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Whales from Space: Counting Southern Right Whales by
Satellite
Peter T. Fretwell
1
*, Iain J. Staniland
2
, Jaume Forcada
2
1Mapping and Geographic Information Centre, British Antarctic Survey, Cambridge, United Kingdom, 2Ecosystems Department, British Antarctic Survey, Cambridge,
United Kingdom
Abstract
We describe a method of identifying and counting whales using very high resolution satellite imagery through the example
of southern right whales breeding in part of the Golfo Nuevo, Penı
´nsula Valde
´s in Argentina. Southern right whales have
been extensively hunted over the last 300 years and although numbers have recovered from near extinction in the early
20
th
century, current populations are fragmented and are estimated at only a small fraction of pre-hunting total. Recent
extreme right whale calf mortality events at Penı
´nsula Valde
´s, which constitutes the largest single population, have raised
fresh concern for the future of the species. The WorldView2 satellite has a maximum 50 cm resolution and a water
penetrating coastal band in the far-blue part of the spectrum that allows it to see deeper into the water column. Using an
image covering 113 km
2
, we identified 55 probable whales and 23 other features that are possibly whales, with a further 13
objects that are only detected by the coastal band. Comparison of a number of classification techniques, to automatically
detect whale-like objects, showed that a simple thresholding technique of the panchromatic and coastal band delivered the
best results. This is the first successful study using satellite imagery to count whales; a pragmatic, transferable method using
this rapidly advancing technology that has major implications for future surveys of cetacean populations.
Citation: Fretwell PT, Staniland IJ, Forcada J (2014) Whales from Space: Counting Southern Right Whales by Satellite. PLoS ONE 9(2): e88655. doi:10.1371/
journal.pone.0088655
Editor: Tom Gilbert, Natural History Museum of Denmark, Denmark
Received October 3, 2013; Accepted January 12, 2014; Published February 12, 2014
Copyright: ß2014 Fretwell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: ptf@bas.ac.uk
Introduction
‘‘How many are there?’’ Is a question that is often difficult to
address in ecology particularly for marine species that are
generally inaccessible and cryptic. This is clearly demonstrated
in whales where, despite their enormous size, robust population
estimates are very difficult to obtain. The extreme size of whales
means that they have a high per-capita rate of food consumption
and hence a potentially massive impact on their prey populations
as well as the marine ecosystem. Accurate population estimates are
also essential to inter alia assess the recovery of depleted
populations, evaluate conservation threats and also to use whales
as indicators of the health of local ecosystems. Here we investigate
the use of available Very High Resolution (VHR) satellite imagery
to detect and count baleen whales as a proof-of-concept to
augment current population studies. We target Southern right
whales (Eubalaena australis) as a test species to evaluate; the southern
right whale is an ideal subject for this work for many of the same
reasons as it was an ideal whale to hunt, specifically its large size
(maximum size ,15 m) and a tendency, in the breeding season,
to bask near the surface in large aggregations around sheltered
coastal waters. This is particularly true for mothers that use
shallow water areas to raise their calves to the surface during their
first months of life. The techniques described in this paper may
also be relevant to other species of baleen whales, especially other
large whales that, like the southern right, breed in calm coastal
waters. Further work to test availability and perception bias of
counting whales by satellite will need to be completed before the
techniques described here can be used to independently assess
populations, such a system would reduce the observer cost and
effort and improve the accuracy of population estimates and
trajectories.
Southern right whales have a circumpolar distribution in the
Southern Hemisphere. The distribution in winter, at least for
breeding animals, is concentrated in shallow coastal waters in the
northern part of their range [1]. In summer right whales are found
mainly in latitudes 40–50uS [2] but have been seen, especially in
recent years, in the Antarctic as far south as 65uS [3,4] and around
South Georgia [5,6].
Southern right whales were hunted extensively from the 17
th
through to the 20th century. The total number processed is
conservatively estimated at about 155,000. The pre-whaling
population was estimated at 55,000–70,000 dropping to a low of
about 300 animals by the 1920s. After 1935 they were legally
protected but over 3,000 more were thought to have been taken by
illegal whaling in the 1960’s [7].
Since the cessation of whaling several southern right whale
breeding populations (Argentina/Brazil, South Africa, and Aus-
tralia) have shown a strong recovery [8,9,10] but the other
breeding populations are still very small. In 1997 the estimated
total population size was 7,500 animals and the three main
populations have continued to increase [3,11,12]. Overall the
population appears to have grown strongly since the cessation of
whaling but is still at ,15% of even conservative historical
estimates.
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Of current concern is the unprecedented mortality of southern
right whales on their nursery grounds at Penı
´nsula Valde´s,
Argentina, in what are the most extreme mortality events ever
observed in a baleen whale [13]. Over 420 whale deaths in recent
years, the majority of which were calves, suggests that this
population and its ecosystem may be less healthy and robust than
previously thought [13].
The traditional methods by which cetacean population abun-
dance estimates are obtained use counts of whales along transects
from platforms such as aircraft or ships, or counts from land-based
vantage points [14]. These can be very labour intensive involving
long hours of recording by trained researchers and, as whales
range over large geographic areas, these survey methods can be
costly and inefficient. Additionally, not all individual whales are
present at once, and if present they are not easily detectable (so
called availability and perception bias, respectively). Detection
probabilities for whales are typically high for shipboard surveys,
but for the study area, where surveys are typically carried out by
small airplanes, they can be down to 40% [15]. In addition, there
are not many precision estimates for southern right whale
abundance, particularly for the study area. Typically abundance
is assessed with line transect methods and for right whales from the
same population in the Scotia Sea coefficients of variation are
wide, ranging from 65 to 185% [16].
A previous attempt to count whales using satellite remote
sensing data and had limited success [17]. Using the first
generation of VHR imagery from the Ikonos satellite, with a
resolution of 0.8 m in the panchromatic and 3.3 m in the colour
bands, two areas were looked at: the orca pools at SeaWorld
theme park in San Diego, and a section of coastal water around
Maui known to have large numbers of humpback whales [17].
Although objects which were probably whales were identified in
the IKONOS imagery, the lower resolution and the cluster and
noise associated with waves sea-surface state meant that definitive
sightings were difficult to prove. Since 2002 the spectral, spatial
and temporal accuracy of high resolution satellites has improved
and cost of acquiring such imagery has decreased. A number of
recent studies have used VHR satellites to count animals such as
penguins and seals from space [18,19,20]. The highest accuracy
satellite, the Worldview2 satellite, has an on-the-ground pixel size
of 50 cm in the panchromatic and 2 m in its eight colour spectral
bands. One of these bands, termed the coastal band, uses the far
blue part of the spectrum to penetrate the water column and is
routinely used for hydrographic mapping [21].
Here we describe a method of identifying and counting
southern right whales breeding in part of the Golfo Nuevo in
Argentina using satellite imagery from the WorldView2 satellite
count. This is an ideal location to evaluate our methods because
every year, from July to November, whales concentrate in high
densities to calve and mate. These enclosed bays are characterized
by calm and shallow waters increasing the chances of obtaining
images with optimum conditions of visibility.
Materials and Methods
We acquired a single WorldView2 satellite image of a region of
the Golfo Nuevo Bay, the southern of two bays which separate
Penı
´nsula Valde´s from the mainland of Argentina (figure 1).
The location
Golfo Nuevo, the southern gulf of the Penı
´nsula Valde´s, is a
roughly circular shaped bay and between 80 – 100 km wide. The
sheltered waters attract southern right whales in great numbers
and, together with a similar sized bay just to the north, they hold
one of the world’s largest breeding aggregations of the species.
This represents one of the best studied populations of southern
right whales, with an ongoing programme detailing the natural
history and ecology of the species [5]. From July to November
(winter and early spring), much of the population is on the nursery
ground at Penı
´nsula Valde´s [21] (42uS, 64uW). In 1997, the
population was estimated at 2,577 whales [22], with an annual
growth rate of 6.9% per year [10]. Current estimates are
unavailable and are required as whale calf mortality has increased
sharply since 2005 when the population has experienced several
severe mortality events, particularly in Golfo Nuevo [13].
The image
A section of a single WorldView2 image (Catalog ID:
103001001C8C0300) covering an area of 113 km
2
and taken on
the 19
th
of September 2012 was purchased from the commercial
provider Digital globe. The image was chosen from the Digital
Globe archive for three reasons:
1. It covers the middle of the Golfo Nuevo Bay, an area with a
high density of southern right whales.
2. The timing corresponds with the middle of the breeding/calf
rearing season, which lasts between July and November.
3. It is cloud free with a calm sea-state.
Sea surface waves have a very strong influence on the ability to
detect submarine features [17] Our previous analyses using VHR
imagery in the Southern Ocean show that choppy water or sea
swell refracts the sunlight making practical detection of whales
almost impossible. This is especially true if attempting to construct
routines to automatically identify targets. When choosing imagery
from archival footage an online reduced resolution library is
usually viewed. These reduced resolution ‘‘quick-looks’’ do not
allow judgement on the sea-state, as they are too coarse to show
surface waves – although whitecaps and swell lines can occasion-
ally be seen. However several key features can indicate suitable
Figure 1. The area of the study and the location of places
named in the text. The red box denotes the area of imagery acquired
for this study. The grey area gives an indication of the possible swath
width of a single satellite pass.
doi:10.1371/journal.pone.0088655.g001
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calm conditions; these include sediment patterns and algal blooms
and lack of surf at the coast.
The image acquired consists of nine bands of information; eight
colour bands with an on the ground resolution of ,2 m per pixel,
(Digital globe http://www.digitalglobe.com/downloads/
WorldView2-DS-WV2-Web.pdf) and one panchromatic band
with an on the ground resolution of 50 cm. The fifth of the eight
bands is termed the coastal band and collects light of wavelengths
between 400 nm and 450 nm. This far-blue or violet light
penetrates deeper into the water column with less absorption
and attenuation than longer wavelengths (dependent upon water
clarity and turbidity). This data is routinely used by hydrographic
institutions for mapping coastal bathymetry [23].
We assessed the returns of each band over a cross section of
pixels through whale-like features of two types; surface features
and assumed submarine features (figure 2). As can be seen in figure
3, all bands responded to surface features, with the strongest
response in the panchromatic band, although this band also
showed the most noise. In the submerged cross section only the
coastal band (band 5) responded, no other band showed evidence
of any feature. This also shows the noisy nature of the
panchromatic data in an area of open water (figure 3).
Previous attempts to identify whales using IKONOS imagery
show that attenuation of light through the atmosphere is weak in
comparison to the two major components of image degradation;
scattering from surface roughness of the sea and attenuation of
light through the water column due to water turbidity [17]. As
these major components could not be quantified to absolute
reflectance, absolute values for the subsurface features could not be
retrieved, we therefore used raw Digital Number (DN) values from
the satellite to give an indication of relative illumination across the
image.
Figure 2. A selection of 20 comparable false colour image chips (bands 1-8-5) of probable whales found by the automated analysis.
Several of the images could be interpreted as whale pairs, or as a mother and calf, others may be displaying behaviour such as tail slapping, rolling or
blowing. On several images there is a strong return at one end of the feature which is mostly likely the calluses on the whales head. Reprinted under
a CC BY license with permission from British Antarctic Survey and DigitalGlobe.
doi:10.1371/journal.pone.0088655.g002
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Automatic detection
Using ENVI5 image processing software and ArcGIS automatic
detection of whale-like features in the water column was tested
using maximum likelihood supervised classification, unsupervised
classification (isoData and k-means) and thresholding of specific
bands.
Supervised classifications need the signatures input information
of the pixel values for each class in order to classify the image.
These signatures are usually manually input by the user. The
algorithm then segregates all the pixels in the image into classes
representing the signatures.
Unsupervised classifications classify the image into component
parts based solely on information held within the image; isoData
uses a clustering algorithm to determine the natural grouping of
cells, while k-means calculates initial class means evenly distributed
in the data space, then iteratively clusters the pixels into the
nearest class using a minimum-distance technique.
Histogram thresholding [24] requires a degree of experimen-
tation to calculate the best thresholds to use. Through an iterative
process we formulated thresholds that maximized signal (in this
case suspected whales), and reduced the amount of noise or false
positives (single pixels from small objects and mixed pixels). As
whales are large features they should be represented by multiple
bright pixels, noise will result in single pixels, although inevitably
there could be a small number of whales at depths that return only
single pixels, so that some valid single pixels should still occur.
Using the histograms of whale DN values as a guide we built
thresholds that maximized the ratio of multiple pixels to single
pixels in the panchromatic and coastal bands.
To construct a test dataset the image was divided up into a grid
and whale-like features were manually digitized and coded into
three classes: probable whale (features that were whale-shape and
whale-sized) possible whale (including weaker signals, bubble slicks
and some groups of seabirds are classed as possible whales),or
features only visible in Band 5 (The third class are objects
identified only in the water penetrating coastal band, are
interpreted as sub-surface feature that are also potentially whales).
This process was conducted multiple times to ensure the lowest
possible errors of omission.
Results
Visual inspection of the image showed that a number of offshore
objects, that were both the right shape and size (5 – 15 m) to be
whales, could be identified in both the colour and the panchro-
matic bands (see figure 2). Most of these objects were visible across
all bands although in most cases the high resolution of the
panchromatic band rendered the objects in greater detail (figure
4). Visual inspection can only utilize three bands as the red, blue
and green element of the onscreen image, we compared
combinations of pansharpened bands to find the band-combina-
tions in which whales were most visible, the best overall results
were retrieved using a combination of bands 1 (red),8 (NIR2) and
5 (coastal). The panchromatic band alone displayed a higher noise
ratio that other bands; possibly a result of the higher resolution
picking up more surface refraction from wavelets, ripples and
small waves. Other surface features such as aggregations of
seabirds were also visible in this band. These smaller features were
a confusing element when attempting to develop automatic
Figure 3. The sensor response through cross sections through two whale-like (correct shape and size) features assumed to be
whales. The left hand figure is from a feature at the surface, the right hand figure shows a submerged feature. Note that while all bands show the
surface feature, only band 5 (the Coastal Band) identifies the submerged feature.
doi:10.1371/journal.pone.0088655.g003
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recognition algorithms. The coastal band (band 5) identified a
number of features not apparent in the other data that were
interpreted as sub-surface features. Manual counts of the gridded
dataset found 55 probable whales, 23 possible whales and 13
objects identified in Band 5 only.
Returns from the four automatic analysis routines were assessed
against the manually digitized data (figure 5). In this analysis we
assume that manual detection detects all whales visible on the
surface. Results from the supervised maximum likelihood classi-
fication returned many errors of commission in comparison to the
unsupervised classification. The supervised classification also has
the disadvantage of needing the input of user-derived signatures
which take additional effort and inserts user bias into any
classification. No meaningful results could be obtained using this
method. The two unsupervised classification methods gave
reasonable results, but the results that best matched the manually
counted data came from a simple thresholding of single bands
(table 1). The two most effective bands were the panchromatic and
the water penetrating Band 5, which slightly outperformed the
more detailed panchromatic analysis (see table 1). The single best
routine was thresholding of the coastal Band 5; this technique
found 84.6% of all manually digitized whales and 89% of the
objects manually classed as probable whales, with 23.7% false
positives. However, thresholding also requires user input to
identify thresholds and therefore the greater accuracy of the
technique needs to be balanced against the need for extra manual
input.
Discussion
How do we know it is a Southern right whale?
There are many objects in the image that resemble whales, but
the question remains; how do we know it is a Southern right
whale? The answer to this can be broken into three criteria used to
identify any objects in remotely sensed imagery:
1. The object is the right size and shape to be a whale
2. The object is in a place we would expect to find whales
3. There are no (or few) other types of objects that could be
misclassified as whales to cause errors of commission.
In this study we have digitized and automatically identified
objects that are the right size (up to 16 m long) and shape.
Although the size of the whales has an upper limit the lower limit is
difficult to assess as the deeper the whale in the water column the
less we are likely to see. The shape is generally ellipsoidal, although
this can vary due to rolling, tail slapping and bubbles and other
ripples associated with the animal. In the location of the study at
the time the image was taken we expect to see a high density of
whales in the image, especially mothers which, at this time of year,
are forced to swim at the surface to support their calves.
There are only a limited number of other confounding artefacts
that could cause errors of commission: No other large marine
mammals are reported to frequent this bay, right whales are the
only large whale species that regularly use the shallow calving
grounds of Peninsula Valde´s [24]. Orcas, much smaller in size, are
common in the area, although at a different time of the year, and
Figure 4. A single band images of a probable right whale in the satellite image from each of the eight multispectral bands and the
panchromatic band of the WorldView2 data. Note that the higher resolution of the panchromatic band gives more detail, but it this increased
detail also renders the object into several parts. Other bands show less detail, but have the advantage of homogenizing the object into one group of
pixels, an important consideration when attempting to build automatic identification routines. Reprinted under a CC BY license with permission from
British Antarctic Survey and DigitalGlobe.
doi:10.1371/journal.pone.0088655.g004
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are unlikely to be confused with right whales. This is an important
criteria for the study area as it seems unlikely that different baleen
species could be differentiated with the resolution of currently
available satellite data. Of the other possible confounding factors
the most likely are subsurface rocks in very shallow areas, seabird
groups, surface bubbles and boats. Surface bubbles and seabird
groups may include whales beneath them but it is unlikely that a
single image of this resolution can elucidate whether a whale exists
within these features. Therefore, when digitizing we classed whale-
like features as ‘‘probable whales’’ and weaker signals, that may
include seabird groups and surface bubbles, as ‘‘possible whales’’.
Some of these issues, such as discrimination between whales and
Figure 5. Comparison between manually identified and automatically identified whales. Manually identified whales (top) have been
broken into three classes; shapes that are whale-like and whale-sized are classed as probable whales, other objects are classed as possible whales, but
may include bubble slicks and some groups of seabirds. The third class are objects identified only in the water penetrating coastal band, these are
interpreted as sub-surface feature that are potentially whales. The bottom image shows the whale-like objects identified from the thresholding
analysis of the coastal band.
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subsurface rocks, could be resolved with the purchase of multiple
imagery or stereo-pairs where movement of whales between
images would eliminate the possibility of rocks awash or at the
surface. Boats should be identifiable by their uniform pale
colouration, wakes or strong outlines which discriminate them
visually from the typical signatures of whales. In the previous
Abileah study using lower resolution imagery stationary boats
could be clearly identified [17]. The WorldView2 imagery used in
our study has 2.5 times as many pixels per unit area as the
IKONOS data and we would therefore expect that boats either
stationary or moving could be discriminated from whales in the
manual search. In the section of Golfo Nuevo contained in our
image no such features were identified.
On several potential whale objects there is a strong return at one
end of the feature which is likely to be from calluses on the whale’s
head, a feature which could aid automatic detection. Several
objects identified as whales could be interpreted as pairs, or as a
mother and calf, others may be displaying behaviour such as tail
slapping, rolling or blowing (see figure 6). These behaviours
present challenges for automatic analyses.
The results from the automated analysis suggest that a
thresholding of the water penetrating Band5 returns the best
results, finding 89% of features classed as probable whales in the
manual count. Thresholding a single band is a very simple
technique, although it does require some user input to identify the
best thresholds. The greater accuracy of the technique (in relation
to the more automated unsupervised analysis) needs to be
balanced against the need for extra manual input in relation to
other methods. These results however are promising and suggested
that larger surveys over whole calving areas, which could
potentially measure thousands of square kilometres, could be
automated with a degree of success using these techniques.
Table 1. Assessment of results from four automatic detection techniques in relation to manually digitized whales; results from two
unsupervised classification techniques and two Thresholding analyses.
Manually
digitized
Unsupervied iso
means
Unsupervised
kmeans
Threshold
Panchromatic Threshold Band 5
total signals 91 total signals 158 102 64 101
probable 55 probable matches 44 42 43 49
possible 23 possible matches 16 11 14 15
Band 5 only 13 band 5 matches 1 0 0 13
total found 61 53 57 77
% found 67.0 58.2 62.6 84.6
% of probable 80.0 76.4 78.2 89.1
total missed 30 38 34 14
% missed 33.0 41.8 37.4 15.4
false positives 97 49 7 24
% false positives 61.4 48.0 10.9 23.8
% good 38.6 52.0 89.1 76.2
doi:10.1371/journal.pone.0088655.t001
Figure 6. Examples of possible confounding features found in the image (false colour bands 1-8-5). The top row shows examples of
surface features that are probably bubbles from subsurface whales. Whether the whales are still under the bubble areas is difficult to ascertain. The
lower row show clusters white dots, probably seabirds. Seabirds have been recorded to feed on whales at Penı
´nsula Valde
´s (see discussion). The third
(and possible fourth) of these images shows a larger white object that could be a whale (or whale carcass) although once more it is impossible to tell
with any certainty in this imagery. Reprinted under a CC BY license with permission from British Antarctic Survey and DigitalGlobe.
doi:10.1371/journal.pone.0088655.g006
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Challenges and Future Improvements
The next challenge is to determine detection probabilities and
understand whether counts from images can be used as a reliable
index for population size, or presence. This paper shows that
automated analysis of satellite imagery can achieve a good match
with manual counts, but more work is needed to ensure that these
manual counts are commensurate with the real number of surface
whales. Once an estimate of visible whales has been formulated
the ratio of visible whales versus invisible whales (those at depth or
not at the breeding locality) is required to ascertain the total
population size. One critical factor is estimating how deep the
satellite sensor is seeing into the water column; the greater the
penetration, the larger the proportion of the total population that
will be identified. Penetration varies with water turbidity and
surface roughness, two factors that may change over short time-
spans and spatially within the image. Some estimation of turbidity
may be made by comparison of the infra-red bands to the visible
bands [25] although this would not account for scattering due to
surface roughness. To give some true indication of water column
penetration we suggest that any larger study should have
submarine reflectance panels placed at depths of 5 m to 30 m
large enough to be seen in the satellite image and to enable a
pragmatic estimation of the depth at which whales may be visible.
We have compared a number of automated techniques that will
aid the up scaling of similar studies, an important consideration if
remote sensing of whales using VHR optical imagery is to be
expanded to cover larger areas. Our studies have concentrated on
pixel based analysis [26], but object-orientated analysis or textual
analysis [27,28] may also provide comparable results (although
confounding behaviours such as rolling, bubble blowing and
associated surface waves may make this approach difficult).
The behaviour of right whales, with mothers calving in very
shallow waters in protected bays, makes them an ideal candidate
for the automated analysis of satellite imagery. The right whale
population at Peninsula Valde´s was previously thought to be
recovering well, but recent years have seen persistent events of calf
mass mortalities, suggesting major changes which require re-
assessment; the latest available population estimates are over a
decade old [12]. In addition, satellite image analysis offers the
opportunity to repeatedly assess the number of dead whale calves
washed up on beaches and even those at sea which are separated
from their mothers. An additional recent threat to the southern
right whale population at Peninsula Valde´s has been predation by
seabirds that peck blubber from calves which, when young, stay
near the surface [29]. Given the potential for the WorldView2
images to identify sea bird assemblages in relation to whales it may
be possible to use them to monitor, and even quantify to some
degree, occurrences of this behaviour (figure. 6).
Conclusions
We have shown that the use of current satellite imagery can be
used to identify individual whales both at, and just below, the
surface. The methods described here readily lend themselves to the
calculation of population abundance estimates and suggest that
behavioural patterns could also be elucidated. The automation of
the methods means that counts can be carried out more quickly
and efficiently than using traditional methods. This will allow a
greater frequency of counts, both within and between years, that
should lead to more robust population estimates, and the build up
of a time series to asses trends. The important differences between
our approach and a previous relatively unsuccessful attempt to
identify whales from satellite imagery are the improvements in the
on-the-ground resolution of panchromatic imagery and the use of
the costal band (band 5) that penetrates to subsurface whales.
These improvements allow a reasonable confidence to be assigned
to the identification of individual whales thus allowing counts of
whales in the wild as opposed to observations of animals in captive
tanks.
A working system of whale population assessment by remote
sensing will be an important new method that is potentially
applicable other species of whale. Many species of whale breed in
areas of calm water where, in order to protect their vulnerable
calves, females remain close to the surface e.g. Humpbacks
(Megaptera novaeangliae). Such behaviours would allow the methods
outlined here to be used for population estimates.
Our methods can potentially help providing within and between
season population estimates, changes in distribution and use of the
breeding grounds, both for right whales and other species of whale
that breed in sheltered locations. Importantly, future satellite
platforms planned in 2013 and 2014 will increase the on-the-
ground resolution of panchromatic imagery from ,50 cm to
34 cm and coastal band from ,2 m to 1.24 m (Worldview3
planned launch 2014). This will result significantly higher quality
imagery and therefore, greater confidence in identifying whales
and differentiating mother calf pairs. Such improvements will also
provide the opportunity to expand similar methodologies to other
whale species.
Acknowledgments
This work forms part of Ecosystems programme and MAGIC within the
Polar Science for Planet Earth (PSPE) strategic science framework of the
British Antarctic Survey (BAS).
Author Contributions
Conceived and designed the experiments: PTF JF IS. Performed the
experiments: PTF. Analyzed the data: PTF. Contributed reagents/
materials/analysis tools: PTF JF IS. Wrote the paper: PTF JF IS.
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