We present a method for a supervised classification of Normalized Difference Vegetation Index (NDVI) time series that identifies vegetation type and vegetation coverage, absolute in %coverage or relative to a reference NDVI cycle. The shape of the NDVI cycle, which is diagnostic for certain vegetation types, is our primary classifier. A Discrete Fourier Filter is applied to time series data in order to minimize the influence of high‐frequency noise on class assignments. Similarity between filtered NDVI cycles is evaluated using a linear regression technique. The correlation coefficients calculated between the Fourier filtered reference cycle and likewise filtered target cycles describe the similarity of their phenology, and the corresponding regression coefficients are an expression of coverage
relative to the reference. The regression coefficients are correlated with field measured vegetation coverage. The Fourier Filtered Cycle Similarity method (FFCS) compensates phenological shifts, which are typical in areas with a strong climate gradient, and prevents the break‐up of classes of identical vegetation types on the basis of vegetation coverage. Some other advantages compared to traditional unsupervised classifications are: synoptic visualization of vegetation type and coverage variation, independence from scene statistics, and consistent classification of biophysical characteristics only, without rock/soil reflectance dominating class assignment as it often does in unsupervised classifications of sparsely vegetated areas. Using the FFCS classification we differentiated a total
of five rangeland vegetation types for the area of Syria including their intra‐class coverage variation. Classified classes are dominated by one of two shrub types, one of two annual grass types or a bare soil/sparsely vegetated type.
Figure 11: Supervised classification of a SPOT NDVI time-series using Fourier Filtered
Cycle Similarity (FFCS). The majority of unclassified pixels represent non-steppe vegetation
covers and NDVI-cycles with a low SNR. For description of classes see figure 9.
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Last updated 31st January 2013