24.4.13.8.4 Burned Area Detection, Fire Damage Assessment, Post-Fire Analysis

Chapter Contents (Back)
Forest Changes. Forest Fires. Fires. Burned Area.

Gerard, F., Plummer, S., Wadsworth, R., Sanfeliu, A.F., Iliffe, L., Balzter, H., Wyatt, B.,
Forest fire scar detection in the boreal forest with multitemporal spot-vegetation data,
GeoRS(41), No. 11, November 2003, pp. 2575-2585.
IEEE Abstract. 0311
BibRef

Brewer, C.K.[C. Kenneth], Winne, J.C.[J. Chris], Redmond, R.L.[Roland L.], Opitz, D.W.[David W.], Mangrich, M.V.[Mark V.],
Classifying and Mapping Wildfire Severity: A Comparison of Methods,
PhEngRS(71), No. 11, November 2005, pp. 1311-1320.
WWW Link. 0602
A comparison of six remote sensing methods for classifying and mapping wildfire severity on forests and rangelands: artificial networks, principal component analysis, and normalized temporal image differencing. BibRef

Henry, M.C.[Mary C.],
Comparison of Single- and Multi-date Landsat Data for Mapping Wildfire Scars in Ocala National Forest, Florida,
PhEngRS(74), No. 7, July 2008, pp. 881-892.
WWW Link. 0804
Datasets classified using a traditional maximum likelihood classification method and a non-parametric classification and regression tree technique. BibRef

Mitri, G.H., Gitas, I.Z.,
Mapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery,
GeoRS(48), No. 3, March 2010, pp. 1613-1618.
IEEE DOI 1003
BibRef

Lhermitte, S., Verbesselt, J., Verstraeten, W.W., Veraverbeke, S., Coppin, P.,
Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index,
PandRS(66), No. 1, January 2011, pp. 17-27.
Elsevier DOI 1101
Forest fire; Monitoring; Temporal; Spatial; Vegetation BibRef

Tanase, M.A., Perez-Cabello, F., de la Riva, J., Santoro, M.,
TerraSAR-X Data for Burn Severity Evaluation in Mediterranean Forests on Sloped Terrain,
GeoRS(48), No. 2, February 2010, pp. 917-929.
IEEE DOI 1002
BibRef

Zhang, X.Y.[Xiao-Yang], Kondragunta, S., Quayle, B.,
Estimation of Biomass Burned Areas Using Multiple-Satellite-Observed Active Fires,
GeoRS(49), No. 11, November 2011, pp. 4469-4482.
IEEE DOI 1112
BibRef

Magnussen, S., Wulder, M.,
Post-Fire Canopy Height Recovery in Canada's Boreal Forests Using Airborne Laser Scanner (ALS),
RS(4), No. 6, June 2012, pp. 1600-1616.
DOI Link 1208
BibRef

Polychronaki, A., Gitas, I.,
Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis,
RS(4), No. 2, February 2012, pp. 424-438.
DOI Link 1203
BibRef

Bastarrika, A., Chuvieco, E., Martin, M.P.,
Automatic Burned Land Mapping From MODIS Time Series Images: Assessment in Mediterranean Ecosystems,
GeoRS(49), No. 9, September 2011, pp. 3401-3413.
IEEE DOI 1109
BibRef

Libonati, R., da Camara, C.C., Pereira, J.M.C., Peres, L.F.,
Retrieving Middle-Infrared Reflectance Using Physical and Empirical Approaches: Implications for Burned Area Monitoring,
GeoRS(50), No. 1, January 2012, pp. 281-294.
IEEE DOI 1201
BibRef

Veraverbeke, S., Gitas, I., Katagis, T., Polychronaki, A., Somers, B., Goossens, R.,
Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability,
PandRS(68), No. 1, March 2012, pp. 28-39.
Elsevier DOI 1204
BibRef
And: Erratum: PandRS(68), No. 1, March 2012, pp. 191.
Elsevier DOI 1204
Forestry; Vegetation; Forest fire; Landsat; Spectral BibRef

Leon, J., van Leeuwen, W., Casady, G.,
Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments,
RS(4), No. 3, March 2012, pp. 598-621;.
DOI Link 1204
BibRef

Sedano, F., Kempeneers, P., Strobl, P., McInerney, D.O., San Miguel-Ayanz, J.,
Increasing Spatial Detail of Burned Scar Maps Using IRS-AWiFS Data for Mediterranean Europe,
RS(4), No. 3, March 2012, pp. 726-744;.
DOI Link 1204
BibRef

Kempeneers, P., Sedano, F., Strobl, P., McInerney, D.O., San Miguel-Ayanz, J.,
Increasing Robustness of Postclassification Change Detection Using Time Series of Land Cover Maps,
GeoRS(50), No. 9, September 2012, pp. 3327-3339.
IEEE DOI 1209
BibRef

Stroppiana, D., Bordogna, G., Carrara, P., Boschetti, M., Boschetti, L., Brivio, P.A.,
A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm,
PandRS(69), No. 1, April 2012, pp. 88-102.
Elsevier DOI 1202
Fuzzy set theory; Fire perimeters; Multi-criteria approach; Mediterranean environment BibRef

Moreira de Araújo, F., Ferreira, L., Arantes, A.,
Distribution Patterns of Burned Areas in the Brazilian Biomes: An Analysis Based on Satellite Data for the 2002-2010 Period,
RS(4), No. 7, July 2012, pp. 1929-1946.
DOI Link 1208
BibRef

Chu, T.[Thuan], Guo, X.[Xulin],
Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review,
RS(6), No. 1, 2013, pp. 470-520.
DOI Link 1402
BibRef

Yi, K.P.[Kun-Peng], Tani, H.[Hiroshi], Zhang, J.Q.[Ji-Quan], Guo, M.[Meng], Wang, X.F.[Xiu-Feng], Zhong, G.S.[Guo-Sheng],
Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China,
RS(5), No. 12, 2013, pp. 6938-6957.
DOI Link 1402
BibRef

Pleniou, M.[Magdalini], Koutsias, N.[Nikos],
Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area,
PandRS(79), No. 1, May 2013, pp. 199-210.
Elsevier DOI 1305
Spectral properties; Sub-pixel; Burned surfaces; LANDSAT; ASTER; IKONOS BibRef

Huang, S.L.[Sheng-Li], Jin, S.[Suming], Dahal, D.[Devendra], Chen, X.X.[Xue-Xia], Young, C.[Claudia], Liu, H.P.[He-Ping], Liu, S.G.[Shu-Guang],
Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska,
PandRS(79), No. 1, May 2013, pp. 94-105.
Elsevier DOI 1305
Alaska; Fire; Land surface; Landsat; Image reconstruction; NDVI BibRef

Polychronaki, A.[Anastasia], Gitas, I.Z.[Ioannis Z.], Veraverbeke, S.[Sander], Debien, A.[Annekatrien],
Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification,
RS(5), No. 11, 2013, pp. 5680-5701.
DOI Link 1312
BibRef

Moreno Ruiz, J.A.[José Andrés], García Lázaro, J.R.[José Rafael], del Águila Cano, I.[Isabel], Hernández Leal, P.[Pedro],
Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001-2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products,
RS(6), No. 1, 2014, pp. 815-840.
DOI Link 1402
BibRef

Tsela, P.[Philemon], Wessels, K.[Konrad], Botai, J.[Joel], Archibald, S.[Sally], Swanepoel, D.[Derick], Steenkamp, K.[Karen], Frost, P.[Philip],
Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa,
RS(6), No. 2, 2014, pp. 1275-1293.
DOI Link 1403
BibRef

Schepers, L.[Lennert], Haest, B.[Birgen], Veraverbeke, S.[Sander], Spanhove, T.[Toon], Vanden Borre, J.[Jeroen], Goossens, R.[Rudi],
Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX),
RS(6), No. 3, 2014, pp. 1803-1826.
DOI Link 1404
BibRef

Parks, S.A.[Sean A.], Dillon, G.K.[Gregory K.], Miller, C.[Carol],
A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio,
RS(6), No. 3, 2014, pp. 1827-1844.
DOI Link 1404
BibRef
And: Correction: RS(6), No. 12, 2014, pp. 12509-12510.
DOI Link 1412
BibRef

Padilla, M.[Marc], Stehman, S.V.[Stephen V.], Litago, J.[Javier], Chuvieco, E.[Emilio],
Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products,
RS(6), No. 3, 2014, pp. 2050-2068.
DOI Link 1404
BibRef

Kalogirou, V., Ferrazzoli, P., Della Vecchia, A., Foumelis, M.,
On the SAR Backscatter of Burned Forests: A Model-Based Study in C-Band, Over Burned Pine Canopies,
GeoRS(52), No. 10, October 2014, pp. 6205-6215.
IEEE DOI 1407
Backscatter BibRef

da Silva Cardozo, F.[Francielle], Pereira, G.[Gabriel], Shimabukuro, Y.E.[Yosio Edemir], Moraes, E.C.[Elisabete Caria],
Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest,
RS(6), No. 9, 2014, pp. 8002-8025.
DOI Link 1410
BibRef

Dragozi, E.[Eleni], Gitas, I.Z.[Ioannis Z.], Stavrakoudis, D.G.[Dimitris G.], Theocharis, J.B.[John B.],
Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery,
RS(6), No. 12, 2014, pp. 12005-12036.
DOI Link 1412
BibRef

Nioti, F.[Foula], Xystrakis, F.[Fotios], Koutsias, N.[Nikos], Dimopoulos, P.[Panayotis],
A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece,
RS(7), No. 6, 2015, pp. 7712.
DOI Link 1507
BibRef

Dragozi, E.[Eleni], Gitas, I.Z.[Ioannis Z.], Bajocco, S.[Sofia], Stavrakoudis, D.G.[Dimitris G.],
Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece,
RS(8), No. 7, 2016, pp. 566.
DOI Link 1608
BibRef

Bishop, B.D.[Brian D.], Dietterick, B.C.[Brian C.], White, R.A.[Russell A.], Mastin, T.B.[Tom B.],
Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR,
RS(6), No. 3, 2014, pp. 1954-1972.
DOI Link 1404
BibRef

Bastarrika, A.[Aitor], Alvarado, M.[Maite], Artano, K.[Karmele], Martinez, M.P.[Maria Pilar], Mesanza, A.[Amaia], Torre, L.[Leyre], Ramo, R.[Rubén], Chuvieco, E.[Emilio],
BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data,
RS(6), No. 12, 2014, pp. 12360-12380.
DOI Link 1412
BibRef

Bernhard, E.M.[Eva-Maria], Twele, A.[André], Martinis, S.[Sandro],
The Effect of Vegetation Type and Density on X-Band SAR Backscatter after Forest Fires,
PFG(2014), No. 4, 2014, pp. 275-285.
DOI Link 1410
BibRef

Chen, G.[Gang], Metz, M.R.[Margaret R.], Rizzo, D.M.[David M.], Dillon, W.W.[Whalen W.], Meentemeyer, R.K.[Ross K.],
Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery,
PandRS(102), No. 1, 2015, pp. 38-47.
Elsevier DOI 1503
GEOBIA BibRef

Stroppiana, D.[Daniela], Azar, R.[Ramin], Calò, F.[Fabiana], Pepe, A.[Antonio], Imperatore, P.[Pasquale], Boschetti, M.[Mirco], Silva, J.M.N.[João M. N.], Brivio, P.A.[Pietro A.], Lanari, R.[Riccardo],
Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions,
RS(7), No. 2, 2015, pp. 1320-1345.
DOI Link 1503
BibRef

de Carvalho Júnior, O.A.[Osmar Abílio], Guimarães, R.F.[Renato Fontes], Silva, C.R.[Cristiano Rosa], Gomes, R.A.T.[Roberto Arnaldo Trancoso],
Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset,
RS(7), No. 6, 2015, pp. 6950.
DOI Link 1507

See also New Approach to Change Vector Analysis Using Distance and Similarity Measures, A. BibRef

Libonati, R.[Renata], DaCamara, C.C.[Carlos C.], Setzer, A.W.[Alberto W.], Morelli, F.[Fabiano], Melchiori, A.E.[Arturo E.],
An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4µm MODIS Imagery,
RS(7), No. 11, 2015, pp. 15782.
DOI Link 1512
BibRef

Sánchez, J.M.[Juan M.], Bisquert, M.[Mar], Rubio, E.[Eva], Caselles, V.[Vicente],
Impact of Land Cover Change Induced by a Fire Event on the Surface Energy Fluxes Derived from Remote Sensing,
RS(7), No. 11, 2015, pp. 14899.
DOI Link 1512
BibRef

Huang, H.Y.[Hai-Yan], Roy, D.P.[David P.], Boschetti, L.[Luigi], Zhang, H.K.[Hankui K.], Yan, L.[Lin], Kumar, S.S.[Sanath Sathyachandran], Gomez-Dans, J.[Jose], Li, J.[Jian],
Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination,
RS(8), No. 10, 2016, pp. 873.
DOI Link 1609
BibRef

Verhegghen, A.[Astrid], Eva, H.[Hugh], Ceccherini, G.[Guido], Achard, F.[Frederic], Gond, V.[Valery], Gourlet-Fleury, S.[Sylvie], Cerutti, P.O.[Paolo Omar],
The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests,
RS(8), No. 12, 2016, pp. 986.
DOI Link 1612
BibRef

Soulard, C.E.[Christopher E.], Albano, C.M.[Christine M.], Villarreal, M.L.[Miguel L.], Walker, J.J.[Jessica J.],
Continuous 1985-2012 Landsat Monitoring to Assess Fire Effects on Meadows in Yosemite National Park, California,
RS(8), No. 5, 2016, pp. 371.
DOI Link 1606
BibRef

Sparks, A.M.[Aaron M.], Kolden, C.A.[Crystal A.], Talhelm, A.F.[Alan F.], Smith, A.M.S.[Alistair M.S.], Apostol, K.G.[Kent G.], Johnson, D.M.[Daniel M.], Boschetti, L.[Luigi],
Spectral Indices Accurately Quantify Changes in Seedling Physiology Following Fire: Towards Mechanistic Assessments of Post-Fire Carbon Cycling,
RS(8), No. 7, 2016, pp. 572.
DOI Link 1608
BibRef

Llovería, R.M.[Raquel Montorio], Pérez-Cabello, F.[Fernando], García-Martín, A.[Alberto],
Assessing post-fire ground cover in Mediterranean shrublands with field spectrometry and digital photography,
PandRS(119), No. 1, 2016, pp. 187-197.
Elsevier DOI 1610
Fire severity BibRef

Sato, L.Y.[Luciane Yumie], Gomes, V.C.F.[Vitor Conrado Faria], Shimabukuro, Y.E.[Yosio Edemir], Keller, M.[Michael], Arai, E.[Egidio], dos-Santos, M.N.[Maiza Nara], Brown, I.F.[Irving Foster], Oliveira e Cruz de Aragão, L.E.[Luiz Eduardo],
Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia,
RS(8), No. 10, 2016, pp. 839.
DOI Link 1609
BibRef

Zhao, F.R.[Feng R.], Meng, R.[Ran], Huang, C.Q.[Cheng-Quan], Zhao, M.S.[Mao-Sheng], Zhao, F.A.[Feng A.], Gong, P.[Peng], Yu, L.[Le], Zhu, Z.L.[Zhi-Liang],
Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack,
RS(8), No. 11, 2016, pp. 898.
DOI Link 1612
BibRef

Nogueira, J.M.P.[Joana M. P.], Ruffault, J.[Julien], Chuvieco, E.[Emilio], Mouillot, F.[Florent],
Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Fraser, R.H.[Robert H.], van der Sluijs, J.[Jurjen], Hall, R.J.[Ronald J.],
Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Ayhan, B.[Bulent], Kwan, C.[Chiman],
On the use of radiance domain for burn scar detection under varying atmospheric illumination conditions and viewing geometry,
SIViP(11), No. 4, May 2017, pp. 605-612.
WWW Link. 1704
BibRef

Hess, K.A.[Katherine A.], Cullen, C.[Cheila], Cobian-Iñiguez, J.[Jeanette], Ramthun, J.S.[Jacob S.], Lenske, V.[Victor], Magness, D.R.[Dawn R.], Bolten, J.D.[John D.], Foster, A.C.[Adrianna C.], Spruce, J.[Joseph],
Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
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Morresi, D.[Donato], Vitali, A.[Alessandro], Urbinati, C.[Carlo], Garbarino, M.[Matteo],
Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Campanharo, W.A.[Wesley A.], Lopes, A.P.[Aline P.], Anderson, L.O.[Liana O.], da Silva, T.F.M.R.[Thiago F. M. R.], Aragão, L.E.O.C.[Luiz E. O. C.],
Translating Fire Impacts in Southwestern Amazonia into Economic Costs,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Zhou, L.[Lei], Wang, Y.H.[Yu-Hang], Chi, Y.G.[Yong-Gang], Wang, S.Q.[Shao-Qiang], Wang, Q.[Quan],
Contrasting Post-Fire Dynamics between Africa and South America based on MODIS Observations,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
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Saha, M.V.[Michael V.], d'Odorico, P.[Paolo], Scanlon, T.M.[Todd M.],
Kalahari Wildfires Drive Continental Post-Fire Brightening in Sub-Saharan Africa,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
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Ayhan, B.[Bulent], Kwan, C.[Chiman],
Tree, Shrub, and Grass Classification Using Only RGB Images,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
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Shan, T.C.[Tian-Chan], Wang, C.L.[Chang-Lin], Chen, F.[Fang], Wu, Q.C.[Qin-Chun], Li, B.[Bin], Yu, B.[Bo], Shirazi, Z.[Zeeshan], Lin, Z.Y.[Zheng-Yang], Wu, W.[Wei],
A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
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Engelbrecht, J.[Jeanine], Theron, A.[Andre], Vhengani, L.[Lufuno], Kemp, J.[Jaco],
A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
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Pereira, A.A.[Allan A.], Pereira, J.M.C.[José M. C.], Libonati, R.[Renata], Oom, D.[Duarte], Setzer, A.W.[Alberto W.], Morelli, F.[Fabiano], Machado-Silva, F.[Fausto], de Carvalho, L.M.T.[Luis Marcelo Tavares],
Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
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Pereira, A.A.[Allan A.], Libonati, R.[Renata], Rodrigues, J.A.[Julia A.], Nogueira, J.[Joana], Santos, F.L.M.[Filippe L. M.], Oom, D.[Duarte], Sanches, W.[Waislan], Alvarado, S.T.[Swanni T.], Pereira, J.M.C.[José M. C.],
Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Ramo, R.[Rubén], Chuvieco, E.[Emilio],
Developing a Random Forest Algorithm for MODIS Global Burned Area Classification,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
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Wang, J.J.[Jun-Jie], Wang, C.Z.[Cui-Zhen], Zang, S.Y.[Shu-Ying],
Assessing Re-Composition of Xing'an Larch in Boreal Forests after the 1987 Fire, Northeast China,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Mithal, V.[Varun], Nayak, G.[Guruprasad], Khandelwal, A.[Ankush], Kumar, V.[Vipin], Nemani, R.[Ramakrishna], Oza, N.C.[Nikunj C.],
Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
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Axel, A.C.[Anne C.],
Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Tane, Z.[Zachary], Roberts, D.[Dar], Veraverbeke, S.[Sander], Casas, Á.[Ángeles], Ramirez, C.[Carlos], Ustin, S.[Susan],
Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Hislop, S.[Samuel], Jones, S.[Simon], Soto-Berelov, M.[Mariela], Skidmore, A.[Andrew], Haywood, A.[Andrew], Nguyen, T.H.[Trung H.],
Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

See also Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data, A. BibRef

Melchiorre, A.[Andrea], Boschetti, L.[Luigi],
Global Analysis of Burned Area Persistence Time with MODIS Data,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Boonprong, S.[Sornkitja], Cao, C.X.[Chun-Xiang], Chen, W.[Wei], Bao, S.N.[Shan-Ning],
Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring: Multilevel RF-VIMP,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

García-Lázaro, J.R.[José R.], Moreno-Ruiz, J.A.[José A.], Riaño, D.[David], Arbelo, M.[Manuel],
Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982-2015 Time Series,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Cabral, A.I.R.[Ana I.R.], Silva, S.[Sara], Silva, P.C.[Pedro C.], Vanneschi, L.[Leonardo], Vasconcelos, M.J.[Maria J.],
Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees,
PandRS(142), 2018, pp. 94-105.
Elsevier DOI 1807
Burned area mapping, Genetic Programming, Savana woodlands, Classification and Regression Trees, Maximum Likelihood, Landsat ETM+/OLI BibRef

Li, X.D.[Xue-Dong], Zhang, H.Y.[Hong-Yan], Yang, G.B.[Guang-Bin], Ding, Y.L.[Yan-Ling], Zhao, J.J.[Jian-Jun],
Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Fornacca, D.[Davide], Ren, G.[Guopeng], Xiao, W.[Wen],
Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Maffei, C.[Carmine], Alfieri, S.M.[Silvia Maria], Menenti, M.[Massimo],
Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Santana, N.C.[Níckolas Castro], de Carvalho Júnior, O.A.[Osmar Abílio], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarães, R.F.[Renato Fontes],
Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Plank, S.[Simon], Martinis, S.[Sandro],
A Fully Automatic Burnt Area Mapping Processor Based on AVHRR Imagery: A TIMELINE Thematic Processor,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

See also Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery: A TIMELINE Thematic Processor, A. BibRef

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DOI Link 1702
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Fernández-García, V.[Víctor], Quintano, C.[Carmen], Taboada, A.[Angela], Marcos, E.[Elena], Calvo, L.[Leonor], Fernández-Manso, A.[Alfonso],
Remote Sensing Applied to the Study of Fire Regime Attributes and Their Influence on Post-Fire Greenness Recovery in Pine Ecosystems,
RS(10), No. 5, 2018, pp. xx-yy.
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Ryu, J.H.[Jae-Hyun], Han, K.S.[Kyung-Soo], Hong, S.[Sungwook], Park, N.W.[No-Wook], Lee, Y.W.[Yang-Won], Cho, J.[Jaeil],
Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea,
RS(10), No. 6, 2018, pp. xx-yy.
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Michael, Y.[Yaron], Lensky, I.M.[Itamar M.], Brenner, S.[Steve], Tchetchik, A.[Anat], Tessler, N.[Naama], Helman, D.[David],
Economic Assessment of Fire Damage to Urban Forest in the Wildland-Urban Interface Using Planet Satellites Constellation Images,
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DOI Link 1810
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Vasilakos, C.[Christos], Tsekouras, G.E.[George E.], Palaiologou, P.[Palaiologos], Kalabokidis, K.[Kostas],
Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Henry, M.C.[Mary C.], Maingi, J.K.[John K.], McCarty, J.[Jessica],
Fire on the Water Towers: Mapping Burn Scars on Mount Kenya Using Satellite Data to Reconstruct Recent Fire History,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
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Ba, R.[Rui], Song, W.G.[Wei-Guo], Li, X.L.[Xiao-Lian], Xie, Z.X.[Zi-Xi], Lo, S.M.[Siu-Ming],
Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data,
RS(11), No. 3, 2019, pp. xx-yy.
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Fang, L.[Lei], Crocker, E.V.[Ellen V.], Yang, J.[Jian], Yan, Y.[Yan], Yang, Y.Z.[Yuan-Zheng], Liu, Z.H.[Zhi-Hua],
Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery,
RS(11), No. 6, 2019, pp. xx-yy.
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Filipponi, F.[Federico],
Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires,
RS(11), No. 6, 2019, pp. xx-yy.
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Long, T.F.[Teng-Fei], Zhang, Z.M.[Zhao-Ming], He, G.J.[Guo-Jin], Jiao, W.L.[Wei-Li], Tang, C.[Chao], Wu, B.F.[Bing-Fang], Zhang, X.O.[Xia-Omei], Wang, G.Z.[Gui-Zhou], Yin, R.[Ranyu],
30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
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Fernandez-Manso, A.[Alfonso], Quintano, C.[Carmen], Roberts, D.A.[Dar A.],
Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data,
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Elsevier DOI 1908
Burn severity, EO-1 Hyperion, LiDAR, MaxEnt BibRef

Quintano, C.[Carmen], Fernández-Manso, A.[Alfonso], Calvo, L.[Leonor], Roberts, D.A.[Dar A.],
Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
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Otón, G.[Gonzalo], Ramo, R.[Rubén], Lizundia-Loiola, J.[Joshua], Chuvieco, E.[Emilio],
Global Detection of Long-Term (1982-2017) Burned Area with AVHRR-LTDR Data,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
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And: Correction: RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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Quintano, C.[Carmen], Fernandez-Manso, A.[Alfonso], Marcos, E.[Elena], Calvo, L.[Leonor],
Burn Severity and Post-Fire Land Surface Albedo Relationship in Mediterranean Forest Ecosystems,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
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Wozniak, E.[Edyta], Aleksansdrowicz, S.[Sebastian],
Self-Adjusting Thresholding for Burnt Area Detection Based on Optical Images,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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Wang, P.[Peng], Zhang, L.[Lei], Zhang, G.[Gong], Jin, B.Z.[Ben-Zhou], Leung, H.[Henry],
Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information,
RS(11), No. 22, 2019, pp. xx-yy.
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Wang, L.G.[Li-Guo], Hao, S.Y.[Si-Yuan], Wang, Q.M.[Qun-Ming], Wang, Y.[Ying],
Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation,
PandRS(97), No. 1, 2014, pp. 123-137.
Elsevier DOI 1410
Hyperspectral imagery BibRef

Wang, P.[Peng], Wang, L.G.[Li-Guo], Leung, H.[Henry], Zhang, G.[Gong],
Super-Resolution Mapping Based on Spatial-Spectral Correlation for Spectral Imagery,
GeoRS(59), No. 3, March 2021, pp. 2256-2268.
IEEE DOI 2103
Correlation, Imaging, Euclidean distance, Optimization, Resource management, Probability density function, super-resolution mapping (SRM) BibRef

Belenguer-Plomer, M.A.[Miguel A.], Chuvieco, E.[Emilio], Tanase, M.A.[Mihai A.],
Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas,
RS(11), No. 22, 2019, pp. xx-yy.
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Maffei, C.[Carmine], Menenti, M.[Massimo],
Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements,
PandRS(158), 2019, pp. 263-278.
Elsevier DOI 1912
MODIS, Perpendicular Moisture Index (PMI), Fire Weather Index (FWI), Live fuel moisture content (LFMC), Probability of extreme events BibRef

Walker, J.J.[Jessica J.], Soulard, C.E.[Christopher E.],
Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
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Zhang, P.Z.[Pu-Zhao], Nascetti, A.[Andrea], Ban, Y.F.[Yi-Fang], Gong, M.[Maoguo],
An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data,
PandRS(158), 2019, pp. 50-62.
Elsevier DOI 1912
Sentinel-1 SAR, Burnt area mapping, InSAR coherence, Change detection, Fully Convolutional Networks (FCN), Radar Convolutional Burn Index (RCBI) BibRef

Kibler, C.L.[Christopher L.], Parkinson, A.M.L.[Anne-Marie L.], Peterson, S.H.[Seth H.], Roberts, D.A.[Dar A.], d'Antonio, C.M.[Carla M.], Meerdink, S.K.[Susan K.], Sweeney, S.H.[Stuart H.],
Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series,
RS(11), No. 24, 2019, pp. xx-yy.
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Chen, Y.P.[Ya-Ping], Lara, M.J.[Mark Jason], Hu, F.S.[Feng Sheng],
A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems,
PandRS(159), 2020, pp. 101-113.
Elsevier DOI 1912
Burn severity, Disturbance, Global Environmental Monitoring Index, Multispectral index, Wildfire BibRef

Chen, D.[Dong], Loboda, T.V.[Tatiana V.], Hall, J.V.[Joanne V.],
A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems,
PandRS(159), 2020, pp. 63-77.
Elsevier DOI 1912
Boreal forest, Tundra, ABoVE, Alaska, Canada, Remote sensing, Disturbance, Wildfire, Burn severity, dNBR, Landsat BibRef

Wang, J.M.[Jian-Min], Zhang, X.Y.[Xiao-Yang],
Investigation of wildfire impacts on land surface phenology from MODIS time series in the western US forests,
PandRS(159), 2020, pp. 281-295.
Elsevier DOI 1912
Land surface phenology, Forest, Wildfire, Timing, Greenness, MODIS, Western US BibRef

Adrianto, H.A.[Hari A.], Spracklen, D.V.[Dominick V.], Arnold, S.R.[Stephen R.], Sitanggang, I.S.[Imas S.], Syaufina, L.[Lailan],
Forest and Land Fires Are Mainly Associated with Deforestation in Riau Province, Indonesia,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
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Lizundia-Loiola, J.[Joshua], Pettinari, M.L.[M. Lucrecia], Chuvieco, E.[Emilio],
Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Tanase, M.A.[Mihai A.], Belenguer-Plomer, M.A.[Miguel A.], Roteta, E.[Ekhi], Bastarrika, A.[Aitor], Wheeler, J.[James], Fernández-Carrillo, Á.[Ángel], Tansey, K.[Kevin], Wiedemann, W.[Werner], Navratil, P.[Peter], Lohberger, S.[Sandra], Siegert, F.[Florian], Chuvieco, E.[Emilio],
Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa,
RS(12), No. 2, 2020, pp. xx-yy.
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Liu, M.[Meng], Popescu, S.[Sorin], Malambo, L.[Lonesome],
Feasibility of Burned Area Mapping Based on ICESAT-2 Photon Counting Data,
RS(12), No. 1, 2019, pp. xx-yy.
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Malambo, L.[Lonesome], Heatwole, C.D.[Conrad D.],
Automated training sample definition for seasonal burned area mapping,
PandRS(160), 2020, pp. 107-123.
Elsevier DOI 2001
Fire, Burned area, Automatic training, Abrupt change, Clustering, Fuzzy c-means, Landsat, Random Forest, Zambia, Southern Africa BibRef

Pinto, M.M.[Miguel M.], Libonati, R.[Renata], Trigo, R.M.[Ricardo M.], Trigo, I.F.[Isabel F.], DaCamara, C.C.[Carlos C.],
A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images,
PandRS(160), 2020, pp. 260-274.
Elsevier DOI 2001
Burned areas, Wildfires, VIIRS, Segmentation, Deep learning BibRef

Franco, M.G.[María Guadalupe], Mundo, I.A.[Ignacio A.], Veblen, T.T.[Thomas T.],
Field-Validated Burn-Severity Mapping in North Patagonian Forests,
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Philipp, M.B.[Marius B.], Levick, S.R.[Shaun R.],
Exploring the Potential of C-Band SAR in Contributing to Burn Severity Mapping in Tropical Savanna,
RS(12), No. 1, 2019, pp. xx-yy.
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Saulino, L.[Luigi], Rita, A.[Angelo], Migliozzi, A.[Antonello], Maffei, C.[Carmine], Allevato, E.[Emilia], Garonna, A.P.[Antonio Pietro], Saracino, A.[Antonio],
Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Zheng, Z.[Zhong], Wang, J.F.[Jin-Fei], Shan, B.[Bo], He, Y.J.[Yong-Jun], Liao, C.H.[Chun-Hua], Gao, Y.H.[Yang-Hua], Yang, S.Q.[Shi-Qi],
A New Model for Transfer Learning-Based Mapping of Burn Severity,
RS(12), No. 4, 2020, pp. xx-yy.
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Pulvirenti, L.[Luca], Squicciarino, G.[Giuseppe], Fiori, E.[Elisabetta], Fiorucci, P.[Paolo], Ferraris, L.[Luca], Negro, D.[Dario], Gollini, A.[Andrea], Severino, M.[Massimiliano], Puca, S.[Silvia],
An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data,
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Syifa, M.[Mutiara], Panahi, M.[Mahdi], Lee, C.W.[Chang-Wook],
Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA,
RS(12), No. 4, 2020, pp. xx-yy.
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Humber, M.L., Boschetti, L., Giglio, L.,
Assessing the Shape Accuracy of Coarse Resolution Burned Area Identifications,
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Accuracy assessment, MODIS Burned Area, North America, wildfire BibRef

Fernández-Manso, A.[Alfonso], Quintano, C.[Carmen], Roberts, D.A.[Dar A.],
Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?,
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Fernández-Manso, A.[Alfonso], Quintano, C.[Carmen],
A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots,
RS(12), No. 5, 2020, pp. xx-yy.
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Pereira-Pires, J.E.[João E.], Aubard, V.[Valentine], Ribeiro, R.A.[Rita A.], Fonseca, J.M.[José M.], Silva, J.M.N.[João M. N.], Mora, A.[André],
Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network,
RS(12), No. 6, 2020, pp. xx-yy.
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Pádua, L.[Luís], Guimarães, N.[Nathalie], Adão, T.[Telmo], Sousa, A.[António], Peres, E.[Emanuel], Sousa, J.J.[Joaquim J.],
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Viana-Soto, A.[Alba], Aguado, I.[Inmaculada], Salas, J.[Javier], García, M.[Mariano],
Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests,
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Adagbasa, E.G.[Efosa G.], Adelabu, S.A.[Samuel A.], Okello, T.W.[Tom W.],
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Elsevier DOI 2005
Remote sensing, GIS, Post-fire recovery, Adaptive strategy, Fire severity, Environmental variables BibRef

Fernández-Guisuraga, J.M.[José Manuel], Calvo, L.[Leonor], Suárez-Seoane, S.[Susana],
Comparison of pixel unmixing models in the evaluation of post-fire forest resilience based on temporal series of satellite imagery at moderate and very high spatial resolution,
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McCaffrey, D.[David], Hopkinson, C.[Chris],
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Smiraglia, D.[Daniela], Filipponi, F.[Federico], Mandrone, S.[Stefania], Tornato, A.[Antonella], Taramelli, A.[Andrea],
Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images,
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Kganyago, M.[Mahlatse], Shikwambana, L.[Lerato],
Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018-2019 Using Multi-Source Satellite Products,
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Scholtz, R.[Rheinhardt], Prentice, J.[Jayson], Tang, Y.[Yao], Twidwell, D.[Dirac],
Improving on MODIS MCD64A1 Burned Area Estimates in Grassland Systems: A Case Study in Kansas Flint Hills Tall Grass Prairie,
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Nolde, M.[Michael], Plank, S.[Simon], Riedlinger, T.[Torsten],
An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time,
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Huang, Z.B.[Zhi-Bin], Cao, C.X.[Chun-Xiang], Chen, W.[Wei], Xu, M.[Min], Dang, Y.F.[Yong-Feng], Singh, R.P.[Ramesh P.], Bashir, B.[Barjeece], Xie, B.[Bo], Lin, X.J.[Xiao-Juan],
Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts,
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Steiner, J.L.[Jean L.], Wetter, J.[Jeffrey], Robertson, S.[Shelby], Teet, S.[Stephen], Wang, J.[Jie], Wu, X.C.[Xiao-Cui], Zhou, Y.T.[Yu-Ting], Brown, D.[David], Xiao, X.M.[Xiang-Ming],
Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery,
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Potter, C.[Christopher],
Recovery Rates of Wetland Vegetation Greenness in Severely Burned Ecosystems of Alaska Derived from Satellite Image Analysis,
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Khodaee, M.[Mahsa], Hwang, T.H.[Tae-Hee], Kim, J.[JiHyun], Norman, S.P.[Steven P.], Robeson, S.M.[Scott M.], Song, C.H.[Cong-He],
Monitoring Forest Infestation and Fire Disturbance in the Southern Appalachian Using a Time Series Analysis of Landsat Imagery,
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de Bem, P.P.[Pablo Pozzobon], de Carvalho Júnior, O.A.[Osmar Abílio], de Carvalho, O.L.F.[Osmar Luiz Ferreira], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarães, R.F.[Renato Fontes],
Performance Analysis of Deep Convolutional Autoencoders with Different Patch Sizes for Change Detection from Burnt Areas,
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Burned Area Mapping Using Multi-Temporal Sentinel-2 Data by Applying the Relative Differenced Aerosol-Free Vegetation Index (RdAFRI),
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Talucci, A.C.[Anna C.], Forbath, E.[Elena], Kropp, H.[Heather], Alexander, H.D.[Heather D.], DeMarco, J.[Jennie], Paulson, A.K.[Alison K.], Zimov, N.S.[Nikita S.], Zimov, S.[Sergei], Loranty, M.M.[Michael M.],
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Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand,
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Shimabukuro, Y.E.[Yosio Edemir], Dutra, A.C.[Andeise Cerqueira], Arai, E.[Egidio], Duarte, V.[Valdete], Cassol, H.L.G.[Henrique Luís Godinho], Pereira, G.[Gabriel], da Silva Cardozo, F.[Francielle],
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Elsevier DOI 2106
Burn severity, Deimos-2, Forest fire, Fractional vegetation cover, PROSAIL-D, Resilience BibRef

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Delcourt, C.J.F.[Clement J. F.], Combee, A.[Alisha], Izbicki, B.[Brian], Mack, M.C.[Michelle C.], Maximov, T.[Trofim], Petrov, R.[Roman], Rogers, B.M.[Brendan M.], Scholten, R.C.[Rebecca C.], Shestakova, T.A.[Tatiana A.], van Wees, D.[Dave], Veraverbeke, S.[Sander],
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van Gerrevink, M.J.[Max J.], Veraverbeke, S.[Sander],
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Xulu, S.[Sifiso], Mbatha, N.[Nkanyiso], Peerbhay, K.[Kabir],
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Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data,
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Oliveira, E.R.[Eduardo R.], Disperati, L.[Leonardo], Alves, F.L.[Fátima L.],
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Seydi, S.T.[Seyd Teymoor], Hasanlou, M.[Mahdi], Chanussot, J.[Jocelyn],
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Florath, J.[Janine], Keller, S.[Sina],
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Katagis, T.[Thomas], Gitas, I.Z.[Ioannis Z.],
Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems,
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Guz, J.[Jaclyn], Sangermano, F.[Florencia], Kulakowski, D.[Dominik],
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White, L.A.[Laura A.], Gibson, R.K.[Rebecca K.],
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Pontes-Lopes, A.[Aline], Dalagnol, R.[Ricardo], Dutra, A.C.[Andeise Cerqueira], de Jesus Silva, C.V.[Camila Valéria], de Alencastro Graça, P.M.L.[Paulo Maurício Lima], de Oliveira e Cruz de Aragão, L.E.[Luiz Eduardo],
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Alcaras, E.[Emanuele], Costantino, D.[Domenica], Guastaferro, F.[Francesca], Parente, C.[Claudio], Pepe, M.[Massimiliano],
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Sun, L.[Li], Yang, L.[Lei], Xia, X.G.[Xian-Gao], Wang, D.D.[Dong-Dong], Zhang, T.N.[Tie-Ning],
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Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning,
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Chen, X.[Xu], Chen, W.[Wei], Xu, M.[Min],
Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors,
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Extending the National Burned Area Composite Time Series of Wildfires in Canada,
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Silva-Cardoza, A.I.[Adrián Israel], Vega-Nieva, D.J.[Daniel José], Briseño-Reyes, J.[Jaime], Briones-Herrera, C.I.[Carlos Ivan], López-Serrano, P.M.[Pablito Marcelo], Corral-Rivas, J.J.[José Javier], Parks, S.A.[Sean A.], Holsinger, L.M.[Lisa M.],
Evaluating a New Relative Phenological Correction and the Effect of Sentinel-Based Earth Engine Compositing Approaches to Map Fire Severity and Burned Area,
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RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery,
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Xu, H.Z.[Hai-Zhou], Zhang, G.[Gui], Zhou, Z.M.[Zhao-Ming], Zhou, X.B.[Xia-Bing], Zhang, J.[Jia], Zhou, C.[Cui],
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Stroppiana, D.[Daniela], Sali, M.[Matteo], Busetto, L.[Lorenzo], Boschetti, M.[Mirco], Ranghetti, L.[Luigi], Franquesa, M.[Magí], Pettinari, M.L.[M. Lucrecia], Chuvieco, E.[Emilio],
Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019,
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Elsevier DOI 2208
Accuracy assessment, Regional and continental products, Sampling approach, Reference dataset, Validation methodology, Burned area BibRef

Gersh, M.[Max], Gleason, K.E.[Kelly E.], Surunis, A.[Anton],
Forest Fire Effects on Landscape Snow Albedo Recovery and Decay,
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Franquesa, M.[Magí], Rodriguez-Montellano, A.M.[Armando M.], Chuvieco, E.[Emilio], Aguado, I.[Inmaculada],
Reference Data Accuracy Impacts Burned Area Product Validation: The Role of the Expert Analyst,
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Kurbanov, E.[Eldar], Vorobev, O.[Oleg], Lezhnin, S.[Sergey], Sha, J.M.[Jin-Ming], Wang, J.L.[Jin-Liang], Li, X.M.[Xiao-Mei], Cole, J.[Janine], Dergunov, D.[Denis], Wang, Y.[Yibo],
Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review,
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Lasaponara, R.[Rosa], Abate, N.[Nicodemo], Fattore, C.[Carmen], Aromando, A.[Angelo], Cardettini, G.[Gianfranco], di Fonzo, M.[Marco],
On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Tomshin, O.[Oleg], Solovyev, V.[Vladimir],
Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning,
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DOI Link 2210
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Howe, A.A.[Alexander A.], Parks, S.A.[Sean A.], Harvey, B.J.[Brian J.], Saberi, S.J.[Saba J.], Lutz, J.A.[James A.], Yocom, L.L.[Larissa L.],
Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Koutsias, N.[Nikos], Karamitsou, A.[Anastasia], Nioti, F.[Foula], Coutelieris, F.[Frank],
Assessment of Fire Regimes and Post-Fire Evolution of Burned Areas with the Dynamic Time Warping Method on Time Series of Satellite Images: Setting the Methodological Framework in the Peloponnese, Greece,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Fernández-Guisuraga, J.M.[José Manuel], Suárez-Seoane, S.[Susana], Quintano, C.[Carmen], Fernández-Manso, A.[Alfonso], Calvo, L.[Leonor],
Comparison of Physical-Based Models to Measure Forest Resilience to Fire as a Function of Burn Severity,
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DOI Link 2211
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Negri, R.G.[Rogério G.], Luz, A.E.O.[Andréa E. O.], Frery, A.C.[Alejandro C.], Casaca, W.[Wallace],
Mapping Burned Areas with Multitemporal-Multispectral Data and Probabilistic Unsupervised Learning,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Fernández-Alonso, J.M.[José María], Llorens, R.[Rafael], Sobrino, J.A.[José Antonio], Ruiz-González, A.D.[Ana Daría], Alvarez-González, J.G.[Juan Gabriel], Vega, J.A.[José Antonio], Fernández, C.[Cristina],
Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Avetisyan, D.[Daniela], Velizarova, E.[Emiliya], Filchev, L.[Lachezar],
Post-Fire Forest Vegetation State Monitoring through Satellite Remote Sensing and In Situ Data,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Gholamrezaie, H.[Houri], Hasanlou, M.[Mahdi], Amani, M.[Meisam], Mirmazloumi, S.M.[S. Mohammad],
Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Amroussia, M.[Mouna], Viedma, O.[Olga], Achour, H.[Hammadi], Abbes, C.[Chaabane],
Predicting Spatially Explicit Composite Burn Index (CBI) from Different Spectral Indices Derived from Sentinel 2A: A Case of Study in Tunisia,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Ye, J.[Junhong], Wang, N.[Nan], Sun, M.[Min], Liu, Q.Q.[Qin-Qin], Ding, N.[Ning], Li, M.[Mingshi],
A New Method for the Rapid Determination of Fire Disturbance Events Using GEE and the VCT Algorithm: A Case Study in Southwestern and Northeastern China,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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East, A.[Alyson], Hansen, A.[Andrew], Armenteras, D.[Dolors], Jantz, P.[Patrick], Roberts, D.W.[David W.],
Measuring Understory Fire Effects from Space: Canopy Change in Response to Tropical Understory Fire and What This Means for Applications of GEDI to Tropical Forest Fire,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Pirotti, F.[Francesco], Adedipe, O.[Opeyemi], Leblon, B.[Brigitte],
Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Oliveira, E.R.[Eduardo R.], Disperati, L.[Leonardo], Alves, F.L.[Fátima L.],
MINDED-FBA: An Automatic Remote Sensing Tool for the Estimation of Flooded and Burned Areas,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Sudiana, D.[Dodi], Lestari, A.I.[Anugrah Indah], Riyanto, I.[Indra], Rizkinia, M.[Mia], Arief, R.[Rahmat], Prabuwono, A.S.[Anton Satria], Sumantyo, J.T.S.[Josaphat Tetuko Sri],
A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Chan, A.H.Y.[Aland H. Y.], Guizar-Coutiño, A.[Alejandro], Kalamandeen, M.[Michelle], Coomes, D.A.[David A.],
Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat,
RS(15), No. 6, 2023, pp. 1489.
DOI Link 2304
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Valles, H.E.P.[Harold E. Pineda], Nunes, G.M.[Gustavo Manzon], Berlinck, C.N.[Christian Niel], Gonçalves, L.G.[Luiz Gustavo], Pires-de Mello-Ribeiro, G.H.[Gabriel Henrique],
Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil,
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DOI Link 2306
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Lin, S.[Simei], Zhang, H.Q.[Hui-Qing], Liu, S.B.[Shang-Bo], Gao, G.[Ge], Li, L.Y.[Lin-Yuan], Huang, H.G.[Hua-Guo],
Characterizing Post-Fire Forest Structure Recovery in the Great Xing'an Mountain Using GEDI and Time Series Landsat Data,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Wang, X.Q.[Xiao-Qiong], Yan, J.[Jun], Tian, Q.J.[Qing-Jiu], Li, X.[Xianyi], Tian, J.[Jia], Zhu, C.C.[Cui-Cui], Li, Q.J.[Qian-Jing],
Estimation of Forest Fire Burned Area by Distinguishing Non-Photosynthetic and Photosynthetic Vegetation Using Triangular Space Method,
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DOI Link 2307
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Fernández-García, V.[Víctor], Alonso-González, E.[Esteban],
Global Patterns and Dynamics of Burned Area and Burn Severity,
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DOI Link 2307
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Wang, X.Y.[Xue-Yan], Di, Z.H.[Zhen-Hua], Liu, J.G.[Jian-Guo],
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Ribeiro, T.F.R.[Tiago F.R.], Silva, F.[Fernando], Moreira, J.[José], de-C.-Costa, R.L.[Rogério Luís],
Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks,
PandRS(202), 2023, pp. 565-580.
Elsevier DOI 2308
Forest fires, Burned area segmentation, UAV, Deep learning, Benchmark BibRef

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Exploring Effective Detection and Spatial Pattern of Prickly Pear Cactus (Opuntia Genus) from Airborne Imagery before and after Prescribed Fires in the Edwards Plateau,
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Elsevier DOI 2310
Wildfire, Burned area, SAR, Deep learning, Segmentation, U-Net, Total variation, Sentinel-1 BibRef

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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Forest Change Evaluation, Change Detection, Temporal Analysis .


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