[IEEE IEEE International IEEE International IEEE International Geoscience and Remote Sensing...

Temporal snow cover variation and terrain characteristics of Peary caribou habitats in the Canadian Arctic using optical and InSAR data Paul Budkewitsch, Katrin Molch¹, Robert McGregor¹ Canada Centre for Remote Sensing (CCRS) Natural Resources Canada Ottawa, Ontario K1A 0Y7 E-mail: [email protected] ¹MIR Télédétection inc., Longueuil, Québec (on contract to CCRS) Andrew Maher, Paul Treitz Department of Geography Queen's University Kingston, Ontario K7L 3N6 E-mail: [email protected] Michael A.D. Ferguson Nunavut Wildlife Service Department of Environment Government of Nunavut Pond Inlet, Nunavut X0A 0S0 E-mail: [email protected] Abstract — Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect winter foraging patterns of Peary caribou and the inter-annual consistency of snow cover patterns within our specific study area. In many parts of Canada’s High Arctic, annual snow fall may exceed 100 cm; however, total accumulation on various types of land surfaces is highly variable due to blowing snow processes. On the other hand, inter-annual variation on specific sites may be minimal because terrain and prevailing winds in turn lead to prevalent snow cover conditions that caribou populations may learn to exploit. A temporal series of InSAR data pairs throughout a given winter can provide insight on the changing spatial pattern of snow cover. To test this hypothesis, we examined a series of SAR coherence images from study areas on Cameron Island and parts of neighbouring Bathurst Island, Nunavut, in the Canadian Arctic. Results indicate that trace or minimal snow accumulation exists over barren areas at higher elevations as they are often swept clear by winds. These areas can be mapped by their high temporal phase coherence. At lower elevations, local topography strongly controls the snow accumulation; vegetation and surface roughness to a much lesser extent. A stronger phase decorrelation is observed in areas of greater snow accumulation. Early spring field observations and optical satellite data (IKONOS and Landsat) are used to support the interpretations of SAR-based results. The next step is to assess the relative interannual variability in snow accumulation between years within these specific terrain areas. Development of these tools will enable us to better understand and eventually predict the impacts that future climate change may have on such endangered wildlife populations at the same landscape scales that animals can respond. 0-7803-8742-2/04/$20.00 (C) 2004 IEEE 4609

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Transcript of [IEEE IEEE International IEEE International IEEE International Geoscience and Remote Sensing...

Page 1: [IEEE IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 - Anchorage, AK, USA (20-24 Sept. 2004)]

Temporal snow cover variation and terrain characteristics of Peary caribou habitats in the Canadian Arctic using optical and InSAR data

Paul Budkewitsch, Katrin Molch¹, Robert

McGregor¹ Canada Centre for Remote

Sensing (CCRS) Natural Resources Canada Ottawa, Ontario K1A 0Y7

E-mail: [email protected]

¹MIR Télédétection inc., Longueuil, Québec

(on contract to CCRS)

Andrew Maher, Paul Treitz

Department of Geography Queen's University

Kingston, Ontario K7L 3N6 E-mail: [email protected]

Michael A.D. Ferguson Nunavut Wildlife Service

Department of Environment Government of Nunavut

Pond Inlet, Nunavut X0A 0S0 E-mail: [email protected]

Abstract — Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect winter foraging patterns of Peary caribou and the inter-annual consistency of snow cover patterns within our specific study area.

In many parts of Canada’s High Arctic, annual snow fall may exceed 100 cm; however, total accumulation on various types of land surfaces is highly variable due to blowing snow processes. On the other hand, inter-annual variation on specific sites may be minimal because terrain and prevailing winds in turn lead to prevalent snow cover conditions that caribou populations may learn to exploit.

A temporal series of InSAR data pairs throughout a given winter can provide insight on the changing spatial

pattern of snow cover. To test this hypothesis, we examined a series of SAR coherence images from study areas on Cameron Island and parts of neighbouring Bathurst Island, Nunavut, in the Canadian Arctic.

Results indicate that trace or minimal snow accumulation exists over barren areas at higher elevations as they are often swept clear by winds. These areas can be mapped by their high temporal phase coherence. At lower elevations, local topography strongly controls the snow accumulation; vegetation and surface roughness to a much lesser extent. A stronger phase decorrelation is observed in areas of greater snow accumulation. Early spring field observations and optical satellite data (IKONOS and Landsat) are used to support the interpretations of SAR-based results. The next step is to assess the relative interannual variability in snow accumulation between years within these specific terrain areas. Development of these tools will enable us to better understand and eventually predict the impacts that future climate change may have on such endangered wildlife populations at the same landscape scales that animals can respond.