# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 4 Discussion
### Future Research  
Automated algorithms for detecting and delineating walrus aggregations might provide faster and more objective ways to implement broad-scale haulout monitoring than the manual interpretation methods used in this study. Automated walrus detection based on spectral and change detection algorithms have been explored with high-resolutionimagery [12]. The multiple bands of a multispectral sensor like DigitalGlobe's lend rigor to development of automated detection algorithms. Assessments of lower resolution and less spectrally diverse optical imagery like PlanetScope and Sentinel-2 also warrant a review to ascertain if algorithms could provide reliable and repeatable methods for haulout monitoring objectives. Similarly, the high contrast of walrus aggregations in C-band and X-band SAR imagery relative to non-occupied areas at Point Lay suggests that automated algorithms might offer a repeatable and reliable alternative (or complement) to manual interpretation. Compared to other haulout sites across the Pacific walrus range [6], the local topography at Point Lay is exceptionally flat and comprised of relatively uniform fine-grained sandy sediments, lending to ideal conditions for minimizing SAR backscatter from non-occupied areas. In contrast, some of the other large and regularly used walrus haulouts abut bluffs and cliffs, and some beaches have variable sediment sizes including large cobbles or boulders. Such features would likely add several complications to the interpretation of SAR imagery and possibly compromise the ability to detect and discriminate walrus aggregations from non-occupied areas. At haulouts next to cliffs or bluffs, walruses could be rendered undetectable if they occurred within'shadows' cast by oblique radar look angles. On the other hand, for SAR look angles facing into cliffs, double-bounced backscatter could produce confounding spatial anomalies, while rocky beaches could return backscatter sufficient to make walrus discrimination entirely ambiguous. Research is needed to evaluate SAR at haulouts with different types of terrain and substrates. Acquiring reference images of vacant beaches may provide baselines for detecting backscatter changes (including interferometry) produced by walrus occupancy. Field observations of walrus aggregations collected at the same time as SAR acquisitions are necessary to robustly evaluate methods designed to discriminate walruses at topographically complex haulout areas. The positive bias of aggregation areas created from visual interpretation of Sentinel-1 imagery, as well as the variability among observers, was largely driven by differences in deciding how many pixels to include in an aggregation and not by disagreements in the presence or general locations of aggregations. Visual interpretations of the Sentinel-1 imagery could be improved through simultaneous collections of aerial imagery to quantify more precise backscatter thresholds for the delineation of aggregation peripheries. In this study, we estimated the area occupied by walruses by outlining walrus aggregations apparent in satellite imagery. Knowledge of the occurrence and location of these aggregations can help managers mitigate potential disturbances in near-real time; yet, managers also need estimates of the number of walruses present on shore. We could not address abundance directly in this study because none of the satellite imagery types that we evaluated enabled counting individual walruses when they were gathered closely together onshore, and we could not address abundance indirectly because the densities of walruses when hauled out on shore are known to vary. Aggregation densities at the Point Lay haulout averaged 0.87 walruses per square meter (sd 0.17, n 26), and ranged from 0.67 to 1.5 [25]. If we were to estimate walrus abundance based on the product of area occupied and the published densities (mean and variance), confidence intervals would likely be broad.