Can Landsat data detect variations in snow cover within habitats of arctic ungulates?

• Published in: Wildlife Biology
• Main Authors: Maher, Andrew I., Treitz, Paul M., Ferguson, Michael A.D.
• Other Authors: Natural Sciences and Engineering Research Council of Canada, Høgskolen i Hedmark
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2012
• Subjects: Bathurst Island; Climate change; Rangifer tarandus
• Online Access: http://dx.doi.org/10.2981/11-055; https://onlinelibrary.wiley.com/doi/pdf/10.2981/11-055; https://onlinelibrary.wiley.com/doi/full-xml/10.2981/11-055

With climate change, modelling has suggested that increased inaccessibility of forage through snow may endanger some populations of arctic ungulates; however, contemporaneous data on snow‐cover conditions, other ecological factors and ungulate responses are lacking at the landscape scale. Researchers have increasingly used remote sensing to map snow cover with higher accuracy, but such tools have not been utilized in research and management of arctic ungulate populations. We estimated field‐measured percent snow‐covered area (F‐SCA) in wintering areas of endangered Peary caribou Rangifer tarandus pearyi in the Bathurst Island complex (BIC) and developed a threshold for a normalized difference snow index (NDSI) using Landsat data. We used our NDSI threshold and another threshold to estimate snow‐covered area (SCA) in Peary caribou habitats in the BIC during 1993‐2003, compared these estimates with snow data from the nearest weather station and assessed the adequacy of Landsat data for arctic ungulate research. Our calculated NDSI threshold of 0.70 reflected field observations better than the published threshold, and our estimated SCAs showed greater variation between study areas, between years and during snow melt. Estimated SCAs were not correlated with total snowfall or snow depth at the nearest weather station. We conclude that SCA using remotely‐sensed data for ungulate habitats would be more useful than weather‐station data. Our methods could detect winters with relatively mild snow‐cover conditions, but not those with very severe conditions; therefore, we recommend development of NDSI thresholds corresponding to ≥ 75% F‐SCA, instead of ≥ 50%. NDSI‐derived SCA methods should prove more useful for southerly arctic regions where sun angles would be less limiting than in the BIC. Higher resolution imagery may be more suited than Landsat for the assessment of snow cover in arctic ungulate ecology. With climate change, further development of remotely‐sensed indices of snow cover, such as NDSI and SCA, should ...