23.4.12.8 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.

Shephard, M.W., Kennelly, E.J.,
Effect of band-to-band coregistration on fire property retrievals,
GeoRS(41), No. 11, November 2003, pp. 2648-2661.
IEEE Abstract. 0311
BibRef

Briz, S., de Castro, A.J., Aranda, J.M., Meléndez, J., López, F.,
Reduction of false alarm rate in automatic forest fire infrared surveillance systems,
RSE(86), No. 1, 30 June 2003, pp. 19-29.
Elsevier DOI 0309
BibRef

Csiszar, I.A., Morisette, J.T., Giglio, L.,
Validation of Active Fire Detection From Moderate-Resolution Satellite Sensors: The MODIS Example in Northern Eurasia,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1757-1764.
IEEE DOI 0606
BibRef

Umamaheshwaran, R., Bijker, W., Stein, A.,
Image Mining for Modeling of Forest Fires From Meteosat Images,
GeoRS(45), No. 1, January 2007, pp. 246-253.
IEEE DOI 0701
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Martinez-de Dios, J.R., Arrue, B.C., Ollero, A., Merino, L., Gomez-Rodriguez, F.,
Computer vision techniques for forest fire perception,
IVC(26), No. 4, April 2008, pp. 550-562.
Elsevier DOI 0711
Perception systems; Infrared and visual image processing; Forest fires; Sensor fusion BibRef

Harris, S., Veraverbeke, S., Hook, S.,
Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data,
RS(3), No. 11, November 2011, pp. 2403-2419.
DOI Link 1203
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Sifakis, N., Iossifidis, C., Kontoes, C.C., Keramitsoglou, I.,
Wildfire Detection and Tracking over Greece Using MSG-SEVIRI Satellite Data,
RS(3), No. 3, March 2011, pp. 524-538.
DOI Link 1203
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Gunay, O., Toreyin, B.U., Kose, K., Cetin, A.E.,
Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video,
IP(21), No. 5, May 2012, pp. 2853-2865.
IEEE DOI 1204
BibRef

Orozco, C.V.[Carmen Vega], Tonini, M.[Marj], Conedera, M.[Marco], Kanveski, M.[Mikhail],
Cluster recognition in spatial-temporal sequences: The case of forest fires,
GeoInfo(16), No. 4, October 2012, pp. 653-673.
WWW Link. 1210
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Bernhard, E.M.[Eva-Maria], Twele, A.[André], Gähler, M.[Monika],
Rapid Mapping of Forest Fires in the European Mediterranean Region: A Change Detection Approach Using X-Band SAR-Data,
PFG(2011), No. 4, 2011, pp. 261-270.
WWW Link. 1211
BibRef

Maier, S.W.[Stefan W.], Russell-Smith, J.[Jeremy], Edwards, A.C.[Andrew C.], Yates, C.[Cameron],
Sensitivity of the MODIS fire detection algorithm (MOD14) in the savanna region of the Northern Territory, Australia,
PandRS(76), No. 1, February 2013, pp. 11-16.
Elsevier DOI 1301
Forest fire; Thermal; Performance; Hazards; Monitoring; Detection BibRef

Elvidge, C.D.[Christopher D.], Zhizhin, M.[Mikhail], Hsu, F.C.[Feng-Chi], Baugh, K.E.[Kimberly E.],
VIIRS Nightfire: Satellite Pyrometry at Night,
RS(5), No. 9, 2013, pp. 4423-4449.
DOI Link 1310
BibRef

Paugam, R., Wooster, M.J., Roberts, G.,
Use of Handheld Thermal Imager Data for Airborne Mapping of Fire Radiative Power and Energy and Flame Front Rate of Spread,
GeoRS(51), No. 6, 2013, pp. 3385-3399.
IEEE DOI 1307
flames; infrared imaging; georeferencing algorithm BibRef

Pennypacker, C.R.[Carlton R.], Jakubowski, M.K.[Marek K.], Kelly, M.[Maggi], Lampton, M.[Michael], Schmidt, C.[Christopher], Stephens, S.[Scott], Tripp, R.[Robert],
FUEGO: Fire Urgency Estimator in Geosynchronous Orbit: A Proposed Early-Warning Fire Detection System,
RS(5), No. 10, 2013, pp. 5173-5192.
DOI Link 1311
BibRef

Pennypacker, C.R.[Carlton R.],
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
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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

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.
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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,
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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
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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
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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
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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
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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.Y.[Rui-Yuan], Yang, H.[Hao], Chen, D.[Danlu], Chen, Z.Y.[Zi-Yue], Gao, B.B.[Bing-Bo], He, B.[Bin],
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DOI Link 1804
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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
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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.
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And: Correction: RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Hally, B.[Bryan], Wallace, L.[Luke], Reinke, K.[Karin], Jones, S.[Simon], Engel, C.[Chermelle], Skidmore, A.[Andrew],
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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],
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Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa,
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Xie, Z.X.[Zi-Xi], Song, W.G.[Wei-Guo], Ba, R.[Rui], Li, X.L.[Xiao-Lian], Xia, L.[Long],
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Fire detection, convolutional neural network, industrial applications, multiscale feature extraction, channel attention mechanism BibRef

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FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001-2019),
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Park, M.S.[Min-Soo], Tran, D.Q.[Dai Quoc], Jung, D.[Daekyo], Park, S.[Seunghee],
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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,
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Guo, J.Y.[Jin-Yun], Hou, R.[Rui], Zhou, M.S.[Mao-Sheng], Jin, X.[Xin], Li, C.M.[Cheng-Ming], Liu, X.[Xin], Gao, H.[Hao],
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Silva, C.A.[Claudia Arantes], Santilli, G.[Giancarlo], Sano, E.E.[Edson Eyji], Laneve, G.[Giovanni],
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van Gerrevink, M.J.[Max J.], Veraverbeke, S.[Sander],
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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,
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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,
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DOI Link 2104
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Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia,
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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,
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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,
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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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Zhang, N.[Ning], Sun, L.[Lin], Sun, Z.D.[Zhen-Dong], Qu, Y.[Yu],
Detecting Low-Intensity Fires in East Asia Using VIIRS Data: An Improved Contextual Algorithm,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Li, X.D.[Xing-Dong], Gao, H.[Hewei], Zhang, M.X.[Ming-Xian], Zhang, S.Y.[Shi-Yu], Gao, Z.M.[Zhi-Ming], Liu, J.Q.[Jiu-Qing], Sun, S.[Shufa], Hu, T.X.[Tong-Xin], Sun, L.[Long],
Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Kim, T.H.[Tae-Hee], Hwang, S.[Suyeon], Choi, J.[Jinmu],
Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Zhang, Q.[Qi], Ge, L.L.[Lin-Lin], Zhang, R.H.[Rui-Heng], Metternicht, G.I.[Graciela Isabel], Liu, C.[Chang], Du, Z.Y.[Zhe-Yuan],
Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Nolde, M.[Michael], Mueller, N.[Norman], Strunz, G.[Günter], Riedlinger, T.[Torsten],
Assessment of Wildfire Activity Development Trends for Eastern Australia Using Multi-Sensor Earth Observation Data,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Li, R.[Rui], Fu, Y.[Yuyun], Bergeron, Y.[Yves], Valeria, O.[Osvaldo], Chavardès, R.D.[Raphaël D.], Hu, J.[Jiheng], Wang, Y.[Yipu], Duan, J.W.[Jia-Wei], Li, D.[Dong], Cheng, Y.X.[Yuan-Xi],
Assessing forest fire properties in Northeastern Asia and Southern China with satellite microwave Emissivity Difference Vegetation Index (EDVI),
PandRS(183), 2022, pp. 54-65.
Elsevier DOI 2201
Satellite remote sensing, Fire count, Fire Radiative Power (FRP), Vegetation water content, Fire Weather Index (FWI) BibRef

Mazzeo, G.[Giuseppe], de Santis, F.[Fortunato], Falconieri, A.[Alfredo], Filizzola, C.[Carolina], Lacava, T.[Teodosio], Lanorte, A.[Antonio], Marchese, F.[Francesco], Nolè, G.[Gabriele], Pergola, N.[Nicola], Pietrapertosa, C.[Carla], Satriano, V.[Valeria],
Integrated Satellite System for Fire Detection and Prioritization,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Dvornikov, Y.[Yury], Novenko, E.[Elena], Korets, M.[Mikhail], Olchev, A.[Alexander],
Wildfire Dynamics along a North-Central Siberian Latitudinal Transect Assessed Using Landsat Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Higa, L.[Leandro], Junior, J.M.[José Marcato], Rodrigues, T.[Thiago], Zamboni, P.[Pedro], Silva, R.[Rodrigo], Almeida, L.[Laisa], Liesenberg, V.[Veraldo], Roque, F.[Fábio], Libonati, R.[Renata], Gonçalves, W.N.[Wesley Nunes], Silva, J.[Jonathan],
Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Rostami, A.[Amirhossein], Shah-Hosseini, R.[Reza], Asgari, S.[Shabnam], Zarei, A.[Arastou], Aghdami-Nia, M.[Mohammad], Homayouni, S.[Saeid],
Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Ostroukhov, A.[Andrey], Klimina, E.[Elena], Kuptsova, V.[Viktoriya], Naito, D.[Daisuke],
Estimating Long-Term Average Carbon Emissions from Fires in Non-Forest Ecosystems in the Temperate Belt,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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bin Azami, M.H.[Muhammad Hasif], Orger, N.C.[Necmi Cihan], Schulz, V.H.[Victor Hugo], Oshiro, T.[Takashi], Cho, M.[Mengu],
Earth Observation Mission of a 6U CubeSat with a 5-Meter Resolution for Wildfire Image Classification Using Convolution Neural Network Approach,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Morante-Carballo, F.[Fernando], Bravo-Montero, L.[Lady], Carrión-Mero, P.[Paúl], Velastegui-Montoya, A.[Andrés], Berrezueta, E.[Edgar],
Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Stoyanova, J.S.[Julia S.], Georgiev, C.G.[Christo G.], Neytchev, P.N.[Plamen N.],
Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Yuan, S.[Shuyun], Bao, F.[Fangwen], Zhang, X.C.[Xiao-Chuan], Li, Y.[Ying],
Severe Biomass-Burning Aerosol Pollution during the 2019 Amazon Wildfire and Its Direct Radiative-Forcing Impact: A Space Perspective from MODIS Retrievals,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Harkat, H.[Houda], Nascimento, J.M.P.[José M. P.], Bernardino, A.[Alexandre], Ahmed, H.F.T.[Hasmath Farhana Thariq],
Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Riet, M.[Mats], Veraverbeke, S.[Sander],
How Much of a Pixel Needs to Burn to Be Detected by Satellites? A Spectral Modeling Experiment Based on Ecosystem Data from Yellowstone National Park, USA,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Osswald, T.[Tobias], Fernandes, A.P.[Ana Patrícia], Miranda, A.I.[Ana Isabel], Gouveia, S.[Sónia],
Dynamic PCA Based Statistical Monitoring of Air Pollutant Concentrations in Wildfire Scenarios,
IbPRIA22(680-692).
Springer DOI 2205
BibRef

Xu, H.Z.[Hai-Zhou], Zhang, G.[Gui], Zhou, Z.M.[Zhao-Ming], Zhou, X.B.[Xia-Bing], Zhou, C.[Cui],
Forest Fire Monitoring and Positioning Improvement at Subpixel Level: Application to Himawari-8 Fire Products,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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Špulák, P.[Pavel],
Wildland Fires in the Czech Republic: Review of Data Spanning 20 Years,
IJGI(11), No. 5, 2022, pp. xx-yy.
DOI Link 2206
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Fu, J.[Jing], Song, S.T.[Shi-Tao], Guo, L.[Li], Chen, W.W.[Wei-Wei], Wang, P.[Peng], Duanmu, L.J.[Ling-Jian], Shang, Y.J.[Yi-Jing], Shi, B.[Bowen], He, L.[Luyan],
Interprovincial Joint Prevention and Control of Open Straw Burning in Northeast China: Implications for Atmospheric Environment Management,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Pletsch, M.A.J.S.[Mikhaela A. J. S.], Körting, T.S.[Thales S.], Morita, F.C.[Felipe C.], Silva-Junior, C.H.L.[Celso H. L.], Anderson, L.O.[Liana O.], Aragão, L.E.O.C.[Luiz E. O. C.],
Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Guan, Z.H.[Zhi-Hao], Miao, X.Y.[Xin-Yu], Mu, Y.J.[Yun-Jie], Sun, Q.[Quan], Ye, Q.[Qiaolin], Gao, D.[Demin],
Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Schag, G.M.[Gavin M.], Stow, D.A.[Douglas A.], Riggan, P.J.[Philip J.], Nara, A.[Atsushi],
Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Tian, J.X.[Jia-Xin], Chen, X.N.[Xiao-Ning], Cao, Y.F.[Yun-Feng], Chen, F.[Feng],
Satellite Observational Evidence of Contrasting Changes in Northern Eurasian Wildfires from 2003 to 2020,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Zhao, Y.[Yu], Ban, Y.F.[Yi-Fang],
GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Tian, Y.P.[Yu-Ping], Wu, Z.C.[Ze-Chuan], Li, M.Z.[Ming-Ze], Wang, B.[Bin], Zhang, X.D.[Xiao-Di],
Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Kaaret, P.[Philip], Tammes, S.[Steve], Wang, J.[Jun], Schnell, T.[Thomas], Linderman, M.[Marc], Richey, C.H.[Carlton H.], Packard, C.M.[Colin M.], Zhou, M.[Meng], Fuller, C.A.[Chase A.],
On the Potential of Flaming Hotspot Detection at Night via Multiband Visible/Near-Infrared Imaging,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
Flares BibRef

Jin, R.[Ri], Lee, K.S.[Kyoo-Seock],
Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Rafaqat, W.[Warda], Iqbal, M.[Mansoor], Kanwal, R.[Rida], Weiguo, S.[Song],
Evaluation of Wildfire Occurrences in Pakistan with Global Gridded Soil Properties Derived from Remotely Sensed Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Ye, X.X.[Xin-Xin], Deshler, M.[Mina], Lyapustin, A.[Alexi], Wang, Y.J.[Yu-Jie], Kondragunta, S.[Shobha], Saide, P.[Pablo],
Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Zhou, Y.K.[Yi-Kang], Ji, S.P.[Shun-Ping], Warner, T.A.[Timothy A.],
Regional Spatiotemporal Patterns of Fire in the Eurasian Subarctic Based on Satellite Imagery,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Muksimova, S.[Shakhnoza], Mardieva, S.[Sevara], Cho, Y.I.[Young-Im],
Deep Encoder-Decoder Network-Based Wildfire Segmentation Using Drone Images in Real-Time,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Lian, C.Q.[Chen-Qin], Xiao, C.[Chiwei], Feng, Z.M.[Zhi-Ming],
Spatiotemporal Characteristics and Regional Variations of Active Fires in China since 2001,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Bao, Y.L.[Yu-Long], Shinoda, M.[Masato], Yi, K.[Kunpeng], Fu, X.[Xiaoman], Sun, L.[Long], Nasanbat, E.[Elbegjargal], Li, N.[Na], Xiang, H.[Honglin], Yang, Y.[Yan], DavdaiJavzmaa, B.[Bulgan], Nandintsetseg, B.[Banzragch],
Satellite-Based Analysis of Spatiotemporal Wildfire Pattern in the Mongolian Plateau,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Sofan, P.[Parwati], Yulianto, F.[Fajar], Sakti, A.D.[Anjar Dimara],
Characteristics of False-Positive Active Fires for Biomass Burning Monitoring in Indonesia from VIIRS Data and Local Geo-Features,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
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Tagestad, J.D.[Jerry D.], Saltiel, T.M.[Troy M.], Coleman, A.M.[André M.],
Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Amaral, B.[Bernardo], Niknejad, M.[Milad], Barata, C.[Catarina], Bernardino, A.[Alexandre],
Weakly Supervised Fire and Smoke Segmentation in Forest Images with CAM and CRF,
ICPR22(442-448)
IEEE DOI 2212
Measurement, Image segmentation, Uncertainty, Sociology, Fires, Forestry, fire segmentation, conditional random fields BibRef

Pan, H.Y.[Hong-Yi], Badawi, D.[Diaa], Chen, C.[Chang], Watts, A.[Adam], Koyuncu, E.[Erdem], Cetin, A.E.[Ahmet Enis],
Deep Neural Network with Walsh-Hadamard Transform Layer For Ember Detection during a Wildfire,
PBVS22(256-265)
IEEE DOI 2210
Deep learning, Computational modeling, Neural networks, Fires, Transforms, Data models BibRef