{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T22:44:53Z","timestamp":1769035493902,"version":"3.49.0"},"publisher-location":"Cham","reference-count":77,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030870065","type":"print"},{"value":"9783030870072","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87007-2_11","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T17:02:22Z","timestamp":1631293342000},"page":"139-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Combined Use of Sentinel-1 and Sentinel-2 for Burn Severity Mapping in a Mediterranean Region"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4740-6468","authenticated-orcid":false,"given":"Giandomenico","family":"De Luca","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5201-9836","authenticated-orcid":false,"given":"Jo\u00e3o M. N.","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9244-3487","authenticated-orcid":false,"given":"Duarte","family":"Oom","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0388-0256","authenticated-orcid":false,"given":"Giuseppe","family":"Modica","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Chuvieco, E.: Earth Observation of Wildland Fires in Mediterranean Ecosystems. Springer, Berlin Heidelberg (2009), https:\/\/doi.org\/10.1007\/978-3-642-01754-4","DOI":"10.1007\/978-3-642-01754-4"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.rse.2017.12.029","volume":"206","author":"V Fern\u00e1ndez-Garc\u00eda","year":"2018","unstructured":"Fern\u00e1ndez-Garc\u00eda, V., Santamarta, M., Fern\u00e1ndez-Manso, A., Quintano, C., Marcos, E., Calvo, L.: Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens. Environ. 206, 205\u2013217 (2018). https:\/\/doi.org\/10.1016\/j.rse.2017.12.029","journal-title":"Remote Sens. Environ."},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jag.2011.09.005","volume":"20","author":"A Lanorte","year":"2013","unstructured":"Lanorte, A., Danese, M., Lasaponara, R., Murgante, B.: Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. Int. J. Appl. Earth Obs. Geoinf. 20, 42\u201351 (2013). https:\/\/doi.org\/10.1016\/j.jag.2011.09.005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Saulino, L., et al.: Detecting burn severity across Mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sens. 12, 1\u201321 (2020), https:\/\/doi.org\/10.3390\/rs12040741","DOI":"10.3390\/rs12040741"},{"key":"11_CR5","doi-asserted-by":"publisher","unstructured":"San-Miguel-Ayanza, J., et al.: Forest fires in Europe, Middle East and North Africa 2018. JRC Technical Report. Publications Office of the European Union (2019), https:\/\/doi.org\/10.2760\/1128","DOI":"10.2760\/1128"},{"issue":"2","key":"11_CR6","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/s10113-018-1415-6","volume":"19","author":"JMN Silva","year":"2018","unstructured":"Silva, J.M.N., Moreno, M.V., Le Page, Y., Oom, D., Bistinas, I., Pereira, J.M.C.: Spatiotemporal trends of area burnt in the Iberian Peninsula, 1975\u20132013. Reg. Environ. Change 19(2), 515\u2013527 (2018). https:\/\/doi.org\/10.1007\/s10113-018-1415-6","journal-title":"Reg. Environ. Change"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.jag.2011.09.001","volume":"20","author":"GH Mitri","year":"2012","unstructured":"Mitri, G.H., Gitas, I.Z.: Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 20, 60\u201366 (2012). https:\/\/doi.org\/10.1016\/j.jag.2011.09.001","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"6499","DOI":"10.1080\/01431161.2018.1460508","volume":"39","author":"M H\u00e4usler","year":"2018","unstructured":"H\u00e4usler, M., et al.: Assessment of the indirect impact of wildfire (severity) on actual evapotranspiration in eucalyptus forest based on the surface energy balance estimated from remote-sensing techniques. Int. J. Remote Sens. 39, 6499\u20136524 (2018). https:\/\/doi.org\/10.1080\/01431161.2018.1460508","journal-title":"Int. J. Remote Sens."},{"key":"11_CR9","doi-asserted-by":"publisher","DOI":"10.5424\/fs\/2015243-07855","author":"G Modica","year":"2015","unstructured":"Modica, G., Merlino, A., Solano, F., Mercurio, R.: An index for the assessment of degraded Mediterranean forest ecosystems. For. Syst. (2015). https:\/\/doi.org\/10.5424\/fs\/2015243-07855","journal-title":"For. Syst."},{"key":"11_CR10","doi-asserted-by":"publisher","unstructured":"Morresi, D., Vitali, A., Urbinati, C., Garbarino, M.: Forest spectral recovery and regeneration dynamics in stand-replacing wildfires of central Apennines derived from Landsat time series. Remote Sens. 11 (2019), https:\/\/doi.org\/10.3390\/rs11030308","DOI":"10.3390\/rs11030308"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ecoleng.2019.04.004","volume":"134","author":"T Semeraro","year":"2019","unstructured":"Semeraro, T., Vacchiano, G., Aretano, R., Ascoli, D.: Application of vegetation index time series to value fire effect on primary production in a Southern European rare wetland. Ecol. Eng. 134, 9\u201317 (2019). https:\/\/doi.org\/10.1016\/j.ecoleng.2019.04.004","journal-title":"Ecol. Eng."},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Di Fazio, S., Modica, G., Zoccali, P.: Evolution trends of land use\/land cover in a mediterranean forest landscape in Italy. In: Murgante, B., et al. (eds.) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. LNCS, vol. 6782, pp. 284\u2013299. Springer, Berlin, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21928-3_20","DOI":"10.1007\/978-3-642-21928-3_20"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1071\/WF07049","volume":"18","author":"JE Keeley","year":"2009","unstructured":"Keeley, J.E.: Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildl. Fire. 18, 116\u2013126 (2009). https:\/\/doi.org\/10.1071\/WF07049","journal-title":"Int. J. Wildl. Fire."},{"key":"11_CR14","unstructured":"Key, C.H., Benson, N.C.: Landscape Assessment (LA) sampling and analysis methods. In: FIREMON: Fire Effects Monitoring and Inventory System (2006)"},{"key":"11_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-58814-4_5","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2020","author":"G De Luca","year":"2020","unstructured":"De Luca, G., Modica, G., Fattore, C., Lasaponara, R.: Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12253, pp. 63\u201377. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58814-4_5"},{"key":"11_CR16","doi-asserted-by":"publisher","DOI":"10.5772\/20571","author":"I Gitas","year":"2012","unstructured":"Gitas, I., Mitri, G., Veraverbeke, S., Polychronaki, A.: Advances in remote sensing of post-fire vegetation recovery monitoring - a review. Remote Sens. Biomass - Princ. Appl. (2012). https:\/\/doi.org\/10.5772\/20571","journal-title":"Remote Sens. Biomass - Princ. Appl."},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.rse.2018.03.019","volume":"210","author":"R Meng","year":"2018","unstructured":"Meng, R., Wu, J., Zhao, F., Cook, B.D., Hanavan, R.P., Serbin, S.P.: Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques. Remote Sens. Environ. 210, 282\u2013296 (2018). https:\/\/doi.org\/10.1016\/j.rse.2018.03.019","journal-title":"Remote Sens. Environ."},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.rse.2008.10.011","volume":"113","author":"A De Santis","year":"2009","unstructured":"De Santis, A., Chuvieco, E.: GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sens. Environ. 113, 554\u2013562 (2009). https:\/\/doi.org\/10.1016\/j.rse.2008.10.011","journal-title":"Remote Sens. Environ."},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Ot\u00f3n, G., Ramo, R., Lizundia-Loiola, J., Chuvieco, E.: Global detection of long-term (1982\u20132017) burned area with AVHRR-LTDR data. Remote Sens. 11 (2019), https:\/\/doi.org\/10.3390\/rs11182079","DOI":"10.3390\/rs11182079"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.rse.2006.11.022","volume":"108","author":"A De Santis","year":"2007","unstructured":"De Santis, A., Chuvieco, E.: Burn severity estimation from remotely sensed data: performance of simulation versus empirical models. Remote Sens. Environ. 108, 422\u2013435 (2007). https:\/\/doi.org\/10.1016\/j.rse.2006.11.022","journal-title":"Remote Sens. Environ."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.rse.2015.08.025","volume":"170","author":"MA Tanase","year":"2015","unstructured":"Tanase, M.A., Kennedy, R., Aponte, C.: Radar Burn Ratio for fire severity estimation at canopy level: an example for temperate forests. Remote Sens. Environ. 170, 14\u201331 (2015). https:\/\/doi.org\/10.1016\/j.rse.2015.08.025","journal-title":"Remote Sens. Environ."},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","volume":"225","author":"E Chuvieco","year":"2019","unstructured":"Chuvieco, E., et al.: Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens. Environ. 225, 45\u201364 (2019). https:\/\/doi.org\/10.1016\/j.rse.2019.02.013","journal-title":"Remote Sens. Environ."},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs13040586","volume":"13","author":"S Pratic\u00f2","year":"2021","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., Modica, G.: Machine learning classification of Mediterranean forest habitats in google earth engine based on seasonal sentinel-2 time-series and input image composition optimisation. Remote Sens. 13, 1\u201328 (2021)","journal-title":"Remote Sens."},{"key":"11_CR24","unstructured":"ESA Sentinel Homepage (2021): https:\/\/sentinels.copernicus.eu\/web\/sentinel\/home. Accessed 11 Mar 2021"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Silva, J.M.N., Cadima, J.F.C.L., Pereira, J.M.C., Gr\u00e9goire, J.M.: Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data. Int. J. Remote Sens. (2004)","DOI":"10.1080\/01431160412331291251"},{"key":"11_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-60164-4_7","author":"JMC Pereira","year":"1999","unstructured":"Pereira, J.M.C., S\u00e1, A.C.L., Sousa, A.M.O., Silva, J.M.N., Santos, T.N., Carreiras, J.M.B.: Spectral characterisation and discrimination of burnt areas. Remote Sens. Large Wildfires (1999). https:\/\/doi.org\/10.1007\/978-3-642-60164-4_7","journal-title":"Remote Sens. Large Wildfires"},{"key":"11_CR27","doi-asserted-by":"publisher","first-page":"456","DOI":"10.3390\/rs4020456","volume":"4","author":"CA Cansler","year":"2012","unstructured":"Cansler, C.A., McKenzie, D.: How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods. Remote Sens. 4, 456\u2013483 (2012). https:\/\/doi.org\/10.3390\/rs4020456","journal-title":"Remote Sens."},{"key":"11_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2005.03.002","author":"J Epting","year":"2005","unstructured":"Epting, J., Verbyla, D., Sorbel, B.: Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sens. Environ. (2005). https:\/\/doi.org\/10.1016\/j.rse.2005.03.002","journal-title":"Remote Sens. Environ."},{"key":"11_CR29","doi-asserted-by":"publisher","unstructured":"Fornacca, D., Ren, G., Xiao, W.: 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 Sens. 10 (2018), https:\/\/doi.org\/10.3390\/rs10081196","DOI":"10.3390\/rs10081196"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Mallinis, G., Mitsopoulos, I., Chrysafi, I.: Evaluating and comparing sentinel 2A and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience Remote Sens. (2018)","DOI":"10.1080\/15481603.2017.1354803"},{"key":"11_CR31","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.rse.2008.08.008","volume":"113","author":"A De Santis","year":"2009","unstructured":"De Santis, A., Chuvieco, E., Vaughan, P.J.: Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sens. Environ. 113, 126\u2013136 (2009). https:\/\/doi.org\/10.1016\/j.rse.2008.08.008","journal-title":"Remote Sens. Environ."},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Schepers, L., et al..: Burned area detection and burn severity assessment of a heathland fire in Belgium using airborne imaging spectroscopy (APEX). Remote Sens. 6, 1803\u20131826 (2014)","DOI":"10.3390\/rs6031803"},{"key":"11_CR33","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.rse.2013.04.017","volume":"136","author":"C Quintano","year":"2013","unstructured":"Quintano, C., Fern\u00e1ndez-Manso, A., Roberts, D.A.: Multiple endmember spectral mixture analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sens. Environ. 136, 76\u201388 (2013)","journal-title":"Remote Sens. Environ."},{"key":"11_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2016.03.005","author":"A Fern\u00e1ndez-Manso","year":"2016","unstructured":"Fern\u00e1ndez-Manso, A., Fern\u00e1ndez-Manso, O., Quintano, C.: SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int. J. Appl. Earth Obs. Geoinf. (2016). https:\/\/doi.org\/10.1016\/j.jag.2016.03.005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Filipponi, F.: BAIS2: Burned Area Index for Sentinel-2. 5177 (2018)","DOI":"10.3390\/ecrs-2-05177"},{"key":"11_CR36","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/j.rse.2008.11.009","volume":"113","author":"JD Miller","year":"2009","unstructured":"Miller, J.D., et al.: Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645\u2013656 (2009)","journal-title":"Remote Sens. Environ."},{"key":"11_CR37","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.3390\/rs6031827","volume":"6","author":"SA Parks","year":"2014","unstructured":"Parks, S.A., Dillon, G.K., Miller, C.: A new metric for quantifying burn severity: the relativized burn ratio. Remote Sens. 6, 1827\u20131844 (2014). https:\/\/doi.org\/10.3390\/rs6031827","journal-title":"Remote Sens."},{"key":"11_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2015.11.002","author":"Z Zheng","year":"2016","unstructured":"Zheng, Z., Zeng, Y., Li, S., Huang, W.: A new burn severity index based on land surface temperature and enhanced vegetation index. Int. J. Appl. Earth Obs. Geoinf. (2016). https:\/\/doi.org\/10.1016\/j.jag.2015.11.002","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Addison, P., Oommen, T.: Utilizing satellite radar remote sensing for burn severity estimation. Int. J. Appl. Earth Obs. Geoinf. (2018)","DOI":"10.1016\/j.jag.2018.07.002"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Kurum, M.: C-Band SAR Backscatter Evaluation of 2008 Gallipoli Forest Fire. 12, 1091\u20131095 (2015)","DOI":"10.1109\/LGRS.2014.2382716"},{"key":"11_CR41","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/LGRS.2018.2888641","volume":"16","author":"R Lasaponara","year":"2019","unstructured":"Lasaponara, R., Tucci, B.: Identification of burned areas and severity. IEEE Geosci. Remote Sens. Lett. 16, 917\u2013921 (2019). https:\/\/doi.org\/10.1109\/LGRS.2018.2888641","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"11_CR42","doi-asserted-by":"publisher","unstructured":"Tanase, M.A., Santoro, M., Aponte, C., De La Riva, J.: Polarimetric properties of burned forest areas at C- and L-band. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2014), https:\/\/doi.org\/10.1109\/JSTARS.2013.2261053","DOI":"10.1109\/JSTARS.2013.2261053"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Tanase, M.A., Santoro, M., Wegm\u00fcller, U., de la Riva, J., P\u00e9rez-Cabello, F.: Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires. Remote Sens. Environ. (2010a)","DOI":"10.1016\/j.rse.2010.04.021"},{"key":"11_CR44","doi-asserted-by":"publisher","first-page":"3663","DOI":"10.1109\/TGRS.2010.2049653","volume":"48","author":"MA Tanase","year":"2010","unstructured":"Tanase, M.A., Santoro, M., De La Riva, J., P\u00e9rez-Cabello, F., Le Toan, T.: Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in Mediterranean pine forests. IEEE Trans. Geosci. Remote Sens. 48, 3663\u20133675 (2010)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"11_CR45","doi-asserted-by":"publisher","unstructured":"Imperatore, P., Azar, R., Calo, F., Stroppiana, D., Brivio, P.A., Lanari, R., Pepe, A.: Effect of the vegetation fire on backscattering: an investigation based on Sentinel-1 observations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 4478\u20134492 (2017), https:\/\/doi.org\/10.1109\/JSTARS.2017.2717039","DOI":"10.1109\/JSTARS.2017.2717039"},{"key":"11_CR46","doi-asserted-by":"publisher","unstructured":"Lasko, K.: 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. Geocarto Int. 1\u201321 (2019), https:\/\/doi.org\/10.1080\/10106049.2019.1608592","DOI":"10.1080\/10106049.2019.1608592"},{"key":"11_CR47","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.rse.2014.09.034","volume":"156","author":"EA Lehmann","year":"2015","unstructured":"Lehmann, E.A., et al.: SAR and optical remote sensing: assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system. Remote Sens. Environ. 156, 335\u2013348 (2015)","journal-title":"Remote Sens. Environ."},{"key":"11_CR48","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.3390\/rs70201320","volume":"7","author":"D Stroppiana","year":"2015","unstructured":"Stroppiana, D., et al.: Integration of optical and SAR data for burned area mapping in Mediterranean regions. Remote Sens. 7, 1320\u20131345 (2015)","journal-title":"Remote Sens."},{"key":"11_CR49","first-page":"1","volume":"00","author":"G De Luca","year":"2021","unstructured":"De Luca, G., Silva, J.M.N., Modica, G.: 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\u201326 (2021)","journal-title":"GIScience Remote Sens."},{"key":"11_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs8120986","volume":"8","author":"A Verhegghen","year":"2016","unstructured":"Verhegghen, A., et al.: The potential of sentinel satellites for burnt area mapping and monitoring in the Congo Basin forests. Remote Sens. 8, 1\u201322 (2016)","journal-title":"Remote Sens."},{"key":"11_CR51","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1071\/WF15059","volume":"24","author":"MA Tanase","year":"2015","unstructured":"Tanase, M.A., Kennedy, R., Aponte, C.: Fire severity estimation from space: a comparison of active and passive sensors and their synergy for different forest types. Int. J. Wildl. Fire. 24, 1062\u20131075 (2015). https:\/\/doi.org\/10.1071\/WF15059","journal-title":"Int. J. Wildl. Fire."},{"key":"11_CR52","unstructured":"The Python Language Reference (2021): https:\/\/docs.python.org\/3\/reference\/. Accessed 15 Mar 2021"},{"key":"11_CR53","unstructured":"Sistema Nacional de Informa\u00e7\u00e3o Geogr\u00e3fica (SNIG) (2021): https:\/\/snig.dgterritorio.gov.pt\/. Accessed 15 Mar 2021"},{"key":"11_CR54","unstructured":"Copernicus Open Access Hub (2021): https:\/\/scihub.copernicus.eu\/. Accessed 15 Mar 2021"},{"key":"11_CR55","unstructured":"Esri ArcGIS World Imagery (2021): https:\/\/www.arcgis.com\/home\/item.html?id=10df2279f9684e4a9f6a7f08febac2a9. Accessed 19 Mar 2021"},{"key":"11_CR56","unstructured":"ESA SNAP Homepage (2021): http:\/\/step.esa.int\/main\/toolboxes\/snap\/. Accessed 11 Mar 2021"},{"key":"11_CR57","unstructured":"ESA SNAP Cookbook (2021): https:\/\/senbox.atlassian.net\/wiki\/spaces\/SNAP\/pages\/24051769\/Cookbook"},{"key":"11_CR58","doi-asserted-by":"publisher","DOI":"10.1109\/36.842003","author":"S Quegan","year":"2000","unstructured":"Quegan, S., Toan, T.L., Yu, J.J., Ribbes, F., Floury, N.: Multitemporal ERS SAR analysis applied to forest mapping. IEEE Trans. Geosci. Remote Sens. (2000). https:\/\/doi.org\/10.1109\/36.842003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"11_CR59","doi-asserted-by":"crossref","unstructured":"Santoso, A.W., Pebrianti, D., Bayuaji, L., Zain, J.M.: Performance of various speckle reduction filters on Synthetic Aperture Radar image. In: 2015 4th Int. Conf. Softw. Eng. Comput. Syst. ICSECS 2015 Virtuous Softw. Solut. Big Data, pp. 11\u201314 (2015)","DOI":"10.1109\/ICSECS.2015.7333103"},{"key":"11_CR60","doi-asserted-by":"publisher","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","volume":"247","author":"D Mandal","year":"2020","unstructured":"Mandal, D., et al.: Remote sensing of environment dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sens. Environ. 247, 111954 (2020). https:\/\/doi.org\/10.1016\/j.rse.2020.111954","journal-title":"Remote Sens. Environ."},{"key":"11_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/app9040655","author":"R Nasirzadehdizaji","year":"2019","unstructured":"Nasirzadehdizaji, R., Sanli, F.B., Abdikan, S., Cakir, Z., Sekertekin, A., Ustuner, M.: Sensitivity analysis of multi-temporal Sentinel-1 SAR parameters to crop height and canopy coverage. Appl. Sci. (2019). https:\/\/doi.org\/10.3390\/app9040655","journal-title":"Appl. Sci."},{"key":"11_CR62","doi-asserted-by":"publisher","first-page":"102214","DOI":"10.1016\/j.jag.2020.102214","volume":"94","author":"AP Nicolau","year":"2021","unstructured":"Nicolau, A.P., Flores-Anderson, A., Griffin, R., Herndon, K., Meyer, F.J.: Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest. Int. J. Appl. Earth Obs. Geoinf. 94, 102214 (2021). https:\/\/doi.org\/10.1016\/j.jag.2020.102214","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"11_CR63","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"11_CR64","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.1890\/07-0539.1","volume":"88","author":"DR Cutler","year":"2007","unstructured":"Cutler, D.R., et al.: Random forests for classification in ecology. Ecology 88, 2783\u20132792 (2007)","journal-title":"Ecology"},{"key":"11_CR65","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR66","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., Green, K.: Assessing the Accuracy of Remotely Sensed Data. Principles and Practices (2019)","DOI":"10.1201\/9780429052729"},{"key":"11_CR67","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-540-31865-1_25","volume-title":"Advances in Information Retrieval","author":"C Goutte","year":"2005","unstructured":"Goutte, C., Gaussier, E.: A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In: Losada, D.E., Fern\u00e1ndez-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345\u2013359. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31865-1_25"},{"key":"11_CR68","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1109\/TGRS.2012.2207123","volume":"51","author":"AO Ok","year":"2013","unstructured":"Ok, A.O., Senaras, C., Yuksel, B.: Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Trans. Geosci. Remote Sens. 51, 1701\u20131717 (2013)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"11_CR69","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/34.761262","volume":"21","author":"JA Shufelt","year":"1999","unstructured":"Shufelt, J.A.: Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans. Pattern Anal. Mach. Intell. 21, 311\u2013326 (1999)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR70","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/11941439_114","volume-title":"AI 2006: Advances in Artificial Intelligence","author":"M Sokolova","year":"2006","unstructured":"Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: Sattar, A., Kang, B.-h (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015\u20131021. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11941439_114"},{"key":"11_CR71","doi-asserted-by":"crossref","unstructured":"Modica, G., Messina, G., De Luca, G., Fiozzo, V., Pratic\u00f2, S.: Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees\u2019 crowns from UAV multispectral imagery. Comput. Electron. Agric. 175, 105500 (2020)","DOI":"10.1016\/j.compag.2020.105500"},{"key":"11_CR72","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45, 427\u2013437 (2009)","journal-title":"Inf. Process. Manag."},{"key":"11_CR73","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1080\/22797254.2021.1951623","volume":"54","author":"G Modica","year":"2021","unstructured":"Modica, G., De Luca, G., Messina, G., Pratic\u00f2, S.: Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery in the framework of precision agriculture. Eur. J. Remote Sens. 54, 431\u2013460 (2021). https:\/\/doi.org\/10.1080\/22797254.2021.1951623","journal-title":"Eur. J. Remote Sens."},{"key":"11_CR74","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1080\/07038992.2020.1735931","volume":"46","author":"MR Gallagher","year":"2020","unstructured":"Gallagher, M.R., et al.: 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. Can. J. Remote Sens. 46, 100\u2013111 (2020). https:\/\/doi.org\/10.1080\/07038992.2020.1735931","journal-title":"Can. J. Remote Sens."},{"key":"11_CR75","doi-asserted-by":"publisher","unstructured":"Inoue, Y., et al.: Reflectance characteristics of major land surfaces in slash \u2010 and \u2010 burn ecosystems in Laos. 1161 (2019), https:\/\/doi.org\/10.1080\/01431160701442039","DOI":"10.1080\/01431160701442039"},{"key":"11_CR76","doi-asserted-by":"publisher","DOI":"10.1071\/WF10013","author":"JJ Picotte","year":"2011","unstructured":"Picotte, J.J., Robertson, K.M.: Validation of remote sensing of burn severity in south-eastern US ecosystems. Int. J. Wildl. Fire. (2011). https:\/\/doi.org\/10.1071\/WF10013","journal-title":"Int. J. Wildl. Fire."},{"key":"11_CR77","doi-asserted-by":"crossref","unstructured":"Verbyla, D.L.V., Kasischke, E.S.K., Hoy, E.E.H.: Seasonal and topographic effects on estimating fire severity from Landsat TM\/ETM + data. 527\u2013534 (2008)","DOI":"10.1071\/WF08038"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87007-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T17:05:57Z","timestamp":1631293557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87007-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030870065","9783030870072"],"references-count":77,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87007-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"466","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}