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

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; GDD5) 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 GDD5 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.受气候变化影响，北极区域已观测到植被变化。本研究针对2004至2018年的高空间分辨率卫星数据（即IKONOS、WorldView-2、WorldView-3）所生成的归一化差分植被指数（normalized difference vegetation index, NDVI）时间序列展开分析，旨在探究努纳武特地区梅尔维尔岛开普邦蒂北极流域观测站范围内的植被丰度是否发生变化。图像数据已校正为大气顶层反射率，并采用伪不变特征（pseudo-invariant feature, PIF）方法完成归一化处理，以适配时间序列分析需求。研究采用植被盖度实测数据，以及基于局地气候数据推导得到的5℃基准生长度日（growing degree days base 5°C, GDD5）指数，以阐明不同植被类型以及活动层滑脱（active layer detachments, ALDs）区域内的NDVI变化趋势。不同植被类型内以及活动层滑脱区域的NDVI变化模式均较为相似。随时间推移，NDVI与GDD5均未出现显著变化。不过，所有植被类型的GDD5与NDVI之间均存在统计学意义上的显著相关性（p<0.05）。结合高空间分辨率遥感数据与野外实测数据，有助于将NDVI变化与植被及地表过程变化建立关联。本研究还探讨了在时间序列分析中整合不同传感器获取的高空间分辨率卫星数据所面临的挑战。