A high spatial resolution satellite remote sensing time series analysis of Cape Bounty, Melville Island, Nunavut (2004-2018)
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Vegetation changes (i.e., areas of ‘greening’ and ‘browning’) have been observed in areas of the circumpolar Arctic due to changing Arctic climate. However, these changes have largely been reported based on coarse spatial resolution satellite data collected since the 1980’s. This study examines a shorter time series (2004-2018) of high spatial resolution satellite data (i.e., IKONOS and Worldview-2,3) to determine if changes in the Normalized Difference Vegetation Index (NDVI) can be detected over a shorter time period at the Cape Bounty Arctic Watershed Observatory (CBAWO) located on Melville Island, Nunavut, Canada. Image data were first corrected to top-of-atmosphere (TOA) reflectance and normalized for the time series analysis using the pseudo-invariant feature (PIF) method to minimize differences in sensor calibration, illumination, sun angle and atmospheric conditions. Local climate data were used to calculate growing degree days (base 5 °C, GDD(5)) and growing season length (GSL). These climate data were combined with percent vegetation cover (PVC) measurements to contextualize trends observed in the time series. NDVI values of different vegetation types (i.e., wet sedge, mesic tundra and polar semi-desert) and within active layer detachments (ALDs) were analyzed. NDVI showed similar patterns over time within the different vegetation types and across the ALDs. It was determined that there was no significant change in NDVI nor in GDD(5) over time. However, there were statistically significant (p < 0.05) relationships between the GDD(5) and NDVI for all vegetation types. ‘Upscaled’ 30 m data presented a very similar trend as the 2 m data analysis at the landscape and plot (1 ha) level, but was not suited to tracking change within the ALDs. Combining field measurements and high spatial resolution remote sensing data helps link observed trends in spectral vegetation indices with processes on the ground. It is anticipated that as longer time series of high spatial resolution remote sensing data and field measures become available, it will become more feasible to examine (and model) changes in biophysical variables associated with warming temperatures. The methods reported here address the challenges of integrating high spatial resolution satellite data from different satellite sensors in a time series analysis.