# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 2 Materials and Methods
### Visual Interpretation  
To recognize and delineate walrus aggregations on shore, we first examined each imagery type before walruses arrived to orient ourselves with how the study area's coastal landscape was depicted in the respective imagery, including how to recognize local features throughout the area such as variations in beach width, banks at the edge of the storm high-tide line, upland vegetation, and remnants of a nearby historic settlement and military infrastructure. We augmented our orientation to local features in the study area with publicly available commercial satellite imagery composites [29] and maps that indicated locations of historic buildings [30]. Walruses disturb beaches and upland vegetation when they gather in large numbers, churning up the soil and defecating. Occupied haulout areas become discolored by these behaviors. Because the discoloration lingers after walruses leave, we examined images in chronological order to more reliably recognize areas that had recently been occupied or vacated. We visualized the Landsat, PlanetScope, and Sentinel-2 multispectral image bands as standard true-color red-green-blue (RGB) images, as well as false-color composites using the near infra-red, red, and green bands for the RGB channels respectively. We also examined the near infrared band individually. We visualized panchromatic imagery from GeoEye-1 and WorldView as single band images. We displayed WorldView multispectral images as RGB false color composites using the near IR2, near IR1, and red edge bands, respectively. For display, we stretched each band's color ramp from the 2 nd to the 98 th percentile of pixel values within the study area, and if useful, we adjusted brightness and contrast settings to optimize visual discrimination among landscape features. If thin clouds were partially present over the study area, we applied image enhancement methods separately to areas with and without clouds. We classified images as \"clear view\" if features over the entire haulout area could be readily observed, including the near-IR band if it effectively penetrated thin clouds that had contaminated the visible bands. We made no attempt to detect walruses in optical imagery that lacked clear views (i.e., cloudy images). Each satellite mission collected no more than one image per day, except for PlanetScope which sometimes acquired two or three images from different satellites. For calculations of clear view revisit rates, we only considered the PlanetScope clear view acquisition time collected closest to solar noon for any given day. We recognized walrus aggregations in the true-color imagery by a distinctive red-brown coloration along the ocean-facing beach that contrasted with the adjacent brown beach, green upland vegetation, and dark brown trampled upland vegetation (or sometimes white fresh snow). Walruses in the false-color image composites were recognizable by similar patterns but with different contrasting colorations. We recognized walrus aggregations in the near infra-red band by distinctive mid-level intensity values that contrasted with darker levels of the adjacent beach, variable levels of the adjacent uplands, and brighter levels in the nearshore surf zone. We detected walnves in the Sentinel-1 cross-polarization normalized backscatter (\(\sigma\)0, sigma nought) imagery when displayed as a single band stretched from \(-\)25 to 0 dB. We displayed the TerraSAR-X staring spotlight (single polarization) \(\sigma\)0 images by stretching values from 1 to 512. We displayed the TerrSAR-X stripmap dual-polarized imagery as a false color RGB image with VV assigned to red, VH assigned to blue (each stretched from 75 to 250), and we left green empty. We recognized walnvs aggregations as large contiguous clusters of pixels with high radar backscatter on the ocean-facing side of the barrier island that contrasted distinctly with the surrounding coastal landscape.