23.4.13.8 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.

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
BibRef

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
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

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
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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],
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
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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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RS(10), No. 9, 2018, pp. xx-yy.
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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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Tran, B.N.[Bang Nguyen], Tanase, M.A.[Mihai A.], Bennett, L.T.[Lauren T.], Aponte, C.[Cristina],
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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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Satellite broadcasting, Spatial resolution, MODIS, Geostationary satellites, Detection algorithms, geostationary satellite data BibRef

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Fire detection, convolutional neural network, industrial applications, multiscale feature extraction, channel attention mechanism BibRef

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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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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.],
Regional Spatiotemporal Patterns of Fire in the Eurasian Subarctic Based on Satellite Imagery,
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Muksimova, S.[Shakhnoza], Mardieva, S.[Sevara], Cho, Y.I.[Young-Im],
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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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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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Schreck, J.S.[John S.], Petzke, W.[William], Jiménez, P.A.[Pedro A.], Brummet, T.[Thomas], Knievel, J.C.[Jason C.], James, E.[Eric], Kosovic, B.[Branko], Gagne, D.J.[David John],
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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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Microwave vegetation water content, Weather conditions, Fire activity BibRef

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DOI Link 2309
BibRef

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