Tools and Technology Testing Methods for Using High-Resolution Satellite Imagery to Monitor Polar Bear Abundance and Distribution MICHELLE A. LARUE, 1 Department of Earth Sciences, University of Minnesota, Minneapolis, MN 55455, USA SETH STAPLETON, Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN 55108, USA CLAIRE PORTER, Polar Geospatial Center, University of Minnesota, St. Paul, MN 55108, USA STEPHEN ATKINSON, Department of Environment, Government of Nunavut, Igloolik, Nunavut X0A 0L0, Canada TODD ATWOOD, United States Geological Survey, Alaska Science Center, Anchorage, AK 99508, USA MARKUS DYCK, Department of Environment, Government of Nunavut, Igloolik, Nunavut X0A 0L0, Canada NICOLAS LECOMTE, 2 Department of Environment, Government of Nunavut, Igloolik, Nunavut X0A 0L0, Canada ABSTRACT High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV <15%). Satellite imagery may be an effective monitoring tool in certain areas, but large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses. Ó 2015 The Wildlife Society. KEY WORDS abundance estimation, Arctic, marine mammal, polar bear, remote sensing, resampling, satellite imagery, Ursus maritimus. Recent advances in remote sensing technologies and detection methods are providing new opportunities for estimating wildlife abundances and distributions. In partic- ular, researchers are turning to very high resolution (i.e., VHR; 0.5–5.0-m pixel size) satellite imagery to assess populations and the impacts of a changing climate, primarily in the polar regions (e.g., LaRue et al. 2011, 2013; Fretwell et al. 2012; Lynch and LaRue 2014; Stapleton et al. 2014a). By providing remote access to study sites and eliminating concerns about human safety and wildlife disturbance, satellite imagery can yield data on wildlife abundance and distribution and thus may be integrated into larger scale monitoring programs. The polar bear (Ursus maritimus) is one species for which researchers have examined the feasibility of satellite imagery as a monitoring tool (Stapleton et al. 2014a). Physical mark– recapture has been the primary technique used to inventory polar bear populations in North America for decades (e.g., DeMaster et al. 1980, Lunn et al. 1997, Regehr et al. 2007, Stirling et al. 2011). Despite intensive public interest, the bear’s cultural importance to northern communities, and its status as a symbol of climate change (Slocum 2004, O’Neill et al. 2008), data on abundance, status, and trends are lacking for many populations (IUCN/PBSG 2014), necessitating the development of a global monitoring framework (Vongraven et al. 2012). These gaps in knowledge are the result of several factors, including the costs and significant logistical challenges of implementing capture-based popula- tion studies in remote parts of the Arctic. This reality, coupled with the recognition that research techniques can Received: 24 February 2015; Accepted: 17 August 2015 Published: 18 September 2015 1 E-mail: larue010@umn.edu 2 Present address: 310 Pillsbury Drive SE, University of Minnesota, Minneapolis, MN 55455, USA Wildlife Society Bulletin 39(4):772–779; 2015; DOI: 10.1002/wsb.596 772 Wildlife Society Bulletin  39(4)