Effects of environmental conditions on the use of forward‐looking infrared for bear den detection in the Alaska Arctic

Abstract

Abstract Industrial off‐road activity in winter overlaps denning habitat of polar bear (Ursus maritimus) and grizzly bear (Ursus arctos) in the North Slope oilfields of Alaska (United States). To prevent disturbance of dens, managers have used forward‐looking infrared (FLIR) cameras to detect dens, but the effectiveness of FLIR under different environmental conditions is unresolved. Our objective was to evaluate the effects of environmental variables on FLIR‐based techniques for arctic bear den detection. Using a FLIR‐equipped unmanned aircraft system (UAS), we conducted observations of artificial polar bear (APD) and grizzly bear (AGD) dens from horizontal and vertical perspectives between December 2016 and April 2017. We recorded physical characteristics of artificial dens and weather conditions present during each observation. We captured 291 images and classified each as detection or nondetection based on the number of pixels representative of a den “hot spot.” We used logistic regression to model the effects of four weather variables on the odds of detection (detection). We found that UAS‐FLIR detects APDs two times better than AGDs, and that for both species detections are four times more likely from the vertical than horizontal perspective. Lower air temperature and wind speed, and the absence of precipitation and sunlight increased detection for APDs. A 1°C increase in air temperature lowered detection by 12% for APDs and by 8% for AGDs. We recommend that UAS‐FLIR surveys be conducted early in the denning season, on cold, clear days, with calm winds, in the absence of sunlight (e.g., civil twilight). Our study further refines the application of FLIR techniques for arctic bear den detection and offers practical recommendations for optimizing detection. Putative den locations should be confirmed by a secondary method to minimize disturbance as anthropogenic activity continues in the Arctic.

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CONTRIBUTED PAPER
Effects of environmental conditions on the use of forward-
looking infrared for bear den detection in the Alaska Arctic
Nils J. Pedersen
1
| Todd J. Brinkman
1
| Richard T. Shideler
2
| Craig J. Perham
3
1
Institute of Arctic Biology, University of
Alaska Fairbanks, Fairbanks, Alaska
2
Alaska Department of Fish and Game,
Fairbanks, Alaska
3
Bureau of Land Management, Alaska
State Office, Anchorage, Alaska
Correspondence
Nils J. Pedersen, Institute of Arctic
Biology, University of Alaska Fairbanks,
323B Murie Building, 2090 Koyukuk
Drive, Fairbanks, AK 99775.
Email: n.pedersen@
windriverbearinstitute.org
Funding information
Defenders of Wildlife; National Fish and
Wildlife Foundation, Grant/Award
Number: 50059; University of Alaska
Fairbanks
Abstract
Industrial off-road activity in winter overlaps denning habitat of polar bear
(Ursus maritimus) and grizzly bear (Ursus arctos) in the North Slope oilfields
of Alaska (United States). To prevent disturbance of dens, managers have used
forward-looking infrared (FLIR) cameras to detect dens, but the effectiveness
of FLIR under different environmental conditions is unresolved. Our objective
was to evaluate the effects of environmental variables on FLIR-based tech-
niques for arctic bear den detection. Using a FLIR-equipped unmanned air-
craft system (UAS), we conducted observations of artificial polar bear (APD)
and grizzly bear (AGD) dens from horizontal and vertical perspectives between
December 2016 and April 2017. We recorded physical characteristics of
artificial dens and weather conditions present during each observation. We
captured 291 images and classified each as detection or nondetection based on
the number of pixels representative of a den “hot spot.”We used logistic
regression to model the effects of four weather variables on the odds of detec-
tion (detection). We found that UAS-FLIR detects APDs two times better than
AGDs, and that for both species detections are four times more likely from the
vertical than horizontal perspective. Lower air temperature and wind speed,
and the absence of precipitation and sunlight increased detection for APDs. A
1C increase in air temperature lowered detection by 12% for APDs and by 8%
for AGDs. We recommend that UAS-FLIR surveys be conducted early in the
denning season, on cold, clear days, with calm winds, in the absence of sun-
light (e.g., civil twilight). Our study further refines the application of FLIR
techniques for arctic bear den detection and offers practical recommendations
for optimizing detection. Putative den locations should be confirmed by a
secondary method to minimize disturbance as anthropogenic activity con-
tinues in the Arctic.
KEYWORDS
FLIR, grizzly bear, human-bear conflict, polar bear, remote sensing, unmanned aircraft system
Received: 3 December 2019 Revised: 20 March 2020 Accepted: 17 April 2020
DOI: 10.1111/csp2.215
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2020 The Authors. Conservation Science and Practice published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology
Conservation Science and Practice. 2020;2:e215. wileyonlinelibrary.com/journal/csp2 1of11
https://doi.org/10.1111/csp2.215

1|INTRODUCTION
Winter denning by grizzly bear (Ursus arctos)andpolar
bear (Ursus maritimus) in the North Slope oilfields of
Alaska (United States) is a complex ecological strategy to
reduce energy expenditures during periods of unfavorable
environmental conditions (Watts, 1990). Grizzly bears of
both sexes and all age classes use winter dens (Manchi &
Swenson, 2005), but only pregnant female polar bears
exhibit denning behavior (Ferguson, Taylor, Rosing-Asvid,
Born, & Messier, 2000; Watts, 1990). Pregnant female polar
bears in Alaska establish maternal dens predominantly
within large drifts of snow (Amstrup, York, McDonald,
Nielsen, & Simac, 2004). They enter the den in November
and exit in March or April (Amstrup & Gardner, 1994;
Smith, Partridge, Amstrup, & Schliebe, 2007). Grizzly bears
dig earthen dens between late September and early
November and exit between March and May, with males
and nonpregnant females entering and exiting dens earlier
and later, respectively, than pregnant females (Shideler &
Hechtel, 2000). Both species give birth in the den during
the winter to protect their young from the harsh winter
conditions during their most vulnerable stage of life
(Linnell, Swenson, Anderson, & Barnes, 2000). If disturbed,
a bear may abandon its den resulting in higher bear mor-
tality, family dissolution, and subsequent cub mortality due
to exposure, starvation, or predation (Amstrup, Stirling,
Smith, Perham, & Thiemann, 2006; Linnell et al., 2000;
Swenson, Sandegren, Brunberg, & Wabakken, 1997).
Losses in critical arctic sea ice habitat have placed the
long-term viability of the polar bear in question, resulting
in “threatened”status under the U.S. Endangered Species
Act since 2008 (Federal Registrar, 2008). Reduced access
to sea ice may be contributing to increased selection of
land-based dens, raising the chances of adverse human-
bear interactions with residents of coastal villages and
personnel associated with industrial activity on the North
Slope (Amstrup & Gardner, 1994; Fischbach, Amstrup, &
Douglas, 2007; Rode, Robbins, Nelson, & Amstrup, 2015).
Since the 1970s, extensive petroleum exploration and
extraction on the North Slope has occurred and may fur-
ther overlap with denning habitat for both polar bears
(Wilson & Durner, 2020) and grizzly bears. It is crucial
for bear conservation that the oil industry has reliable
methods for locating and avoiding occupied dens during
development activities in the Arctic. The U.S. Fish and
Wildlife Service (USFWS) manages the southern Beaufort
Sea polar bear stock on the Alaska North Slope and has a
federally codified regulation (50 CFR §18.128 1(b)(2))
requiring minimal industrial activity ≤1.6 km from
known polar bear dens. Likewise, the Alaska Department
of Natural Resources and the U.S. Bureau of Land Man-
agement have specific permit stipulations on off-road
exploration and construction projects during winter to
minimize industrial activity ≤0.8 km from an occupied
grizzly bear den (Reed, 2018). These buffer zones around
occupied bear dens are established for bear conservation
and human safety purposes (Löe & Röskaft, 2004;
Naughton-Treves, Grossberg, & Treves, 2003; Voorhees,
Sparks, Huntington, & Rode, 2014). In order to reduce
negative human-bear interactions and threats to worker
safety, it is critical that managers are able to accurately
identify the location of active bear dens for avoidance.
PolarbeardenninghabitatintheNorthSlopeofAlaska
oilfields is associated with landscape features that allow
drifting snow to accumulate deep enough for excavation
(Liston, Perham, Shideler, & Cheuvront, 2016). These fea-
tures are widely dispersed and difficult to differentiate from
the surrounding environment (Amstrup & Gardner, 1994;
Durner,Amstrup,&Ambrosius,2001;Durner,Amstrup,&
Fischbach, 2003). Grizzly bear denning habitat can be even
more difficult to identify, as it constitutes a variety of land-
scape features (e.g., riverbanks, pingos, and terraces) that
provide relief in the landscape (Shideler & Hechtel, 2000).
Den entrances are discrete, and they are quickly covered by
drifting snow, which can impede visual den detection tech-
niques (Amstrup & Gardner, 1994; Clark, Stirling, &
Calvert, 1997; Ramsay & Stirling, 1990). For this reason, it
is preferable to identify and map suitable den habitat to
facilitate knowledge of where occupied dens are more likely
to occur (Durner et al., 2001, 2003; Liston et al., 2016).
Polarbeardeninteriorscanbe30
Chigherthan
outside air temperatures and the snow surface tempera-
ture above the den can be 10C warmer than surround-
ing snow (Watts, 1983). A denning grizzly bear can
emit heat signatures less than or equal to polar bears
(Watts, 1990). Forward-looking infrared (FLIR) cam-
eras can detect minor heat differences in the landscape
by measuring emissivity: the ability of a substance to
release thermal energy (Hyll, 2012). The USFWS has
used FLIR techniques to detect polar bear dens in
advance of industrial operations on the North Slope of
Alaska since 2004. Sensors on the airborne FLIR imager
used in these surveys can measure 0.1C differences in
surface temperature (Amstrup et al., 2004) and produce
an image that represents relative temperature differen-
tials. Body heat of a denning bear is emitted through
the snow surface creating a “hot spot”in a FLIR image.
This is represented by a discrete cluster of image pixels
on the snow surface above the den, or den entrance
that can be differentiated from the surrounding surface
(Figure 1). Previous studies on the effectiveness of
FLIR imagery have documented both false positives
(hot spots are detected at unoccupied dens) and false
negatives (hot spots are not detected at occupied den).
False positives occur because landscape structures
2of11 PEDERSEN ET AL.

(e.g., human-made infrastructure, exposed soil) can absorb
and re-emit solar radiation, or have substantially different
emissivity that can be confused with the infrared
(IR) signature of an occupied den (Amstrup et al., 2004;
Shideler & Perham, 2013). Weather conditions and den
physical characteristics (i.e., den snow wall ceiling thick-
ness) can inhibit the ability of FLIR sensors to differentiate
temperatures on the snow surface, causing a false nega-
tive. False negatives are a much greater conservation and
safety concern than false positives because occupied bear
dens are not detected.
Aerial and handheld FLIR camera platforms have been
evaluated to detect occupied and human-made artificial
polar bear and grizzly bear dens (Amstrup et al., 2004; Rob-
inson, Smith, Larsen, & Kirschhoffer, 2014; Shideler &
Perham, 2013). Each of these previous studies revealed limi-
tations on the effectiveness of FLIR cameras under varying
ambient conditions that may influence odds of detection
(detection). However, each of these previous studies was
limited in scope, leaving ample room for testing and optimi-
zation of the FLIR-based technique. For example, either
logistics hindered sufficient data collection for precise and
conclusive results (Amstrup et al., 2004; Shideler &
Perham, 2013), or data collection occurred over short obser-
vation period (19 days) when polar bears begin exiting the
den (March) using images that were only captured from a
horizontal perspective (Robinson et al., 2014). Den detection
using ground-based horizontal images are more likely to be
influenced by convection and blowing snow. Previous stud-
ies indicated that a more meaningful evaluation of FLIR
techniques could be obtainedbyovercominglogistical
issues to collect a robust sample size of FLIR observations
from the horizontal and vertical perspective, across the
denning season, using dens with known physical character-
istics and known weather conditions for comparison
(Amstrup et al., 2004; Robinson et al., 2014; Shideler &
Perham, 2013).
To overcome technical and logistical problems, we
employed an Unmanned Aircraft System (UAS) platform
equipped with a FLIR camera to obtain a greater sample
of images of artificial polar bear (APD) and grizzly bear
(AGD) dens under different environmental conditions
that may affect detection. Our objectives were to capture
images of artificial dens throughout most of bear denning
season (December to April) to (a) estimate differences in
detection from horizontal and vertical perspectives;
(b) model the relative influence of environmental vari-
ables on detection, and (c) opportunistically collect imag-
ery of occupied bear dens for comparison with artificial
dens and to demonstrate the ability of UAS-FLIR to sur-
vey and monitor bear dens in arctic winter conditions.
Drawing from previous research, we hypothesized that
detection would be optimized using vertical perspectives
under certain environmental conditions, which includes
low wind speed and relative humidity, along with the
absence of direct solar radiation and precipitation.
2|METHODS
We constructed six artificial bear dens during October
and December 2016 within the North Slope oilfields of
Alaska (Figure 2). We used the same methods and similar
dimensions specified in an earlier study (Shideler, 2014).
AGDs were excavated in the soil in late October, and the
entrances were covered with slabs of snow to allow for
additional drifted snow to form a snow wall over the
entrance and soil wall (Supporting Information). Internal
den dimensions were measured upon excavation of the
dens and verified at the end of the winter monitoring
period (Table 1). We created APDs in prominent snow-
drifts in December and covered entrances with slabs of
snow to allow the entrance to fill in with drifted snow to
form a wall (Table 1). Two artificial dens were
FIGURE 1 From left to right: forward-looking infrared image of an artificial polar bear den at a distance of 50 m vertical above the
den, post-processed using a “segment”feature to eliminate colder pixels from image (a), to partially segmented image (b), and leave only the
warmer pixels associated with the den “hot spot”in image (c) to be counted using a “region of interest”(circle) as a means to evaluate the
quality of the detection in comparison to other detections at this distance
PEDERSEN ET AL.3of11

established at drill site 2M (DS2M; 1 AGD and 1 APD)
and four artificial dens were established at the Kuparuk
Industrial Center (KIC; 2 AGDs and 2 APDs; Figure 2).
We monitored snow wall thickness (cm) throughout the
denning season by inserting a measuring stick through
the den entrance wall of AGDs and through the ceiling
wall of APDs. We measured snow wall thickness of artifi-
cial dens at the beginning and end of the denning season
and during each observation period. Each AGD had a
60 W silicone heating unit, and each APD had a 120 W
FIGURE 2 Study area:
North Slope oilfields of Alaska
(United States). One artificial
grizzly bear and one artificial
polar bear den located at Drill
Site 2M and two artificial grizzly
bear and two artificial polar bear
dens located at the Kuparuk
Industrial Center. The location
of the one occupied polar bear
den and three grizzly bear dens
are labeled
TABLE 1 A comparison of means
with SD for artificial den dimensions,
wall thickness (cm), and temperature
characteristics (C) of den interior,
snow surface above the den, and
ambient snow surface near the den, as
well as predictor variables: air
temperature (C), wind speed (kph),
and presence or absence of solar
radiation and precipitation during the
forward-looking infrared-based bear
den detection study period in the North
Slope oilfields of Alaska (United States)
Polar bear Grizzly bear
Den dimensions (cm) Depth 136.7 (SD 40.9) 94.3 (SD 21.2)
Width 152.7 (SD 45.5) 126 (SD 41.2)
Height 94.2 (SD 41.8) 86.7 (SD 23.6)
Den wall thickness (cm) Mean 30.9 (SD 16.7) 51.6 (SD 45.8)
Minimum 1 15
Maximum 57 150
Temperature (C) Den interior −2.5 (SD 1.9) −4.4 (SD 4.5)
Den surface −10.1 (SD 6.9) −11.9 (SD 8.4)
Snow surface −18.1 (SD 8.3) −18.1 (SD 8.3)
Air temperature (C) Mean −18.3 (SD 7.7) −18.3 (SD 7.7)
Maximum −9
Minimum −29
Solar radiation Present 9
Absent 32
Wind speed (kph) Mean 25.9 (SD 8.8)
Maximum 36.6
Minimum 5.6
Precipitation Present 20
Absent 21
4of11 PEDERSEN ET AL.

silicone heating unit, placed on the den floor to mimic
temperatures commensurate with the conservatively esti-
mated heat generated by a single grizzly bear or polar
bear (Shideler, 2014). A HOBO Water Temperature Pro
v2 Data Logger, or “thermistor”(Onset Computer Corpo-
ration, Bourne, MA) was placed securely inside each of
the six artificial dens to measure interior temperatures
once every 12 hours for the duration of the study.
To capture IR spectrum imagery from a vertical per-
spective above artificial dens, we used a Ptarmigan
Hexacopter UAS (Northern Embedded Solutions, LLC,
Fairbanks, AK) fitted with a FLIR Vue Pro (FLIR Sys-
tems, Inc., Wilsonville, OR) camera. We used the same
camera for horizontal images from handheld level. We
used Mission Planner (ArduPilot Development Team,
2016) to program the Ptarmigan for a repeat flight pattern
at artificial den sites. We collected vertical and horizontal
images of each den from 20, 50, and 100 m distances to
obtain imagery from comparable distances without
exceeding an altitude of 122 m AGL (Federal Aviation
Administration restriction; Dorr, 2018). We measured
outside air temperature, ambient snow surface tempera-
ture 10 m from the den, and the snow temperature
directly above each artificial den using a digital ther-
mometer. At the time of each flight, we measured average
wind speed and direction with a Kestrel 1000 (Nielsen-
Kellerman, Boothyn, PA) to obtain average wind speed
and direction. We categorized direct sunlight (solar radia-
tion) as either present or absent. Humidity, dew point, and
precipitation were recorded from the nearest airport
(i.e., Kuparuk Airport). We categorized precipitation as
either present or absent. We collected UAS-FLIR imagery
during 1–3 day trips each month, between mid-December
2016 and mid-April 2017, to capture sufficient seasonal
variation throughout the den season. We collected imagery
during civil twilight and daylight hours.
We were able to opportunistically sample occupied bear
dens to compare with, and ensure that, artificial den imag-
ery was representative of occupied dens. We collected verti-
cal imagery of one occupied polar bear den on December
21, 2016 and February 15, 2017, and horizontal imagery on
11 January and March 8, 2017. We collected vertical imag-
ery of three occupied grizzly bear dens, two on February
15, 2017 and one on 17 and April 18, 2019, and collected
horizontal imagery on March 8, 2017. The presence of a
polar bear within the den was confirmed by oilfield worker
observations, UAS-FLIR detection, and camera trap imag-
ery when the female and two cubs emerged in the spring.
The presence of grizzly bears within these dens was con-
firmed by radio and GPS-collar information, indication
from a scent-trained dog, camera trap imagery, and oilfield
personnel observation of den excavation and emergence.
All activities involving occupied bear dens were approved
by the Institutional Animal Care and Use Committee
(Approval Number 716202-1, April 24, 2015).
We conducted 30 UAS-FLIR missions at the KIC and
11 at the DS2M (Figure 2), for a total of 41 UAS-FLIR
missions (Supporting Information). At the KIC site, we
sampled four artificial dens from the horizontal and verti-
cal perspective ([4 dens ×30 observation periods] ×3 dis-
tances: 20, 50, and 100 m) for a total of 360 possible
samples (Supporting Information). At the DS2M, we sam-
pled two artificial dens from the horizontal and vertical
perspective ([2 dens ×11 observation periods] ×3 dis-
tances: 20, 50, 100 m) for a total of 66 possible samples.
Thus, we had 426 (KIC: 360 + DS2M: 66) opportunities
to collect samples of artificial dens. The imagery that we
included in the analysis consisted of two possible out-
comes: nondetections (correct UAS-FLIR position but
no artificial den visible: false negative) and detections
(correct UAS-FLIR position with artificial den visible:
true positive). We used Research IR (FLIR Systems, Inc.,
Wilsonville, OR) software for analysis of the radiometric
properties recorded in each image. The software applied
a color code for the average IR intensity (heat) of each
pixel in the image. We uniformly applied the “segment”
feature to all images to remove colder pixels from the
image until a discrete cluster of the warmest pixels (hot
spot) remained that was approximately the size of a bear
den footprint or smaller: 1.5 m long by 1.3 m wide
(Durner et al., 2003). We summed the pixel count for
each hot spot (Figure 1). This allowed us to highlight the
size of clustered hot spot pixels and classify them as
either a detection (1) or a nondetection (0). We estimated
median hot spot pixel counts of true positives within each
subcategory (e.g., APD, vertical, 20 m; see Supporting
Information) to account for effects of distances, species,
and perspectives on pixel count. We expected pixel count
within the hot spot to decrease with distance because the
camera resolution remained constant. We classified false
negatives and imagery with hot spot pixel counts at or
below the median within each subcategory as non-
detections prior to estimating the effects of environmen-
tal variables.
We used a logistic regression model (Hosmer &
Lemeshow, 2000) to predict den detection (dependent
variable: 0 or 1 categorized using size of den hot spot)
based on environmental variables recorded at the time of
observation. We considered the following environmental
variables as predictors in the logistic regression models:
air temperature, humidity, dew point, temperature dew
point spread, solar radiation, wind speed, cloud cover,
and precipitation. We excluded variables that would be
unmeasurable in the field when attempting to detect an
occupied bear den, such as den snow wall thickness and
snow surface temperature directly above the den. We
PEDERSEN ET AL.5of11

used variance inflation factor (VIF) and Spearman's rank
(r
s
) correlation matrix (Supporting Information) to reduce
our starting set (n= 8) of environmental variables, and
avoid multicollinearity (VIF score ≥4.0; r
s
> 0.6;
Zar, 1971; O'Brien, 2007). We selected one predictor vari-
able from a set of highly correlated predictor variables for
inclusion in our model based on importance in previous
research (Amstrup et al., 2004; Robinson et al., 2014) and
ability for future researchers to readily measure and
account for. We excluded predictors from the model if
there was insufficient variation within the sample for our
model to converge. We conducted a logistic regression on
all imagery within our four UAS-FLIR observation cate-
gories (Table 2). All statistical analyses were performed
using IBM SPSS (IBM Corporation, 2015, V23). We inter-
preted the results of the logistic regression model by con-
sidering any continuous or categorical predictor variable
with p≤.1 as significant and used the beta coefficients to
obtain the odds ratio. Using the odds ratio, we estimated
the effect that a 1-unit change in a continuous variable or
a categorical change in predictor variable would have on
the odds that a detection (1) occurred. We evaluated the
relationship between month of the denning season with
ambient air temperature and den wall thickness for AGD
and APD using a Kruskal–Wallis test to provide insight
on the best time of year to locate dens.
3|RESULTS
Out of 426 sample collection opportunities, we success-
fully captured 291 images (68%) of the artificial dens for
inclusion in our analysis. The images were distributed as
follows: AGD vertical (n= 52), AGD horizontal (n= 78),
APD vertical (n= 81), and APD horizontal (n= 80). We
classified 41% as detections (n= 119) and 59% as nonde-
tection (n= 172; Supporting Information). The APD
images comprised 55% of observations (n= 161), and
AGD comprised 45% of observations (n= 130); 54% of
images were taken from the horizontal perspective
TABLE 2 Logistic regression model estimates of strength of influence (exponential beta coefficient, Exp (B)) of ambient weather
predictor variables on odds of detection (odds of outcome “detection”instead of reference “nondetection”) in forward-looking infrared
images (n= 291) of artificial grizzly bear and polar bear dens observed from the vertical and horizontal perspective
Coefficients 95% CI
Species Perspective Variable p-value Exp (B) Lower Upper
Grizzly bear (n= 130) Horizontal (n= 78) Constant .03 0.00
Air temperature .05 0.92 0.85 1.00
Wind .08 1.18 0.98 1.42
Precipitation
a
.47 3.51 0.12 105.40
Solar
a
(insufficient sample variation)
Vertical (n= 52) Constant .47 0.37
Air temperature .77 0.99 0.91 1.07
Wind .17 1.05 0.98 1.13
Precipitation
a
.68 0.66 0.21 11.00
Solar
a
.43 0.45 0.30 16.76
Polar bear (n= 161) Horizontal (n= 80) Constant .79 0.64
Air temperature .01 0.90 0.84 0.97
Wind .53 0.97 0.86 1.08
Precipitation
a
.06 0.10 0.01 1.05
Solar
a
.88 0.89 0.18 4.32
Vertical (n= 81) Constant .93 1.10
Air temperature .00 0.88 0.82 0.95
Wind .10 0.95 0.90 1.01
Precipitation
a
.03 0.18 0.04 0.87
Solar
a
.05 0.23 0.05 1.01
a
Categorical variables (odds of detection outcome instead of nondetection with one category change: presence instead of absence of precipita-
tion or solar).
6of11 PEDERSEN ET AL.

(n= 158) and 46% were taken from the vertical perspec-
tive (n= 133). Median pixel counts of true positive hot
spots from the vertical perspective were four times
greater than that of hot spots from the horizontal per-
spective. Median APD hot spot pixel counts were approxi-
mately two times greater than that of AGD hot spots at
both the horizontal and vertical perspectives (Figure 3;
Supporting Information).
Our factor reduction analysis indicated that 4 of 8 envi-
ronmental predictor variables (air temperature, wind
speed, precipitation, and solar radiation) explained 81% of
the variation in our dataset. Air temperature was a signifi-
cant predictor of detection in three (vertical and horizontal
APDs, and horizontal AGD) out of four models (Table 2).
For the AGD horizontal model, an increase of 1Cambi-
ent air temperature lowered detection by 8% (p= .05). For
APD horizontal and vertical models, an increase of 1Cin
ambient air temperature lowered detection by 10%
(p< .01) and 12% (p< .01), respectively. Precipitation was
a significant predictor in both APD models, and the pres-
ence of precipitation from the horizontal and vertical per-
spectives lowered detection by 10 times (p=.06)and5.6
times (p= .03), respectively. Solar radiation was a
significant predictor for the vertical APD model, presence
of solar radiation lowered detection by 4.3 times (p= .05).
Wind was a significant predictor in the vertical APD
model. An increase of 1 kph in wind speed lowered detec-
tion by 5% (p= .1). Wind was a significant predictor of the
horizontal AGD model (p= .08). An increase of 1 kph in
wind speed increased detection by 18% (p= .08), however,
we suggest that this finding should be viewed with caution
as it may indicate a failure of our technique. The Kruskal–
Wallis test indicated that ambient air temperature
increased each month (p= .09) and that den wall thick-
ness increased each month for both AGDs and APDs
(p< .01). The temperature differentials between inside
and outside air increased as ambient outside air tempera-
tures decreased. The average air temperature inside of the
artificial dens was warmer than ambient outside air by
13.8 and 15.7C for AGD and APD, respectively (Table 1).
4|DISCUSSION
Warmer air temperatures resulted in lower UAS-FLIR
den detection for three out of four models because of
FIGURE 3 True positive “hot spot”
pixel counts for artificial polar bear
(a) and grizzly bear (b) den imagery
captured with forward-looking infrared
(FLIR) camera from a distance of 20, 50,
and 100 m, at a vertical and horizontal
perspective, in the North Slope oilfields
of Alaska (United States). “Hot spot”
pixel count represents the quality of
detections relative to the distance and
perspective of the FLIR image
PEDERSEN ET AL.7of11

(a) colder ambient air temperatures causing an increased
contrast between den snow wall surface temperature and
surrounding ambient snow temperature, (b) the tendency
for colder temperatures to be associated with clear, dry
conditions, and (c) warming outside air temperatures
that occurred later in the winter season when den walls
are thickest and most insulating. As FLIR imagers
measure relative differences in surface temperatures, col-
der ambient air temperatures increase the differential
between exterior and interior den air temperature. This
draws internal warm air through the snow wall over the
top (APDs), or through the den entrance (AGDs), which
increases the contrast between ambient snow surface
temperatures and den snow surface temperatures.
A lower air temperature was highly correlated with
lower humidity and greater temperature dew point
spread which may also explain the influence that air
temperature had on detection. A previous study
observed that a 1C increase in temperature dew point
spread increased detection, and that the presence of air-
borne moisture reduced detection for occupied polar
bear dens from airborne FLIR, suggesting that this was
due to the FLIR sensor measuring the temperature of
suspended particulate in the air between the sensor and
the den rather than den snow surface temperature dif-
ferentials (Amstrup et al., 2004). The presence of precip-
itation (falling or suspended moisture) decreased
detection for APDs in our study more than previously
reported, and this effect is expected to have an even
greater influence on detection as distance increases for
UAS-FLIR imagery.
In previous studies, solar radiation has been shown to
have a negative effect on detection for both occupied and
artificial polar bear dens (Amstrup et al., 2004; Robinson
et al., 2014). In our study, the presence of solar radiation
decreased detection for APDs at the vertical perspective,
but was not significant in any other models. Solar radia-
tion is expected to reduce detection due to solar reflec-
tance from the snow surface interfering with the FLIR
sensor's ability to distinguish fine-scale temperature dif-
ferentials. We collected samples during daylight hours
and hours of morning and evening civil twilight but our
sample size of observation periods with solar radiation
present was low (n= 9) due to the presence of clouds.
Even with a low sample size, our findings (APD vertical)
agree with earlier studies.
High wind speed was a slightly significant predictor
and reduced detection for vertical APDs, supporting pre-
vious findings of FLIR detection of APDs from the hori-
zontal perspective (Robinson et al., 2014). Wind speeds
≥20 kph were considered to have a negative effect on
detection due to the effect of rolling and suspended snow
particles on the FLIR sensor's ability to measure snow
surface temperature differentials (Amstrup et al., 2004).
Considering that 73% (n= 30) of our observation periods
took place in wind speeds ≥20 kph, a condition normal
for the North Slope, it is possible that we did not sample
sufficiently low wind speeds to measure a threshold at
which wind speed affects detection. Increased wind speed
raised detection for AGDs from the horizontal perspec-
tive, but this may be misleading because the hot spot in
AGD imagery was associated with exposed soil on top of
two AGDs. We believe that these hot spots were the
result of a difference between snow and soil emissivity
rather than a positive relationship between increased
wind speed and the strength of AGD IR signal
(Hyll, 2012). For example, clumps of exposed soil away
from the den surface were also warmer pixels within our
imagery as compared to the snow surface. This suggests
patchy snow conditions may cause problems (i.e., false
positives) when applying the FLIR technique. It is
unlikely that wind friction would generate a measurable
heat difference on exposed soil features, but this warrants
further study.
The positive relationship that air temperature and the
advancing winter months had with den snow wall thick-
ness is intuitive, and its significance is representative of
two things: (a) contrast between ambient snow tempera-
ture and den snow wall temperature decreased as den
snow wall thickness increased, and (b) one AGD accu-
mulated a 125 cm snow wall over the den entrance by
March 6, 2017, after which there was no measurable tem-
perature contrast between ambient snow and den snow
wall surface; it was no longer visible with the UAS-FLIR.
A previous study found that a 1 cm increase in den snow
wall thickness decreased detection by a 1.49% for artifi-
cial polar bear dens from the horizontal perspective, with
a mean den snow wall thickness of 90 cm (Robinson
et al., 2014). In our study, we observed that a threshold
occurred between 80 and 125 cm for AGD. The positive
relationship between an increase in snow wall thickness
and month implies that it is best to conduct UAS-FLIR
surveys early in the winter when snow walls are at their
thinnest levels, before large-scale snow events and subse-
quent drifting snow.
Part of the reason that AGD detections had fewer
pixels in their hot spot than APD detections is explained
by the difference in denning strategies by each species.
Soil and other materials that grizzly bears excavate for a
den are more permanent than snow and provide addi-
tional insulation to snow accumulation over the top of
each den. We speculate that this causes heat to absorb
into the ground or mainly escape through the snow wall
over the grizzly bear den entrance rather than through
the snow wall ceiling of a polar bear den. The APDs were
also 1.9 times larger than AGDs and were equipped with
8of11 PEDERSEN ET AL.

twice the heating elements for an approximately equal
heat output (0.006 W/m
3
). Interior temperatures of APDs
and AGDs remained within the range of occupied bear
dens (Watts, 1983, 1990), but APD interiors averaged
1.8C warmer than AGD interior temperatures. The APD
snow wall surface temperatures also averaged 1.8C
warmer than that of AGDs. The larger, less insulated,
and warmer APDs losing heat through their snow wall
ceilings contributed to better detections with greater
median hot spot pixel counts and surface temperature
contrast than the smaller, more insulated, and less
warm AGDs.
This result was not surprising but the real-world
implications are significant to understanding the efficacy
of using UAS-FLIR for detecting grizzly bear dens. If our
AGDs accurately represented the variation in grizzly bear
den morphology, the size and shape of visible AGD hot
spots imply that UAS-FLIR grizzly bear den detection
surveys will be difficult to perform effectively because
(a) if grizzly bear dens produce small hot spots, they may
not be readily visible to the observer, resulting in false
negatives, and (b) the small size and variable shape of the
grizzly bear den hot spot will make grizzly bear dens
more difficult to distinguish from other landscape fea-
tures with different emissivity (i.e., exposed soil), increas-
ing the chances of both false negatives and false positives.
The known variation in physical den characteristics
introduces uncertainty whether grizzly bear dens, or
polar bear dens established within the earth surface
(Clark et al., 1997), are possible to reliably detect using
UAS-FLIR.
We collected UAS-FLIR images of the occupied polar
bear den from the vertical and handheld perspective
monthly between December 2016 and March 2017
(Supporting Information). The den was possible to detect
with the UAS-FLIR until March, at which point a sub-
stantial (>1 m) accumulation of drifted snow had formed
over the den and an IR signal was no longer visible. We
also collected observations of three occupied grizzly bear
dens on three separate occasions in February and March
2017, and April 2019. Two dens were detectable with
UAS-FLIR from the vertical perspective (Supporting
Information), but one was difficult to discern when
viewed from the horizontal perspective. The other den
was not detected from either perspective, on either of the
two sampling occasions. The fact that two grizzly bear
dens were possible to detect with such certainty from the
UAS-FLIR was a significant accomplishment of our
study, but the inability to detect the other grizzly bear
den under similar conditions calls into question the effec-
tiveness of this technique for broad application when
searching for the location of occupied grizzly bear dens
that are not known in advance. The occupied grizzly bear
dens were known based on radio-collar triangulation,
GPS collar fix, trained scent dog indication, and observed
emergence. Den site selection appeared similar in that
each bear had excavated a den within a small 1.5–2m
tall bank.
Our study found that air temperature, wind speed
(vertical, APD), and presence or absence of precipitation
and solar radiation can be used to predict detection for
artificial dens from both the vertical and horizontal per-
spectives, especially early in the winter season when
den wall thickness is at its minimum. We found that
vertical imagery produced better detection qualities
than that of horizontal imagery. We were unable to cap-
ture and test UAS-FLIR imagery of artificial dens from
an oblique (45–60) perspective due to the difficulty of
maintaining the den location in field of view at a con-
stant angle and distance in the presence of wind. Den
surveys in mountainous terrain with steeper slopes may
need to be able to adjust the angle of view to maintain a
perpendicular perspective to the surface of the ground.
However, our vertical perspective directly above the den
was ideal for the relatively flat terrain on the North
Slope of Alaska, and our vertical perspective was repre-
sentative of manned aircraft surveys conducted in the
area. Given that our artificial dens were representative
of the physical characteristics of occupied polar bear
and grizzly bear dens, and that we sampled our artificial
dens in environmental conditions representative of
those present in bear den habitat of arctic Alaska, we
expect that our results will also apply to UAS-FLIR sur-
veys of occupied bear dens. We recommend UAS-FLIR
surveys be conducted vertically in cold, clear conditions
with calm winds (≤20 kph) at night or during civil twi-
light, at a distance of 100 m (46 ×35 m swath coverage
per image), in December (grizzly bear) and January
(polar bear) before den wall thickness accumulates to its
maximum extent.
We also recommend the use of FLIR cameras with
simultaneous visual and IR spectrum image collection
to help distinguish true positives from false positives.
The visual spectrum may help confirm if other land-
scape characteristics are creating hotspots. Some train-
ing and experience with FLIR-imagery is necessary.
Because the false negatives present the greatest manage-
ment concern for bear conservation and worker safety
we recommend that UAS-FLIR surveys be coupled with
reliable secondary methods, such as repeat surveys or
confirmation with trained scent dogs, to reduce the
occurrence of false negatives. UAS-based FLIR imagery
isausefultoolforarcticbeardendetectionthatmay
enhance worker safety and bear conservation on the
North Slope oilfields of Alaska, with broad application
throughout the north.
PEDERSEN ET AL.9of11

ACKNOWLEDGMENTS
National Fish and Wildlife Foundation (grant #50059),
Defenders of Wildlife, and University of Alaska Fairbanks
assisted with funding. We thank R. McGhee, C. May,
W. Mahan, and N. Visser for support with field logistics.
We thank M. Lindberg, S. Brainerd, and C. Hunt for
review and comments on drafts of this manuscript. We
thank Wind River Bear Institute, Wildlife K-9 “Soledad”
for assistance with confirmation of occupied dens.
CONFLICT OF INTEREST
The authors have no conflict of interest to declare.
AUTHOR CONTRIBUTIONS
Nils J. Pedersen was the lead author of this paper and
helped to conceptualize the study with Todd J. Brinkman
and major input from Richard T. Shideler and Craig
J. Perham. Nils J. Pedersen and Todd J. Brinkman led
data collection, management, and analysis. Nils
J. Pedersen led the writing with major input from all
authors. All authors contributed to the revision and prep-
aration of the final manuscript.
DATA AVAILABILITY STATEMENT
The list of weather conditions present during each sam-
pling period and the Spearman's rho correlation matrix
are available as Appendixes S7 and S8, respectively. Artifi-
cial den detection data requests can be made to
NP. Location of occupied bear dens is considered sensitive.
ORCID
Nils J. Pedersen https://orcid.org/0000-0002-9846-0851
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SUPPORTING INFORMATION
Additional supporting information may be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Pedersen NJ,
Brinkman TJ, Shideler RT, Perham CJ. Effects of
environmental conditions on the use of forward-
looking infrared for bear den detection in the
Alaska Arctic. Conservation Science and Practice.
2020;2:e215. https://doi.org/10.1111/csp2.215
PEDERSEN ET AL.11 of 11