OCS Study MMS 2006-003 

Development of Airborne Remote Sensing Methods 

for Surveys of Pacific Walrus 

Prepared for: 

U.S. Department of the Interior 

Minerals Management Service 

Alaska Outer Continental Shelf Region 
i

OCS Study MMS 2006-003 

Final Report: 

Development of Airborne Remote Sensing Methods for 

Surveys of Pacific Walruses 

by: 

Douglas M. Burn1, Mark S. Udevitz2, Marc A. Webber3 and Joel L. Garlich-Miller1 

1

U.S. Fish and Wildlife Service, Marine Mammals Management, 

1011 East Tudor Road, Anchorage, AK 99503 

2

U.S. Geological Survey, Alaska Science Center, 

1011 East Tudor Road, Anchorage, AK 99503 

3

U.S. Fish and Wildlife Service, Muscatatuck National Wildlife Refuge, 

12589 E. U.S. Highway 50, Seymour, IN 47274 

This study was funded in part by the U.S. Department of the Interior, Minerals Management 

Service (MMS), Alaska Outer Continental Shelf Region, Anchorage, Alaska, through Intraagency Agreement No. 0103RU72398, with the U.S. Fish and Wildlife Service, as part of the 

MMS Alaska Environmental Studies Program. 

The opinions, findings, conclusions, or recommendations expressed in this report or product are 

those of the authors and do not necessarily reflect the views of the U.S. Department of the 

Interior, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use by the Federal Government. 

December 2006 
ii

Table of Contents 

List of Tables ................................................................................................................................. iii 

List of Figures................................................................................................................................ iii 

Abstract ......................................................................................................iv 

Introduction......................................................................................................................................1 

Methods ................................................................................................... 2 

Study area and survey dates.............................................................................................2 

Remote sensing systems ..................................................................................................3 

Survey aircraft .................................................................................................................4 

Flight crew.......................................................................................................................5 

Flight operations ..............................................................................................................6 

Analytical methods ..........................................................................................................6 

Results............................................................................................................

.................................10 

Discussion......................................................................................................................................14 

Discussion......................................................................................................................................14 

Acknowledgments .........................................................................................................................16 

Study Products...............................................................................................................................16 

Literature Cited..............................................................................................................................18 

Appendix 1. Scanner specifications..............................................................................................20 

Appendix 2. Total walrus heat measured for each walrus hotspot recorded ................................22 
iii

List of Tables 

Table 1. Survey blocks and transects flown................................................................................. 12 

Table 2. Total walrus heat measured for each walrus hotspot recorded with corresponding 

walrus counts from digital aerial photography............................................................. 13 

Table 3. Abundance estimates for survey blocks......................................................................... 13 

List of Figures 

Figure 1. Study area for field data collection of AMS thermal imagery and digital aerial 

photography indicating survey blocks, midlines of strip transects flown and the 

location of detected walrus groups................................................................................. 3 

Figure 2. Aero Commander 690B turbine engine aircraft. ............................................................ 4 

Figure 3. Survey flight crew. Pictured left to right are: William Baker (scanner operator), 

Douglas Burn (walrus observer and flight coordinator), Marc Webber (walrus observer 

and camera operator), and Ralph Aiken (pilot).............................................................. 5 

Figure 4. Diagram of survey aircraft indicating the location of flight crew and the configuration 

of the remote sensing systems........................................................................................ 5 

Figure 5. Example of frequency histogram of AMS thermal imagery showing temperature 

threshold between the background environment and walruses that occurs at -2.81°C. 

Pixels to the right of the threshold value have some portion of their area covered by 

walruses. The major peaks of the histogram correspond to environmental features such 

as thick, snow-covered ice, and bare ice of varying thickness....................................... 7 

Figure 6. Poisson regression of walrus counts as a function of total heat index for AMS thermal 

imagery at 4 m spatial resolution. See text for model details. .................................... 11 
iv

Abstract 

In April 2003, we conducted an operational test of an airborne multispectral scanner (AMS) over 

pack ice in the Bering Sea to evaluate the potential of this system as a survey tool for Pacific 

walruses. We scanned a total of 28,875 km2

 of sea ice habitat at a spatial resolution of 4 m and 

collected high resolution photographs from a subset of the thermally detected walrus groups. We 

found a significant positive relationship between walrus group size and the amount of heat 

measured by the AMS and used this relationship to estimate total walrus numbers in the survey 

area. The number of walruses hauled out onto sea ice in our study area was estimated at 4,785 

animals with a 95% confidence interval of 2,499–7,111. We believe that the AMS system as 

configured for this study would be a highly effective tool for surveying large areas of sea ice 

habitat for walrus groups. With a 6 km swath width, it should be possible to sample more 10,000 

km2 in an 8-hr flight. Although walrus groups > 4 animals were easily detected and enumerated 

in the 4 m thermal data, the system was unable to detect individual walruses or seals (Phoca spp.

and Erignathus barbatus). We found that most (94.6%) of the walruses photographed in our 

survey area occurred in groups > 6 animals, therefore we expect the magnitude of any bias due to 

undetected groups of hauled out animals would be relatively small. 
1

Introduction 

The last population survey of Pacific walrus (Odobenus rosmarus divergens) was conducted 

jointly by the United States and the Soviet Union in 1990 (Gilbert et al. 1992) and after nearly 15 

years, the current population size is unknown. The technique used at that time, a visual aerial 

survey, is now considered to be inadequate for measuring population size with sufficient 

precision to monitor trend (Hills and Gilbert 1994, Gilbert 1999). Drawbacks to a visual aerial 

survey include: a narrow survey swath width, observer bias and fatigue, the lack of a permanent 

data record, and safety concerns associated with low-level flight in remote areas. Of these, 

narrow survey swath width is considered to have the greatest impact on the precision of the 

resulting population estimate due to the large geographic area that must be surveyed in a short 

time period (Estes and Gilbert 1978, Gilbert 1999). At an international workshop of walrus 

biologists held by the United States Fish and Wildlife Service (USFWS) and the United States 

Geological Survey (USGS), the consensus opinion was that remote sensing techniques capable 

of collecting data over large areas should be investigated and developed as an alternative to 

visual surveys (Garlich-Miller and Jay 2000). 

The history of aerial surveys of Pacific walrus has been reviewed by Hills and Gilbert (1994), 

Gilbert (1999), and Udevitz et al. (2001). In the fall season when most of the previous surveys 

were conducted, the walrus population is segregated, with some animals associated with the ice 

edge in the Chukchi Sea, while others make use of terrestrial haulouts along the coast of Bristol 

Bay, Alaska in the United States, and the Chukotka and Kamchatka peninsulas in Russia. Fewer 

surveys have been conducted in the winter and early spring, when the entire walrus population 

occurs almost exclusively on the pack ice of the Bering Sea with concentrations in the Gulf of 

Anadyr, south-west of St. Lawrence Island, and south of Nunivak Island (Fay 1982). 

At any time of year, some or all of the walrus population is associated with sea ice, where 

animals haul out of the water and rest in large aggregations on ice floes (Fay 1982). Within 

minutes of leaving the water, their skin temperature becomes noticeably warmer than the 

background environment (Ray and Fay 1968), which provides excellent thermal contrast. In the 

mid-1970s, Wartzok and Ray (1980) experimented with a variety of aerial photography and 

remote sensing techniques, including thermal imagery, to detect marine mammals in the Bering 

Sea. While their results were promising, the development of a survey method using thermal 

imagery was not feasible due to limitations of existing technology. Over a decade later, Barber 

et al. (1991) used a forward-looking infrared system (FLIR) to demonstrate that groups of 

Atlantic walrus (Odobenus rosmarus rosmarus) can be detected by their signatures in the 8-

12μm thermal infrared (IR) band. In addition to FLIR systems, there are other types of thermal 

imagery systems available for survey applications. In contrast to the video image of FLIR 

systems, across-track thermal scanners are capable of producing a continuous vertical photo-like 

digital image in the thermal IR band. 

In April 2002, we tested an Airborne Multispectral Scanner (AMS) to determine if this 

technology could be used to detect walrus groups on sea ice and estimate the number of walrus 

present in each group (Burn et al. 2006). We collected thermal imagery of 37 walrus groups in 

the Bering Sea at spatial resolutions ranging from 1-4 m, and high resolution digital photographs 
2

of the same walrus groups. We found that walrus were considerably warmer than the 

background environment of ice, snow, and seawater and easily detected in the thermal imagery. 

We also found a significant linear relation between walrus group size and the amount of heat 

measured by the thermal sensor at all 4 spatial resolutions tested. We hypothesized that the 

relationship between group size and thermal signature could be used in a double-sampling 

framework to estimate total walrus numbers in an area, by conducting a thermal survey of an 

area and obtaining photographs from a sub sample of the thermally detected groups. 

Based upon the successful test of the AMS system in 2002, we proposed conducting a pilot 

survey for Pacific walrus in the spring of 2003 in the Bering Sea pack ice near St Lawrence 

Island. The study area was selected because major aggregations of walruses typically occur 

during spring in association with polynyas that form around the Island (Fay 1982). The 

objectives of our study were to: 

1. Conduct a full operational test of the AMS system to evaluate the potential of this system 

as a survey tool for Pacific walruses distributed over a wide geographic area. 

2. Evaluate the effectiveness of the AMS system as a survey tool for other marine mammal 

and bird species. 

Methods 

Study area and survey dates 

Our study area consisted of the Bering Sea pack ice near St. Lawrence Island, Alaska (Figure 1). 

The area is characterized by a re-occurring Polynya and known to support large concentrations of 

Pacific walruses during the late winter-early spring breeding season (Fay 1982). Our logistical 

base for the study was Nome, Alaska. We were based in Nome from March 30 through April 15, 

2003, and were able to fly surveys on clear weather days from April 5-10, 2003. 

The study area was partitioned into seven survey bocks (Figure 1) and each survey block was 

partitioned into strip transects with midlines spaced 6 km apart. The spacing of transects allowed 

total coverage of the survey block without overlap. We use the terms “transect” and “strip 

transect” interchangeably - the survey aircraft flew along the midlines of selected strip transects. 

Block size was designed with the goal of sampling 30% of each block, and was influenced by the 

operational range of the aircraft and the distance of the survey block from Nome. Transects were 

oriented north-south and surveyed from west to east so that survey effort could be finished as 

close to Nome as possible. 
3

Figure 1. Study area for field data collection of AMS thermal imagery and digital aerial 

photography indicating survey blocks, midlines of strip transects flown and the location of 

detected walrus groups. 

Remote sensing systems 

We used a Daedelus Airborne Multispectral Scanner (AMS), built by SenSyTech Inc. (now 

Argon ST) of Ann Arbor, Michigan (Appendix 1). The system has a 1.25 milliradian 

instantaneous field of view (IFOV) and collects imagery across a sensor array 1,440 pixels wide. 

When the data are corrected for tangential distortion, the resulting image is 1,493 pixels wide. 

We recorded 6 unique spectral channels of information, with at least one channel in the thermal 

infrared (8.5-12.5μm) range. Each channel was recorded with 12-bit radiometric resolution (able 

to discriminate 212, i.e. 4,096 different temperature levels). Similar to most passive remote 

sensing systems, the AMS was unable see through clouds and fog, and could only be operated in 

clear weather conditions. 

We used a Nikon D1X camera to collect high-resolution digital photographs of walrus groups. 

This 5.47 megapixel camera produced images with dimensions of 3,008 x 1,960 pixels. The 

camera was connected to a notebook computer equipped with Nikon Capture software via an 

IEEE 1394 (firewire) port. The use of the notebook computer and camera control software 
4

allowed the digital photos to be loaded directly to the computer’s hard drive and reviewed in 

flight. The ability to review photos within seconds after collection greatly improved our 

efficiency, as we could quickly tell if a photo pass was successful and repeat the pass if 

necessary. The rapid feedback provided by digital photography also eliminated concerns about 

exposure and focus that are common to film photography. 

Survey aircraft 

We used an Aero Commander 690B turbine engine aircraft owned and operated by Commander 

Northwest of Wenatchee, Washington to conduct our surveys (Figure 2). The aircraft was 

equipped with bubble windows which provided excellent lateral and downward visibility for 

walrus observations. 

Figure 2. Aero Commander 690B turbine engine aircraft. 
5

Flight crew 

Field work was carried out by a four-person flight crew (Figure 3). The location of crew 

members and the configuration of the remote sensing systems in the aircraft are presented in 

Figure 4. 

Figure 3. Survey flight crew. Pictured left to right are: William Baker (scanner operator), 

Douglas Burn (walrus observer and flight coordinator), Marc Webber (walrus observer and 

camera operator), and Ralph Aiken (pilot). 

Figure 4. Diagram of survey aircraft indicating the location of flight crew and the configuration 

of the remote sensing systems. 
6

Flight operations 

Block selection for a given day was driven by local weather conditions. Cloud cover and cloud 

ceiling were instrumental in selecting which survey blocks to be flown. Once a block was 

identified, randomly selected transects (Figure 1) were entered into the aircraft’s GPS for the 

day’s mission. 

Survey transects were flown at 3,200 m above ground level (AGL- all altitudes are reported as 

AGL), with an effective swath width of 6 km and a ground sampling resolution of 4 m (Burn et 

al. 2006). Turns and transits between transects were made as efficiently as possible to maximize 

the amount of survey effort. For each transect, the scanner operator recorded the transect 

number, start and finish times, ground speed, altitude, aircraft heading and the temperature of the 

scanner’s two black bodies. To minimize the risk of data loss, each transect was stored and 

processed as a separate file, and the system and storage media were checked at the start of each 

new file. 

After completing the survey transects, we descended to 762 m to collect digital aerial 

photographs from a subset of the “hotspots” identified with the thermal scanner. Photographs 

were taken using a 180 mm camera lens which produced an effective ground resolution of 3.4 

cm

2 pixels. This resolution was sufficient to resolve and count individual walruses within a group 

(Burn et al. 2006), and was achieved from an altitude at which disturbance to resting animals 

was minimized. A Garmin GPS 3 was linked to the camera through a dedicated port, and aircraft 

position and exposure time were annotated to the metadata of each photograph. At the end of 

each flight day, we archived the digital photographs on compact disc media and an external hard 

drive. 

In some cases digital photographs of walrus groups and corresponding thermal imagery were 

collected opportunistically during transit flights to or from the survey blocks. As walrus groups 

were sighted and photographed in the same general areas on consecutive days, it is possible that 

some of these sightings were duplicative. We considered these photographs appropriate for 

examining the relationship between walrus group size and thermal signature, but did not include 

any data in our analysis of abundance that was not collected on a survey transect. 

Analytical methods 

For the purpose of this study, a walrus group was defined as an aggregation of walruses on an ice 

floe lying in contact with or near to each other. We considered any walrus aggregations 

(including single animals) within 20 m of each other as a single group. Walruses in the water, 

including those that were partially submerged or resting head-up in a breathing hole in the ice, 

were not considered in our analysis. 

We analyzed digital photographs of walrus groups and the AMS imagery with ERDAS Imagine 

(Leica Geosystems, Atlanta, Georgia) software. To count the number of walruses in a group, we 

created annotation layers and manually marked each walrus with a brightly colored symbol. 

Each walrus group was enumerated three times on different dates (without referring back to 
7

previous counts) using a different colored symbol each time. Finally, all three count layers were 

displayed simultaneously to compare the individual counts and arrive at a final, rectified count 

for each group. 

We imported the AMS imagery directly from 8 mm tape into Imagine software using the 

Daedelus import module which corrects for distortion in the final image. To determine the 

threshold temperature value between walrus and the background environment we examined a 

frequency histogram of temperature values in the entire image. Within each image, the first 

histogram bin containing zero pixels was chosen as the temperature threshold value (Figure 5). 

The temperature histogram of an image typically contains one or more modal peaks which 

correspond to the predominant features in the image, such as thick, snow-covered ice, and thin, 

bare ice. 

Figure 5. Example of frequency histogram of AMS thermal imagery showing temperature 

threshold between the background environment and walruses that occurs at -2.81°C. Pixels to 

the right of the threshold value have some portion of their area covered by walruses. The major 

peaks of the histogram correspond to environmental features such as thick, snow-covered ice, 

and bare ice of varying thickness. 
8

Pixels with temperatures warmer than the threshold value were classified as having some portion 

of their area covered by walruses. After determining the threshold temperature for each image, 

we calculated an index of the total amount of heat produced by each walrus group as: 

= ∑( − ), i i j Ti h a t

where hi was the index for group i, a was the pixel area (m2

), tij was the temperature for pixel j of 

group i, Ti was the threshold temperature for group i, and the summation was over all pixels with 

temperature values above the threshold (i.e., pixels with tij > Ti). 

We estimated the scanner’s detection limit based on photographic counts for the smallest 

thermally detected walrus groups, and the largest groups that were not thermally detected. There 

were 6 groups, ranging in size from 1 to 4 walruses that were photographed but were not 

detectable in the thermal imagery. The smallest photographed groups that were detected in 

thermal imagery contained 4 walruses (2 groups). We therefore assumed that groups with less 

than 4 walruses were not detectable. 

Data from all of the photographed groups were used to develop a regression model relating 

group size to the thermal index. Preliminary examination of the data indicated that variances of 

the photographic counts were proportional to the mean counts. Therefore, we used a generalized 

linear model (McCullagh and Nelder 1989) with an identity link and a Poisson distribution to 

estimate the relation between numbers of individuals and the thermal index values for the 

photographed groups. The form of this model was: 

( ) , ( ) ( ), i i i hi E y = α + β h Var y = φ α + β

where yi is the number of walruses and hi is the thermal index for group i, α is the minimum size 

group that can be detected by the scanner (i.e., the scanner detection limit), β is the regression 

coefficient estimated with maximum likelihood, and φ is the dispersion parameter estimated as 

Pearson’s chi square divided by the degrees of freedom. We assessed model fit using deviance 

residuals (McCullagh and Nelder 1989) and used a Wald test to assess the regression parameter. 

This model was then used to estimate the number of walruses in each thermally detected group 

on a surveyed transect that was not photographed. The total number of hauled-out walruses on a 

surveyed transect was estimated by summing the counts of individuals in all the photographed 

groups and the estimated counts in all the detected groups that were not photographed on that 

transect. For transect t in block b: 

), ˆ ( ˆ

1 1

∑ ∑ = + =

= + +

t b b t

t b

c

g

G

g c

tb g tb hg tb N y α β

where ygtb is the number of walruses in group g on transect t of block b, photographed groups are 

indexed 1, ... , ctb, and groups that were not photographed are indexed ctb+1, ..., Gtb. If there 

were no photographed groups on a transect, then ctb = 0. 
9

The total population size was estimated as a sum of separate ratio estimators (Cochran 1977) of 

the totals for each survey block: 

, ˆ ˆ ˆ

1 1 1

∑ ∑ ∑ = = =

=













 =

B

b

b

B

b

T

t

N Rb Atb N b

where: 

,

ˆ

ˆ

1

1

∑

∑

=

= = b

b

t

t

tb

t

t

tb

b

A

N

R

Atb is the area of transect t in block b, Tb is the number of transects in block b, tb is the number of 

surveyed transects in block b, and B is the number of survey blocks. 

We estimated variance of the population estimate with a bootstrap (Efron 1982) procedure based 

on the general approach of Booth et al. (1994) for finite populations. The procedure involved 

generating of series of simulated populations, estimating statistics of interest by resampling from 

each simulated population, and then averaging these statistics over the simulated populations. 

We generated simulated populations of transects (with associated walrus observations) for each 

block by first replicating the complete set of surveyed transects in the block as many times as 

possible without exceeding the total number of potential transects in the block. We then added a 

random sample without replacement from the surveyed transects to complete the population of 

potential transects. Bootstrap survey samples were obtained by drawing random samples 

without replacement from the simulated populations to give the same number of transects as in 

the original survey. 

For each bootstrap survey sample, we also obtained a bootstrap sample of photographic counts 

for fitting the regression model. A bootstrap sample of photographed groups included all of the 

photographed groups in the bootstrap sample of surveyed transects if the number of those groups 

was ≤ the number on surveyed transects in the original sample. Otherwise, we sampled without 

replacement from the photographed groups in the bootstrap sample of transects to obtain the 

same number as in the original survey. We then completed the bootstrap sample of 

photographed groups by sampling with replacement from the entire original sample of groups 

photographed off survey transects to obtain the same total sample size (i.e., number of groups 

photographed on transects + number of groups photographed off transects) as in the original 

survey. This resampling strategy was designed to approximate the survey protocol which 

supplemented the essentially random distribution of group sizes photographed on survey 

transects with additional off-transect photographs emphasizing larger groups, thereby obtaining a 

more even distribution of group sizes for the calibration regression. 

Estimation for each bootstrap sample followed the same procedure as for the original sample. 

We obtained 100 bootstrap samples and associated estimates of population size for each 

simulated population and then calculated the standard error and 2.5 and 97.5 percentiles of those 
10

estimates. We repeated this process for 500 simulated populations and took the average of the 

standard errors and 2.5 and 97.5 percentiles as our estimates of standard errors and 95% 

confidence limits for the estimates from the original survey. 

Results 

During the two-week field period we were able collect data on six consecutive days (April 5-10) 

and were able to survey six of seven survey blocks. Block 5 (Figure 1) was missed due to a 

technical problem encountered with the scanner system on April 11. After April 11, weather 

conditions deteriorated, and we were unable to fly again during the remainder of our deployment. 

We sampled a total area of 28,875 km2

 with the AMS system at a spatial resolution of 4 m, and 

encountered many walrus groups scattered throughout the survey area (Figure 1). 

Survey effort is summarized in Table 1. Survey efficiency, measured as the proportion of the 

survey block sampled with the AMS system, ranged from 21 to 38 percent. The number of 

square kilometers scanned per day ranged from 3,622 to 6,051 with a mean of 4,813. Air speed, 

which ranged from 333 to 444 km/hr, was not limited by scanner operations but was affected by 

wind speed and direction. 

During the 2003 survey effort, threshold temperatures ranged from -2.98 to -4.32 °C. In general, 

the threshold temperatures were lower when ambient temperatures were lower. We photographed 

a total of 46 walrus groups during the survey period and were able to match 25 of these to 

corresponding hotspots in the thermal imagery. Walrus group sizes and their corresponding 

thermal values are presented in Table 2. Thermal values for all walrus hotspots recorded during 

the survey are presented in Appendix 2. There was a strong linear relationship (X1

2 =90.02, P < 

0.01) between the numbers of walruses in a group and the index of total walrus heat (Figure 6), 

with a slope parameter estimate of β = 0.67 (standard error = 0.0070). Plots of deviance 

residuals did not show any lack of fit to the linear model or the Poisson variance function. 

Abundance estimates for survey blocks are presented in Table 3. The total number of walruses 

in the 6 surveyed blocks was estimated at 4,785 animals with a standard error of 1,186 (C.V. = 

0.25) and a 95 % confidence interval of 2,499–7,111. 
11

Figure 6. Poisson regression of walrus counts as a function of total heat index for AMS thermal 

imagery at 4 m spatial resolution. See text for model details. 

During flight operations we observed, but were unable to photograph, several small flocks of 

birds. We also observed and photographed several seals (Phoca spp. or bearded seal Erignathus 

barbatus). No bird aggregations or seals could be confirmed in the scanner data, probably 

because they presented too small of a thermal target at the four meter resolution obtained at 

3,200 m. 

Thermal index

0 1000 2000 3000 4000 5000

Walruses

0

50

100

150

200

250

300

350
12

Table 1. Survey blocks and transects flown. 

Date Block 

Block Area 

(km2

) Transect 

Transect Length 

(km) 

Sampled Area a

 (km2) 

452 105.9 635.7 

455 113.2 679.1 

456 112.9 677.5 

458 112.1 672.8 

459 189.8 1,138.7 

466 142.5 854.8 

4/05/2003 3 18,455 

Subtotal 776.4 4,658.4 

356 144.1 864.9 

358 142.8 857.0 

360 145.2 871.4 

363 143.4 860.6 

364 144.1 864.9 

366 144.7 868.2 

368 144.2 865.4 

4/06/2003 7 15,900 

Subtotal 1,008.5 6,051.0 

437 176.8 1,060.9 

438 180.7 1,083.9 

446 191.7 1,150.5 

447 190.9 1,145.1 

449 190.3 1,141.8 

4/07/2003 2 15,903 

Subtotal 930.4 5,582.4 

416 65.3 391.8 

419 52.9 317.6 

420 64.1 384.6 

422 86.4 518.5 

424 100.8 605.0 

425 109.0 653.9 

426 115.4 692.3 

430 123.3 739.9 

4/08/2003 1 13,808 

Subtotal 717.2 4,303.2 

336 107.6 645.5 

337 107.9 647.5 

339 127.0 762.3 

345 144.5 866.7 

347 144.4 866.4 

350 144.9 869.2 

4/09/2003 6 15,310 

Subtotal 776.3 4,657.8 

314 213.6 1,281.7 

315 224.7 1,348.0 

312 165.4 992.4 

4/10/2003 4 17,525 

Subtotal 603.7 3,622.2 

Total 4,812.5 28,875.0 a Sampled Area (km2

) is equal to transect length (km) multiplied by the scanner swath width (6 km). 
13

Table 2. Total walrus heat measured for each walrus hotspot recorded with corresponding 

walrus counts from digital aerial photography.

Date Total Walrus Heat Walrus Count 

4/07/2003 797.62 43 

 329.47 17 

 427.78 57 

 329.47 23 

 1,887.07 155 

4/08/2003 39.70 4 

 135.30 4 

 4,652.21 294 

 127.40 25 

 3,006.84 315 

4/09/2003 157.64 54 

 113.52 49 

 137.75 32 

4/10/2003 84.08 13 

 1.07 5 

 271.71 11 

 54.74 7 

 120.93 15 

 1,704.84 97 

 322.61 24 

 893.64 39 

 2,436.68 116 

 152.24 11 

 1,413.85 53 

 1,651.65 79 

Table 3. Abundance estimates for survey blocks. 

Block Total Walruses Standard Error 95% Conf. Interval 

1 424 166 131–761 

2 2,057 846 395–3693 

3 509 349 40–1252 

4 1,278 509 555–2259 

6 505 317 3–1050 

7 12 9 0–29 
14

Discussion 

Objective 1. Conduct a full operational test of the AMS system to evaluate the potential of this 

system as a survey tool for Pacific walruses distributed over a wide geographic area 

The development of an aerial survey method using thermal imaging technology would address 

many of the shortcomings of visual aerial surveys described by Gilbert (1999). Visual surveys 

have historically been flown at relatively low altitudes (150-300 m) and airspeeds (250 km/hr) 

that allowed for both detection and counting of walrus groups in a relatively narrow swath, 

usually 1.86 km or less. The proportion of the study area that could be searched by visual 

methods was typically low, resulting in population estimates of low precision (Estes and Gilbert 

1978, Gilbert 1999). By flying at a higher altitude (3,200 m) and at faster airspeeds (370 km/hr), 

and using a thermal scanner with an effective swath width of 6 km to detect walrus groups, it is 

theoretically possible to sample up to 400% more area per hour of flight time than with 

traditional visually based survey methods (Burn et al. 2006). 

Our operational test of the AMS system indicated that walrus groups > 4 animals were readily 

detected at a spatial resolution of 4 m. Walrus range in size from 1-3 m, and we suspect that 

individual animals and small groups may have been missed at this spatial resolution. Walruses 

are extremely gregarious animals and normally haul out on ice floes in large groups (Fay 1982). 

We found that most (94.6%) of the walruses photographed during the survey occurred in groups 

> 6 animals. Results of previous visual aerial surveys also indicate that walruses typically occur 

in group sizes that would be detectable at this spatial resolution (Estes and Gilbert 1978, GarlichMiller and Jay 2000; Burn et al. 2006), therefore we consider the magnitude of any bias due to 

undetected groups to be relatively small. 

The 95% confidence interval for the number of walruses hauled out in the 6 surveyed blocks of 

our study area was 2,499 -7,111 animals. It is important to point out that this abundance estimate 

does not account for animals in the water during the survey. Developing a correction factor for 

walruses in the water during our aerial survey was beyond the scope of this project. Recently, 

Jay et al. (2006) have developed satellite radio transmitter tags for walruses that record wet and 

dry intervals from which correction factors for aerial surveys can be developed. These tags can 

be deployed with crossbows, and are attached to animals via a harpoon head lodged underneath 

the skin. USGS is currently modeling telemetry data collected in 2004 and 2005 to estimate the 

number of tags necessary to deploy in support of a range wide population survey (Udevitz et al.

2004). Data collected during this pilot survey is being used along with the telemetry data to 

estimate the amount of survey effort necessary to obtain an abundance estimate for the Pacific 

walrus population with an acceptable level of precision (Udevitz et al. 2004, USFWS and USGS 

In prep). 

We believe that the AMS system as configured for this study would be a highly effective tool for 

surveying large areas of sea ice habitat for walrus groups. With a 6 km swath width, it should be 

possible to sample more than 10,000 km2

 in an 8-hr flight. The system could theoretically be 

flown at higher altitudes with larger spatial resolutions, covering an even larger area; however, 

the proportion of the walrus population that would be missed would also increase. Alternatively, 
15

as systems with smaller IFOVs and larger pixel arrays become available, it may be possible to 

survey larger areas with the same 4 m resolution. Based on the results and experience gained 

during this pilot survey, the USFWS contracted with ArgonST to build a detector with a 0.625 

milliradian IFOV and 3,000 pixel array. This system, tested in the spring of 2005, generated 4 m 

spatial resolution data with a swath width of approximately 12 km from edge to edge when flown 

at 6,400 m. Additionally, at this altitude, the 690B Turbine Commander was considerably more 

fuel efficient and could cruise at speeds up to 463 km/hr. These factors combined to increase 

survey efficiency by roughly 250% over the results obtained in this study (USFWS, unpublished 

data). 

In addition to thermal imagery, high-resolution digital photographs, suitable for counting 

individual walruses, need to be obtained from a representative sample of walrus groups to allow 

estimation of group sizes from the thermal data. Given the relative importance of large walrus 

groups to the overall number of walruses observed, it is important to photograph the full range of 

walrus group sizes. We recommend a double-sampling design (Thompson 2002) in which a 

random sample of survey transects are scanned using the AMS system and a random sample of 

the thermally detected walrus groups photographed. Based upon the experience gained in this 

study, and in consideration of the cost in fuel and flight time associated with repeatedly changing 

altitudes between scanner operation (3,200 m) and photography (762 m), we recommend that a 

separate (second) aircraft be deployed to collect photographs. The scanner and photography 

airplanes could work together at different altitudes and relay information back and forth by radio 

or satellite phone on the position and size of groups to enhance the effectiveness of data 

collection. 

Although we believe this test survey demonstrates the utility of airborne thermal imagery in 

surveys of Pacific walrus, additional field work conducted under extremely cold conditions in 

April 2005 indicated that the ability to detect walrus groups in thermal imagery is in part a 

function of ambient temperatures. Based on these results, we are exploring alternative analytical 

procedures for identifying walrus groups in thermal imagery, as well as alternative calculations 

of thermal index values that accounts for variations in ambient temperature. In addition to the 

apparent influence of ambient temperature on detection of walrus groups, the requirement of 

cloud-free conditions may also be a limiting factor for survey operations. An analysis of 

historical weather information should be incorporated into any study plan for surveying the 

Pacific walrus population. 

Based on the success of this pilot survey and recent innovations in satellite telemetry packages 

capable of tracking walrus movements and haulout patterns (Jay et al. 2006) the USFWS and 

USGS, in cooperation with Russian scientists, have begun to develop a study plan for a rangewide survey of the Pacific walrus population (USFWS and USGS 2006). The study plan outlines 

an operational strategy to estimate the size of the Pacific walrus population using an AMS 

system and digital photography. The proposed survey would occur in spring, when the Pacific 

walrus population is distributed near the southern edge of the Bering Sea ice pack. Prior to the 

aerial survey, satellite transmitters would be deployed on a representative sample of walruses 

distributed across Bering Sea pack ice. The transmitters would record the proportion of time 

each tagged walrus is hauled out on the ice or in the water and this information used to develop a 

correction factor for animals not detected during the aerial survey (Udevitz 2005). 
16

Objective 2. Evaluate the effectiveness of the AMS system as a survey tool for other marine 

mammal and bird species 

We had limited success evaluating the AMS system as a survey tool for other marine mammal or 

bird species in the study area. We had intended to systematically search the area for other 

pinniped and bird species and collect thermal imagery from a variety of spatial resolutions in 

order to evaluate the capability of the AMS system in detecting and enumerating these species. 

Unfortunately, weather conditions deteriorated towards the end of our study period and we were 

unable to complete this aspect of the mission. 

Our survey effort for walrus groups was conducted from 3,200 m with a ground sample 

resolution of 4 m. The tendency for walruses to aggregate into large groups permits sampling at 

this relatively course resolution with its correspondingly broad swath width (Burn et al. 2006). 

While the 4 m spatial resolution is sufficient for detecting and enumerating walrus groups, it 

apparently is insufficient for detecting individual seals. During our walrus survey effort, we 

sighted and were able to photograph several single seals (Phoca spp. and Erignathus barbatus) 

from an altitude of 762 m. None of these seals were detected in the corresponding 4 m thermal 

data. Large flocks of spectacled eiders are known to occur in the study area (Petersen et al.

1999); however we did not encounter any aggregations during our surveys. 

Theoretically, the AMS system should be capable of detecting any endothermic species in an ice 

environment providing that there is sufficient resolution to detect the thermal contrast between 

the target and its back ground. Unfortunately, flying at lower altitudes to obtain a greater 

resolution will also reduce the area covered by the scanner, and affect the utility of the scanner as 

a survey tool. We also anticipate challenges in distinguishing between species that are similar in 

size unless the target species selects unique habitat features or has striking and consistent 

behavioral attributes such as group size or configuration. 

Acknowledgments 

We thank the other members of our survey team, Ralph Aiken and William Baker, for their skill 

and expertise during the field portion of this study. David Weintraub (Commander Northwest) 

was instrumental in ensuring the survey aircraft would meet the necessary specifications. Fred 

Osterwisch, Tom Ory, and Jason Jester (ArgonST) provided valuable technical support for the 

AMS system. Jan Bennett, Lark Wuerth, and Bud Walters (U.S. Department of Interior Office 

of Aircraft Services) insured that the study was conducted in accordance with safety guidelines. 

We also thank staff from the National Weather Service in Nome, Alaska for their assistance with 

weather forecasts and mission planning. This study was conducted under U.S. Department of 

Interior Letter of Confirmation MA-039582. 

Study Products 

Reports and publications 
17

Burn, D., M. Webber and M. Udevitz. (2006). Application of airborne thermal imagery to 

surveys of Pacific walrus Odobenus rosmarus divergens). Wildlife Society Bulletin 

34:51–58. 

Udevitz, M.S. 2005. Statistical procedures for estimating Pacific walrus population size. 

Technical report, Alaska Science Center, U.S. Geological Survey, Anchorage, Alaska. 9 

pp. 

USFWS and USGS. (2006.) Study Plan for estimating the size of the Pacific walrus population. 

Udevitz, M.S., D.M. Burn and M.A. Webber. (In prep) Estimation of walrus populations on sea 

ice with infrared imagery and aerial photography. 

Presentations 

Burn, D., M. Webber and M. Udevitz. 2003. Application of Airborne Thermal Remote Sensing 

to Surveys of Pacific walrus (Odobenus rosmarus divergens). Presented at: 15th Biennial 

Conference on the Biology of Marine Mammals, 14-19 December, Greensboro NC. 

Burn, D., M. Webber and M. Udevitz. 2004.. Using Airborne Thermal Remote Sensing to 

Survey Pacific walrus (Odobenus rosmarus divergens) Presented at: Marine Mammals 

of the Holarctic III, 11-17 October, Koktebel, Ukraine. 

Udevitz, M. S., D. B. Burn, A. S. Fischbach, C. V. Jay, and M. A. Webber. 2004. Survey design 

for estimating Pacific walrus population size. Presented at: Marine Mammals of the 

Holarctic III, 11-17 October, Koktebel, Ukraine. 
18

Literature Cited 

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20

Appendix 1. Scanner specifications 
21
22

Appendix 2. Total walrus heat measured for each walrus hotspot recorded

Date Transect Number a Total Walrus Heat Walrus Count 

452 68.79 

452 45.98 

452 572.82 

452 391.05 

452 209.76 

452 8.48 

459 84.61 

4/05/2003 

466 55.03 

4/06/2003 366 5.46 

437 20.66 

438 19.96 

446 10.90 

446 23.67 

446 6.89 

446 52.80 

 71.69 

 8.84 

 797.62 43 

 86.62 

 329.47 17 

 9.03 

 97.47 

 58.70 

447 51.69 

447 578.04 

447 45.04 

447 340.23 

447 3,818.46 

449 760.65 

449 59.89 

449 78.28 

449 52.97 

449 427.78 57 

449 329.47 23 

449 304.78 

449 10.91 

4/07/2003 

449 1,887.07 155 

4/08/2003 422 15.25 

 422 6.67 

 424 39.70 4 

 424 363.66 

 424 78.53 

 424 135.30 4 

 424 92.96 
23

Date Transect Number a Total Walrus Heat Walrus Count 

4/08/2003 424 79.85 

 424 10.74 

 425 161.64 

 425 105.81 

 426 13.82 

426 105.94 

426 9.06 

426 26.88 

 208.00 

 4,652.21 294 

 127.40 25 

 3,006.84 315 

345 18.30 

345 157.64 54 

345 3.31 

345 113.52 49 

345 137.75 32 

347 6.46 

347 1.54 

4/09/2003 

 26.00 

4/10/2003 312 7.99 

 312 18.91 

 312 84.08 13 

 312 14.90 

 312 25.34 

 312 1.07 5 

 314 28.46 

 314 6.99 

 314 5.04 

 314 3.26 

 314 11.56 

 314 26.71 

 314 8.55 

 314 107.66 

 314 43.76 

 314 29.80 

 314 1.10 

 314 62.92 

 314 11.22 

 314 79.81 

 314 28.78 

 314 5.59 

 314 53.41 

 314 1.11 

 314 22.50 
24

Date Transect Number a Total Walrus Heat Walrus Count 

4/10/2003 314 1.80 

 314 39.74 

 314 135.06 

 314 308.83 

 314 47.93 

 314 8.68 

 314 21.84 

 314 12.27 

 314 16.84 

 315 25.00 

 315 65.65 

 315 56.60 

 315 15.56 

 315 26.89 

 315 8.83 

 315 19.86 

 271.71 11 

 695.27 

 11.93 

 156.71 

 557.54 

 198.29 

 25.59 

 54.74 7 

 120.93 15 

 1,704.84 97 

 322.61 24 

 893.64 39 

 2,436.68 116 

 152.24 11 

 1.41 

 1,413.85 53 

 4.57 

 72.89 

 0.80 

 1,651.65 79 a Hotspots with missing transect number were recorded supplemental to survey effort.