22.5.11.5.1 Burned Area Detection

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

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

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

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

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

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

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

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.[Haiyan], 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

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

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
BibRef

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
BibRef

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
BibRef

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
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
BibRef

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

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
BibRef

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.
DOI Link 1902
BibRef

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.
DOI Link 1903
BibRef

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.
DOI Link 1903
BibRef

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
BibRef

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,
PandRS(155), 2019, pp. 102-118.
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
BibRef

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
BibRef

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
BibRef

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
BibRef

Wang, P.[Peng], ZhG, R.[Rei], Zhang, G.[Gong], Jin, B.[Benzhou], Leung, H.[Henry],
Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
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.
DOI Link 1911
BibRef

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
BibRef

Zhang, P.[Puzhao], Nascetti, A.[Andrea], Ban, Y.[Yifang], 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.
DOI Link 1912
BibRef

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.[Jianmin], 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
BibRef

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
BibRef

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.
DOI Link 2001
BibRef

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.
DOI Link 2001
BibRef

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, Computer vision 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,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

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.
DOI Link 2001
BibRef


Attaf, D., Djerriri, K., Mansour, D., Hamdadou, D.,
Mapping of Burned Area Using Presence and Background Learning Framework On The Google Earth Engine Platform,
Gi4DM19(37-41).
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Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Forest Change Evaluation, Change Detection, Temporal Analysis .


Last update:Feb 20, 2020 at 21:34:09