Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series
AbstractA detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result. View Full-Text
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Inglada, J.; Vincent, A.; Arias, M.; Tardy, B.; Morin, D.; Rodes, I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens. 2017, 9, 95.
Inglada J, Vincent A, Arias M, Tardy B, Morin D, Rodes I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sensing. 2017; 9(1):95.Chicago/Turabian Style
Inglada, Jordi; Vincent, Arthur; Arias, Marcela; Tardy, Benjamin; Morin, David; Rodes, Isabel. 2017. "Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series." Remote Sens. 9, no. 1: 95.
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