Smoke from Forest Fires, Smoke from Wildfires

Chapter Contents (Back)
Smoke Detection. Forest Fires. Smoke. More surveillance:
See also Surveillance Systems for Smoke Detection, Aerial Image Smoke Detection.
See also Forest Fire Evaluation, Wildfire Analysis, Brushfire Analysis, Fire Detection.

Mims, S.R., Kahn, R.A., Moroney, C.M., Gaitley, B.J., Nelson, D.L., Garay, M.J.,
MISR Stereo Heights of Grassland Fire Smoke Plumes in Australia,
GeoRS(48), No. 1, January 2010, pp. 25-35.

Jakovevic, T.[Toni], Stipanicev, D.[Darko], Krstinic, D.[Damir],
Visual spatial-context based wildfire smoke sensor,
MVA(24), No. 4, May 2013, pp. 707-719.
WWW Link. 1304

Labati, R.D.[R. Donida], Genovese, A., Piuri, V., Scotti, F.,
Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation,
SMCS(43), No. 4, 2013, pp. 1003-1012.
lattice Boltzmann; neural networks; wildfire BibRef

Ko, B.C.[Byoung-Chul], Park, J.O.[Jun-Oh], Nam, J.Y.[Jae-Yeal],
Spatiotemporal bag-of-features for early wildfire smoke detection,
IVC(31), No. 10, 2013, pp. 786-795.
Elsevier DOI 1310
Wildfire smoke detection BibRef

Park, J.[Jun_Oh], Ko, B.[Byoung_Chul], Nam, J.Y.[Jae-Yeal], Kwak, S.[Soo_Yeong],
Wildfire smoke detection using spatiotemporal bag-of-features of smoke,

Bugaric, M.[Marin], Jakovcevic, T.[Toni], Stipanicev, D.[Darko],
Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index,
CVIU(118), No. 1, 2014, pp. 184-196.
Elsevier DOI 1312
Smoke detection BibRef

Fisher, D., Muller, J.P., Yershov, V.N.,
Automated Stereo Retrieval of Smoke Plume Injection Heights and Retrieval of Smoke Plume Masks From AATSR and Their Assessment With CALIPSO and MISR,
GeoRS(52), No. 2, February 2014, pp. 1249-1258.
geophysical techniques BibRef

Li, X.L.[Xiao-Lian], Song, W.G.[Wei-Guo], Lian, L.P.[Li-Ping], Wei, X.G.[Xiao-Ge],
Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data,
RS(7), No. 4, 2015, pp. 4473-4498.
DOI Link 1505

Martin, M.V.[Maria Val], Kahn, R.A.[Ralph A.], Tosca, M.G.[Mika G.],
A Global Analysis of Wildfire Smoke Injection Heights Derived from Space-Based Multi-Angle Imaging,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Várnai, T.[Tamás], Gatebe, C.[Charles], Gautam, R.[Ritesh], Poudyal, R.[Rajesh], Su, W.Y.[Wen-Ying],
Developing an Aircraft-Based Angular Distribution Model of Solar Reflection from Wildfire Smoke to Aid Satellite-Based Radiative Flux Estimation,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

Ba, R.[Rui], Chen, C.[Chen], Yuan, J.[Jing], Song, W.G.[Wei-Guo], Lo, S.[Siuming],
SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Li, X., Chen, Z., Wu, Q.M.J., Liu, C.,
3D Parallel Fully Convolutional Networks for Real-Time Video Wildfire Smoke Detection,
CirSysVideo(30), No. 1, January 2020, pp. 89-103.
convolutional neural nets, feature extraction, geophysical image processing, image classification, natural scene BibRef

Zhu, G.D.[Guo-Dong], Chen, Z.X.[Zhen-Xue], Liu, C.Y.[Cheng-Yun], Rong, X.W.[Xue-Wen], He, W.K.[Wei-Kai],
3D video semantic segmentation for wildfire smoke,
MVA(31), No. 6, August 2020, pp. Article50.
Springer DOI 2008

Lu, X.M.[Xiao-Man], Zhang, X.Y.[Xiao-Yang], Li, F.J.[Fang-Jun], Cochrane, M.A.[Mark A.], Ciren, P.[Pubu],
Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Mo, Y.H.[Yu-Hao], Yang, X.[Xin], Tang, H.[Hong], Li, Z.G.[Zhi-Gang],
Smoke Detection from Himawari-8 Satellite Data over Kalimantan Island Using Multilayer Perceptrons,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Wang, Z.W.[Ze-Wei], Yang, P.F.[Peng-Fei], Liang, H.T.[Hao-Tian], Zheng, C.[Change], Yin, J.Y.[Ji-Yan], Tian, Y.[Ye], Cui, W.B.[Wen-Bin],
Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Zheng, X.[Xin], Chen, F.[Feng], Lou, L.M.[Li-Ming], Cheng, P.[Pengle], Huang, Y.[Ying],
Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Dewangan, A.[Anshuman], Pande, Y.[Yash], Braun, H.W.[Hans-Werner], Vernon, F.[Frank], Perez, I.[Ismael], Altintas, I.[Ilkay], Cottrell, G.W.[Garrison W.], Nguyen, M.H.[Mai H.],
FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202

Nakata, M.[Makiko], Sano, I.[Itaru], Mukai, S.[Sonoyo], Kokhanovsky, A.[Alexander],
Characterization of Wildfire Smoke over Complex Terrain Using Satellite Observations, Ground-Based Observations, and Meteorological Models,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206

Konovalov, I.B.[Igor B.], Golovushkin, N.A.[Nikolai A.], Beekmann, M.[Matthias], Turquety, S.[Solène],
Using Multi-Platform Satellite Observations to Study the Atmospheric Evolution of Brown Carbon in Siberian Biomass Burning Plumes,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Lemmouchi, F.[Farouk], Cuesta, J.[Juan], Eremenko, M.[Maxim], Derognat, C.[Claude], Siour, G.[Guillaume], Dufour, G.[Gaëlle], Sellitto, P.[Pasquale], Turquety, S.[Solène], Tran, D.[Dung], Liu, X.[Xiong], Zoogman, P.[Peter], Lutz, R.[Ronny], Loyola, D.[Diego],
Three-Dimensional Distribution of Biomass Burning Aerosols from Australian Wildfires Observed by TROPOMI Satellite Observations,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Chen, J.[Jie], Zheng, W.[Wei], Wu, S.[Shuang], Liu, C.[Cheng], Yan, H.[Hua],
Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Martins, L.[Leonardo], Guede-Fernández, F.[Federico], de Almeida, R.V.[Rui Valente], Gamboa, H.[Hugo], Vieira, P.[Pedro],
Real-Time Integration of Segmentation Techniques for Reduction of False Positive Rates in Fire Plume Detection Systems during Forest Fires,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Engel, C.B.[Chermelle B.], Jones, S.D.[Simon D.], Reinke, K.J.[Karin J.],
Fire Radiative Power (FRP) Values for Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) Hotspots Derived from the Advanced Himawari Imager (AHI),
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Deng, Z.[Zhen], Hu, S.H.[Shu-Hao], Yin, S.B.[Shi-Bai], Wang, Y.[Yibin], Basu, A.[Anup], Cheng, I.[Irene],
Multi-step implicit Adams predictor-corrector network for fire detection,
IET-IPR(16), No. 9, 2022, pp. 2338-2350.
DOI Link 2206

Zhao, L.[Liang], Liu, J.[Jixue], Peters, S.[Stefan], Li, J.Y.[Jiu-Yong], Oliver, S.[Simon], Mueller, N.[Norman],
Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

López-Cayuela, M.Á.[María-Ángeles], Herrera, M.E.[Milagros E.], Córdoba-Jabonero, C.[Carmen], Pérez-Ramírez, D.[Daniel], Carvajal-Pérez, C.V.[Clara Violeta], Dubovik, O.[Oleg], Guerrero-Rascado, J.L.[Juan Luis],
Retrieval of Aged Biomass-Burning Aerosol Properties by Using GRASP Code in Synergy with Polarized Micro-Pulse Lidar and Sun/Sky Photometer,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Lv, Z.H.[Zheng-Han], Shi, Y.S.[Yu-Sheng], Guo, D.F.[Dian-Fan], Zhu, Y.[Yue], Man, H.R.[Hao-Ran], Zhang, Y.[Yang], Zang, S.Y.[Shu-Ying],
High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Yazdi, A.[Amirhessam], Qin, H.Y.[He-Yang], Jordan, C.B.[Connor B.], Yang, L.[Lei], Yan, F.[Feng],
Nemo: An Open-Source Transformer-Supercharged Benchmark for Fine-Grained Wildfire Smoke Detection,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Adam, M.[Mariana], Fragkos, K.[Konstantinos], Solomos, S.[Stavros], Belegante, L.[Livio], Andrei, S.[Simona], Talianu, C.[Camelia], Marmureanu, L.[Luminita], Antonescu, B.[Bogdan], Ene, D.[Dragos], Nicolae, V.[Victor], Amiridis, V.[Vassilis],
Methodology for Lidar Monitoring of Biomass Burning Smoke in Connection with the Land Cover,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

de Rosa, B.[Benedetto], Amato, F.[Francesco], Amodeo, A.[Aldo], d'Amico, G.[Giuseppe], Dema, C.[Claudio], Falconieri, A.[Alfredo], Giunta, A.[Aldo], Gumà-Claramunt, P.[Pilar], Kampouri, A.[Anna], Solomos, S.[Stavros], Mytilinaios, M.[Michail], Papagiannopoulos, N.[Nikolaos], Summa, D.[Donato], Veselovskii, I.[Igor], Mona, L.[Lucia],
Characterization of Extremely Fresh Biomass Burning Aerosol by Means of Lidar Observations,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Attiya, A.A.[Ali A.], Jones, B.G.[Brian G.],
Impact of Smoke Plumes Transport on Air Quality in Sydney during Extensive Bushfires (2019) in New South Wales, Australia Using Remote Sensing and Ground Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Bar, S.[Somnath], Parida, B.R.[Bikash Ranjan], Pandey, A.C.[Arvind Chandra], Kumar, N.[Navneet],
Pixel-Based Long-Term (2001-2020) Estimations of Forest Fire Emissions over the Himalaya,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Li, J.[Jian], Liu, H.[Hua], Du, J.[Jia], Cao, B.[Bin], Zhang, Y.W.[Yi-Wei], Yu, W.L.[Wei-Lin], Zhang, W.J.[Wei-Jian], Zheng, Z.[Zhi], Wang, Y.[Yan], Sun, Y.[Yue], Chen, Y.[Yuanhui],
Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Bhamra, J.K.[Jaspreet Kaur], Ramaprasad, S.A.[Shreyas Anantha], Baldota, S.[Siddhant], Luna, S.[Shane], Zen, E.[Eugene], Ramachandra, R.[Ravi], Kim, H.[Harrison], Schmidt, C.[Chris], Arends, C.[Chris], Block, J.[Jessica], Perez, I.[Ismael], Crawl, D.[Daniel], Altintas, I.[Ilkay], Cottrell, G.W.[Garrison W.], Nguyen, M.H.[Mai H.],
Multimodal Wildland Fire Smoke Detection,
RS(15), No. 11, 2023, pp. 2790.
DOI Link 2306

Chen, G.[Gong], Cheng, R.[Renxi], Lin, X.F.[Xu-Feng], Jiao, W.G.[Wan-Guo], Bai, D.[Di], Lin, H.F.[Hai-Feng],
LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308

Tao, H.[Huanjie], Duan, Q.[Qianyue], Lu, M.H.[Ming-Hao], Hu, Z.[Zhenwu],
Learning discriminative feature representation with pixel-level supervision for forest smoke recognition,
PR(143), 2023, pp. 109761.
Elsevier DOI 2310
Deep neural network, Component separation, Forest smoke recognition, Supervision information BibRef

Mukai, S.[Sonoyo], Hioki, S.[Souichiro], Nakata, M.[Makiko],
Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI,
RS(15), No. 22, 2023, pp. 5405.
DOI Link 2311

Yang, H.Y.[Huan-Yu], Wang, J.[Jun], Wang, J.[Jiacun],
Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning,
RS(15), No. 23, 2023, pp. 5527.
DOI Link 2312

Damiano, R.[Riccardo], Amoruso, S.[Salvatore], Sannino, A.[Alessia], Boselli, A.[Antonella],
Lidar Optical and Microphysical Characterization of Tropospheric and Stratospheric Fire Smoke Layers Due to Canadian Wildfires Passing over Naples (Italy),
RS(16), No. 3, 2024, pp. 538.
DOI Link 2402

Sowden, M.[Miles], Hanigan, I.C.[Ivan C.], Robbins, D.J.V.[Daniel Jamie Victor], Cope, M.[Martin], Silver, J.D.[Jeremy D.], Noonan, J.[Julie],
Characterizing Smoke Haze Events in Australia Using a Hybrid Approach of Satellite-Based Aerosol Optical Depth and Chemical Transport Modeling,
RS(16), No. 7, 2024, pp. 1266.
DOI Link 2404

Mulena, G.C.[Gabriela Celeste], Asmi, E.M.[Eija Maria], Ruiz, J.J.[Juan José], Pallotta, J.V.[Juan Vicente], Jin, Y.[Yoshitaka],
Biomass Burning Aerosol Observations and Transport over Northern and Central Argentina: A Case Study,
RS(16), No. 10, 2024, pp. 1780.
DOI Link 2405

Gupta, T.[Taanya], Liu, H.Y.[Heng-Yue], Bhanu, B.[Bir],
Early Wildfire Smoke Detection in Videos,
Training, Fires, Vegetation, Object segmentation, Forestry, Unmanned aerial vehicles, Pattern recognition BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Forest Fire Prediction, Fire Hazard, Mitigation, Risk, Susceptibility .

Last update:Jun 5, 2024 at 10:22:22