Alignment of growth seasons from satellite data

Abstract

This work concerns the alignment of growth seasons based on satellite data. This work is motivated by a high mountain vegetation classification problem in Norway. Vegetation classes are characterized by their temporal evolution through a growth season. Data of high spatial resolution, like LANDSAT data, are often temporally sparse. In order to get a longer sequence of images, data from different years can be combined into one single synthetic sequence. We describe a method for determining the correspondence between the chronological time of the image acquisition and the time at which the phenological state of the vegetation cover shown in the image would typically occur. The task is considered as a minimization problem and is solved by dynamic programming. The methodology is based on the normalized difference vegetation index (NDVI) computed from data having a coarse spatial resolution such as MODIS or AVHRR data. The proposed methodology has been tested on data from several years covering a region in Norway including mountainous areas. It is evident from plots of the original data that NDVI curves from different seasons are shifted relative to one another. By applying the proposed time warping methodology to adjust the time scale within each year the shifts become less apparent. We conclude that the methodology can be used for alignment of growth seasons from satellite data.