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

Pennypacker, C.[Carlton],
FUEGO: a satellite system for rapid location of wildfires,
SPIE(Newsroom), February 14, 2014
DOI Link 1402
Combining imaging, computation, software modeling, and satellite hosting systems with firefighting methods may enable cost-effective detection and monitoring of wildland fires in their first few minutes. BibRef

Freeborn, P.H.[Patrick H.], Wooster, M.J.[Martin J.], Roberts, G.[Gareth], Xu, W.D.[Wei-Dong],
Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product,
RS(6), No. 3, 2014, pp. 1890-1917.
DOI Link 1404
BibRef

Le, G.E.[George E.], Breysse, P.N.[Patrick N.], McDermott, A.[Aidan], Eftim, S.E.[Sorina E.], Geyh, A.[Alison], Berman, J.D.[Jesse D.], Curriero, F.C.[Frank C.],
Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly,
IJGI(3), No. 2, 2014, pp. 713-731.
DOI Link 1407
BibRef

Montealegre, A.L.[Antonio Luis], Lamelas, M.T.[María Teresa], Tanase, M.A.[Mihai A.], de la Riva, J.[Juan],
Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment,
RS(6), No. 5, 2014, pp. 4240-4265.
DOI Link 1407
BibRef

Vlassova, L.[Lidia], Pérez-Cabello, F.[Fernando], Mimbrero, M.R.[Marcos Rodrigues], Llovería, R.M.[Raquel Montorio], García-Martín, A.[Alberto],
Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images,
RS(6), No. 7, 2014, pp. 6136-6162.
DOI Link 1408
BibRef

Safronov, A.N.[Alexander N.], Fokeeva, E.V.[Ekaterina V.], Rakitin, V.S.[Vadim S.], Grechko, E.I.[Eugene I.], Shumsky, R.A.[Roman A.],
Severe Wildfires Near Moscow, Russia in 2010: Modeling of Carbon Monoxide Pollution and Comparisons with Observations,
RS(7), No. 1, 2014, pp. 395-429.
DOI Link 1502
BibRef

Gross, B.[Barry], Wu, Y.H.[Yong-Hua], Moshary, F.[Fred], Delgado, R.[Ruben], Hoff, R.[Ray], Su, J.[Jia], Lee, R.[Robert], McCormick, P.[Pat],
Using lidar networks to explore aloft plume properties,
SPIE(Newsroom), December 30, 2014
DOI Link 1504
A coordinated lidar network in the northeastern United States explored the optical properties of transported plumes from fires and dust and diagnosed chemical transport model concentration biases. 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
BibRef

Toulouse, T., Rossi, L., Akhloufi, M., Celik, T., Maldague, X.,
Benchmarking of wildland fire colour segmentation algorithms,
IET-IPR(9), No. 12, 2015, pp. 1064-1072.
DOI Link 1512
fires BibRef

Li, P.[Peng], Feng, Z.M.[Zhi-Ming],
Extent and Area of Swidden in Montane Mainland Southeast Asia: Estimation by Multi-Step Thresholds with Landsat-8 OLI Data,
RS(8), No. 1, 2016, pp. 44.
DOI Link 1602
slash and burn analysis. BibRef

Benali, A.[Akli], Russo, A.[Ana], Sá, A.C.L.[Ana C. L.], Pinto, R.M.S.[Renata M. S.], Price, O.[Owen], Koutsias, N.[Nikos], Pereira, J.M.C.[José M. C.],
Determining Fire Dates and Locating Ignition Points With Satellite Data,
RS(8), No. 4, 2016, pp. 326.
DOI Link 1604
BibRef

Lin, L.[Lei], Meng, Y.[Yu], Yue, A.Z.[An-Zhi], Yuan, Y.[Yuan], Liu, X.Y.[Xiao-Yi], Chen, J.B.[Jing-Bo], Zhang, M.M.[Meng-Meng], Chen, J.S.[Jian-Sheng],
A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data,
RS(8), No. 5, 2016, pp. 403.
DOI Link 1606
BibRef

Polivka, T.N., Wang, J., Ellison, L.T., Hyer, E.J., Ichoku, C.M.,
Improving Nocturnal Fire Detection With the VIIRS Day: Night Band,
GeoRS(54), No. 9, September 2016, pp. 5503-5519.
IEEE DOI 1609
remote sensing BibRef

Oom, D.[Duarte], Silva, P.C.[Pedro C.], Bistinas, I.[Ioannis], Pereira, J.M.C.[José M. C.],
Highlighting Biome-Specific Sensitivity of Fire Size Distributions to Time-Gap Parameter Using a New Algorithm for Fire Event Individuation,
RS(8), No. 8, 2016, pp. 663.
DOI Link 1609
BibRef

de Grandi, E.C.[Elsa Carla], Mitchard, E.[Edward], Hoekman, D.[Dirk],
Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia,
RS(8), No. 8, 2016, pp. 641.
DOI Link 1609
BibRef

Xie, H.[Huan], Du, L.[Li], Liu, S.[Sicong], Chen, L.[Lei], Gao, S.[Sa], Liu, S.[Shuang], Pan, H.Y.[Hai-Yan], Tong, X.H.[Xiao-Hua],
Dynamic Monitoring of Agricultural Fires in China from 2010 to 2014 Using MODIS and GlobeLand30 Data,
IJGI(5), No. 10, 2016, pp. 172.
DOI Link 1610
BibRef

Tian, G., Ren, Y., Zhou, M.,
Dual-Objective Scheduling of Rescue Vehicles to Distinguish Forest Fires via Differential Evolution and Particle Swarm Optimization Combined Algorithm,
ITS(17), No. 11, November 2016, pp. 3009-3021.
IEEE DOI 1609
Engines BibRef

Wickramasinghe, C.H.[Chathura H.], Jones, S.[Simon], Reinke, K.[Karin], Wallace, L.[Luke],
Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8,
RS(8), No. 11, 2016, pp. 932.
DOI Link 1612
BibRef

Plank, S.[Simon], Fuchs, E.M.[Eva-Maria], Frey, C.[Corinne],
A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery: A TIMELINE Thematic Processor,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702

See also Fully Automatic Burnt Area Mapping Processor Based on AVHRR Imagery: A TIMELINE Thematic Processor, A. BibRef

Fukuhara, T., Kouyama, T., Kato, S., Nakamura, R., Takahashi, Y., Akiyama, H.,
Detection of Small Wildfire by Thermal Infrared Camera With the Uncooled Microbolometer Array for 50-kg Class Satellite,
GeoRS(55), No. 8, August 2017, pp. 4314-4324.
IEEE DOI 1708
Brightness temperature, Cameras, Instruments, Low earth orbit satellites, Satellite broadcasting, Spatial resolution, Infrared imaging, remote sensing, satellite, applications BibRef

Lin, Z., Chen, F., Li, B., Yu, B., Shirazi, Z., Wu, Q., Wu, W.,
FengYun-3C VIRR Active Fire Monitoring: Algorithm Description and Initial Assessment Using MODIS and Landsat Data,
GeoRS(55), No. 11, November 2017, pp. 6420-6430.
IEEE DOI 1711
Algorithm design and analysis, Earth, Heuristic algorithms, MODIS, Active fire monitoring. BibRef

Fornacca, D.[Davide], Ren, G.[Guopeng], Xiao, W.[Wen],
Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Zhang, X.X.[Xiao-Xiang], Yao, J.[Jing], Sila-Nowicka, K.[Katarzyna],
Exploring Spatiotemporal Dynamics of Urban Fires: A Case of Nanjing, China,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801
BibRef
Earlier: A2, A1, Only:
Spatial-temporal Dynamics Of Urban Fire Incidents: A Case Study Of Nanjing, China,
ISPRS16(B2: 63-69).
DOI Link 1610
BibRef

Cho, K.J.[Kang-Joon], Kim, Y.H.[Yong-Hyun], Kim, Y.[Yongil],
Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Garg, S.[Saurabh], Forbes-Smith, N.[Nicholas], Hilton, J.[James], Prakash, M.[Mahesh],
SparkCloud: A Cloud-Based Elastic Bushfire Simulation Service,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Wu, P., Chu, F., Che, A., Zhou, M.,
Bi-Objective Scheduling of Fire Engines for Fighting Forest Fires: New Optimization Approaches,
ITS(19), No. 4, April 2018, pp. 1140-1151.
IEEE DOI 1804
Earthquakes, Emergency services, Engines, Heuristic algorithms, Processor scheduling, Routing, Scheduling, Forest fires, optimization BibRef

Zhuang, Y.[Yan], Li, R.[Ruiyuan], Yang, H.[Hao], Chen, D.[Danlu], Chen, Z.[Ziyue], Gao, B.[Bingbo], He, B.[Bin],
Understanding Temporal and Spatial Distribution of Crop Residue Burning in China from 2003 to 2017 Using MODIS Data,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

di Biase, V.[Valeria], Laneve, G.[Giovanni],
Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Parks, S.A.[Sean A.], Holsinger, L.M.[Lisa M.], Voss, M.A.[Morgan A.], Loehman, R.A.[Rachel A.], Robinson, N.P.[Nathaniel P.],
Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhang, T.R.[Tian-Ran], Wooster, M.J.[Martin J.], de Jong, M.C.[Mark C.], Xu, W.D.[Wei-Dong],
How Well Does the 'Small Fire Boost' Methodology Used within the GFED4.1s Fire Emissions Database Represent the Timing, Location and Magnitude of Agricultural Burning?,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Hally, B.[Bryan], Wallace, L.[Luke], Reinke, K.[Karin], Jones, S.[Simon], Engel, C.[Chermelle], Skidmore, A.[Andrew],
Estimating Fire Background Temperature at a Geostationary Scale: An Evaluation of Contextual Methods for AHI-8,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Liu, X.Z.[Xiang-Zhuo], He, B.B.[Bin-Bin], Quan, X.W.[Xing-Wen], Yebra, M.[Marta], Qiu, S.[Shi], Yin, C.M.[Chang-Ming], Liao, Z.M.[Zhan-Mang], Zhang, H.G.[Hong-Guo],
Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

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
BibRef

Roberts, G.[Gareth], Wooster, M.J.[Martin J.], Xu, W.D.[Wei-Dong], He, J.P.[Jiang-Ping],
Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Tran, B.N.[Bang Nguyen], Tanase, M.A.[Mihai A.], Bennett, L.T.[Lauren T.], Aponte, C.[Cristina],
Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Xie, Z.[Zixi], Song, W.G.[Wei-Guo], Ba, R.[Rui], Li, X.L.[Xiao-Lian], Xia, L.[Long],
A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Jang, E.[Eunna], Kang, Y.J.[Yoo-Jin], Im, J.H.[Jung-Ho], Lee, D.W.[Dong-Won], Yoon, J.M.[Jong-Min], Kim, S.K.[Sang-Kyun],
Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Sofan, P.[Parwati], Bruce, D.[David], Jones, E.[Eriita], Marsden, J.[Jackie],
Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef
Earlier: Correction: RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Weber, H.[Helga], Wunderle, S.[Stefan],
Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Elvidge, C.D.[Christopher D.], Zhi-Zhin, M.[Mikhail], Baugh, K.[Kimberly], Hsu, F.C.[Feng Chi], Ghosh, T.[Tilottama],
Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Ozaki, M.[Mitsuhiro], Aryal, J.[Jagannath], Fox-Hughes, P.[Paul],
Dynamic Wildfire Navigation System,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Cruz-López, M.I.[María Isabel], Manzo-Delgado, L.D.[Lilia De_Lourdes], Aguirre-Gómez, R.[Raúl], Chuvieco, E.[Emilio], Equihua-Benítez, J.A.[Julián Alberto],
Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Ecoregions,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Kaur, I.[Inderpreet], Hüser, I.[Imke], Zhang, T.[Tianran], Gehrke, B.[Berit], Kaiser, J.W.[Johannes W.],
Correcting Swath-Dependent Bias of MODIS FRP Observations with Quantile Mapping,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
FRP: Fire Radiative Power. Active fire monitoring. BibRef

Klein, D., Richter, R., Strobl, C., Schläpfer, D.,
Solar Influence on Fire Radiative Power Retrieved With the Bispectral Method,
GeoRS(57), No. 7, July 2019, pp. 4521-4528.
IEEE DOI 1907
Table lookup, Cameras, Sensors, MODIS, Solar radiation, Fires, Bispectral method, fire radiative power (FRP), TET-1 BibRef

Várnai, T.[Tamás], Gatebe, C.[Charles], Gautam, R.[Ritesh], Poudyal, R.[Rajesh], Su, W.[Wenying],
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
BibRef

Lin, Z., Chen, F., Li, B., Yu, B., Jia, H., Zhang, M., Liang, D.,
A Contextual and Multitemporal Active-Fire Detection Algorithm Based on FengYun-2G S-VISSR Data,
GeoRS(57), No. 11, November 2019, pp. 8840-8852.
IEEE DOI 1911
Satellite broadcasting, Spatial resolution, MODIS, Geostationary satellites, Detection algorithms, geostationary satellite data BibRef

Shah, S.B., Grübler, T., Krempel, L., Ernst, S., Mauracher, F., Contractor, S.,
Real-time Wildfire Detection From Space - a Trade-off Between Sensor Quality, Physical Limitations and Payload Size,
PIA19(209-213).
DOI Link 1912
BibRef

Hesam, S., Valizadeh Kamran, K.,
Intelligent Management Occurrence and Spread of Front Fire in GIS by Using Cellular Automata. Case Study: Golestan Forest,
SMPR19(475-481).
DOI Link 1912
BibRef

Jahdi, R., Salis, M., Arabi, M., Arca, B.,
Fire Modelling to Assess Spatial Patterns of Wildfire Exposure In Ardabil, Nw Iran,
SMPR19(577-581).
DOI Link 1912
BibRef

Ying, L.X.[Ling-Xiao], Shen, Z.[Zehao], Yang, M.Z.[Ming-Zheng], Piao, S.L.[Shi-Long],
Wildfire Detection Probability of MODIS Fire Products under the Constraint of Environmental Factors: A Study Based on Confirmed Ground Wildfire Records,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Govil, K.[Kinshuk], Welch, M.L.[Morgan L.], Ball, J.T.[J. Timothy], Pennypacker, C.R.[Carlton R.],
Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Kumar, S.S.[Sanath Sathyachandran], Hult, J.[John], Picotte, J.[Joshua], Peterson, B.[Birgit],
Potential Underestimation of Satellite Fire Radiative Power Retrievals over Gas Flares and Wildland Fires,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Valero, M.M.[Mario M.], Verstockt, S.[Steven], Mata, C.[Christian], Jimenez, D.[Dan], Queen, L.[Lloyd], Rios, O.[Oriol], Pastor, E.[Elsa], Planas, E.[Eulàlia],
Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

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.
IEEE DOI 2002
convolutional neural nets, feature extraction, geophysical image processing, image classification, natural scene BibRef

Varotsos, C.A.[Costas A.], Krapivin, V.F.[Vladimir F.], Mkrtchyan, F.A.[Ferdenant A.],
A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Pham, H.X., La, H.M., Feil-Seifer, D., Deans, M.C.,
A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking,
SMCS(50), No. 4, April 2020, pp. 1537-1548.
IEEE DOI 2004
Mathematical model, Unmanned aerial vehicles, Robot sensing systems, Decentralized control, Task analysis, Color, networked robots BibRef

Zhang, X.X.[Xiao-Xiang], Yao, J.[Jing], Sila-Nowicka, K.[Katarzyna], Jin, Y.[Yuhao],
Urban Fire Dynamics and Its Association with Urban Growth: Evidence from Nanjing, China,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Pan, H.Y.[Hong-Yi], Badawi, D.[Diaa], Zhang, X.[Xi], Cetin, A.E.[Ahmet Enis],
Additive neural network for forest fire detection,
SIViP(14), No. 4, June 2020, pp. 675-682.
WWW Link. 2005
BibRef

Udahemuka, G.[Gustave], van Wyk, B.J.[Barend J.], Hamam, Y.[Yskandar],
Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Li, F.J.[Fang-Jun], Zhang, X.Y.[Xiao-Yang], Kondragunta, S.[Shobha],
Biomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire Product,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Wei, X.[Xikun], Wang, G.[Guojie], Chen, T.[Tiexi], Hagan, D.F.T.[Daniel Fiifi Tawia], Ullah, W.[Waheed],
A Spatio-Temporal Analysis of Active Fires over China during 2003-2016,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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
BibRef

Salguero, J.[John], Li, J.J.[Jing-Jing], Farahmand, A.[Alireza], Reager, J.T.[John T.],
Wildfire Trend Analysis over the Contiguous United States Using Remote Sensing Observations,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Li, S., Yan, Q., Liu, P.,
An Efficient Fire Detection Method Based on Multiscale Feature Extraction, Implicit Deep Supervision and Channel Attention Mechanism,
IP(29), 2020, pp. 8467-8475.
IEEE DOI 2008
Fire detection, convolutional neural network, industrial applications, multiscale feature extraction, channel attention mechanism BibRef

Fu, Y.Y.[Yu-Yun], Li, R.[Rui], Wang, X.[Xuewen], Bergeron, Y.[Yves], Valeria, O.[Osvaldo], Chavardès, R.D.[Raphaël D.], Wang, Y.[Yipu], Hu, J.H.[Ji-Heng],
Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Weber, K.T.[Keith T.], Yadav, R.[Rituraj],
Spatiotemporal Trends in Wildfires across the Western United States (1950-2019),
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Hayasaka, H.[Hiroshi], Sokolova, G.V.[Galina V.], Ostroukhov, A.[Andrey], Naito, D.[Daisuke],
Classification of Active Fires and Weather Conditions in the Lower Amur River Basin,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Barmpoutis, P.[Panagiotis], Stathaki, T.[Tania], Dimitropoulos, K.[Kosmas], Grammalidis, N.[Nikos],
Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Castillo, E.B.[Elgar Barboza], Cayo, E.Y.T.[Efrain Y. Turpo], de Almeida, C.M.[Cláudia Maria], López, R.S.[Rolando Salas], Briceño, N.B.R.[Nilton B. Rojas], López, J.O.S.[Jhonsy Omar Silva], Gurbillón, M.Á.B.[Miguel Ángel Barrena], Oliva, M.[Manuel], Espinoza-Villar, R.[Raul],
Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010
BibRef
And: Erratum: IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Ciullo, V.[Vito], Rossi, L.[Lucile], Pieri, A.[Antoine],
Experimental Fire Measurement with UAV Multimodal Stereovision,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Yin, C.M.[Chang-Ming], He, B.B.[Bin-Bin], Quan, X.W.[Xing-Wen], Yebra, M.[Marta], Lai, G.K.[Geng-Ke],
Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Balch, J.K.[Jennifer K.], Denis, L.A.S.[Lise A. St.], Mahood, A.L.[Adam L.], Mietkiewicz, N.P.[Nathan P.], Williams, T.M.[Travis M.], McGlinchy, J.[Joe], Cook, M.C.[Maxwell C.],
FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001-2019),
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Park, M.S.[Min-Soo], Tran, D.Q.[Dai Quoc], Jung, D.[Daekyo], Park, S.[Seunghee],
Wildfire-Detection Method Using DenseNet and CycleGAN Data Augmentation-Based Remote Camera Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Janiec, P.[Piotr], Gadal, S.[Sébastien],
A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Lu, X.[Xiaoman], 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
BibRef

Guo, J.[Jinyun], Hou, R.[Rui], Zhou, M.[Maosheng], Jin, X.[Xin], Li, C.M.[Cheng-Ming], Liu, X.[Xin], Gao, H.[Hao],
Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Silva, C.A.[Claudia Arantes], Santilli, G.[Giancarlo], Sano, E.E.[Edson Eyji], Laneve, G.[Giovanni],
Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

van Gerrevink, M.J.[Max J.], Veraverbeke, S.[Sander],
Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

da Penha Pacheco, A.[Admilson], da Silva Junior, J.A.[Juarez Antonio], Ruiz-Armenteros, A.M.[Antonio Miguel], Henriques, R.F.F.[Renato Filipe Faria],
Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Nolde, M.[Michael], Plank, S.[Simon], Richter, R.[Rudolf], Klein, D.[Doris], Riedlinger, T.[Torsten],
The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Amici, S.[Stefania], Piscini, A.[Alessandro],
Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Engel, C.B.[Chermelle B.], Jones, S.D.[Simon D.], Reinke, K.J.[Karin J.],
Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Engel, C.B.[Chermelle B.], Jones, S.D.[Simon D.], Reinke, K.[Karin],
A Seasonal-Window Ensemble-Based Thresholding Technique Used to Detect Active Fires in Geostationary Remotely Sensed Data,
GeoRS(59), No. 6, June 2021, pp. 4947-4956.
IEEE DOI 2106
Australia, Clouds, Cloud computing, Fires, MODIS, Advanced Baseline Imager, Advanced Himawari Imager, Himawari-8 BibRef

Farguell, A.[Angel], Mandel, J.[Jan], Haley, J.[James], Mallia, D.V.[Derek V.], Kochanski, A.[Adam], Hilburn, K.[Kyle],
Machine Learning Estimation of Fire Arrival Time from Level-2 Active Fires Satellite Data,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Tariq, A.[Aqil], Shu, H.[Hong], Li, Q.[Qingting], Altan, O.[Orhan], Khan, M.R.[Mobushir Riaz], Baqa, M.F.[Muhammad Fahad], Lu, L.L.[Lin-Lin],
Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Hysa, A.[Artan], Spalevic, V.[Velibor], Dudic, B.[Branislav], Ro?ca, S.[Sanda], Kuriqi, A.[Alban], Bila?co, ?.[?tefan], Sestras, P.[Paul],
Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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de Almeida Pereira, G.H.[Gabriel Henrique], Fusioka, A.M.[Andre Minoro], Nassu, B.T.[Bogdan Tomoyuki], Minetto, R.[Rodrigo],
Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study,
PandRS(178), 2021, pp. 171-186.
Elsevier DOI 2108
Active fire detection, Active fire segmentation, Active fire dataset, Convolutional neural network, Landsat-8 imagery BibRef

Zheng, Y.M.[Yue-Ming], Liu, J.[Jian], Jian, H.[Hongdeng], Fan, X.T.[Xiang-Tao], Yan, F.[Fuli],
Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Gong, A.[Adu], Li, J.[Jing], Chen, Y.L.[Yan-Ling],
A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Lourenço, M.[Miguel], Oliveira, L.B.[Luís B.], Oliveira, J.P.[João P.], Mora, A.[André], Oliveira, H.[Henrique], Santos, R.[Rui],
An Integrated Decision Support System for Improving Wildfire Suppression Management,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
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Ghali, R.[Rafik], Akhloufi, M.A.[Moulay A.], Jmal, M.[Marwa], Mseddi, W.S.[Wided Souidene], Attia, R.[Rabah],
Wildfire Segmentation Using Deep Vision Transformers,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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Mo, Y.[Yuhao], 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
BibRef

Gupta, T.[Taanya], Liu, H.[Hengyue], Bhanu, B.[Bir],
Early Wildfire Smoke Detection in Videos,
ICPR21(8523-8530)
IEEE DOI 2105
Training, Fires, Vegetation, Object segmentation, Forestry, Unmanned aerial vehicles, Pattern recognition BibRef

Pan, H.Y.[Hong-Yi], Badawi, D.[Diaa], Cetin, A.E.[Ahmet Enis],
Fourier Domain Pruning of MobileNet-V2 with Application to Video Based Wildfire Detection,
ICPR21(1015-1022)
IEEE DOI 2105
Performance evaluation, Training, Image edge detection, Neural networks, Transfer learning, Fires, Forestry, pruning and slimming BibRef