Forest Fire Evaluation, Wildfire Analysis, Fire Detection

Chapter Contents (Back)
Forest. Forest Fires. Smoke Detection. Fire Detection.
See also Forest Fire Prediction, Fire Hazard, Mitigation, Risk, Susceptibility.
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.
See also Surveillance Systems, Applied to Fire and Flame Detection.

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

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.

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

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

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.

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

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

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

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

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.
flames; infrared imaging; georeferencing algorithm BibRef

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,

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

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

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

Huo, H.Y.[Hong-Yuan], Jiang, X.G.[Xiao-Guang], Song, X.F.[Xian-Feng], Li, Z.L.[Zhao-Liang], Ni, Z.[Zhuoya], Gao, C.[Caixia],
Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China,
RS(6), No. 2, 2014, pp. 1234-1259.
DOI Link 1403

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

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

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

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

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

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

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

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

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.
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

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

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

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.
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

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.
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.
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

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
Earlier: A2, A1, Only:
Spatial-temporal Dynamics Of Urban Fire Incidents: A Case Study Of Nanjing, China,
ISPRS16(B2: 63-69).
DOI Link 1610

Cho, K.[Kangjoon], 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

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

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.
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

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

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

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

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

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

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

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

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

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

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

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
Earlier: Correction: RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

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

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

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

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

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.
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

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.
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,
DOI Link 1912

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

Jahdi, R., Salis, M., Arabi, M., Arca, B.,
Fire Modelling to Assess Spatial Patterns of Wildfire Exposure In Ardabil, Nw Iran,
DOI Link 1912

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

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

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

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

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

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

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.
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

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

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

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

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

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

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

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.
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

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

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

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