A Simple Transformation for Visualizing Non-seasonal Landscape Change From Dense Time Series of Satellite Data

We present the Change, Aftereffect, and Trend (CAT) transform for visualizing and analyzing landscape dynamics from dense, multi-annual satellite vegetation index (VI) time series. The transform compresses a temporally detailed, multi-annual VI dataset into three new variables capturing change events and trends occurring within that period. First, peak annual greenness is extracted from each year. Then a series of simple calculations generate the three CAT variables: 1) Change: the maximum interannual absolute difference in peak greenness between consecutive years; 2) Aftereffect: the mean peak greenness after Change occurred; and 3) Trend: the slope of a linear regression applied to the entire annual peak greenness time series. We demonstrate the CAT transform by applying it to a MODIS 16-day 250-m normalized difference VI (NDVI) dataset covering the province of Alberta, Canada, for 2001 through 2011. We find that the CAT variables capture much of the non-seasonal change in the original NDVI time series. When displayed as an RGB color composite (the CAT image), the transform provides a striking visualization of both drastic and gradual decadal-scale landscape dynamics. Its application to quantitative analyses is demonstrated by an urban sprawl case study conducted around the city of Calgary, Alberta, where a simple decision-tree-based classification of the CAT transform variables was superior to a bitemporal, image-differencing approach. The simple yet powerful CAT transform is easily applicable to other study areas and datasets, and could foster a wider usage and understanding of the many archived high-temporal-resolution satellite datasets currently available.