English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/167503
COMPARTIR / IMPACTO:
Estadísticas
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Título

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

AutorKordelas, Georgios A.; Manakos, Ioannis; Aragonés, David ; Díaz-Delgado, Ricardo ; Bustamante, Javier
Palabras claveInundation mapping;
Automatic thresholding
Sentinel-2
Marshland
Rice-paddies
Temporary ponds
Fecha de publicación2018
EditorMolecular Diversity Preservation International
CitaciónRemore sensing, 10: 910 (2018)
ResumenSatellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics
Versión del editorhttps://doi.org/10.3390/rs10060910
URIhttp://hdl.handle.net/10261/167503
DOI10.3390/rs10060910
Aparece en las colecciones: (EBD) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
remotesensing-10-00910-v2.pdf4,78 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.