Abstract

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 < 0.05) relationships between the GDD₅ 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.

摘要

由于气候变化，在北极地区已观察到植被的变化。这项研究研究了高空间分辨率卫星数据（即IKONOS，WorldView-2，WorldView-3）的归一化植被指数（NDVI）时间序列（2004–2018），以确定在开普恩特角北极流域上植被的丰度是否发生了变化努纳武特梅尔维尔岛天文台。使用伪不变特征（PIF）方法将图像数据校正到大气最高反射率并进行标准化，以进行时间序列分析。根据当地气候数据得出的植被覆盖率百分比和指数（生长天数以5°C为基础； GDD ₅）被用于情境化不同植被类型和活动层脱离（ALD）中的NDVI趋势。NDVI在不同植被类型内和整个ALD中表现出相似的模式。随着时间的推移，NDVI和GDD ₅均无显着变化。但是，对于所有植被类型 ，GDD ₅和NDVI之间存在统计学上显着的（p <0.05）关系。使用具有高空间分辨率遥感数据的野外测量有助于将NDVI的变化与植被和地球表面过程的变化联系起来。还讨论了在时间序列分析中集成来自不同传感器的高空间分辨率卫星数据的挑战。