Canadian Forest Service Publications
A simple transformation for visualizing non-seasonal landscape change from dense time series of satellite data. 2015. Hird, J.N.; Castilla, G.; McDermid, G.J.; Bueno, I.T. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(8):3372-3383.
Available from: Northern Forestry Centre
Catalog ID: 38257
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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.
Plain Language Summary
The growing archive of satellite imagery provides exciting opportunities for detecting and characterizing past and present landscape changes anywhere on the globe. However, there are no standard methods to quickly assess change from a stack of hundreds of images of the same area covering a period of a decade or more. We developed a simple method that compresses such a stack into just one image. In that image, different types of nonseasonal, year-to-year changes involving vegetation loss or gain can be easily spotted regardless of when they occurred within the time period. We applied our method to a stack of 121 satellite images of the province of Alberta taken between 2001 and 2011, where each image represents the amount of green vegetation in pixels of 250 metres over a specific 16-day period during the growing season of those years. We obtained a striking color visualization that tells the story of Alberta's landscapes during that decade. In addition, we conducted a small study of urban sprawl around Calgary to demonstrate that our method can also be used for quantitative analyses. Our method has the potential to become a standard exploratory tool for detecting and visualizing landscape changes.
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