Attribute_Accuracy_Report:
Data were produced by repeatable methods involving visual interpretation of walruses apparent at the indicated scales using a standard desktop GIS. Grid cells were generated for each survey, clipped to the outline of the walrus herd apparent in each survey and randomly selected for visual interpretation using standard desktop GIS methods.
Walrus herd outlines, sampling grids, and digitized walrus centroids of randomly selected grid cells are provided for all 26 aerial survey orthoimages.
Methodology:
Methodology_Type: Field
Methodology_Description:
We flew a small unoccupied aerial system (sUAS; Solo 3D Robotics, Berkeley, CA) over walruses hauled out on shore near Point Lay, Alaska. We fit the sUAS with a 41 degree angle gimbal-mounted camera (Peaupro41; Peau Productions, San Diego, CA) and flew transects over walrus haulouts at 107 to 112 m altitude using autonomous flight commands (Tower app:
https://github.com/DroidPlanner/Tower ). We georeferenced aerial images with coordinates estimated on-board the sUAS at approximately 1.2 second intervals during image collection based on a Global Navigation Satellite System (GNSS) which received and processed signals from both the U.S. Global Navigation System constellation (GPS) and the Russian global navigation system (GLONASS; Russian: ГЛОНАСС, Глобальная навигационная спутниковая система). Location was interpolated using input from a printed circuit board inertial measurement unit (IMU). Coordinates were recorded in the WGS84 coordinate reference system (EPSG:7660), with elevation specified in meters relative to the WGS84 ellipsoid. We assigned approximate image acquisition coordinates to the image metadata from the image timestamp and locations collected on-board the sUAS using the GeoSetter program (v.3.5.0, www.geosetter.de, Friedmann Schmidt) which is based on exiftools (ver. 12.21,
https://exiftool.org, Phil Harvey). Because we have no survey-grade ground control points for cross-validation and calibration of the GNSS locations collected on-board the UAS, these locations are provided without a specified location accuracy report. Surveys were flown under FAA part 107 rules that stipulated visual line of sight be maintained by the remote pilot or by a trained visual observer throughout the UAS flight. All surveys were conducted under Marine Mammals Protection Act permit number MA801652-7 at altitudes and in a manner that minimized potential disturbances to walruses resting on land. Although it was not possible to conduct independent observations of walruses during the surveys, inspection of the survey imagery and field observers detected no changes in walrus behavior, such as head raises or displacements, in apparent response to sUAS overflights. Access to the lands was arranged through the landowners and authorized under the North Slope Borough permit 18-449.
Methodology:
Methodology_Type: Lab
Methodology_Description:
We interpreted orthoimages generated from 26 UAS aerial surveys to identify the perimeter of groups of walruses resting on shore. We then digitized geospatial polygon outlines of these walrus groups apparent in the aerial imagery at a scale of 1:400. We generated a grid of 10 m by 10 m cells across these walrus group outlines. For each survey we clipped these grids by the walrus group outlines and retained all portions of grid cells within each survey's walrus group outlines. We then used a uniform random number generator to select at least 10 percent of the clipped grid cells. Working at a scale of 1 to 40 in a desktop GIS, we digitized spatial points over the centroid of walruses with most of their apparent body within selected grid cells. We then followed the statistical procedures outlined in Battaile et al. (2017) to estimate the coefficient of variation for the walrus abundance in each survey. We used the estimated coefficient of variation to determine if additional grid cells needed to be examined. If the coefficient of variation exceeded five percent, we randomly selected additional grid cells and repeated the process until the estimated coefficient of variation was less than five percent.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Fischbach, A.S.
Originator: Jay, C.V.
Originator: Adams, J.D.
Publication_Date: 2021
Title:
Walrus Haulout Aerial Survey Data Near Point Lay Alaska, Autumn 2018 and 2019
Geospatial_Data_Presentation_Form:
raster digital data, geotagged digital imagery, tabular digital data
Publication_Information:
Publication_Place: Anchorage, Alaska
Publisher: U.S. Geological Survey, Alaska Science Center
Other_Citation_Details:
Fischbach, A.S., Jay, C.V., Adams, J.D. 2021, Walrus haulout aerial survey data near Point Lay Alaska, autumn 2018 and 2019: U.S. Geological Survey data release,
https://doi.org/10.5066/P9X1C0WX
Type_of_Source_Media: digital image files
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20180906
Ending_Date: 20190905
Source_Currentness_Reference: observed
Source_Citation_Abbreviation: USGS Alaska Science Center 2021
Source_Contribution:
The dataset containing all aerial survey images that were used to derive the data presented in this data package. The high resolution images are considered sensitive. All images are archived at the U.S. Geological Survey, Alaska Science Center (a USGS Trusted Digital Repository). Only the FGDC metadata record describing the iamge dataset is publicly accessible.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Battaile, B.C.
Originator: Jay, C.V.
Originator: Udevitz, M.S.
Originator: Fischbach, A.S.
Publication_Date: 2017
Title:
Evaluation of a method using survey counts and tag data to estimate the number of Pacific walruses (Odobenus rosmarus divergens) using a coastal haulout in northwestern Alaska
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Polar Biology
Issue_Identification: 40:1359–1369
Publication_Information:
Publication_Place: online
Publisher: Springer
Other_Citation_Details:
Battaile, B.C., Jay, C.V., Udevitz, M.S., Fischbach, A.S. 2017. Evaluation of a method using survey counts and tag data to estimate the number of Pacific walruses (Odobenus rosmarus divergens) using a coastal haulout in northwestern Alaska. Polar Biology 40:1359–1369 doi:10.1007/s00300-016-2060-5
Online_Linkage: https://doi.org/10.1007/s00300-016-2060-5
Type_of_Source_Media: journal article
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2017
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: Battaile et al. 2017
Source_Contribution:
Provides statistical calculation of the onshore walrus herd size coefficient of variation. This metric was used to determine the number of grid cells to select for visual interpretation of walruses and determination of walrus density within the apparent walrus group.
Process_Step:
Process_Description:
We generated orthoimages with approximately 1.5 to 2.2 cm/pixel resolution from the georeferenced images using structure from motion software (Agisoft version 1.5.5, Saint Petersburg, Russia). We aligned the images, performed an initial bundle adjustment for the following parameters: f, cx, cy, k1, k2, k3, p1, p2, then performed gradual selection to systematically reduce spatial error in the sparse cloud. For the gradual selection, we iteratively culled sparse points based on a reconstruction uncertainty threshold of 10 and followed by a bundled adjustment using the same parameters as before. We then culled sparse points that exceeded the projection accuracy threshold of 3, performing a bundle adjustment as before. We then culled sparse points that exceeded a re-projection error threshold of .3, performing a bundle adjustment on all parameters (f, cx, cy, k1, k2, k3, b1, b2, p1, p, p3, p4). We iterated each of these gradual selection processes until the specified parameter level was attained using the python API for the Agisoft software if the images were of superior quality. If survey images suffered image blur or erratic camera angles due to gusty winds, we relaxed error reduction constraints by using a reconstruction uncertainty threshold of 12, a projection accuracy threshold of 3, and a reconstruction uncertainty threshold of 4, and limited iterations to not cull more than 50% of the sparse points during the reconstruction uncertainty and projection accuracy culling and not allow culling of more than 10% of the points during the re-projection accuracy culling, so as to enable construction of a structure from motion model across the walrus haulout. We then built a dense cloud, using a medium depth filtering and built a medium density height field mesh with interpolation enable. From this mesh we built the orthoimages in a WGS84 coordinate system from a mosaic of images overlain on the mesh. The U.S. Geological Survey, Advanced Research Computing Center and the Pacific Coastal and Marine Science Center supported this processing.
Process_Date: Unknown
Process_Step:
Process_Description:
We processed survey orthoimagery (USGS Alaska Science Center 2021) by counting walruses in a GIS with a projected coordinate reference system that minimized aerial distortion within the study area, Lambert azimuthal equal area centered on the study area (proj4string: +proj=laea +lat_0=69.5 +lon_0=-163.5 +x_0=0 +y_0=0 +ellps=WGS84 +units=m). We digitized the perimeter of the onshore walrus herd in a desktop GIS (QGIS version 3.14) at a scale of 1 to 400, excluding walruses resting in the surf zone; including isolated walrus groups of two or more within one body length of each other; and digitizing bare beach areas within the herd polygon where gaps of 10 m or more extended between walruses.
Process_Date: Unknown
Process_Step:
Process_Description:
We generated a 10 m by 10 m grid across the study area and clipped grid cells by the herd polygons, retaining any portion of grid cells found to be within the herd outline. We then used a random uniform distribution to select at least 10 percent of the grid cells retained from each survey, whereby each grid cell had an equal probability of selection regardless of the clipped area.
Process_Date: Unknown
Process_Step:
Process_Description:
Working at a scale of 1 to 40 in a desktop GIS (QGIS version 3.14), we digitized spatial points over the apparent centroid of each walrus that had most of its apparent body within a randomly selected grid cell.
Process_Date: Unknown
Process_Step:
Process_Description:
To determine whether additional grid cells were required, we estimated the onshore walrus herd size coefficient of variation following the statistical procedures established by Battaile et al (2017). If the calculated onshore walrus herd size coefficient of variation exceeded five percent, we randomly selected additional grid cells for counting. We then visually interpreted these additional grid cells and estimated the onshore walrus herd size coefficient of variation again. We iterated this process until the estimated onshore walrus herd size coefficient of variation was below five percent.
Process_Date: Unknown