24.4.13.8.2 Forest Fire Prediction, Fire Hazard, Mitigation, Risk, Susceptibility

Chapter Contents (Back)
Forest Fires. Fire Risk. One big subset
See also Fuel Load for Forest Fire Prediction.

Listopad, C., Drake, J., Masters, R., Weishampel, J.,
Portable and Airborne Small Footprint LiDAR: Forest Canopy Structure Estimation of Fire Managed Plots,
RS(3), No. 7, July 2011, pp. 1284-1307.
DOI Link 1203
BibRef

Lewis, S., Robichaud, P., Hudak, A., Austin, B., Liebermann, R.,
Utility of Remotely Sensed Imagery for Assessing the Impact of Salvage Logging after Forest Fires,
RS(4), No. 7, July 2012, pp. 2112-2132.
DOI Link 1208
BibRef

Bisquert, M.[Mar], Sánchez, J.M.[Juan Manuel], Caselles, V.[Vicente],
Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images,
RS(6), No. 1, 2014, pp. 540-554.
DOI Link 1402
BibRef

Freeborn, P.H.[Patrick H.], Cochrane, M.A.[Mark A.], Wooster, M.J.[Martin J.],
A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic,
RS(6), No. 5, 2014, pp. 4061-4089.
DOI Link 1407
BibRef

Ling, B.[Bohua], Goodin, D.G.[Douglas G.], Mohler, R.L.[Rhett L.], Laws, A.N.[Angela N.], Joern, A.[Anthony],
Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA,
RS(6), No. 5, 2014, pp. 4430-4453.
DOI Link 1407
BibRef

Katagis, T.[Thomas], Gitas, I.Z.[Ioannis Z.], Mitri, G.H.[George H.],
An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors,
RS(6), No. 6, 2014, pp. 5480-5496.
DOI Link 1407
BibRef

Daldegan, G.A.[Gabriel Antunes], de Carvalho, O.A.[Osmar Abílio], Guimarães, R.F.[Renato Fontes], Gomes, R.A.T.[Roberto Arnaldo Trancoso], de Figueiredo Ribeiro, F.[Fernanda], McManus, C.[Concepta],
Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil,
RS(6), No. 10, 2014, pp. 9873-9894.
DOI Link 1411
BibRef

Chowdhury, E.H.[Ehsan H.], Hassan, Q.K.[Quazi K.],
Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data,
RS(7), No. 3, 2015, pp. 2431-2448.
DOI Link 1504
BibRef

Chowdhury, E.H.[Ehsan H.], Hassan, Q.K.[Quazi K.],
Operational perspective of remote sensing-based forest fire danger forecasting systems,
PandRS(104), No. 1, 2015, pp. 224-236.
Elsevier DOI 1505
Fire occurrence BibRef

Abdollahi, M.[Masoud], Islam, T.[Tanvir], Gupta, A.[Anil], Hassan, Q.K.[Quazi K.],
An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Borrelli, P.[Pasquale], Armenteras, D.[Dolors], Panagos, P.[Panos], Modugno, S.[Sirio], Schütt, B.[Brigitta],
The Implications of Fire Management in the Andean Paramo: A Preliminary Assessment Using Satellite Remote Sensing,
RS(7), No. 9, 2015, pp. 11061.
DOI Link 1511
BibRef

Bui, D.T.[Dieu Tien], Le, K.T.T.[Kim-Thoa Thi], Nguyen, V.C.[Van Cam], Le, H.D.[Hoang Duc], Revhaug, I.[Inge],
Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression,
RS(8), No. 4, 2016, pp. 347.
DOI Link 1604
BibRef

García, M.[Mariano], Saatchi, S.[Sassan], Casas, A.[Angeles], Koltunov, A.[Alexander], Ustin, S.L.[Susan L.], Ramirez, C.[Carlos], Balzter, H.[Heiko],
Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Myoung, B.[Boksoon], Kim, S.H.[Seung Hee], Nghiem, S.V.[Son V.], Jia, S.[Shenyue], Whitney, K.[Kristen], Kafatos, M.C.[Menas C.],
Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Jia, S.[Shenyue], Kim, S.H.[Seung Hee], Nghiem, S.V.[Son V.], Kafatos, M.[Menas],
Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Kelly, M.[Maggi], Su, Y.J.[Yan-Jun], di Tommaso, S.[Stefania], Fry, D.L.[Danny L.], Collins, B.M.[Brandon M.], Stephens, S.L.[Scott L.], Guo, Q.H.[Qing-Hua],
Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Rozario, P.F.[Papia F.], Madurapperuma, B.D.[Buddhika D.], Wang, Y.J.[Yi-Jun],
Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Kim, S.J.[Sea Jin], Lim, C.H.[Chul-Hee], Kim, G.S.[Gang Sun], Lee, J.Y.[Jong-Yeol], Geiger, T.[Tobias], Rahmati, O.[Omid], Son, Y.[Yowhan], Lee, W.K.[Woo-Kyun],
Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables,
RS(11), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Ying, H.[Hong], Shan, Y.[Yu], Zhang, H.Y.[Hong-Yan], Yuan, T.[Tao], Rihan, W.[Wu], Deng, G.R.[Guo-Rong],
The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001-2018 Based on MODIS,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Abdollahi, M.[Masoud], Dewan, A.[Ashraf], Hassan, Q.K.[Quazi K.],
Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Jaafari, A.[Abolfazl], Mafi-Gholami, D.[Davood], Pham, B.T.[Binh Thai], Bui, D.T.[Dieu Tien],
Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Carvajal-Ramírez, F.[Fernando], Marques da Silva, J.R.[José Rafael], Agüera-Vega, F.[Francisco], Martínez-Carricondo, P.[Patricio], Serrano, J.[João], Moral, F.J.[Francisco Jesús],
Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Parks, S.A.[Sean A.], Holsinger, L.M.[Lisa M.], Koontz, M.J.[Michael J.], Collins, L.[Luke], Whitman, E.[Ellen], Parisien, M.A.[Marc-André], Loehman, R.A.[Rachel A.], Barnes, J.L.[Jennifer L.], Bourdon, J.F.[Jean-François], Boucher, J.[Jonathan], Boucher, Y.[Yan], Caprio, A.C.[Anthony C.], Collingwood, A.[Adam], Hall, R.J.[Ron J.], Park, J.[Jane], Saperstein, L.B.[Lisa B.], Smetanka, C.[Charlotte], Smith, R.J.[Rebecca J.], Soverel, N.[Nick],
Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Wei, X.Y.[Xin-Yuan], Larsen, C.P.S.[Chris P. S.],
Methods to Detect Edge Effected Reductions in Fire Frequency in Simulated Forest Landscapes,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Ahmed, M.R.[M. Razu], Hassan, Q.K.[Quazi K.], Abdollahi, M.[Masoud], Gupta, A.[Anil],
Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Rihan, W.[Wu], Zhao, J.J.[Jian-Jun], Zhang, H.Y.[Hong-Yan], Guo, X.Y.[Xiao-Yi], Ying, H.[Hong], Deng, G.R.[Guo-Rong], Li, H.[Hui],
Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Szpakowski, D.M.[David M.], Jensen, J.L.R.[Jennifer L. R.],
A Review of the Applications of Remote Sensing in Fire Ecology,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Stefanidou, A.[Alexandra], Gitas, I.Z.[Ioannis Z.], Stavrakoudis, D.[Dimitris], Eftychidis, G.[Georgios],
Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Pérez-Rodríguez, L.A.[Luis A.], Quintano, C.[Carmen], Marcos, E.[Elena], Suarez-Seoane, S.[Susana], Calvo, L.[Leonor], Fernández-Manso, A.[Alfonso],
Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Farahmand, A.[Alireza], Stavros, E.N.[E. Natasha], Reager, J.T.[John T.], Behrangi, A.[Ali],
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Razavi-Termeh, S.V.[Seyed Vahid], Sadeghi-Niaraki, A.[Abolghasem], Choi, S.M.[Soo-Mi],
Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Donovan, V.M.[Victoria M.], Wonkka, C.L.[Carissa L.], Wedin, D.A.[David A.], Twidwell, D.[Dirac],
Land-Use Type as a Driver of Large Wildfire Occurrence in the U.S. Great Plains,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

García, M.[Mariano], Riaño, D.[David], Yebra, M.[Marta], Salas, J.[Javier], Cardil, A.[Adrián], Monedero, S.[Santiago], Ramirez, J.[Joaquín], Martín, M.P.[M. Pilar], Vilar, L.[Lara], Gajardo, J.[John], Ustin, S.[Susan],
A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Guldåker, N.[Nicklas],
Geovisualization and Geographical Analysis for Fire Prevention,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Graff, C.A., Coffield, S.R., Chen, Y., Foufoula-Georgiou, E., Randerson, J.T., Smyth, P.,
Forecasting Daily Wildfire Activity Using Poisson Regression,
GeoRS(58), No. 7, July 2020, pp. 4837-4851.
IEEE DOI 2006
Predictive models, Weather forecasting, MODIS, Atmospheric modeling, Forecasting, Satellites, vapor pressure deficit (VPD) BibRef

Marino, E.[Eva], Yebra, M.[Marta], Guillén-Climent, M.[Mariluz], Algeet, N.[Nur], Tomé, J.L.[José Luis], Madrigal, J.[Javier], Guijarro, M.[Mercedes], Hernando, C.[Carmen],
Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Zhu, L.J.[Liu-Jun], Webb, G.I.[Geoffrey I.], Yebra, M.[Marta], Scortechini, G.[Gianluca], Miller, L.[Lynn], Petitjean, F.[François],
Live fuel moisture content estimation from MODIS: A deep learning approach,
PandRS(179), 2021, pp. 81-91.
Elsevier DOI 2108
Live fuel moisture content, MODIS, Convolutional neural network, Time series analysis, Fire risk, Fire danger BibRef

Song, Y.J.[Yong-Jia], Wang, Y.H.[Yu-Hang],
Global Wildfire Outlook Forecast with Neural Networks,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Laneve, G.[Giovanni], Pampanoni, V.[Valerio], Shaik, R.U.[Riyaaz Uddien],
The Daily Fire Hazard Index: A Fire Danger Rating Method for Mediterranean Areas,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Aubard, V.[Valentine], Pereira-Pires, J.E.[João E.], Campagnolo, M.L.[Manuel L.], Pereira, J.M.C.[José M. C.], Mora, A.[André], Silva, J.M.N.[João M. N.],
Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Afonso, R.[Ricardo], Neves, A.[André], Damásio, C.V.[Carlos Viegas], Pires, J.M.[João Moura], Birra, F.[Fernando], Santos, M.Y.[Maribel Yasmina],
Assessment of Interventions in Fuel Management Zones Using Remote Sensing,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Adaktylou, N.[Nektaria], Stratoulias, D.[Dimitris], Landenberger, R.[Rick],
Wildfire Risk Assessment Based on Geospatial Open Data: Application on Chios, Greece,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Domingo, D.[Darío], de la Riva, J.[Juan], Lamelas, M.T.[María Teresa], García-Martín, A.[Alberto], Ibarra, P.[Paloma], Echeverría, M.[Maite], Hoffrén, R.[Raúl],
Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Kalantar, B.[Bahareh], Ueda, N.[Naonori], Idrees, M.O.[Mohammed O.], Janizadeh, S.[Saeid], Ahmadi, K.[Kourosh], Shabani, F.[Farzin],
Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

See also Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. BibRef

Novo, A.[Ana], Fariñas-Álvarez, N.[Noelia], Martínez-Sánchez, J.[Joaquín], González-Jorge, H.[Higinio], Fernández-Alonso, J.M.[José María], Lorenzo, H.[Henrique],
Mapping Forest Fire Risk: A Case Study in Galicia (Spain),
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Barreto, J.S.[Joan Sebastian], Armenteras, D.[Dolors],
Open Data and Machine Learning to Model the Occurrence of Fire in the Ecoregion of 'Llanos Colombo-Venezolanos',
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Krsnik, G.[Goran], Olivé, E.B.[Eduard Busquets], Nicolau, M.P.[Míriam Piqué], Larrañaga, A.[Asier], Cardil, A.[Adrián], García-Gonzalo, J.[Jordi], Olabarría, J.R.G.[José Ramón González],
Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Mathews, L.E.H.[Lauren E. H.], Kinoshita, A.M.[Alicia M.],
Urban Fire Severity and Vegetation Dynamics in Southern California,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Sulova, A.[Andrea], Arsanjani, J.J.[Jamal Jokar],
Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Mauro, F.[Francisco], Hudak, A.T.[Andrew T.], Fekety, P.A.[Patrick A.], Frank, B.[Bryce], Temesgen, H.[Hailemariam], Bell, D.M.[David M.], Gregory, M.J.[Matthew J.], McCarley, T.R.[T. Ryan],
Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Revilla, S.[Sergio], Lamelas, M.T.[María Teresa], Domingo, D.[Darío], de la Riva, J.[Juan], Montorio, R.[Raquel], Montealegre, A.L.[Antonio Luis], García-Martín, A.[Alberto],
Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation: Application to Fuel Type Mapping,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Smith, C.W.[Christopher William], Panda, S.K.[Santosh K.], Bhatt, U.S.[Uma Suren], Meyer, F.J.[Franz J.],
Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Badola, A.[Anushree], Panda, S.K.[Santosh K.], Roberts, D.A.[Dar A.], Waigl, C.F.[Christine F.], Bhatt, U.S.[Uma S.], Smith, C.W.[Christopher W.], Jandt, R.R.[Randi R.],
Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wu, Z.C.[Ze-Chuan], Li, M.Z.[Ming-Ze], Wang, B.[Bin], Quan, Y.[Ying], Liu, J.Y.[Jian-Yang],
Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Al-Fugara, A.[A'kif], Mabdeh, A.N.[Ali Nouh], Ahmadlou, M.[Mohammad], Pourghasemi, H.R.[Hamid Reza], Al-Adamat, R.[Rida], Pradhan, B.[Biswajeet], Al-Shabeeb, A.R.[Abdel Rahman],
Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Naderpour, M.[Mohsen], Rizeei, H.M.[Hossein Mojaddadi], Ramezani, F.[Fahimeh],
Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Tomar, J.S.[Jagpal Singh], Kranjcic, N.[Nikola], Ðurin, B.[Bojan], Kanga, S.[Shruti], Singh, S.K.[Suraj Kumar],
Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Philogene, S.[Sheena], Ni-Meister, W.[Wenge],
Relationship between Fire Events and Land Use Changes in the State of São Paulo, Brazil,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Pickell, P.D.[Paul D.], Chavardès, R.D.[Raphaël D.], Li, S.J.[Shuo-Jie], Daniels, L.D.[Lori D.],
FuelNet: An Artificial Neural Network for Learning and Updating Fuel Types for Fire Research,
GeoRS(59), No. 9, September 2021, pp. 7338-7352.
IEEE DOI 2109
Fuels, Forestry, Remote sensing, Neural networks, Meteorology, Indexes, Earth, Artificial neural network, wildfire BibRef

Costa-Saura, J.M.[José M.], Balaguer-Beser, Á.[Ángel], Ruiz, L.A.[Luis A.], Pardo-Pascual, J.E.[Josep E.], Soriano-Sancho, J.L.[José L.],
Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhao, P.C.[Peng-Cheng], Zhang, F.[Fuquan], Lin, H.F.[Hai-Feng], Xu, S.[Shuwen],
GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Park, M.S.[Min-Soo], Tran, D.Q.[Dai Quoc], Lee, S.[Seungsoo], Park, S.[Seunghee],
Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Maffei, C.[Carmine], Lindenbergh, R.[Roderik], Menenti, M.[Massimo],
Combining multi-spectral and thermal remote sensing to predict forest fire characteristics,
PandRS(181), 2021, pp. 400-412.
Elsevier DOI 2110
Fire danger, MODIS, Land surface temperature (LST), Live fuel moisture content (LFMC), Fire Weather Index (FWI), Probability of extreme events BibRef

Fitzgerald, A.M.[Alissa M.],
A New Wildfire Watchdog: Alerts About Forest Fires Shouldn't Depend on Pets Smelling Smoke. We Need Smart Infrastructure, and that Needs Zero-Power Sensors,
Spectrum(58), No. 11, November 2021, pp. 38-43.
IEEE DOI 2112
Fires, Automobiles BibRef

Dragozi, E.[Eleni], Giannaros, T.M.[Theodore M.], Kotroni, V.[Vasiliki], Lagouvardos, K.[Konstantinos], Koletsis, I.[Ioannis],
Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Arkin, J.[Jeremy], Coops, N.C.[Nicholas C.], Daniels, L.D.[Lori D.], Plowright, A.[Andrew],
Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Alonso-Rego, C.[Cecilia], Arellano-Pérez, S.[Stéfano], Guerra-Hernández, J.[Juan], Molina-Valero, J.A.[Juan Alberto], Martínez-Calvo, A.[Adela], Pérez-Cruzado, C.[César], Castedo-Dorado, F.[Fernando], González-Ferreiro, E.[Eduardo], Álvarez-González, J.G.[Juan Gabriel], Ruiz-González, A.D.[Ana Daría],
Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Cui, L.[Lilu], Luo, C.J.[Chuan-Jiang], Yao, C.L.[Chao-Long], Zou, Z.B.[Zheng-Bo], Wu, G.J.[Gui-Ju], Li, Q.[Qiong], Wang, X.L.[Xiao-Long],
The Influence of Climate Change on Forest Fires in Yunnan Province, Southwest China Detected by GRACE Satellites,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Piralilou, S.T.[Sepideh Tavakkoli], Einali, G.[Golzar], Ghorbanzadeh, O.[Omid], Nachappa, T.G.[Thimmaiah Gudiyangada], Gholamnia, K.[Khalil], Blaschke, T.[Thomas], Ghamisi, P.[Pedram],
A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Sakti, A.D.[Anjar Dimara], Fauzi, A.I.[Adam Irwansyah], Takeuchi, W.[Wataru], Pradhan, B.[Biswajeet], Yarime, M.[Masaru], Vega-Garcia, C.[Cristina], Agustina, E.[Elprida], Wibisono, D.[Dionisius], Anggraini, T.S.[Tania Septi], Theodora, M.O.[Megawati Oktaviani], Ramadhanti, D.[Desi], Muhammad, M.F.[Miqdad Fadhil], Aufaristama, M.[Muhammad], Perdana, A.M.P.[Agung Mahadi Putra], Wikantika, K.[Ketut],
Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Apostolakis, A.[Alexis], Girtsou, S.[Stella], Giannopoulos, G.[Giorgos], Bartsotas, N.S.[Nikolaos S.], Kontoes, C.[Charalampos],
Estimating Next Day's Forest Fire Risk via a Complete Machine Learning Methodology,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Shaik, R.U.[Riyaaz Uddien], Laneve, G.[Giovanni], Fusilli, L.[Lorenzo],
An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach,
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DOI Link 2203
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Trencanová, B.[Bianka], Proença, V.[Vânia], Bernardino, A.[Alexandre],
Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Palaiologou, P.[Palaiologos], Kalabokidis, K.[Kostas], Day, M.A.[Michelle A.], Ager, A.A.[Alan A.], Galatsidas, S.[Spyros], Papalampros, L.[Lampros],
Modelling Fire Behavior to Assess Community Exposure in Europe: Combining Open Data and Geospatial Analysis,
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DeCastro, A.L.[Amy L.], Juliano, T.W.[Timothy W.], Kosovic, B.[Branko], Ebrahimian, H.[Hamed], Balch, J.K.[Jennifer K.],
A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Rafaqat, W.[Warda], Iqbal, M.[Mansoor], Kanwal, R.[Rida], Song, W.G.[Wei-Guo],
Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan,
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DOI Link 2205
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Sharma, S.K.[Saroj Kumar], Aryal, J.[Jagannath], Rajabifard, A.[Abbas],
Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Luz, A.E.O.[Andréa Eliza O.], Negri, R.G.[Rogério G.], Massi, K.G.[Klécia G.], Colnago, M.[Marilaine], Silva, E.A.[Erivaldo A.], Casaca, W.[Wallace],
Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection,
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He, W.Q.[Wan-Qin], Shirowzhan, S.[Sara], Pettit, C.J.[Christopher James],
GIS and Machine Learning for Analysing Influencing Factors of Bushfires Using 40-Year Spatio-Temporal Bushfire Data,
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Michael, Y.[Yaron], Kozokaro, G.[Gilad], Brenner, S.[Steve], Lensky, I.M.[Itamar M.],
Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices,
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Camprubí, À.C.[Àngel Cunill], González-Moreno, P.[Pablo], de Dios, V.R.[Víctor Resco],
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data,
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Cheng, S.[Sibo], Jin, Y.F.[Yu-Fang], Harrison, S.P.[Sandy P.], Quilodrán-Casas, C.[César], Prentice, I.C.[Iain Colin], Guo, Y.K.[Yi-Ke], Arcucci, R.[Rossella],
Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling,
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Brook, A.[Anna], Hamzi, S.[Seham], Roberts, D.[Dar], Ichoku, C.[Charles], Shtober-Zisu, N.[Nurit], Wittenberg, L.[Lea],
Total Carbon Content Assessed by UAS Near-Infrared Imagery as a New Fire Severity Metric,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Sun, Y.Y.[Yan-Yan], Zhang, F.[Fuquan], Lin, H.F.[Hai-Feng], Xu, S.[Shuwen],
A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm,
RS(14), No. 17, 2022, pp. xx-yy.
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Chen, J.H.[Jia-Hui], Yang, Y.[Yi], Peng, L.[Ling], Chen, L.J.[Luan-Jie], Ge, X.T.[Xing-Tong],
Knowledge Graph Representation Learning-Based Forest Fire Prediction,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Nur, A.S.[Arip Syaripudin], Kim, Y.J.[Yong Je], Lee, C.W.[Chang-Wook],
Creation of Wildfire Susceptibility Maps in Plumas National Forest Using InSAR Coherence, Deep Learning, and Metaheuristic Optimization Approaches,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Xie, L.X.[Ling-Xiao], Zhang, R.[Rui], Zhan, J.Y.[Jun-Yu], Li, S.[Song], Shama, A.[Age], Zhan, R.[Runqing], Wang, T.[Ting], Lv, J.[Jichao], Bao, X.[Xin], Wu, R.Z.[Ren-Zhe],
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Xie, J.J.[Jiang-Jian], Qi, T.[Tao], Hu, W.[Wanjun], Huang, H.G.[Hua-Guo], Chen, B.B.[Bei-Bei], Zhang, J.[Junguo],
Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model,
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DOI Link 2209
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Mlynarczyk, A.[Adam], Konatowska, M.[Monika], Królewicz, S.[Slawomir], Rutkowski, P.[Pawel], Piekarczyk, J.[Jan], Kowalewski, W.[Wojciech],
Spectral Indices as a Tool to Assess the Moisture Status of Forest Habitats,
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Costa-Saura, J.M.[Jose Maria], Bacciu, V.[Valentina], Ribotta, C.[Claudio], Spano, D.[Donatella], Massaiu, A.[Antonella], Sirca, C.[Costantino],
Predicting and Mapping Potential Fire Severity for Risk Analysis at Regional Level Using Google Earth Engine,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Li, W.J.[Wen-Jun], Li, P.[Peng], Feng, Z.M.[Zhi-Ming],
Delineating Fire-Hazardous Areas and Fire-Induced Patterns Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires in Northeast China,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Pang, Y.Q.[Yong-Qi], Li, Y.D.[Yu-Dong], Feng, Z.K.[Zhong-Ke], Feng, Z.[Zemin], Zhao, Z.Y.[Zi-Yu], Chen, S.L.[Shi-Lin], Zhang, H.[Hanyue],
Forest Fire Occurrence Prediction in China Based on Machine Learning Methods,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Zhou, Q.[Qing], Zhang, H.[Heng], Wu, Z.W.[Zhi-Wei],
Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Cimdins, R.[Reinis], Krasovskiy, A.[Andrey], Kraxner, F.[Florian],
Regional Variability and Driving Forces behind Forest Fires in Sweden,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
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Ren, H.G.[Hong-Ge], Zhang, L.[Li], Yan, M.[Min], Chen, B.[Bowei], Yang, Z.Y.[Zhen-Yu], Ruan, L.L.[Lin-Lin],
Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Li, Y.H.[Yu-Heng], Xu, S.X.[Shu-Xing], Fan, Z.F.[Zhao-Fei], Zhang, X.[Xiao], Yang, X.H.[Xiao-Hui], Wen, S.[Shuo], Shi, Z.J.[Zhong-Jie],
Risk Factors and Prediction of the Probability of Wildfire Occurrence in the China-Mongolia-Russia Cross-Border Area,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Li, Y.X.[Yan-Xi], He, B.B.[Bin-Bin],
A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Zhang, X.[Xiao], Lan, M.[Meng], Ming, J.[Jinke], Zhu, J.P.[Ji-Ping], Lo, S.[Siuming],
Spatiotemporal Heterogeneity of Forest Fire Occurrence Based on Remote Sensing Data: An Analysis in Anhui, China,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Nur, A.S.[Arip Syaripudin], Kim, Y.J.[Yong Je], Lee, J.[Junho], Lee, C.W.[Chang-Wook],
Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Jo, H.W.[Hyun-Woo], Krasovskiy, A.[Andrey], Hong, M.[Mina], Corning, S.[Shelby], Kim, W.[Whijin], Kraxner, F.[Florian], Lee, W.K.[Woo-Kyun],
Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
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Chrysafis, I.[Irene], Damianidis, C.[Christos], Giannakopoulos, V.[Vasileios], Mitsopoulos, I.[Ioannis], Dokas, I.M.[Ioannis M.], Mallinis, G.[Giorgos],
Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives: The Case of the Region of East Macedonia and Thrace, Greece,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Calvo, R.C.[Roberto Crespo], Martínez, M.Á.V.[Ma Ángeles Varo], Gómez, F.R.[Francisco Ruiz], Salamanca, A.J.A.[Antonio Jesús Ariza], Navarro-Cerrillo, R.M.[Rafael M.],
Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests,
RS(15), No. 8, 2023, pp. 2023.
DOI Link 2305
BibRef

Barmpoutis, P.[Panagiotis], Kastridis, A.[Aristeidis], Stathaki, T.[Tania], Yuan, J.[Jing], Shi, M.J.[Meng-Jie], Grammalidis, N.[Nikos],
Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer,
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Badola, A.[Anushree], Panda, S.K.[Santosh K.], Thompson, D.R.[David R.], Roberts, D.A.[Dar A.], Waigl, C.F.[Christine F.], Bhatt, U.S.[Uma S.],
Estimation and Validation of Sub-Pixel Needleleaf Cover Fraction in the Boreal Forest of Alaska to Aid Fire Management,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Yue, W.T.[Wei-Ting], Ren, C.[Chao], Liang, Y.[Yueji], Liang, J.[Jieyu], Lin, X.Q.[Xiao-Qi], Yin, A.[Anchao], Wei, Z.K.[Zhen-Kui],
Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Penglase, K.[Kim], Lewis, T.[Tom], Srivastava, S.K.[Sanjeev K.],
A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Chang, C.[Chang], Chang, Y.[Yu], Xiong, Z.P.[Zai-Ping], Ping, X.Y.[Xiao-Ying], Zhang, H.[Heng], Guo, M.[Meng], Hu, Y.M.[Yuan-Man],
Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Sim, M.S.[Min-Sung], Wee, S.J.[Shi-Jun], Alcantara, E.[Enner], Park, E.[Edward],
Deforestation as the Prominent Driver of the Intensifying Wildfire in Cambodia, Revealed through Geospatial Analysis,
RS(15), No. 13, 2023, pp. 3388.
DOI Link 2307
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Truong, T.X.[Tran Xuan], Nhu, V.H.[Viet-Ha], Phuong, D.T.N.[Doan Thi Nam], Nghi, L.T.[Le Thanh], Hung, N.N.[Nguyen Nhu], Hoa, P.V.[Pham Viet], Bui, D.T.[Dieu Tien],
A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas,
RS(15), No. 14, 2023, pp. 3458.
DOI Link 2307
BibRef

Fernández-García, V.[Víctor], Calvo, L.[Leonor], Suárez-Seoane, S.[Susana], Marcos, E.[Elena],
Remote Sensing Advances in Fire Science: From Fire Predictors to Post-Fire Monitoring,
RS(15), No. 20, 2023, pp. 4930.
DOI Link 2310
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Kanwal, R.[Rida], Rafaqat, W.[Warda], Iqbal, M.[Mansoor], Weiguo, S.[Song],
Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN),
RS(15), No. 21, 2023, pp. 5099.
DOI Link 2311
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Wang, X.C.[Xue-Cheng], Gao, X.[Xing], Wu, Y.M.[Yu-Ming], Jiang, H.[Hou], Wang, P.[Peng],
Spatio-Temporal Characteristics of Ice-Snow Freezing and Its Impact on Subtropical Forest Fires in China,
RS(15), No. 21, 2023, pp. 5118.
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Gong, A.[Adu], Huang, Z.Q.[Zhi-Qing], Liu, L.F.[Long-Fei], Yang, Y.Q.[Yu-Qing], Ba, W.[Wanru], Wang, H.[Haihan],
Development of an Index for Forest Fire Risk Assessment Considering Hazard Factors and the Hazard-Formative Environment,
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Celis, N.[Nathalia], Casallas, A.[Alejandro], Lopez-Barrera, E.A.[Ellie Anne], Felician, M.[Martina], de Marchi, M.[Massimo], Pappalardo, S.E.[Salvatore E.],
Climate Change, Forest Fires, and Territorial Dynamics in the Amazon Rainforest: An Integrated Analysis for Mitigation Strategies,
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Agrawal, N.[Nikita], Nelson, P.V.[Peder V.], Low, R.D.[Russanne D.],
A Novel Approach for Predicting Large Wildfires Using Machine Learning towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States,
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Quintano, C.[Carmen], Fernández-Manso, A.[Alfonso], Fernández-Guisuraga, J.M.[José Manuel], Roberts, D.A.[Dar A.],
Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models,
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Prapas, I.[Ioannis], Bountos, N.I.[Nikolaos Ioannis], Kondylatos, S.[Spyros], Michail, D.[Dimitrios], Camps-Valls, G.[Gustau], Papoutsis, I.[Ioannis],
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting,
AIHADR23(3756-3761)
IEEE DOI 2401
BibRef

Akay, A.E.[Abdullah E.],
Assessment of the Visibility Capabilities of Forest Fire Lookout Towers: The Case of Gemlik, Bursa, Turkey,
SmartCityApp21(27-32).
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BibRef

Akay, A.E., Erdogan, A.,
Developing Validation of Forest Fire Risk Maps Based on Historical Fire Incidences,
SmartCityApp21(33-38).
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Apostolakis, A.[Alexis], Girtsou, S.[Stella], Kontoes, C.[Charalampos], Papoutsis, I.[Ioannis], Tsoutsos, M.[Michalis],
Implementation of a Random Forest Classifier to Examine Wildfire Predictive Modelling in Greece Using Diachronically Collected Fire Occurrence and Fire Mapping Data,
MMMod21(II:318-329).
Springer DOI 2106
BibRef

Moyo, T., Musakwa, W., Nyathi, N.A., Mpofu, E., Gumbo, T.,
Modelling of Natural Fire Occurrences: A Case of South Africa,
ISPRS20(B3:1477-1482).
DOI Link 2012
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Nasanbat, E., Lkhamjav, O., Balkhai, A., Tsevee-Oirov, C., Purev, A., Dorjsuren, M.,
A Spatial Distributionmap Of The Wildfire Risk In Mongolia Using Decision Support System,
Gi4DM18(357-362).
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Nasanbat, E.[Elbegjargal], Lkhamjav, O.[Ochirkhuyag],
Wild Fire Risk Map In The Eastern Steppe Of Mongolia Using Spatial Multi-criteria Analysis,
ISPRS16(B1: 469-473).
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BibRef

Shadlouei, A.J.[A. Jalilzadeh], Delavar, M.R.,
The Zoning of Forest Fire Potential of Gulestan Province Forests Using Granular Computing and MODIS Images,
SMPR13(365-370).
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Canale, S., de Santis, A., Iacoviello, D., Pirri, F., Sagratella, S.,
High-Resolution SAR images for fire susceptibility estimation in urban forestry,
HighRes11(xx-yy).
PDF File. 1106
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Fuel Load for Forest Fire Prediction .


Last update:Mar 16, 2024 at 20:36:19