Title :
Phenology parameter extraction from time-series of satellite vegetation index data using phenosat
Author :
Rodrigues, Arlete ; Marcal, Andre R S ; Cunha, Mario
Author_Institution :
Centro de Investig. em Cienc. Geo-Espaciais, Univ. do Porto, Porto, Portugal
Abstract :
PhenoSat is an experimental software tool that extracts phenological information from satellite vegetation index time-series. Temporal satellite NDVI data provided by VEGETATION sensor from three different vegetation types (Vineyard, Closed Deciduous Forest and Deciduous Shrubland with Sparse Trees) and for different geographical locations were used to test the ability of the software in extracting vegetation dynamics information. Six noise reduction filters were tested: piecewise-logistic, Savitzky-Golay, cubic smoothing splines, Gaussian models, Fourier series and polynomial curve fitting. The results showed that PhenoSat is an useful tool to extract phenological NDVI metrics, providing similar results to those obtained from field measurements. The best results presented correlations of 0.89 (n=6; p<;0.01) and 0.71 (n=6; p<;0.06) for the green-up and maximum stages, respectively. In the fitting process, the polynomial and Gaussian algorithms over smoothed the peak related with a double-growth season, the opposite to the other methods that could detect more accurately this peak.
Keywords :
remote sensing; vegetation; Fourier series; Gaussian algorithm; Gaussian models; PhenoSat; Savitzky-Golay test; cubic smoothing splines; experimental software tool; noise reduction filters; phenological information; phenology parameter extraction; piecewise-logistic test; polynomial algorithm; polynomial curve fitting; satellite vegetation index data; satellite vegetation index time-series; temporal satellite NDVI data; vegetation dynamics information; vegetation sensor; Cascading style sheets; Correlation; Data mining; Measurement; Satellites; Smoothing methods; Vegetation mapping; NDVI; PhenoSat; Phenology; SPOT VGT; Time-series;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6352507