22.5.11.7.1 Forest Fire Prediction, Fire Hazard, Mitigation, Risk

Chapter Contents (Back)
Forest Fires.

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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Kim, S.J.[Sea Jin], Lim, C.H.[Chul-Hee], Kim, G.S.[Gang Sun], Lee, J.[Jongyeol], 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Li, Z.P.[Zheng-Peng], Shi, H.[Hua], Vogelmann, J.E.[James E.], Hawbaker, T.J.[Todd J.], Peterson, B.[Birgit],
Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
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Guldåker, N.[Nicklas],
Geovisualization and Geographical Analysis for Fire Prevention,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
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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
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Song, Y.[Yongjia], Wang, Y.H.[Yu-Hang],
Global Wildfire Outlook Forecast with Neural Networks,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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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
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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).
DOI Link 1805
BibRef

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).
DOI Link 1610
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).
HTML Version. 1311
BibRef

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
BibRef

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Burned Area Detection, Fire Damage Assessment, Post-Fire Analysis .


Last update:Sep 14, 2020 at 15:32:18