Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data

References

• Alcaras, E., D. Costantino, F. Guastaferro, C. Parente, and M. Pepe. 2022. “Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery.” Remote Sensing 14 (7): 1727. doi:10.3390/rs14071727.
   Web of Science ®Google Scholar
• ASF. 2022. Accessed July 21. https://asf.alaska.edu/.
   Google Scholar
• Axel, A. C., 2018. Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data. Remote Sens. https://doi.org/10.3390/rs10030371
   Google Scholar
• Ban, Y., P. Zhang, A. Nascetti, A. R. Bevington, and M. A. Wulder. 2020. “Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning.” Scientific Reports 10 (1): 1–15. doi:10.1038/s41598-019-56967-x.
   PubMed Web of Science ®Google Scholar
• Barbosa, P. M., J. M. C. Pereira, and J.-M. Grégoire. 1998. “Compositing Criteria for Burned Area Assessment Using Multitemporal Low Resolution Satellite Data.” Remote Sensing of Environment 65 (1): 38–49.
   Web of Science ®Google Scholar
• Barbosa, P. M., D. Stroppiana, J. M. Grégoire, and J. M. C. Pereira. 1999. “An Assessment of Vegetation Fire in Africa (1981-1991): Burned Areas, Burned Biomass, and Atmospheric Emissions.” Global Biogeochemical Cycles 13 (4): 933–950. doi:10.1029/1999GB900042.
   Web of Science ®Google Scholar
• Belenguer-Plomer, M. A., E. Chuvieco, and M. A. Tanase. 2019. “Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas.” Remote Sensing 11 (22): 2661. doi:10.3390/rs11222661.
   Web of Science ®Google Scholar
• Boschetti, L., D. P. Roy, C. O. Justice, and M. L. Humber. 2015. “MODIS–Landsat Fusion for Large Area 30m Burned Area Mapping.” Remote Sens. Environ 161: 27–42. doi:10.1016/j.rse.2015.01.022.
   Web of Science ®Google Scholar
• Breiman L. 2001. Machine Learning. 45(1): 5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Cabral, A., M. J. P. De Vasconcelos, J. M. C. Pereira, É. Bartholomé, and P. Mayaux. 2003. “Multi-temporal Compositing Approaches for SPOT-4 Vegetation.” International Journal of Remote Sensing 24 (16): 3343–3350. doi:10.1080/0143116031000075936.
   Web of Science ®Google Scholar
• Carreiras, J. M. B., S. Quegan, K. Tansey, and S. Page. 2020. “Sentinel-1 Observation Frequency Significantly Increases Burnt Area Detectability in Tropical SE Asia.” Environmental Research Letters 15 (5): 054008. doi:10.1088/1748-9326/ab7765.
   Web of Science ®Google Scholar
• Cascio, W. E. 2018. “Wildland Fire Smoke and Human Health.” The Science of the Total Environment 624: 586–595. doi:10.1016/j.scitotenv.2017.12.086.
   PubMed Web of Science ®Google Scholar
• Christopoulou, A., N. M. Fyllas, P. Andriopoulos, N. Koutsias, P. G. Dimitrakopoulos, and M. Arianoutsou. 2014. “Post-fire Regeneration Patterns of Pinus Nigra in a Recently Burned Area in Mount Taygetos, Southern Greece: The Role of Unburned Forest Patches.” Forest Ecology and Management 327: 148–156. doi:10.1016/j.foreco.2014.05.006.
   Web of Science ®Google Scholar
• Chuvieco, E. 2009. “Earth Observation of Wildland Fires in Mediterranean Ecosystems, Earth Observation of Wildland Fires in Mediterranean Ecosystems.” Springer Berlin Heidelberg. doi:10.1007/978-3-642-01754-4.
   Google Scholar
• Chuvieco, E., and M. P. Martin. 1994. “Global Fire Mapping and Fire Danger Estimation Using AVHRR Images.” Photogramm. Eng. Remote Sensing 60: 563–570.
   Web of Science ®Google Scholar
• Chuvieco, E., F. Mouillot, G. R. van der Werf, J. San Miguel, M. Tanasse, N. Koutsias, M. García, et al. 2019. “Historical Background and Current Developments for Mapping Burned Area from Satellite Earth Observation.” Remote Sens. Environ 225: 45–64. doi:10.1016/j.rse.2019.02.013.
   Web of Science ®Google Scholar
• Chuvieco, E., G. Ventura, M. P. Martín, and I. Gómez. 2005. “Assessment of Multitemporal Compositing Techniques of MODIS and AVHRR Images for Burned Land Mapping.” Remote Sensing of Environment 94 (4): 450–462. doi:10.1016/j.rse.2004.11.006.
   Web of Science ®Google Scholar
• Chuvieco, E., C. Yue, A. Heil, F. Mouillot, I. Alonso-Canas, M. Padilla, J. M. Pereira, D. Oom, and K. Tansey. 2016. “A New Global Burned Area Product for Climate Assessment of Fire Impacts.” Global Ecology and Biogeography 25 (5): 619–629. doi:10.1111/geb.12440.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green, 2019. Assessing the Accuracy of Remotely Sensed Data. Principles and Practices, CRC Press
   Google Scholar
• Copernicus Long Term Archive Access. 2022. Accessed July 21. https://scihub.copernicus.eu/userguide/LongTermArchive
   Google Scholar
• COS. 2018. Accessed July 21 2012. https://www.dgterritorio.gov.pt/Carta-de-Uso-e-Ocupacao-do-Solo-para-2018
   Google Scholar
• Crowley, M. A., J. A. Cardille, J. C. White, and M. A. Wulder. 2019. “Generating intra-year Metrics of Wildfire Progression Using Multiple open-access Satellite Data Streams.” Remote Sens. Environ 232: 111295. doi:10.1016/j.rse.2019.111295.
   Web of Science ®Google Scholar
• Cutler D. R., T. C. Edwards, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson and J. J. Lawler. 2007. “Random Forests for Classification in Ecology.“ Ecology 88 (11): 2783–2792. doi:10.1890/07-0539.1
   PubMed Web of Science ®Google Scholar
• De Luca, G., N. Silva, J. M. Cerasoli, S. Araújo, J. Campos, J. Di Fazio, and S. Modica, G. 2019. “Object-Based Land Cover Classification of Cork Oak Woodlands Using UAV Imagery and Orfeo ToolBox.” Remote Sensing 11 (10): 1238. doi:10.3390/rs11101238.
   Web of Science ®Google Scholar
• De Luca, G., J. M. N. Silva, and G. Modica. 2021. “A Workflow Based on Sentinel-1 SAR Data and open-source Algorithms for Unsupervised Burned Area Detection in Mediterranean Ecosystems.” GIScience Remote Sens 00: 1–26. doi:10.1080/15481603.2021.1907896.
   Google Scholar
• Donezar, U., T. De Blas, A. Larrañaga, F. Ros, L. Albizua, A. Steel, and M. Broglia. 2019. “Applicability of the Multitemporal Coherence Approach to Sentinel-1 for the Detection and Delineation of Burnt Areas in the Context of the Copernicus Emergency Management Service.” Remote Sensing 11 (22): 2607. doi:10.3390/rs11222607.
   Web of Science ®Google Scholar
• EFFIS Annual Fire Reports. 2021. Accessed May 04. https://effis.jrc.ec.europa.eu/reports-and-publications/annual-fire-reports
   Google Scholar
• EFFIS Rapid Damage Assessment. 2021. Accessed September 26. https://effis.jrc.ec.europa.eu/about-effis/technicavl-background/rapid-damage-assessment
   Google Scholar
• Epting, J., D. Verbyla, and B. Sorbel. 2005. “Evaluation of Remotely Sensed Indices for Assessing Burn Severity in Interior Alaska Using Landsat TM and ETM+.” Remote Sensing of Environment 96 (3–4): 328–339. doi:10.1016/j.rse.2005.03.002.
   Web of Science ®Google Scholar
• ESA Sentinel Homepage. 2021. Accessed March 11. https://sentinel.esa.int/web/sentinel/home
   Google Scholar
• Esri ArcGIS World Imagery. 2021. Accessed March 19. https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9
   Google Scholar
• European Environment Agency. 2021. Accessed July 21. https://www.eea.europa.eu/data-and-maps/daviz/burnt-forest-area-in-five-3/#tab-chart_5
   Google Scholar
• Eva, H., and E. F. Lambin. 1998. “Burnt Area Mapping in Central Africa Using ATSR Data.” International Journal of Remote Sensing 19 (18): 3473–3497. doi:10.1080/014311698213768.
   Web of Science ®Google Scholar
• Farr, T. G., P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, et al. 2007. “The Shuttle Radar Topography Mission.” Reviews of Geophysics 45 (2): 1–33. doi:10.1029/2005RG000183.
   Web of Science ®Google Scholar
• Fernández-García, V., M. Santamarta, A. Fernández-Manso, C. Quintano, E. Marcos, and L. Calvo. 2018. “Burn Severity Metrics in fire-prone Pine Ecosystems along a Climatic Gradient Using Landsat Imagery.” Remote Sens. Environ 206: 205–217. doi:10.1016/j.rse.2017.12.029.
   Web of Science ®Google Scholar
• Fernández, A., P. Illera, and J. L. Casanova. 1997. “Automatic Mapping of Surfaces Affected by Forest Fires in Spain Using AVHRR NDVI Composite Image Data.” Remote Sens. Environ 60 (2): 153–162. doi:10.1016/S0034-4257(96).
   Web of Science ®Google Scholar
• Filipponi, F. 2018. “BAIS2: Burned Area Index for Sentinel-2.” Proceedings 5177. doi:10.3390/ecrs-2-05177.
   Google Scholar
• Filipponi, F. 2019. “Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires.” Remote Sensing 11 (6): 622. doi:10.3390/rs11060622.
   Web of Science ®Google Scholar
• Fornacca, D., G. Ren, and W. Xiao. 2018. “Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several post-fire Dates in a Mountainous Region of Northwest Yunnan, China.” Remote Sensing 10 (8): 1196. doi:10.3390/rs10081196.
   Web of Science ®Google Scholar
• Fraser, R. H., Z. Li, J. Cihlar, Z. Li, J. Cihlar, and J. Cihlar. 2000. “Hotspot and NDVI Differencing Synergy (HANDS): A New Technique for Burned Area Mapping over Boreal Forest.” Remote Sensing of Environment 74 (3): 362–376. doi:10.1016/S0034-4257(00).
   Web of Science ®Google Scholar
• Fukunaga, K., and L. D. Hostetler. 1975. “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition.” IEEE Transactions on Information Theory 21 (1): 32–40. doi:10.1109/TIT.1975.1055330.
   Web of Science ®Google Scholar
• Gallagher, M. R., N. S. Skowronski, M. R. Gallagher, N. S. Skowronski, E. J. Green, R. G. Lathrop, E. J. Green, et al. 2020. “An Improved Approach for Selecting and Validating Burn Severity Indices in Forested Landscapes an Improved Approach for Selecting and Validating Burn Severity Indices in Feux Dans Des Milieux Forestiers.” . Canadian Journal of Remote Sensing 46 (1): 100–111. doi:10.1080/07038992.2020.1735931.
   Web of Science ®Google Scholar
• Georgopoulos, N., D. Stavrakoudis, and I. Z. Gitas, 2019. Object-Based Burned Area Mapping Using Sentinel-2 Imagery and Supervised Learning Guided by Empirical Rules in: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 28 July - 2 August 2019, Yokohama, Japan, pp. 9980–9983. 10.1109/IGARSS.2019.8900134
   Google Scholar
• Giglio, L., L. Boschetti, D. P. Roy, M. L. Humber, and C. O. Justice. 2018. “The Collection 6 MODIS Burned Area Mapping Algorithm and Product.” Remote Sens. Environ 217: 72–85. doi:10.1016/j.rse.2018.08.005.
   PubMed Web of Science ®Google Scholar
• Gimeno, M., J. San-Miguel-Ayanz, and G. Schmuck. 2004. “Identification of Burnt Areas in Mediterranean Forest Environments from ERS-2 SAR Time Series.” International Journal of Remote Sensing 25 (22): 4873–4888. doi:10.1080/01431160412331269715.
   Web of Science ®Google Scholar
• Gitas, I. Z., G. H. Mitri, and G. Ventura. 2004. “Object-based Image Classification for Burned Area Mapping of Creus Cape, Spain, Using NOAA-AVHRR Imagery.” Remote Sensing of Environment 92 (3): 409–413. doi:10.1016/j.rse.2004.06.006.
   Web of Science ®Google Scholar
• Gitas, I., G. Mitri, S. Veraverbeke, and A. Polychronaki. 2012. “Advances in Remote Sensing of Post-Fire Vegetation Recovery Monitoring - A Review.” Remote Sens. Biomass - Princ. Appl. doi:10.5772/20571.
   Google Scholar
• Goodwin, N. R., and L. J. Collett. 2014. “Development of an Automated Method for Mapping Fire History Captured in Landsat TM and ETM+ Time Series across Queensland, Australia.” Remote Sens. Environ 148: 206–221. doi:10.1016/j.rse.2014.03.021.
   Web of Science ®Google Scholar
• Google Earth. 2021. Accessed March 19. https://www.google.com/earth/index.html
   Google Scholar
• Goutte, C., and E. Gaussier. 2005. “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.” Lect. Notes Comput. Sci 3408: 345–359. doi:10.1007/978-3-540-31865-1_25.
   Google Scholar
• Hachani, A., M. Ouessar, S. Paloscia, E. Santi, and S. Pettinato. 2019. “Soil Moisture Retrieval from Sentinel-1 Acquisitions in an Arid Environment in Tunisia: Application of Artificial Neural Networks Techniques.” International Journal of Remote Sensing 40 (24): 9159–9180. doi:10.1080/01431161.2019.1629503.
   Web of Science ®Google Scholar
• Hardy, C. C. 2005. “Wildland Fire Hazard and Risk: Problems, Definitions, and Context.” Forest Ecology and Management 211 (1–2): 73–82. doi:10.1016/j.foreco.2005.01.029.
   Web of Science ®Google Scholar
• Hawbaker, T. J., M. K. Vanderhoof, Y.-J. Beal, J. D. Takacs, G. L. Schmidt, J. T. Falgout, B. Williams, et al. 2017. “Mapping Burned Areas Using Dense time-series of Landsat Data.” Remote Sensing of Environment 198: 504–522. doi:10.1016/j.rse.2017.06.027.
   Web of Science ®Google Scholar
• Holben, B. N. 1986. “Characteristics of maximum-value Composite Images from Temporal AVHRR Data.” International Journal of Remote Sensing 7 (11): 1417–1434. doi:10.1080/01431168608948945.
   Web of Science ®Google Scholar
• Hosseini, M., and H. McNairn. 2017. “Using multi-polarization C- and L-band Synthetic Aperture Radar to Estimate Biomass and Soil Moisture of Wheat Fields.” Int. J. Appl. Earth Obs. Geoinf 58: 50–64. doi:10.1016/j.jag.2017.01.006.
   Web of Science ®Google Scholar
• ICNF. 2017. Accessed May 18. https://www.icnf.pt/api/file/doc/2c45facee8d3e4f8
   Google Scholar
• Imperatore, P., R. Azar, F. Calo, D. Stroppiana, P. A. Brivio, R. Lanari, and A. Pepe. 2017. “Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (10): 4478–4492. doi:10.1109/JSTARS.2017.2717039.
   Web of Science ®Google Scholar
• Inoue, Y., J. Qi, A. Olioso, Y. Kiyono, T. Horie, H. Asai, K. Saito, et al. 2008. “Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos.” International Journal of Remote Sensing 29 (7): 2011–2019. doi:10.1080/01431160701442039.
   Web of Science ®Google Scholar
• Kasischke, E. S., N. H. F. French, P. Harrell, N. L. Christensen, S. L. Ustin, and D. Barry. 1993. “Monitoring of Wildfires in Boreal Forests Using Large Area AVHRR NDVI Composite Image Data.” Remote Sensing of Environment 45 (1): 61–71. doi:10.1016/0034-4257(93).
   Web of Science ®Google Scholar
• Kaufman, Y. J., and L. A. Remer. 1994. “Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies.” IEEE Transactions on Geoscience and Remote Sensing 32 (3): 672–683. doi:10.1109/36.297984.
   Web of Science ®Google Scholar
• Key, C. H., and N. C. Benson, 2006. Landscape Assessment (LA) Sampling and Analysis Methods, In: FIREMON: Fire Effects Monitoring and Inventory System
   Google Scholar
• Kim, Y., T. Jackson, R. Bindlish, S. Hong, G. Jung, and K. Lee. 2014. “Retrieval of Wheat Growth Parameters with Radar Vegetation Indices.” IEEE Geoscience and Remote Sensing Letters 11 (4): 808–812. doi:10.1109/LGRS.2013.2279255.
   Web of Science ®Google Scholar
• Kim, Y., T. Jackson, R. Bindlish, H. Lee, and S. Hong. 2012. “Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean.” IEEE Geoscience and Remote Sensing Letters 9 (4): 564–568. doi:10.1109/LGRS.2011.2174772.
   Web of Science ®Google Scholar
• Kim, Y., and J. J. Van Zyl. 2009. “A time-series Approach to Estimate Soil Moisture Using Polarimetric Radar Data.” IEEE Trans. Geosci. Remote Sens. doi:10.1109/TGRS.2009.2014944.
   Web of Science ®Google Scholar
• Knopp, L., M. Wieland, M. Rättich, and S. Martinis. 2020. “A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data.” Remote Sensing 12 (15): 2422. doi:10.3390/RS12152422.
   Web of Science ®Google Scholar
• Kurum, M., 2015. C-Band SAR Backscatter Evaluation of 2008 Gallipoli Forest Fire 12, 1091–1095.
   Google Scholar
• Lasaponara, R., and B. Tucci. 2019. “Identification of Burned Areas and Severity Using SAR Sentinel-1.” IEEE Geoscience and Remote Sensing Letters 16 (6): 917–921. doi:10.1109/LGRS.2018.2888641.
   Web of Science ®Google Scholar
• Lasko, K. 2019. “Incorporating Sentinel-1 SAR Imagery with the MODIS MCD64A1 Burned Area Product to Improve Burn Date Estimates and Reduce Burn Date Uncertainty in Wildland Fire Mapping Incorporating Sentinel-1 SAR Imagery with the MODIS MCD64A1 Burned Area Product to Imp.” Geocarto International 0: 1–21. doi:10.1080/10106049.2019.1608592.
   Google Scholar
• Lee, J.-S., and E. Pottier. 2009. “Filtering of Single Polarization SAR Data, In: CRC Press. (Ed.), Polarimetric Radar Imaging. From Basics to Applications.” CRC Press., Boca Raton. doi:10.1201/9781420054989.
   Google Scholar
• Lehmann, E. A., P. Caccetta, K. Lowell, A. Mitchell, Z.-S. Zhou, A. Held, T. Milne, and I. Tapley. 2015. “SAR and Optical Remote Sensing: Assessment of Complementarity and Interoperability in the Context of a large-scale Operational Forest Monitoring System.” Remote Sensing of Environment 156: 335–348. doi:10.1016/j.rse.2014.09.034.
   Web of Science ®Google Scholar
• Llorens, R., J. A. Sobrino, C. Fernández, J. M. Fernández-Alonso, and J. A. Vega. 2021. “A Methodology to Estimate Forest Fires Burned Areas and Burn Severity Degrees Using Sentinel-2 Data. Application to the October 2017 Fires in the Iberian Peninsula. Int. J. Appl. Earth Obs.” Geoinf 95: 102243. doi:10.1016/j.jag.2020.102243.
   Google Scholar
• Martins, F. S. R. V., J. R. Dos Santos, L. S. Galvao, and H. A. M. Xaud. 2016. “Sensitivity of ALOS/PALSAR Imagery to Forest Degradation by Fire in Northern Amazon.” Int. J. Appl. Earth Obs. Geoinf 49: 163–174. doi:10.1016/j.jag.2016.02.009.
   Web of Science ®Google Scholar
• Mayaux, P., V. Gond, and E. Bartholome. 2000. “A near-real Time forest-cover Map of Madagascar Derived from SPOT-4 VEGETATION Data.” International Journal of Remote Sensing 21 (16): 3139–3144. doi:10.1080/01431160050145018.
   Web of Science ®Google Scholar
• Meddens, A. J. H., C. A. Kolden, and J. A. Lutz. 2016. “Detecting Unburned Areas within Wildfire Perimeters Using Landsat and Ancillary Data across the Northwestern United States.” Remote Sensing of Environment 186: 275–285. doi:10.1016/j.rse.2016.08.023.
   Web of Science ®Google Scholar
• Michel, J., D. Youssefi, and M. Grizonnet. 2015. “Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 952–964. doi:10.1109/TGRS.2014.2330857.
   Web of Science ®Google Scholar
• Miettinen, J., E. Hyer, A. S. Chia, L. K. Kwoh, and S. C. Liew. 2013. “Detection of Vegetation Fires and Burnt Areas by Remote Sensing in Insular Southeast Asian Conditions: Current Status of Knowledge and Future Challenges.” International Journal of Remote Sensing 34 (12): 4344–4366. doi:10.1080/01431161.2013.777489.
   Web of Science ®Google Scholar
• Miettinen, J., and S. C. Liew. 2008. “Comparison of Multitemporal Compositing Methods for Burnt Area Detection in Southeast Asian Conditions.” International Journal of Remote Sensing 29 (4): 1075–1092. doi:10.1080/01431160701281031.
   Web of Science ®Google Scholar
• Miller, J. D., and A. E. Thode. 2007. “Quantifying Burn Severity in a Heterogeneous Landscape with a Relative Version of the Delta Normalized Burn Ratio (Dnbr).” Remote Sensing of Environment 109 (1): 66–80. doi:10.1109/LGRS.2011.2174772.
   Web of Science ®Google Scholar
• Mitri, G. H., and I. Z. Gitas. 2004. “A semi-automated object-oriented Model for Burned Area Mapping in the Mediterranean Region Using Landsat-TM Imagery.” International Journal of Wildland Fire 13 (3): 367–376. doi:10.1109/LGRS.2018.2888641.
   Web of Science ®Google Scholar
• Mpakairi, K. S., H. Ndaimani, and B. Kavhu. 2020. “Exploring the Utility of Sentinel-2 MSI Derived Spectral Indices in Mapping Burned Areas in Different land-cover Types.” Scientific African 10: e00565. doi:10.1016/j.rse.2016.08.023.
   Google Scholar
• Navarro, G., I. Caballero, G. Silva, P.-C. Parra, Á. Vázquez, and R. Caldeira. 2017. “Evaluation of Forest Fire on Madeira Island Using Sentinel-2A MSI Imagery.” Int. J. Appl. Earth Obs. Geoinf 58: 97–106. doi:10.1016/j.jag.2017.02.003.
   Web of Science ®Google Scholar
• OTB Homepage. 2021. Accessed March 19. https://www.orfeo-toolbox.org/
   Google Scholar
• Otón, G., R. Ramo, J. Lizundia-Loiola, and E. Chuvieco. 2019. “Global Detection of long-term (1982-2017) Burned Area with AVHRR-LTDR Data.” Remote Sensing 11 (18): 2079. doi:10.3390/rs11182079.
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–2830.
   Web of Science ®Google Scholar
• Pepe, A., D. Stroppiana, F. Calo, P. Imperatore, L. Boschetti, C. Bignami, and P. A. Brivio. 2018. “Exploitation of Copernicus Sentinels Data for Sensing Fire-Disturbed Vegetated Areas.“ IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 22-27 July 2018, (IEEE), Valencia, Spain, 7593–7596. doi:10.1109/IGARSS.2018.8518272.
   Google Scholar
• Pereira, A. A., R. Libonati, D. Oom, L. M. C. Tavares, and J. M. Pereira. 2017. “ Avaliação de técnicas de compósitos multitemporais em imagens PROBA-V para o mapeamento de áreas queimadas.“ XVIII SBSR - Simpósio Brasileiro de Sensoriamento Remoto, 28- 31 May 2017, Santos, Brasil. https://proceedings.science/sbsr/papers/avaliacao-de-tecnicas-de-compositos-multitemporais-em-imagens-proba-v-para-o-mapeamento-de-areas-queimadas.
   Google Scholar
• Pereira, A. A., J. M. C. Pereira, R. Libonati, D. Oom, and L. M. T. Carvalho. 2016. “Multitemporal Image Compositing Evaluation Algorithms for PROBA-V Burned Area Analysis.“ Proba-V Symposium, 26 - 28 January 2016, Ghent, Belgium.
   Google Scholar
• Pereira, J. M. C., A. C. L. Sá, A. M. O. Sousa, J. M. N. Silva, T. N. Santos, and J. M. B. Carreiras. 1999. “ Spectral Characterisation and Discrimination of Burnt Areas. Remote Sensing of Large Wildfires.“ Berlin, Heidelberg: Springer, 123–138. doi:10.1007/978-3-642-60164-4_7.
   Google Scholar
• Periasamy, S. 2018. “Significance of Dual Polarimetric Synthetic Aperture Radar in Biomass Retrieval: An Attempt on Sentinel-1.” Remote Sensing of Environment 217: 537–549. doi:10.1016/j.rse.2018.09.003.
   Web of Science ®Google Scholar
• Pipia, L., J. Muñoz-Marí, E. Amin, S. Belda, G. Camps-Valls, and J. Verrelst. 2019. “Fusing Optical and SAR Time Series for LAI Gap Fillingwith Multioutput Gaussian Processes.” Remote Sensing of Environment 235: 111452. doi:10.1016/j.rse.2019.111452.
   PubMed Web of Science ®Google Scholar
• Polychronaki, A., and I. Z. Gitas. 2010. “The Development of an Operational Procedure for burned-area Mapping Using object-based Classification and ASTER Imagery.” International Journal of Remote Sensing 31 (4): 1113–1120. doi:10.1080/01431160903334497.
   Web of Science ®Google Scholar
• Polychronaki, A., and I. Z. Gitas. 2012. “Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and object-based Image Analysis.” Remote Sensing 4 (2): 424–438. doi:10.3390/rs4020424.
   Web of Science ®Google Scholar
• Polychronaki, A., I. Z. Gitas, S. Veraverbeke, and A. Debien. 2013. “Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using object-based Classification.” Remote Sensing 5 (11): 5680–5701. doi:10.3390/rs5115680.
   Web of Science ®Google Scholar
• Pulvirenti, L., G. Squicciarino, E. Fiori, P. Fiorucci, L. Ferraris, D. Negro, A. Gollini, M. Severino, and S. Puca. 2020. “An Automatic Processing Chain for near real-time Mapping of Burned Forest Areas Using Sentinel-2 Data.” Remote Sensing 12 (4): 674. doi:10.3390/rs12040674.
   Web of Science ®Google Scholar
• Ql, J., and Y. Kerr. 1997. “On Current Compositing Algorithms.” Remote Sensing Reviews 15 (1–4): 235–256. doi:10.1080/02757259709532340.
   Google Scholar
• Radoux, J., and P. Defourny. 2008. Quality Assessment of Segmentation Results Devoted to object-based Classification, In: Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications, 257–271. Berlin Heidelberg: Springer-Verlag Berlin Heidelberg.
   Google Scholar
• Reid, C. E., M. Brauer, F. H. Johnston, M. Jerrett, J. R. Balmes, and C. T. Elliott. 2016. “Critical Review of Health Impacts of Wildfire Smoke Exposure.” Environmental Health Perspectives 124 (9): 1334–1343. doi:10.1289/ehp.1409277.
   PubMed Web of Science ®Google Scholar
• Richards, J. A. 2009. “The Radiation Framework.”Remote Sensing with ImagingRadar, 11-52. Berlin, Heidelberg:Springer-Verlag. doi:10.1007/978-3-642-02020-9.
   Google Scholar
• Rodman, K. C., R. A. Andrus, T. T. Veblen, and S. J. Hart. 2021. “Remote Sensing of Environment Disturbance Detection in Landsat Time Series Is Influenced by Tree Mortality Agent and Severity, Not by Prior Disturbance.” Remote Sensing of Environment 254: 112244. doi:10.1016/j.rse.2020.112244.
   Web of Science ®Google Scholar
• Roteta, E., A. Bastarrika, M. Padilla, T. Storm, and E. Chuvieco. 2019. “Development of a Sentinel-2 Burned Area Algorithm: Generation of a Small Fire Database for sub-Saharan Africa.” Remote Sensing of Environment 222: 1–17. doi:10.1016/j.rse.2018.12.011.
   Web of Science ®Google Scholar
• Ruiz-Ramos, J., A. Marino, and C. P. Boardman. 2018. “Using Sentinel 1-SAR for Monitoring Long Term Variation in Burnt Forest Areas.” Int. Geosci. Remote Sens. Symp, July. 4901–4904. doi:10.1109/IGARSS.2018.8518960.
   Google Scholar
• Saatchi, S. 2019. “SAR Methods for Mapping and Monitoring Forest Biomass.” In The SAR Handbook. Comprehensive Methodologies for Forest Monitoring and Biomass Estimation. NASA, 207–247. doi:10.25966/nr2c-s697.
   Google Scholar
• Sali, M., E. Piaser, M. Boschetti, P. A. Brivio, G. Sona, G. Bordogna, and D. Stroppiana. 2021. “A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing.” Remote Sensing 13 (11): 2214. doi:10.3390/rs13112214.
   Web of Science ®Google Scholar
• San-Miguel-Ayanz, J., T. Durrant, R. Boca, G. Libertà, A. Branco, D. de Rigo, D. Ferrari, et al. 2018. “Forest Fires in Europe, Middle East and North Africa 2017.” Publications Office of the European Union. doi:10.2760/27815.
   Google Scholar
• Schepers, L., B. Haest, S. Veraverbeke, T. Spanhove, J. Borre, and R. Vanden, Goossens. 2014. “Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX).” Remote Sensing 6 (3): 1803–1826. doi:10.3390/rs6031803.
   Web of Science ®Google Scholar
• Sertel, E., and U. Alganci. 2016. “Comparison of Pixel and object-based Classification for Burned Area Mapping Using SPOT-6 Images. Geomatics, Nat.” Geomatics, Natural Hazards and Risk 7 (4): 1198–1206. doi:10.1080/19475705.2015.1050608.
   Web of Science ®Google Scholar
• Setzer, A. W., and M. C. Pereira. 1991. “Amazonia Biomass Burnings in 1987 and an Estimate of Their Tropospheric Emissions.” Ambio. doi:10.2307/4313765.
   Web of Science ®Google Scholar
• Shimabukuro, Y. E., J. Miettinen, R. Beuchle, R. C. Grecchi, D. Simonetti, and F. Achard. 2015. “Estimating Burned Area in Mato Grosso, Brazil, Using an Object-Based Classification Method on a Systematic Sample of Medium Resolution Satellite Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (9): 4502–4508. doi:10.1109/JSTARS.2015.2464097.
   Web of Science ®Google Scholar
• Shufelt, J. A. 1999. “Performance Evaluation and Analysis of Monocular Building Extraction from Aerial Imagery.” IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (4): 311–326. doi:10.1109/34.761262.
   Web of Science ®Google Scholar
• Silva, J. M. N., J. F. C. L. Cadima, J. M. C. Pereira, and J. M. Grégoire. 2004. “Assessing the Feasibility of a Global Model for multi-temporal Burned Area Mapping Using SPOT-VEGETATION Data.” International Journal of Remote Sensing 25 (22): 4889–4913. doi:10.1080/01431160412331291251.
   Web of Science ®Google Scholar
• Silva, J. M. N., M. V. Moreno, Y. Le Page, D. Oom, I. Bistinas, and J. M. C. Pereira. 2019. “Spatiotemporal Trends of Area Burnt in the Iberian Peninsula, 1975–2013.” Regional Environmental Change 19 (2): 515–527. doi:10.1007/s10113-018-1415-6.
   Web of Science ®Google Scholar
• Small, D. 2011. “Flattening Gamma: Radiometric Terrain Correction for SAR Imagery.” IEEE Transactions on Geoscience and Remote Sensing 49 (8): 3081–3093. doi:10.1109/TGRS.2011.2120616.
   Web of Science ®Google Scholar
• Smiraglia, D., F. Filipponi, S. Mandrone, A. Tornato, and A. Taramelli. 2020. “Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images.” Remote Sensing 12 (11): 1862. doi:10.3390/rs12111862.
   Web of Science ®Google Scholar
• Smith, A. M. S., M. J. Wooster, N. A. Drake, F. M. Dipotso, M. J. Falkowski, and A. T. Hudak. 2005. “Testing the Potential of Multispectral Remote Sensing for Retrospectively Estimating Fire Severity in African Savannahs.” Remote Sensing of Environment 97 (1): 92–115. doi:10.1016/j.rse.2005.04.014.
   Web of Science ®Google Scholar
• Sokolova, M., N. Japkowicz, and S. Szpakowicz. 2006. “Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluatio Discriminant.“ 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 1015-1021. Berlin, Heidelberg: Springer. doi:10.1007/11941439_114.
   Google Scholar
• Sousa, A. M. O., J. M. C. Pereira, and J. M. N. Silva. 2003. “Evaluating the Performance of Multitemporal Image Compositing Algorithms for Burned Area Analysis.” International Journal of Remote Sensing 24 (6): 1219–1236. doi:10.1080/01431160110114466.
   Web of Science ®Google Scholar
• Storey, E. A., K. R. Lee West, and D. A. Stow. 2021. “Utility and Optimization of LANDSAT-derived Burned Area Maps for Southern California.” International Journal of Remote Sensing 42 (2): 486–505. doi:10.1080/01431161.2020.1809741.
   Web of Science ®Google Scholar
• Stroppiana, D., R. Azar, F. Calò, A. Pepe, P. Imperatore, M. Boschetti, J. M. N. Silva, P. A. Brivio, and R. Lanari. 2015. “Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions.” Remote Sensing 7 (2): 1320–1345. doi:10.3390/rs70201320.
   Web of Science ®Google Scholar
• Stroppiana, D., S. Pinnock, J. M. C. Pereira, and J.-M. Grégoire. 2002. “Radiometric Analysis of SPOT-VEGETATION Images for Burnt Area Detection in Northern Australia.” Remote Sensing of Environment 82 (1): 21–37. doi:10.1016/S0034-4257(02).
   Web of Science ®Google Scholar
• Szigarski, C., T. Jagdhuber, M. Baur, C. Thiel, M. Parrens, J. Wigneron, M. Piles, and D. Entekhabi. 2018. “Analysis of the Radar Vegetation Index and Potential Improvements.” Remote Sensing 10 (1776): 1–15. doi:10.3390/rs10111776.
   Google Scholar
• Tanase, M. A., M. A. Belenguer-Plomer, E. Roteta, A. Bastarrika, J. Wheeler, Á. Fernández-Carrillo, K. Tansey, et al. 2020. “Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa.” Remote Sensing 12 (2): 334. doi:10.3390/rs12020334.
   Web of Science ®Google Scholar
• Tanase, M., J. de la Riva, M. Santoro, F. Pérez-Cabello, and E. Kasischke. 2011. “Sensitivity of SAR Data to post-fire Forest Regrowth in Mediterranean and Boreal Forests.” Remote Sensing of Environment 115 (8): 2075–2085. doi:10.1016/j.rse.2011.04.009.
   Web of Science ®Google Scholar
• Tanase, M. A., M. Santoro, J. De La Riva, F. Prez-Cabello, and T. Le Toan. 2010a. “Sensitivity of X-, C-, and L-band SAR Backscatter to Burn Severity in Mediterranean Pine Forests.” IEEE Transactions on Geoscience and Remote Sensing 48 (10): 3663–3675. doi:10.1109/TGRS.2010.2049653.
   Web of Science ®Google Scholar
• Tanase, M. A., M. Santoro, U. Wegmüller, J. de la Riva, and F. Pérez-Cabello. 2010b. “. Properties of X-, C- and L-band repeat-pass Interferometric SAR Coherence in Mediterranean Pine Forests Affected by Fires.” Remote Sensing of Environment 114 (10): 2182–2194. doi:10.1016/j.rse.2010.04.021.
   Web of Science ®Google Scholar
• Tansey, K., J.-M. Grégoire, E. Binaghi, L. Boschetti, P. A. Brivio, D. Ershov, S. Flasse, et al. 2004b. “A Global Inventory of Burned Areas at 1 Km Resolution for the Year 2000 Derived from Spot Vegetation Data.” Clim. Change 67: 345–377. doi:10.1007/s10584-004-2800-3.
   Web of Science ®Google Scholar
• Tansey, K., J. Gre, D. Stroppiana, M. C. Pereira, L. Boschetti, M. Maggi, P. A. Brivio, and R. Fraser. 2004a. “Vegetation Burning in the Year 2000.” Global Burned Area Estimates from SPOT VEGETATION Data 109: 1–22. doi:10.1029/2003JD003598.
   Google Scholar
• Trudel, M., F. Charbonneau, and R. Leconte. 2012. “Using RADARSAT-2 Polarimetric and ENVISAT-ASAR dual-polarization Data for Estimating Soil Moisture over Agricultural Fields.” Can. J. Remote Sens 38: 514–527. doi:10.5589/m12-043.
   Web of Science ®Google Scholar
• Vanderhoof, M. K., T. J. Hawbaker, C. Teske, A. Ku, J. Noble, and J. Picotte, 2021. Mapping Wetland Burned Area from Sentinel-2 across the Southeastern United States and Its Contributions Relative to Landsat-8 (2016 – 2019) 8
   Google Scholar
• Van, Shoaie, D. D., S. Leeuwen, and T. Van, Veraverbeke. 2021. “Spectral Signature Analysis of False Positive Burned Area Detection from Agricultural Harvests Using Sentinel-2 Data.” Int. J. Appl. Earth Obs. Geoinf 97: 102296. doi:10.1016/j.jag.2021.102296.
   Web of Science ®Google Scholar
• Verbyla, D. L. V., E. S. K. Kasischke, and E. E. H. Hoy, 2008 . Seasonal and Topographic Effects on Estimating Fire Severity from Landsat TM/ETM + Data 527–534.
   Google Scholar
• Verhegghen, A., H. Eva, G. Ceccherini, F. Achard, V. Gond, S. Gourlet-Fleury, and P. O. Cerutti. 2016. “The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests.” Remote Sens 8 (12): 1–22. doi:10.1016/j.rse.2011.04.009.
   Google Scholar
• Zhang, P., A. Nascetti, Y. Ban, and M. Gong. 2019. “An Implicit Radar Convolutional Burn Index for Burnt Area Mapping with Sentinel-1 C-band SAR Data.” ISPRS Journal of Photogrammetry and Remote Sensing 158: 50–62. doi:10.1016/j.isprsjprs.2019.09.013.
   Web of Science ®Google Scholar