -- IR, Infra-Red, Thermal, Applications. Includeing buring of residue:
See also Crop Residue Analysis.

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Perception systems; Infrared and visual image processing; Forest fires; Sensor fusion BibRef

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Elvidge, C.D.[Christopher D.], Zhizhin, M.[Mikhail], Hsu, F.C.[Feng-Chi], Baugh, K.E.[Kimberly E.],
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Paugam, R., Wooster, M.J., Roberts, G.,
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Wickramasinghe, C.H.[Chathura H.], Jones, S.[Simon], Reinke, K.[Karin], Wallace, L.[Luke],
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IEEE DOI 2106
Australia, Clouds, Cloud computing, Fires, MODIS, Advanced Baseline Imager, Advanced Himawari Imager, Himawari-8 BibRef

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Elsevier DOI 2108
Active fire detection, Active fire segmentation, Active fire dataset, Convolutional neural network, Landsat-8 imagery BibRef

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Satellite remote sensing, Fire count, Fire Radiative Power (FRP), Vegetation water content, Fire Weather Index (FWI) BibRef

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Flares BibRef

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Ye, X.X.[Xin-Xin], Deshler, M.[Mina], Lyapustin, A.[Alexi], Wang, Y.J.[Yu-Jie], Kondragunta, S.[Shobha], Saide, P.[Pablo],
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Zhou, Y.K.[Yi-Kang], Ji, S.P.[Shun-Ping], Warner, T.A.[Timothy A.],
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Muksimova, S.[Shakhnoza], Mardieva, S.[Sevara], Cho, Y.I.[Young-Im],
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Bao, Y.L.[Yu-Long], Shinoda, M.[Masato], Yi, K.[Kunpeng], Fu, X.M.[Xiao-Man], Sun, L.[Long], Nasanbat, E.[Elbegjargal], Li, N.[Na], Xiang, H.[Honglin], Yang, Y.[Yan], DavdaiJavzmaa, B.[Bulgan], Nandintsetseg, B.[Banzragch],
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Tagestad, J.D.[Jerry D.], Saltiel, T.M.[Troy M.], Coleman, A.M.[André M.],
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Fernández-Guisuraga, J.M.[José Manuel], Fernandes, P.M.[Paulo M.],
Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal,
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Ren, X.Y.[Xiao-Yang], Yu, X.[Xin], Wang, Y.[Yi],
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Thangavel, K.[Kathiravan], Spiller, D.[Dario], Sabatini, R.[Roberto], Amici, S.[Stefania], Sasidharan, S.T.[Sarathchandrakumar Thottuchirayil], Fayek, H.[Haytham], Marzocca, P.[Pier],
Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire,
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Yulianti, N.[Nina], Hayasaka, H.[Hiroshi],
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Fu, Y.Y.[Yu-Yun], Hu, J.H.[Ji-Heng], Song, W.G.[Wei-Guo], Cheng, Y.Y.X.[Yuan-Yan-Xi], Li, R.[Rui],
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Elsevier DOI 2308
Microwave vegetation water content, Weather conditions, Fire activity BibRef

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PBVS22(256-265)
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Deep learning, Computational modeling, Neural networks, Fires, Transforms, Data models BibRef

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environmental science computing BibRef

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environmental science computing BibRef

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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Smoke from Forest Fires, Smoke from Wildfires .


Last update:Mar 16, 2024 at 20:36:19