4.3 Article

A High Spatial Resolution Satellite Remote Sensing Time Series Analysis of Cape Bounty, Melville Island, Nunavut (2004-2018)

Journal

CANADIAN JOURNAL OF REMOTE SENSING
Volume 46, Issue 6, Pages 733-752

Publisher

TAYLOR & FRANCIS INC
DOI: 10.1080/07038992.2020.1866979

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Funding

  1. Natural Sciences and Engineering Research Council (NSERC)
  2. Northern Scientific Training Program (NSTP)
  3. Government of Ontario (Ontario Graduate Scholarship)
  4. NSERC (Canadian Graduate Scholarships-Masters)
  5. Queen's University (Tri-Agency Recipient Recognition Award)

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Analysis of high spatial resolution satellite data from 2004 to 2018 showed no significant changes in vegetation abundance in the Cape Bounty Arctic Watershed Observatory, Nunavut. However, there were significant relationships between GDD(5) and NDVI.
Changes in vegetation have been observed in areas of the Arctic due to changing climate. This study examines a normalized difference vegetation index (NDVI) time series (2004-2018) of high spatial resolution satellite data (i.e., IKONOS, WorldView-2, WorldView-3) to determine if vegetation abundance has changed over the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut. Image data were corrected to top-of-atmosphere reflectance and normalized for time series analysis using the pseudo-invariant feature (PIF) method. Percent vegetation cover measurements and indices derived from local climate data (growing degree days base 5 degrees C; GDD(5)) were used to contextualize NDVI trends in different vegetation types and within active layer detachments (ALDs). NDVI showed similar patterns within the different vegetation types and across the ALDs. 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. Using field measurements with high spatial resolution remote sensing data helps link changes in NDVI with changes to vegetation and earth surface processes. The challenges of integrating high spatial resolution satellite data from different sensors in a time series analysis are also discussed.

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