Alignment of Growth Seasons from Satellite Data Ragnar Bang Huseby, Lars Aurdal, Dagrun Vikhamar, Line Eikvil, Anne Solberg, Rune Solberg

What? Determine 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. ?? Landsat image

Why? The 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 are often temporally sparse. In order to get a longer sequence of images, data from different years should be combined into one single synthetic sequence.

Combining image data July 24 th 1994 June 25 th 1995 August 19 th 1995 June 5 th 1997 August 17 th 1998 July 29 th 1999 October 18 th 1999 May 19 th 2002 May 23 rd 2004 May 19 th May 23 rd June 5 th June 25 th July 24 th July 29 th August 17 th August 19 th October 18 th Naive approach

Problems with naive approach... May 23 rd 2004June 5 th 1997

How? NDVI = The normalized difference vegetation index Extract the maximum NDVI from the region of interest.

NDVI in various growth seasons alignment of growth seasons = alignment of NDVI curves

Model of observations is the expected NDVI as a function of phenological ”time”. is the time warping function. is the remainder not explained by the model.

Double logistic function The parameters can be estimated from data from a given year.

Time warping functions Early spring Late spring Normal spring

Minimization problem The discrepancy between model and observations should be small. Minimizing this object function produces weird results. We wish to determine the warping function.

Object function measures the deviation between and 1. and the identity. We wish to determine the warping function. The warping function minimizes the sum

Dynamic programming Discretize time Finite but huge number of feasible solutions Find optimal solution by dynamic programming Similar approaches have been used in speech recognition.

Experiments Dataset for time warping: Global Inventory Modeling and Mapping Studies (GIMMS) A sequence of NDVI composites extracted from NOAA AVHRR data from the period 1982 – images from each year. In addition, MODIS data from 2003 and Test site: Region in Norway including mountain areas.

Results

Images in correct order! Day 156, 1997 Day 144, 2004

Application The results from this study have been used in the work titled Classification of Multitemporal Satellite Images Using Phenological Models also a talk at MultiTemp 2005.

Conclusions We have proposed a methodology for alignment of growth seasons from satellite data. The proposed methodology has been tested on data from several years covering a region in Norway including mountain areas. The results show that it is possible to combine data from several years into a sequence of observations from one season. We expect that the methodology will serve as a useful tool in multi-temporal classification.