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

• Main Authors: V. Freemantle, J. Freemantle, D. Atkinson, P. Treitz
• Format: Dataset
• Language: unknown
• Published: Taylor & Francis 2021
• Subjects: 59999 Environmental Sciences not elsewhere classified; FOS Earth and related environmental sciences; Ecology; FOS Biological sciences; 69999 Biological Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; FOS Computer and information sciences; Inorganic Chemistry; FOS Chemical sciences; Plant Biology; Arctic; Nunavut; Cape Bounty; Melville Island
• Online Access: https://dx.doi.org/10.6084/m9.figshare.13561994.v1; https://tandf.figshare.com/articles/dataset/A_High_Spatial_Resolution_Satellite_Remote_Sensing_Time_Series_Analysis_of_Cape_Bounty_Melville_Island_Nunavut_2004_2018_/13561994/1

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 °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 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.