Remote sensing techniques for automated marine mammals detection: a review of methods and current challenges

Abstract

Marine mammals are under pressure from multiple threats, such as global climate
change, bycatch, and vessel collisions. In this context, more frequent and spatially
extensive surveys for abundance and distribution studies are necessary to inform
conservation efforts. Marine mammal surveys have been performed visually from land,
ships, and aircraft. These methods can be costly, logistically challenging in remote
locations, dangerous to researchers, and disturbing to the animals. The growing use of
imagery from satellite and unoccupied aerial systems (UAS) can help address some of
these challenges, complementing crewed surveys and allowing for more frequent and
evenly distributed surveys, especially for remote locations. However, manual counts
in satellite and UAS imagery remain time and labor intensive, but the automation
of image analyses offers promising solutions. Here, we reviewed the literature for
automated methods applied to detect marine mammals in satellite and UAS imagery.
The performance of studies is quantitatively compared with metrics that evaluate
false positives and false negatives from automated detection against manual counts of
animals, which allows for a better assessment of the impact of miscounts in conservation
contexts. In general, methods that relied solely on statistical differences in the spectral
responses of animals and their surroundings performed worse than studies that used
convolutional neural networks (CNN). Despite mixed results, CNN showed promise,
and its use and evaluation should continue. Overall, while automation can reduce time
and labor, more research is needed to improve the accuracy of automated counts.
With the current state of knowledge, it is best to use semi-automated approaches that
involve user revision of the output. These approaches currently enable the best tradeoff
between time effort and detection accuracy. Based on our analysis, we identified thermal
infrared UAS imagery as a future research avenue for marine mammal detection and
also recommend the further exploration of object-based image analysis (OBIA). Our
analysis also showed that past studies have focused on the automated detection of baleen
whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar
bears, sirenians, and mustelids.

Full text

Submitted 11 February 2022
Accepted 13 May 2022
Published 20 June 2022
Corresponding author
Esteban N. Rodofili, erodofili@ufl.edu
Academic editor
Patricia Gandini
Additional Information and
Declarations can be found on
page 15
DOI 10.7717/peerj.13540
Copyright
2022 Rodofili et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Remote sensing techniques for automated
marine mammals detection: a review of
methods and current challenges
Esteban N. Rodofili1, Vincent Lecours1,2and Michelle LaRue3,4
1School of Natural Resources and Environment, University of Florida, Gainesville, FL, United States of
America
2School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, United States of
America
3School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
4Department of Earth and Environmental Science, University of Minnesota, Minneapolis, MN, United States
of America
ABSTRACT
Marine mammals are under pressure from multiple threats, such as global climate
change, bycatch, and vessel collisions. In this context, more frequent and spatially
extensive surveys for abundance and distribution studies are necessary to inform
conservation efforts. Marine mammal surveys have been performed visually from land,
ships, and aircraft. These methods can be costly, logistically challenging in remote
locations, dangerous to researchers, and disturbing to the animals. The growing use of
imagery from satellite and unoccupied aerial systems (UAS) can help address some of
these challenges, complementing crewed surveys and allowing for more frequent and
evenly distributed surveys, especially for remote locations. However, manual counts
in satellite and UAS imagery remain time and labor intensive, but the automation
of image analyses offers promising solutions. Here, we reviewed the literature for
automated methods applied to detect marine mammals in satellite and UAS imagery.
The performance of studies is quantitatively compared with metrics that evaluate
false positives and false negatives from automated detection against manual counts of
animals, which allows for a better assessment of the impact of miscounts in conservation
contexts. In general, methods that relied solely on statistical differences in the spectral
responses of animals and their surroundings performed worse than studies that used
convolutional neural networks (CNN). Despite mixed results, CNN showed promise,
and its use and evaluation should continue. Overall, while automation can reduce time
and labor, more research is needed to improve the accuracy of automated counts.
With the current state of knowledge, it is best to use semi-automated approaches that
involve user revision of the output. These approaches currently enable the best tradeoff
between time effort and detection accuracy. Based on our analysis, we identified thermal
infrared UAS imagery as a future research avenue for marine mammal detection and
also recommend the further exploration of object-based image analysis (OBIA). Our
analysis also showed that past studies have focused on the automated detection of baleen
whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar
bears, sirenians, and mustelids.
How to cite this article Rodofili EN, Lecours V, LaRue M. 2022. Remote sensing techniques for automated marine mammals detection: a
review of methods and current challenges. PeerJ 10:e13540 http://doi.org/10.7717/peerj.13540

Subjects Conservation Biology, Ecology, Marine Biology, Zoology, Spatial and Geographic
Information Science
Keywords Accuracy metrics, Conservation surveys, Object-based image analysis, Remote sensing,
Thermal infrared
INTRODUCTION
Marine mammals currently face various anthropogenic threats such as fishery bycatch,
vessel strikes, competition for resources with commercial fisheries, noise pollution,
bioaccumulation of pathogens and toxins, harmful algal blooms, and climate change
(Merrick, Silber & De Master, 2018). In this context, surveying efforts to improve knowledge
of marine mammal species’ distributions and abundance are critical for their conservation.
Surveys can be carried out from land, aircraft or ships for cetaceans (Hiby & Hammond,
1989). Pinnipeds have been usually counted from land-based viewing stations, although
aerial photographs have also provided a viable alternative (Moore, Forney & Weller, 2018).
Boat surveys around rookeries are also commonly performed (e.g., Arias-del Razo et
al., 2016;Adame et al., 2017). However, crewed aerial and vessel platforms can bias
observations due to the animals’ reactions to them (Würsig et al., 1998;Born Riget, Dietz
& Andriashek, 1999;Dawson et al., 2004;Luksenburg & Parsons, 2009;Hashim & Jaaman,
2011). In addition, crewed aerial and boat surveys, as well as ground surveys, can be
dangerous to researchers (Sasse, 2003;Hodgson, Kelly & Peel, 2013;Gooday et al., 2018),
expensive, and logistically challenging for remote populations far away from an airstrip or
a port (Hodgson, Kelly & Peel, 2013;Gooday et al., 2018;Höschle et al., 2021).
In this context, satellite and unoccupied aerial systems (UAS) imagery have surged as
complementary tools to count individuals of various taxa of marine mammals (Sirenia,
Ursidae, Pinnipedia, and Cetacea: cf. Table 1). These two platforms share the advantage of
minimizing danger to researchers. Furthermore, the two platforms can provide still images
researchers can revisit and use to cross-validate their counts with other researchers (e.g.,
Gon¸
calves, Spitzbart & Lynch, 2020). In particular, very-high-resolution (VHR) satellite
imagery (i.e., sub-meter spatial resolution) allows monitoring marine mammals in remote
areas (LaRue, Stapleton & Anderson, 2017) without any disturbance to wildlife (LaRue
et al., 2011). As a result of a wide geographic coverage, VHR satellite images have the
potential to fill knowledge gaps on distribution, abundance, density, and population trends
of different marine mammal taxa (cf. Table 1) while supplementing data from field-based
surveys and allowing for continued monitoring when in-person surveys are not possible
(Höschle et al., 2021). A comparison between satellite and ship survey density estimates
showed encouraging results for satellite imagery (i.e., Bamford et al., 2020). On the other
hand, despite still requiring fieldwork and offering less area coverage than satellites, UAS
allow researchers more freedom in terms of survey timing than satellites and permit
coverage in regions of perennial overcast weather (e.g., Goebel et al., 2015). Adame et al.
(2017) found their UAS survey to be more accurate than a traditional boat-based survey
for the study of a California sea lion (Zalophus californianus) rookery. Furthermore, UAS
collect multispectral imagery of higher resolution than satellites, at centimeter (Johnston,
2019) and even sub-centimeter spatial resolutions (Raoult et al., 2020), which is especially
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 2/22

relevant for smaller marine mammals like mustelids (e.g., sea otters - Enhydra lutris).
Moreover, UAS can provide thermal infrared (IR) imagery at sufficient resolution to detect
marine mammals on land (e.g., Seymour et al., 2017), unlike satellite-based thermal IR,
which offers too coarse of a resolution (e.g., United States Geological Survey, 2020).
Despite their advantages, satellite and UAS platforms are not panaceas. The cost of
satellite imagery still constitutes a limiting factor for many studies (Turner et al., 2015;
Höschle et al., 2021). UAS, on the other hand, can be disturbing to animals depending
on their noise profile (Pomeroy, O’Connor & Davies, 2015). Furthermore, high-endurance
UAS recommended to cover large regions (Raoult et al., 2020) are costly, and their use can
be limited based on civil aviation regulations (Fiori et al., 2017). In such cases, satellites
remain the most cost-effective solution to study large-bodied animals over large areas
(Johnston, 2019). In addition to these issues that depend on costs, regulatory framework,
and technological advancements of these platforms, manual counts of animals in remote
sensing data are extremely labor and time-intensive for researchers over large areas (Hollings
et al., 2018;Höschle et al., 2021). For example, the analysis of 425 km2(still a relatively small
area in terms of marine mammal distribution) took 24 h to be analyzed with three replicates
(eight hours per person) for the work of Thums et al. (2018), corresponding to 1.13 min per
km2. Furthermore, Cubaynes et al. (2019) reported three hours and 20 min to scan 100 km2
at a 1:1,500 m. scale (2 min per km2), and Charry et al. (2021) reported an even slower rate
of approximately 2.5 min per km2(at a 1:536 scale). While satellite images have allowed
continental-scale abundance and distribution studies in remote locations (e.g.,LaRue et
al., 2019;LaRue et al., 2020;LaRue et al., 2021), that work required crowdsourcing: LaRue
et al. (2020) enlisted more than 325,000 volunters to analyze 268,611 km2of images while
a previous effort (LaRue et al., 2019) used more than 5,000 volunteers for the Ross Sea
region in Antarctica. These broad-scale studies remain scarce for marine mammals: most
work based on satellite images is performed over smaller areas such as individual islands
(e.g., LaRue et al., 2015;LaRue & Stapleton, 2018).
Automation of image analysis reduces the level of effort and time needed, especially
for large-scale projects (Hollings et al., 2018;Charry et al., 2021;Höschle et al., 2021). Even
semi-automation in Thums et al. (2018) shortened 24 person-hours to 34 min to run the
algorithm and an additional 20 min to revise sub-sections with whale detections and discard
those that were false (a reduction of more than 96% of the analysis time). Automation could
also help with errors caused by human fatigue from reviewing imagery (Hodgson, Kelly &
Peel, 2013). As such, automation could unleash the full potential of VHR satellite images
to cover more extensive study areas, which is ultimately a capability that distinguishes the
use of satellite imagery from crewed surveys. Automating animal detection in UAS imagery
has also been suggested to provide better survey data (Hodgson, Peel & Kelly, 2017). Just as
for satellite imagery, automation would also reduce the labor and time necessary to count
animals from UAS imagery (Linchant et al., 2015). Technologies for automated count of
animals have emerged for areas difficult to access, in which ground counts are difficult, or
for animals that are at low densities over large areas (Hollings et al., 2018). Marine mammals
generally fit all these conditions, given their aquatic habitats and extended ranges, making
them suitable candidates for the application of automated counts. However, automated
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 3/22

Table 1 Marine mammal automated detection studies using satellite or UAS imagery. Imagery type is based on testing imagery, although in sev-
eral studies the training imagery was from the same source. Automated studies results were assessed with Eqs. (3),(4) and (5). Missed animals refer
to Eq. (3), false animals refer to Eq. (4), and total deviation refers to Eq. (5). For automation results assessment calculations, see Eqs. (3),(4) and (5),
and Table S1.
Study Taxa Platform; altitude
(if UAS); imagery
type (spatial resolution)
Automated
method
Automation results
assessment
Mejias Alvarez et
al. (2013)
Dugongs (Dugong
dugon) (Sirenia)
UAS (ScanEagle) with Nikon
12 MP. digital SLR camera
and 50 mm. lens and polar-
izing filter; 500/750/1000 ft.;
RGB (ground sampling reso-
lution not found)
Morphological based detec-
tion, segmentation, shape
profiling on saturation chan-
nel
Not enough data was found to cal-
culate Eqs. (3),(4) and (5)
Maire et al. (2013) Dugongs (Dugong
dugon) (Sirenia)
UAS (not specified) with
Nikon 12 MP. digital
SLR camera and 50 mm.
lens and polarizing filter;
500/750/1000 ft.; RGB
(ground sampling resolution
not found)
Color and morphological fil-
ters, segmentation and shape
analysis
Not enough data was found to cal-
culate Eqs. (3),(4) and (5)
Fretwell, Staniland
& Forcada (2014)
Southern right
whales (Eubalena
australis) (Mysticeti)
Satellite (WorldView-2);
panchromatic (0.5 m.) and
multispectral (∼2 m. but pan-
sharpened)
Unsupervised classification,
supervised classification and
histogram thresholding
Supervised classification:
no meaningful results
Unsupervised classification
Kmeans: 41.76%
(missed), 53.85% (false),
95.6% (total deviation)
Histogram thresholding Band
5: 15.38% (missed), 26.37%
(false), 41.76% (total deviation)
(Best variants within methods
chosen based on Eq. (5) for total
signals, not by probable, possible
and band 5 manual detections)
LaRue et al. (2015) Polar bears (Ursus
maritimus) (Ursi-
dae)
Satellite (WorldView-2/
QuickBird)**; panchromatic
(0.5-0.65 m.)*
Supervised classification and
image differencing
Supervised classifi-
cation: unsuccessful.
Image differencing: not enough
data was found to calculate Eqs.
(3),(4) and (5).
Seymour et al.
(2017)
Grey seals
(Halichoerus
grypus) (Pinnipedia)
UAS (senseFly eBee) with 12
MP. RGB Canon S110 cam-
era and 640 ×512-pixel ther-
mal IR senseFly LLC, Ther-
momapper camera; altitude
not found; RGB (3 cm.) and
thermal IR (8 cm.)
ArcGis Model based on tem-
perature, size and shape
Pups: Saddle Island (simple):
6.45% (missed), 13.55%
(false), 20% (total deviation)
Saddle Island (Complex):
7.04% (missed), 13.9% (false),
20.94% (total deviation)
Adults: Saddle Island (simple):
1% (missed), 23.38% (false),
24.38% (total deviation)
Saddle Island (Complex): 0.98%
(missed), 49.02% (false), 50%
(total deviation) (only models
from prediction site)
(continued on next page)
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 4/22

Table 1 (continued)
Study Taxa Platform; altitude
(if UAS); imagery
type (spatial resolution)
Automated
method
Automation results
assessment
Thums et al. (2018) Humpback whales
(Megaptera no-
vaeangliae) (Mys-
ticeti)
Satellite (WorldView-2/
WorldView-3); panchromatic
(0.4 m.) and multispectral
(but pansharpened) /
panchromatic (0.31 m.)
Unsupervised classification
and supervised classification
(in Worldview 2 imagery)
and semi-automated algo-
rithm based on shape (in
Worldview-3 imagery)
Unsupervised classification and
supervised classification: not
enough data found for Eqs. (3),
(4) and (5). Shape algorithm:
0% (missed), 205.8% (false),
205.8% (total deviation) (calves
and mothers together, all images
combined)
Gray et al. (2019) Blue whales
(Balaenoptera
musculus) and
humpback whales
(Megaptera
novaeangliae)
and Antarctic
minke whales
(Balaenoptera
bonaerensis)
(Mysticeti)
UAS (FreeFly Alta 6/LemHex-
44) with Sony a5100 cam-
era with 50 mm. Focal length
lens, 23.5 ×15.6 mm. Sen-
sor size and 6000 ×4000 pixel
resolution; 30–80 m.; RGB
(see equation in study for
ground sampling distance)
CNN (deep learning) 0% (missed), 1.72% (false), 1.72%
(total deviation) (only for whale
recognition, not species)
Borowicz et al.
(2019)
Southern right
whales (Eubalaena
australis) and
humpback whales
(Megaptera
novaeangliae)
(Mysticeti)
Satellite (WorldView-3); mul-
tispectral (1.24 m. but pan-
sharpened to 0.31 m.)
CNN (deep learning) 0% (missed), 271.88% (false),
271.88% (total deviation) (best
model chosen by authors)
Guirado et al.
(2019)
Various Google Earth Imagery
(included USGS
aerial/WorldView-
3/QuickBird-2/GeoEye-
1/SPOT-6/WorldView-
2); RGB (0.15 m./0.31
m. panchromatic–1.24
m. multispectral/0.61
m. panchromatic–2.5
m. multispectral/0.46
m. panchromatic–1.84
m. multispectral/1.5
m. panchromatic–6 m.
multispectral/0.46 m.
panchromatic–1.84 m.
multispectral)
CNN (deep learning) Detection CNN: 20.59% (missed),
7.35% (false), 27.94% (total de-
viation). Count CNN: 11.43%
(missed), 4.29% (false), 15.71%
(total deviation) (all locations to-
tals)
(continued on next page)
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Table 1 (continued)
Study Taxa Platform; altitude
(if UAS); imagery
type (spatial resolution)
Automated
method
Automation results
assessment
Cubaynes (2019) Southern right
whales (Eubalaena
australis) (Mysticeti)
Satellite (GeoEye-1); mul-
tispectral (1.65 m. but pan-
sharpened to 0.41 m.)
Unsupervised classification,
supervised classification,
thresholding and OBIA
Unsupervised classification
Isodata: 56.82% (missed),
140.91% (false), 197.73%
(total deviation).
Supervised classification
(maximum likelihood): 9.09%
(missed), 3.41% (false),
12.5% (total deviation).
Thresholding (only NIR1):
34.09% (missed), 194.32%
(false), 228.41% (total deviation).
OBIA: 27.27% (missed), 453.41%
(false), 480.68% (total deviation).
(for total whales, not by definite,
probable and possible)
Gon¸
calves,
Spitzbart &
Lynch (2020)
Crabeater
seals (Lobodon
carcinophaga),
Weddell seals
(Leptonychotes
weddellii), leopard
seals (Hydrurga
leptonyx) and Ross
seals (Omnatophoca
rossii) (Pinnipedia)
Satellite (WorldView-3);
panchromatic (0.3 m.)
CNN (deep learning) SealNet: 69.78% (missed), 51.71%
(false), 121.49% (total deviation)
(total for all scenes) (only model
with best F1 for testing)
Zinglersen et al.
(2020)
Walruses (Odobenus
rosmarus) (Pinni-
pedia)
Satellite (Pléiades
1 A/B/WorldView-
2/WorldView-3);
multispectral (2 m. but
pansharpened to 0.5 m./1.84
m. but pansharpened to 0.46
m./1.24 m. but pansharpened
to 0.31 m.)
OBIA Not enough data found for Eqs.
(3),(4) and (5).
Dujon et al. (2021) Australian fur seal
(Arctocephalus pusil-
lus) (Pinnipedia)
UAS (DJI Phantom 4
ProfessionalTM V2) with
built-in camera; 35 m.; RGB
(ground sampling resolution
not found)
CNN (Deep learning) Not enough data found for Eqs.
(3),(4) and (5).
Notes.
∗Information obtained from Hollings et al. (2018)
∗∗Information obtained from Cubaynes (2019)
detection in UAS imagery has been more often applied to terrestrial mammals and birds
than to marine mammals (Corcoran et al., 2021), and Hollings et al. (2018) report a similar
pattern for automated analysis of remote sensing imagery in general.
Automated analysis of remote sensing imagery can be divided into pixel or object-based
methods, depending on the unit of analysis (Blaschke et al., 2014;Wang, Shao & Yue,
2019). While pixel-based methods (e.g., unsupervised or supervised classification and
thresholding) classify individual pixels into animal or background classes based on spectral
information or texture, object-based methods classify groups of homogeneous pixels
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 6/22

into the different classes, which allows for the use of other variables, such as size and
shape (Blaschke et al., 2014;Wang, Shao & Yue, 2019). This contribution reviews the use
of different methods of automated analysis of VHR satellite and UAS imagery for marine
mammal detection, which is a current gap as previously-published reviews are dedicated to
wildlife in general, either to their automated detection in UAS (i.e.,Corcoran et al., 2021)
or manual and automatic detection in UAS, satellite and aerial imagery (i.e., Hollings et al.,
2018;Wang, Shao & Yue, 2019). The detection of marine mammals in their natural habitats
presents different challenges that warrant their own review. The analysis we present also
adds to contributions that focused on using satellite (LaRue, Stapleton & Anderson, 2017;
Höschle et al., 2021) and UAS (Koski, Abgrall & Yazvenko, 2010;Smith et al., 2016;Fiori et
al., 2017;Johnston, 2019;Schofield et al., 2019;Raoult et al., 2020) imagery to study marine
wildlife separately. This review aims to inform the multidisciplinary community of remote
sensing experts, biologists, ecologists, and managers working on marine mammal research
and conservation as to which automated methods and imagery type show more promise,
but also which taxa have been overlooked, practical alternatives to ensure time-saving
and accurate counts, and steps necessary to progress from individual studies to the
regional-scale initiatives that conservation needs. Our analysis is also summarized in Table
1, which improves upon the work of Hollings et al. (2018) and Cubaynes (2019) by the
application of uniform metrics for evaluating detection accuracy that allow comparing the
results of existing studies directly and for a more straightforward assessment of over- and
undercounts in a conservation context.
SURVEY METHODOLOGY
The search engine of the University of Florida Libraries (https://uflib.ufl.edu/find/) Primo
database was used repeatedly between August 2019 to January 2022 to identify peer-
reviewed literature, conference proceedings, grey literature, and theses and dissertations
about combinations of keywords such as ‘‘marine mammals’’, ‘‘remote sensing’’, ‘‘UAV’’,
‘‘UAS’’, ‘‘satellite’’ and ‘‘images’’. The Primo database searches across the majority of
the authoritative electronic and print resources subscribed to and purchased by the
University of Florida Libraries (UF George A. Smathers Libraries, 2022). The literature cited
in these studies was then explored further if relevant to image analysis for marine mammal
detection, either through Primo or Google Scholar. We excluded studies of detection of
marine mammal groups (e.g., Burn & Cody, 2005;Fischbach & Douglas, 2021), traces and
holes (e.g., Platonov, Mordvintsev & Rozhnov, 2013), carcasses (e.g., Fretwell et al., 2019;
Clarke et al., 2021), and works that compared automated detection in separate datasets of
different spatial resolutions (e.g., Fischbach & Douglas, 2021;Corrêa et al., 2022).
CURRENT TRENDS IN MARINE MAMMAL AUTOMATED
DETECTION
We found 13 studies on automated detection of marine mammals ranging from 2013 to
2021, of which five used UAS imagery and eight used satellite imagery (Table 1). While
many different automation approaches have been tested, most studies used pixel-based
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 7/22

approaches (as opposed to object-based workflows). The early attempts in pixel-based
automation used spectral information through unsupervised classifications, supervised
classifications, histogram thresholding, and image differencing (Table 1), for example
for the classification of Southern right whales (Eubalaena australis) (Fretwell, Staniland
& Forcada, 2014) and polar bears (Ursus maritimus) (LaRue et al., 2015). Histogram
thresholding consists of maximizing the class of interest’s signal to reduce the amount
of noise (Fretwell, Staniland & Forcada, 2014). Automated image differencing is a process
to detect differences in pixels between satellite images collected at different times (Singh,
1989;Lu et al., 2004;LaRue et al., 2015). These methods proved not to be very effective in
providing accurate counts (cf. Table 1). This aligns with recommendations from Cubaynes et
al. (2019) to adopt approaches that can use more information (e.g., topological, geometrical,
or textural information) than only spectral responses, such as deep learning or object-based
image analysis (OBIA).
Convolutional neural networks (CNN), a deep learning approach, have been increasingly
used to analyze satellite and UAS images of whale hotspots (Borowicz et al., 2019;Guirado
et al., 2019;Gray et al., 2019) and pinnipeds (Gon¸
calves, Spitzbart & Lynch, 2020;Dujon et
al., 2021) (Table 1). CNN has had mixed results so far, with relative success when applied
to satellite (Guirado et al., 2019) and UAS (Gray et al., 2019) images of whales. Of note,
CNN proved able to distinguish between different species in Gray et al. (2019), something
which had not yet been achieved from satellite images (Höschle et al., 2021). However,
not all CNN applications for whale detection were successful (Borowicz et al., 2019), and
studies that applied CNN for the detection of pinnipeds in satellite (Gon¸
calves, Spitzbart &
Lynch, 2020) and UAS (Dujon et al., 2021) imagery did not show high accuracies. Gon¸
calves,
Spitzbart & Lynch (2020) reported the highest precision and recall among different scenes
for their SealNet model as 0.519 and 0.377, respectively, in their crabeater seals (Lobodon
carcinophaga), Weddell seals (Leptonychotes weddellii), leopard seals (Hydrurga leptonyx)
and Ross seals (Omnatophoca rossii) study. Dujon et al. (2021) reported a 0.27 precision
for Australian fur seal (Arctocephalus pusillus) detection. Borowicz et al. (2019) suggested
including confounding classes such as ships, rocks, and land to minimize false positives
and improve CNN accuracy. This worked relatively well for Guirado et al. (2019) but did
not provide more accurate counts for Gon¸
calves, Spitzbart & Lynch (2020). Another way to
improve detection accuracy was the application of a two-step CNN that first detects whale
presence in individual tiles of an image collection and then locates and counts whales in
tiles that had them (Guirado et al., 2019). A valuable insight the authors offer about their
two-step approach is that they find better results than with one detection model alone, as
a result of the first CNN filtering false positives and therefore allowing the second CNN to
count whales more accurately.
CNN studies have also sparked a discussion on how marine mammal behavior impacts
detection abilities. For example, Dujon et al. (2021) developed a CNN capable of detecting,
as mentioned before, Australian fur seals (Arctocephalus pusillus), but also loggerhead sea
turtles (Caretta caretta), and Australian gannets (Morus serrator) in UAS imagery from
separate sites. The authors reference the aggregation of pups in creches as one of the factors
that made seal detection difficult compared to that of the other taxa analyzed, up to the
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 8/22

point that the CNN was not able to distinguish between multiple aggregated pups. Whales
can also offer particular detection challenges based on their behavior. For instance, Guirado
et al. (2019) concluded that more than 90% of true positives of their detection CNN were
whales in blowing, breaching, peduncle emerged, or logging behavior, whereas 33% of
false negatives were submerged whales—which happened to be the most frequent behavior
observed. The remaining 66% of false negatives were spy-hopping whales (Guirado et al.,
2019). Cubaynes et al. (2019) suggested that some species with less acrobatic behavior were
easier to detect. This is not that clear in Guirado et al.’s (2019) findings, as some whale
behaviors which could be characterized as acrobatic, i.e., with their peduncle emerged and
breaching, were part of most of the true positive cases along with blowing and logging.
However, they recognized that the spyhopping behavior compromised the CNN’s ability
to detect whales.
In terms of detectability and accuracy evaluation, while Guirado et al. (2019) and
Cubaynes et al. (2019) discussed the influence of behaviors displayed on the surface or
submerged—but still visible—on the detectability of animals, there remains the issue of
animals submerged to the point of not being visible. In general, studies that detect marine
mammals automatically in imagery evaluate the accuracy of their automation methods
by comparing the animals captured by the computer with the ones counted manually.
This approach is based on obtaining a count of the detectable animals, i.e., only those
visible in the imagery. This is a common limitation of visual surveys from land, ships,
aircraft, or using UAS or satellite imagery, and especially in marine mammal taxa that are
permanently in the water (i.e., sirenians and cetaceans). While, as mentioned before, UAS
and satellite imagery have the advantage of allowing surveyors to go over them many times
and cross-validate their counts with other researchers—helping to minimize perception
bias compared to field surveys from land, ships or aircraft—they are still visual methods not
able to detect animals submerged to the point of not being visible. Consequently, it is worth
noting that while this review is focused on the automated detection of marine mammals
in imagery to obtain animal counts as a first step, further steps are needed after the counts
to calculate abundance or density estimates. For example, there is a requirement to make
adjustements for submerged animals not showing in the imagery—animals available in the
study area but not at the surface (not detectable), as Bamford et al. (2020) did for whale
densities calculated from satellite imagery. These adjustments could involve additional
information obtained through tracking devices, such as surfacing and submersion times
(e.g., Bamford et al., 2020).
The general approach presented above—comparing objects detected automatically with
user observations assumed to correspond to all animals detectable in the image—aligns with
evaluation methods commonly used in remote sensing. When adopting this approach,
false negatives correspond to detectable animals missed by the computer. In contrast,
false positives refer to anything that is not the detection target, such as water or rocks,
and is classified wrongly as a marine mammal by the computer. False negatives thus
contribute to an undercount of marine mammals, and false positives contribute to an
overcount. However, there is currently a lack of consistency in accuracy metrics to evaluate
automated methods across animal detection studies (Hollings et al., 2018). Furthermore,
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 9/22

when comparing automated detections with manual counts to evaluate detection accuracy,
some studies defined the F1 index metric differently. For example, Borowicz et al. (2019)
used Eq. (1) and Gon¸
calves, Spitzbart & Lynch (2020) used Eq. (2). This can be highly
problematic when comparing different approaches.
F1=2∗precision∗recall
precision+recall .(1)
Equation 1: F1 index as defined by Borowicz et al. (2019).
F1=precision∗recall.(2)
Equation 2: F1 index as defined by Gon¸
calves, Spitzbart & Lynch (2020).
FUTURE DIRECTIONS IN MARINE MAMMAL AUTOMATED
DETECTION
Object-based image analysis (OBIA)
OBIA has been less explored for marine mammal detection compared to pixel-based
methods. While both pixel- and object-based approaches may consider more variables
than spectral information (e.g., texture) (Cubaynes et al., 2019), they are fundamentally
different in their units of analysis (Blaschke et al., 2014). OBIA first segments the imagery,
dividing it into spatially continuous objects whose internal heterogeneity is less than the
heterogeneity of their neighbors (Blaschke et al., 2014). This produces scale-dependent,
potentially more meaningful objects, or segments, made of relatively homogeneous pixels.
The classification algorithm selected is then applied to the objects rather than the individual
pixels making them up. In addition, OBIA provides more information than only the spectral
response to inform the classification: for instance, information about the objects themselves,
such as size, shape, relative or absolute location, boundary conditions, and topological
relationships, can be integrated as features within the classification, something that cannot
be done in pixel-based classifications (Blaschke et al., 2014). In theory, it would make sense
that marine mammals be detected as homogeneous objects in remote sensing imagery.
Some studies have used object features, such as shape (i.e., Seymour et al., 2017;Thums
et al., 2018), and a study used highlighting of elliptical features, segmentation, and feature
extraction, such as length and area, from blobs (i.e., Mejias Alvarez et al., 2013). Another
work by Maire et al. (2013) used shape from blobs obtained after segmentation with a
prior determination of regions of interest using color and morphological filters. Results
have not been conclusive: while the work of Seymour et al. (2017) proved successful in
detecting pinnipeds in thermal IR imagery, Thums et al. (2018),Mejias Alvarez et al. (2013)
and Maire et al. (2013) did not get as good results in detecting a baleen whale species and a
Sirenia species, respectively (cf. Table 1). Furthermore, OBIA was tested with mixed results.
For Southern right whale (Eubalaena australis), Cubaynes (2019) had a moderate amount
of missed (27.27%) animals, but a considerably high number of false positives (453.41%)
(Table 1). Zinglersen et al. (2020) obtained a good object definition for walruses (Odobenus
rosmarus), but this was dependent on site heterogeneity (i.e., the relative composition of
sand and snow impacted the analysis).
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 10/22

OBIA has not been used extensively to detect other animals either, leaving pixel-based
studies as predominant. While OBIA has been suggested to perform better than pixel-based
analyses in land classification, that is still not clear in the examples of OBIA for counting
animals (see Hollings et al., 2018). It is also worth noting that OBIA can be computationally
time-intensive (Hollings et al., 2018). Furthermore, there is a lack of an obvious method to
measure segmentation accuracy and sometimes real-life objects in the image do not match
the segments obtained, making it difficult to assess classification accuracy (Ye, Pontius &
Rakshit, 2018).
Thermal infrared (IR) imagery
Another resource worth exploring in the future for marine mammal automated image
analysis is the use of thermal IR. The combined use of shape, size, and temperature
captured from thermal IR allowed Seymour et al. (2017) to distinguish pinniped pups from
adults and count animals in aggregations, which was a recognized challenge for this taxon.
Hollings et al. (2018) argued that a high contrast between animals and their surroundings
is particularly important for automation methods, which is consistent with Cubaynes
et al. (2019) findings. Based on these arguments, thermal IR UAS imagery is promising
for marine mammal automated detection as it should assist with distinguishing marine
mammals from confounding objects or like-colored surfaces. While Hollings et al. (2018)
found that automation attempts to count animals have shown reasonably high accuracy
in areas that are small relative to a species’ range and/or homogenous environments such
as ice, the accuracy assessment metrics in Table 1 for studies that match this description
suggest this may not be necessarily the case for marine mammal detection (see Fretwell,
Staniland & Forcada, 2014;Borowicz et al., 2019;Gon¸
calves, Spitzbart & Lynch, 2020 in
Table 1). For these cases, thermal IR may be a tool worth exploring to increase the contrast
between the animals and the background. However, thermal IR has only been used for
automated detection of pinnipeds over land (i.e., Seymour et al., 2017), and as such this
recommendation can only be supported by research results in such settings. The potential
of automated analyses on UAS thermal IR data for taxa that are in the water at all times
(i.e., cetaceans and sireneans) remains a knowledge gap at this time.
Evaluation metrics
Here we assess the number of false negatives in the different studies of Table 1 using the
miss rate, or false negative rate (FNR; Eq. (3)). We consider that this metric provides
a straightforward assessment of false negatives—the undercount—in the context of
conservation and management, as its denominator is the total number of animals that
could have been detected (using the manual count as a reference). In order to have a metric
that assesses false positives—the overcount—over the total number of animals that could
have been detected too, we used a false positive over detectable rate (FPDR; Eq. (4)). In the
context of a remote sensing application to inform conservation efforts, we considered it
may be helpful to measure the undercount (all detectable animals missed by the computer)
or the overcount (all false computer detections) against the actual animals detectable in
the image, for a faster and direct assessment of how reliable an automated method would
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 11/22

be if adopted in conservation planning, and what the impact of its errors would be over
counts later used in abundance estimations.
(1−recall)×100 =FN
TP +FN ×100 =FN
Detectable animals ×100.(3)
False negative rate (Equation 3): ratio between the number of animals missed by an
automated method (numerator) and the number of animals detectable in the image (the
animals counted manually) (denominator), presented as a percentage. FN: false negatives.
TP: true positives.
FP
TP +FN ×100 =FP
Detectable animals ×100.(4)
False positive over detectable rate (Eq. (4)): ratio between the number of objects wrongly
classified as animals by an automated method (numerator) and the number of animals
detectable in the image (the animals counted manually) (denominator), presented as a
percentage. FP: false positives. FN: false negatives. TP: true positives.
To summarize these two metrics into one, in the same way the F1 index integrates
precision and recall, we suggest the automated count deviation (Eq. (5)). The automated
count deviation (Eq. (5)) adds false negatives to false positives and measures them against
the detectable animals too, to help in conservation and management, as neither false
positives nor negatives are desirable in that context. The FNR (Eq. (3)), FPDR (Eq. (4))
and automated count deviation (Eq. (5)) can also be used in combination with recall,
precision and the F1 index for a more comprehensive assessment of performance.
Furthermore, we note that even though manual counts are taken as a reference of
the animals detectable in the image, this is not an infallible strategy. The effects of
environmental conditions, UAS flight-related variables, and the angle of imagery capture
on manual detection certainty of different species should continue to be investigated
(see Aniceto et al., 2018;Subhan et al., 2019). Furthermore, the subclassification of manual
detections into probable or possible (e.g., Fretwell, Staniland & Forcada, 2014) together
with cross-validated manual counts among researchers (e.g., Gon¸
calves, Spitzbart & Lynch,
2020) can help in attaining the best manual detection reference possible.
FN +FP
TP +FN ×100 =FN +FP
Detectable animals ×100.(5)
Automated count deviation (Eq. (5)): ratio between the sum of animals missed and
the objects wrongly classified as animals by an automated method (numerator) and the
animals detectable in the image (the animals counted manually) (denominator), presented
as a percentage. FN: false negatives. FP: false positives. TP: true positives.
DISCUSSION
The current threats marine mammals face justify the need for their populations to be
monitored through spatially extensive and frequent surveys for abundance and distribution
studies. Satellite and UAS imagery can help address these challenges and complement
crewed surveys. Satellite images can provide extensive coverage, enabling even continental-
scale counts to use for abundance and distribution estimations in remote locations (e.g.,
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 12/22

LaRue et al., 2019;LaRue et al., 2020;LaRue et al., 2021), making sampling efforts more
globally even. Although UAS provide less spatial coverage than satellite images, they
can allow more control over survey timing and surveying in overcast conditions, as
well as higher spatial resolution, which can be of use for smaller marine mammals like
sea otters (Enhydra lutris) or dolphins of the Cephalorhynchus genus. Nevertheless, the
manual analysis of satellite and UAS imagery is time- and effort-intensive, for which
automation can help (Linchant et al., 2015;Hollings et al., 2018;Charry et al., 2021;Höschle
et al., 2021). While Thums et al. (2018) provided a comparative measure of time saved by
their workflow, we recommend future automated marine mammal studies do the same,
i.e., that they evaluate how efficient different automated methods are in saving time and
effort compared to a manual approach.
We also acknowledge the current challenges of automated approaches in matching the
animals present in the imagery. In the studies we analyzed, automation efforts thus far
have shown considerable deviation of their detections over the animals manually counted,
in particular when it came to false positives (cf. Table 1). This is especially concerning in
a conservation assessment context, as such an error could lead to the lifting of protection
measures when they should stay in place, and therefore calls for future research to improve
detection methods. Until automated methods are better, studies on marine mammal
detection should compromise with semi-automated approaches that involve a user review
(Höschle et al., 2021). However, one positive aspect is that even if a manual review is needed,
a semi-automated method that yields image sub-sections or thumbnails that need to be
revised manually afterwards can still shorten the overall revision time, as we have seen
from Thums et al. (2018).Dujon et al. (2021) also recommend semi-automation in the
sense of leaving full automation for situations of animals that show optimal conditions for
such analysis, and doing manual detections in more challenging settings. Furthermore, the
authors recognized that while their CNN could be used in different species even within a
single site, a sole algorithm would not be able to capture all components of different species
optimally as a result of morphology, spacing behavior, and habitat (Dujon et al., 2021).
Here, we have reviewed the automated approaches for detecting marine mammals
through satellite and UAS images. We observed that the automated methods reliant
only on spectral information did not show high-performance (e.g., Fretwell, Staniland
& Forcada, 2014;LaRue et al., 2015), and spectral contrast between baleen whales and
their surroundings were found to be insufficient for automation, highlighting the need
for other methods that can accommodate more information, such as deep learning and
OBIA (Cubaynes et al., 2019). Deep learning showed high performance in some studies
(i.e., Guirado et al., 2019;Gray et al., 2019) and even allowed for distinguishing between
whale species (i.e., Gray et al., 2019), although this has not yet been achieved in satellite
images (Höschle et al., 2021). Nevertheless, not all deep learning attempts were successful,
and some attempts required hundreds (e.g., Gray et al., 2019) or thousands (e.g., Guirado
et al., 2019) of images for training. An extra complication is that labeled satellite images
are currently limited, highlighting the need for shared training datasets (Höschle et al.,
2021). Overall, CNN shows promise, and future research should continue to improve on
the studies already conducted. For instance, CNNs only successful attempts thus far have
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 13/22

been in whales—although the contrary is not true—for which further studies are due to
see if the method’s results in pinnipeds can be improved, and to evaluate the method in
other smaller marine mammal taxa. The number of studies using CNN is still low, and
more studies need to be undertaken to identify which components of workflows improve
accuracy. For example, the use of more confounding classes seemed useful for Guirado et
al. (2019) but was not a guarantee of success for Gon¸
calves, Spitzbart & Lynch (2020).
We agree with Hollings et al. (2018) in that less costly images and software are necessary
to drive the field forward. The use of free platforms for image analysis, such as Google
Earth Engine, could provide a solution by also providing greater computational power
to researchers. We recommend the development of semi-automated methods that can
be applied to the simultaneous detection of multiple species sharing a common habitat.
Therefore, free, regional, or even continental initiatives that combine training data for
recognition of different species, through the cooperation among different research groups,
government agencies or nonprofit organizations, are what is now necessary to provide a
useful tool for conservation managers to obtain counts for abundances and distribution
studies. Moreover, with readily available and accessible satellite image coverage, Abileah
(2001) and Cubaynes et al. (2019) recommendations to use satellite imagery to study
migrations could be finally implemented at a large scale, and object-based methods
could provide exclusive applications, such as the detection of swimming directions of
different individuals. This could complement the use of tagging devices and lead to
mapping migration routes for policy measures aiming to reduce collisions and bycatch.
Furthermore, accessible satellite image coverage combined with the speed of automation
could help improve distribution estimations, discover unknown feeding and calving
grounds, and keep track of distribution shifts due to climate change. Moreover, it is
worth considering baleen whales have been the focus of most automated studies, and
although some automated studies were applied to pinnipeds, sirenians, and polar bears,
more studies are needed to evaluate different platforms, automation methods, or spectral
bands for these other taxa. Automated studies on toothed whales and mustelids are also
due. Here we focused on automated detection of living marine mammal individuals in
VHR satellite and UAS imagery, but it is noteworthy that other promising satellite and
UAS image analysis applications (automated and manual) exist and focus for example on
detecting marine mammal groups (e.g., Burn & Cody, 2005;Fischbach & Douglas, 2021),
traces and holes (e.g., Platonov, Mordvintsev & Rozhnov, 2013), carcasses (e.g., Fretwell et
al., 2019;Clarke et al., 2021), and on comparing detection success from imagery of different
spatial resolutions (e.g., Fischbach & Douglas, 2021;Corrêa et al., 2022).
Regarding new promising directions to explore, OBIA has been recommended for
automated analysis (Cubaynes et al., 2019), but thus far pixel-based methods have been
more thoroughly explored and the OBIA studies or the studies using object features have
shown mixed results (e.g., Mejias Alvarez et al., 2013;Maire et al., 2013;Seymour et al.,
2017;Thums et al., 2018;Cubaynes, 2019;Zinglersen et al., 2020). Moreover, UAS offer
data at sufficient resolution in the thermal IR, which has proven useful in distinguishing
between pinniped pups and adults and counting individuals in aggregations (i.e., Seymour
et al., 2017). The use of multispectral imagery has been prevalent among studies thus far,
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and we recommend further exploring the use of thermal IR as a solution to distinguish
animals from similarly-colored surroundings, an issue spanning different taxa and surfaces
(e.g., LaRue et al., 2015;Cubaynes et al., 2019;LaRue et al., 2020), or even to study marine
mammals nighttime behavior, or during the polar night (Boehme et al., 2016). Furthermore,
we decided to use the FNR (Eq. (3)), the FPDR (Eq. (4)) and the automated count deviation
(Eq. (5)) proposed as a more straightforward means of assessing how reliable an automated
method would be if adopted in conservation planning, and what the impact of its errors
would be over counts later used in abundance estimations.
Marine mammal abundance and distribution studies can be well served from a
combination of different surveying methods. For instance, UAS and satellite images,
airplane and vessel surveys, to detect animals in the surface; passive acoustic monitoring,
to detect submerged animals’ vocalizations (e.g., Davis et al., 2017;Stanistreet et al., 2018);
and satellite tracking, to obtain detailed movement patterns (e.g., Horton et al., 2017;
Horton et al., 2020). In the last decade, studies using satellite and UAS imagery for marine
mammal surveys have grown considerably in number, but lag behind the number of
automated studies on other taxa. This may occur because of the logistical complexity
of conducting UAS surveys at sea as opposed to on land (e.g., launching and landing
sites, weather conditions), as well as the relative scarcity of archival satellite imagery
over sea compared to terrestrial habitats, leaving researchers only with more expensive
tasking options. Automated methods have the potential to complement airplane and vessel
marine mammal surveys, helping overcome old limitations, such as higher coverage in
remote regions. However, to do that, satellite imagery needs to become more available and
affordable further away from the coast, and high-endurance UAS need to become more
affordable too, while their regulation needs to be flexibilized. In this context, joint initiatives
for further research are necessary to not only to exchange imagery, but also to develop
automated methods that both save researchers time and provide accurate counts, while
being versatile enough to work in different settings and distinguish coexistent species.
ACKNOWLEDGEMENTS
Authors acknowledge Ng¯
ai T¯
u¯
ahuriri as the University of Canterbury’s mana whenua
partner, and the Potano (Timucua) and Seminole Tribes, on whose lands the University of
Florida is located. Thanks are also due to the three reviewers and the handling editor for
their constructive comments that significantly improved earlier versions of this manuscript.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Esteban N. Rodofili is funded jointly by the School of Natural Resources and Environment
and the School of Forest, Fisheries, and Geomatics Sciences (College of Agricultural and
Life Sciences) of the University of Florida. Funds allocated to Vincent Lecours by the
University of Florida Senior Vice President for Agriculture and Natural Resources were
also used to support this work. Support for Michelle LaRue was provided by the School
Rodofili et al. (2022), PeerJ, DOI 10.7717/peerj.13540 15/22

of Earth and Environment, University of Canterbury. There was no additional external
funding received for this study. The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
School of Natural Resources and Environment.
School of Forest, Fisheries, and Geomatics Sciences (College of Agricultural and Life
Sciences) of the University of Florida.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Esteban N. Rodofili conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
article, and approved the final draft.
•Vincent Lecours performed the experiments, analyzed the data, authored or reviewed
drafts of the article, and approved the final draft.
•Michelle LaRue performed the experiments, analyzed the data, authored or reviewed
drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
This work is a literature review and is not data-driven.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.13540#supplemental-information.
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