@InProceedings{GracianiBrogCort:2023:MaÁrAg,
               author = "Graciani, Silvio and Brogioni, Marco and Corti, Marcelo",
          affiliation = "{Universidad Nacional del Litoral (UNL)} and {Consiglio Nazionale 
                         delle Ricerche (CNR)} and {Universidad Nacional del Litoral 
                         (UNL)}",
                title = "Mapeo de {\'a}reas agr{\'{\i}}colas inundadas ante un evento 
                         clim{\'a}tico extremo utilizando im{\'a}genes SAR",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156157",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "SAR, mecanismos de dispersi{\'o}n, umbral manual, detecci{\'o}n 
                         de cambios, SAR, backscatter mechanisms, Manual Threshold, Change 
                         Detection.",
             abstract = "El principal objetivo de esta investigaci{\'o}n fue determinar la 
                         superficie inundada en {\'a}reas de llanura a partir del uso de 
                         im{\'a}genes SAR, Sentinel 1B banda C polarizaci{\'o}n VV y VH. 
                         El {\'a}rea de estudio es la cuenca superior del Arroyo 
                         Culul{\'u}, localizada en el centro oeste de la Provincia de 
                         Santa Fe - Argentina (31º 10´ Sur y 61º 50´ Oeste). Para tal fin 
                         se compararon siete algoritmos de clasificaci{\'o}n, dos no 
                         supervisados: clasificaci{\'o}n polarim{\'e}trica H - alpha y 
                         coherencia interferom{\'e}trica; y cinco supervisados: umbral 
                         manual, detecci{\'o}n de cambios ({\'{\i}}ndice de 
                         inundaci{\'o}n, {\'{\i}}ndice de vegetaci{\'o}n inundada y 
                         cociente) y par{\'a}metros polarim{\'e}tricos. Estos algoritmos 
                         se validaron a trav{\'e}s de la matriz de error obteni{\'e}ndose 
                         una fiabilidad global del 83,4% para el seleccionado, resultante 
                         de la aplicaci{\'o}n conjunta de los m{\'e}todos supervisados de 
                         Umbral Manual y Detecci{\'o}n de Cambios. El mismo presenta como 
                         ventajas: simplicidad y rapidez, la explotaci{\'o}n de los 
                         conjuntos de datos de observaci{\'o}n de la tierra (Big Data EO), 
                         la f{\'a}cil selecci{\'o}n de umbrales y la capacidad para 
                         delimitar tanto las superficies abiertas inundadas como las 
                         cubiertas por ciertos cultivos. ABSTRACT: The main objective of 
                         this research is to contribute to the determination of flooded 
                         surfaces in plain areas, through the use of SAR, Sentinel 1B 
                         satellite, C band and VV-VH polarizations. The case study is a 
                         sector of the upper basin of the Arroyo Culul{\'u}, located in 
                         the Province of Santa Fe - Argentina (31º 10´ South and 61º 50´ 
                         West). To this end, seven classification algorithms were compared, 
                         two unsupervised: H-alpha polarimetric classification and 
                         interferometric coherence; and five supervised: manual threshold, 
                         change detection (flood index, flooded vegetation index and 
                         quotient) and polarimetric parameters. These algorithms were 
                         validated through the error matrix, obtaining an overall 
                         reliability of 83.4% for the selected algorithm, resulting from 
                         the joint application of the supervised methods of Manual 
                         Threshold and Change Detection. It presents as main advantages: 
                         simplicity and speed, the exploitation of Earth observation data 
                         sets (Big Data EO); the easy selection of thresholds and the 
                         ability to delimit both rural areas flooded with open water and 
                         those covered by certain crops.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/48TRE75",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/48TRE75",
           targetfile = "156157.pdf",
                 type = "Sensoriamento remoto de microondas",
        urlaccessdate = "11 maio 2024"
}