Whales from space: Four mysticete species described using new VHR satellite imagery

Large-bodied animals such as baleen whales can now be detected with very high resolution (VHR) satellite imagery, allowing for scien- ti ﬁ c studies of whales in remote and inaccessible areas where traditional survey methods are limited or impractical. Here we present the ﬁ rst study of baleen whales using the WorldView-3 satellite, which has a maximum spatial resolution of 31 cm in the panchromatic band, the highest currently available to nonmilitary professionals. We manually detected, described, and counted four different mysticete species: ﬁ n whales ( Balaenoptera physalus ) in the Ligurian Sea, humpback whales ( Megaptera novaeangliae ) off Hawaii, southern right whales ( Euba- laena australis ) off Península Valdés, and gray whales ( Eschrichtius robustus ) in Laguna San Ignacio. Visual and spectral analyses were conducted for each species, their surrounding waters, and nonwhale objects ( e.g. , boats). We found that behavioral and morphological differences made some species more distinguishable than others. Fin and gray whales were the easiest to discern due to their contrasting body coloration with surrounding water, and their prone body position, which is proximal to the sea surface ( i.e. , body parallel to the sea sur- face). These results demonstrate the feasibility of using VHR satellite technology for monitoring the great whales.

easily detected on 46 cm resolution images, can be seen more clearly on 31 cm resolution images. Based on the morphometric measurements given by Woodward et al. (2006), the fluke surface area of an average sized gray whale takes up about six pixels on a 46 cm resolution image, whereas on a 31 cm resolution image it is comprised of approximately 10 pixels. The ability to differentiate the body outline and features (e.g., fluke) is approximately 1.6 times better with WorldView-3 imagery. The capability of reliably distinguishing characteristic features of whales is essential for identifying an object as a whale, and for differentiating species.
Development of a satellite-based, automated whale detection system requires capturing images that clearly allow identification of different whale species, before questions involving aspects of population biology can be addressed. Comparative species identification remains untested for satellite images, thus, we make the first attempt to characterize the unique spectral signature (i.e., the shape of the spectral reflectance curve), and characteristic features of four whale species found in different marine habitats. With the support of WorldView-3 images, our study focuses on (1) identifying the unique visual characteristics and spectral signature of the focal whale species and (2) developing a method to count whales manually and to categorize counts by level of confidence.
Four highly distinguishable species were targeted for this study (Perrin et al. 2009, Jefferson et al. 2015, including the fin whale in the Pelagos Sanctuary (France, Monaco, and Italy), the humpback whale off Maui Nui (Hawaii), the southern right whale off Península Valdés (Argentina), and the gray whale (Eschrichtius robustus) in Laguna San Ignacio (Mexico). Each species has been well studied on either their feeding or breeding grounds (Rowntree et al. 2001, Herman et al. 2011, Panigada et al. 2011, Ponce et al. 2012 where they occur in relatively high abundance, making the study of each species using VHR satellite technology feasible. Spectral analyses of each species, their surrounding waters, and other nonwhale objects were subsequently conducted to ascertain the signatures are unique to each species in WorldView-3 images. Results of this survey contribute towards the development of an automated detection system that may be able to distinguish whale species and count them from space.

Image Selection
WorldView-3 satellite images were collected from four known baleen whale habitats (Fig. 1). Images were acquired from DigitalGlobe, WorldView-3 imagery provider. All images had a spatial resolution of 31 cm for the panchromatic band (i.e., black and white image) and 1.24 m for the multispectral bands (i.e., color image). Four multispectral bands were acquired for all locations (i.e., blue, green, red, and near infrared 1 (NIR1); DigitalGlobe 2017). The choice of species, and location and time of acquisition of the satellite images was based on the following four prime criteria: (1) morphological differences: the candidate CUBAYNES ET AL.: WHALES FROM SPACE 3 species are morphologically distinct from each other and from other great whale species; (2) whale abundance: to optimize the likelihood of whales being present at the sea surface in the images, images were collected near the known peak in seasonal abundance for each species; (3) sea surface conditions: ideal conditions to observe whales on satellite imagery are few or no white caps, low glare, and low swell (see Fretwell et al. (2014) and Abileah (2002) for details about limitations in manual detection of whales in rough sea state on satellite imagery); and (4) other megafauna: the image locations and times were chosen so that no other large marine animals (e.g., Bryde's whales, B. edeni) of similar size to the studied whales (e.g., fin whales) were likely to be present at the time the images were taken. Although changes in whale distribution are predicted (Learmonth et al. 2006, Schumann et al. 2013, Silber et al. 2017, or have already happened for some species (Ramp et al. 2015), no other marine mammals similar in size to the target species have yet been reported to regularly occur during the periods that satellite images were collected for this study.
At location 1 (Fig. 1), one satellite image (570 km 2 ) of the Au'au Channel in Maui Nui, Hawaii, taken on 9 January 2015 was acquired from DigitalGlobe archives. This region is a well known humpback whale breeding ground from December to April with peak abundance between February and March (Mobley et al. 2001, Herman et al. 2011, Baird et al. 2015. No other marine mammals similar in size to humpback whales are regularly reported in Maui Nui during this season. Rarely, blue whales (B. musculus), fin whales, minke whales (B. acutorostrata), sei whales, and Bryde's whales have been sighted offshore, north of the main Hawaiian Islands (i.e., outside and north of the Au'au Channel; Mobley et al. 2000, Barlow 2006, Smultea et al. 2010. Although sperm whales have been recorded in the region, they tend to stay in deep water away from the main Hawaiian Islands (Mobley et al. 2000, Barlow 2006). Based on OBIS-SEAMAP data (Halpin et al. 2009). The probability of observing one of these species within the acquired satellite image was negligible. Therefore, we assumed that only humpback whales were present in the analyzed satellite image. The Au'au Channel is partly enclosed by four islands, and therefore has low swell and provides ideal sea surface conditions for satellite imagery analysis.
At location 2 ( Fig. 1), one satellite image (80 km 2 ) of Laguna San Ignacio, Mexico, was actively collected on 20 February 2017, which coincides with the calving season for gray whales (Jones andSwartz 1984, Urbán et al. 2003). Although humpback whales and blue whales (B. musculus) are encountered off the coast of Baja California during winter, no sightings have been reported within Laguna San Ignacio (Urbán and Aguayo 1987, Steiger et al. 1991, Mate et al. 1999, Calambokidis and Barlow 2004, Bailey et al. 2009). Based on OBIS-SEAMAP data (Halpin et al. 2009), the probability of encountering whale species other than gray whales was negligible. Therefore, we assumed that only gray whales were present in the analyzed satellite image. Laguna San Ignacio is a small, enclosed area, where the swell was expected to be low.
At location 3 ( Fig. 1), four satellite images (4,230 km 2 ) were actively collected for a region of the Pelagos Sanctuary in the Mediterranean Sea, spanning French, Monégasque, and Italian waters. Three were taken on 19 June 2016 and one was acquired on 26 June 2016. In the summer, fin whales are known to be present in the deep western offshore water of this sanctuary (Forcada et al. 1995, Notarbartolo di Sciara et al. 2003, Panigada et al. 2011. The choice of location for the images was based on the findings of Panigada et al. (2008) who used habitat models to identify an area where fin whale abundance was likely to be the highest.
In the Pelagos Sanctuary no other large marine animals similar in size to the Mediterranean fin whale (i.e., maximum body length of 24 m) have been observed with any regularity or in high abundance. Sperm whales (i.e., maximum body length of 18 m) are usually found next to steep topographic features such as canyons, some of which are located near the northern edge of the studied images (Moulins et al. 2008, Jefferson et al. 2015. Based on OBIS-SEAMAP data (Halpin et al. 2009, Lanfredi and Notarbartolo di Sciara 2011, Boisseau 2014, Lanfredi and Notarbartolo di Sciara 2014, Frey 2015, Van Canneyt 2016, there was a probability of 87% that a large whale species encountered in the acquired satellite image was a fin whale, and a probability of 13.04% that it was a sperm whale. The different body shape should limit misidentification between a fin whale (i.e., sleek, streamlined) and a sperm whale (i.e., CUBAYNES ET AL.: WHALES FROM SPACE log-like; Jefferson et al. 2015). Whale-like objects observed on the images of the Pelagos were included in the visual and spectral analysis only if they had a streamlined body shape. In the northwestern Mediterranean Sea there have been rare observations of humpback whales (Frantzis et al. 2004, Dhermain et al. 2015) measuring a maximum of 18 m (Jefferson et al. 2015). Based on OBIS-SEAMAP data, the probability of observing a humpback whale within the boundaries on the acquired satellite images was negligible. Therefore, we assumed only fin whales were present in the analyzed satellite image. Summer sea conditions in the northern Mediterranean are characterized by calm sea conditions (Panigada et al. 2008). Due to the size and enclosed nature of the Mediterranean basin, the swell was expected to be lower than that in the open ocean.
At location 4 ( Fig. 1), one satellite image (560 km 2 ) taken on 16 October 2014 of Golfo Nuevo in Península Valdés, Argentina, was acquired from DigitalGlobe archives. It coincides with the calving season for the southern right whales. During the past four decades that their population has been monitored, southern right whales inhabit Península Valdés between May and December, with peak abundance from mid-August until early October (Payne 1986, Cooke et al. 2015, IWC 2013, Crespo et al. 2014. Based on OBIS-SEAMAP data (Halpin et al. 2009), the probability of encountering any other large whale species was null. Although killer whales (Orcinus orca) are the only other large marine mammal known to enter this bay, they are much smaller than southern right whales and arrive later, around December (Iñiguez 2001). Consequently, we assumed only southern right whales could be observed on the analyzed satellite image. Regarding the sea surface conditions required for satellite imagery analysis, Golfo Nuevo is sheltered and relatively calm sea conditions were expected compared to the open ocean.

Visual Image Analysis
One observer, experienced in whale identification at sea, visually identified and manually counted all large whale species on each satellite image. Throughout this manuscript, whale identification on satellite imagery refers to the classification of an object as a whale. The manual counting method involved loading all acquired satellite images into ArcGIS 10.4 ESRI 2017. To improve whale detectability, pan-sharpened images (i.e., high resolution color images) were created using the ESRI algorithm in ArcGIS 10.4 ESRI 2017. This algorithm combined the low resolution multispectral images (i.e., 1.24 m) with the high resolution panchromatic images (i.e., 31 cm) to generate high resolution multispectral images (i.e., 31 cm). Each pan-sharpened image was manually and systematically scanned using a grid system at a scale of 1:1,500 m. Detected objects needing more scrutiny were looked at a higher scale (e.g., 1:500). To scan an area of 100 km 2 took approximately 3 h and 20 min.
Determining if an object was a whale, and accounting for the confidence of the observer in the identification of whale-like objects, involved the use of a classification method (see Appendix S1). The classification was trialed using two additional observers in order to check for consistency and identify any classification parameters that vary between observers. A random subset of 10 whale-like objects per species was provided to both observers (see Appendix S1). The classification allowed categorizing each whale-like object as "definite," "probable," "possible." The proportion of "definite" whales among all counted whales (i.e., including "probable" and "possible" individuals) was calculated for each candidate species.
A selection of whale-like objects classified as "definite" were used to describe obvious morphological differences among species when viewed from space. The following anatomy, if visible, was measured in ArcGIS 10.4 ESRI 2017: (1) body length (A in Table 1), (2) body width (B in Table 1), (3) flipper length (C in Table 1), (4) fluke width (D in Table 1). Mean and standard deviation were calculated for all measured anatomy, then compared to known measurements of large whale species (Jefferson et al. 2015). Characterization of surface and near surface water disturbances associated with each species was also recorded ( Table 2). Boats and planes were also recorded as nonwhale objects.

Spectral Image Analysis
One of the motives of this research was also to assess what parameters can be helpful when attempting to automate the detection of whales on satellite images. Each pixel of a satellite image contains quantifiable information (Rees 2013), not visible to the human eye. A human eye can only see the red, blue, and green wavelengths of the electromagnetic spectrum (Nathans et al. 1986), whereas sensors on the WorldView-3 satellite can detect additional wavelengths of the electromagnetic spectrum (DigitalGlobe 2017). The sensors of the WorldView-3 also assign a value to each of the wavelength ranges it can detect (i.e., each multispectral band), which the human eye cannot see. The assigned value is called the spectral radiance (W Á m −2 Á sr −1 Á μm −1 ). It is the amount of light reflected by the target for a specific wavelength (Rees 2013). Each pixel from the WorldView-3 satellite images acquired for this study held four radiances, one for each of the multispectral bands, or wavelength ranges: blue (450-510 nm), green (510-580 nm), red (630-690 nm) and NIR1 (770-895 nm; DigitalGlobe 2017).
Spectral analysis was performed on each image to characterize each whale species, and to investigate if and how they differ from each other, their surrounding environments, and nonwhale objects (e.g., boats and planes). First, we corrected the multispectral image of each location for the top of atmosphere using ENVI software (Harris Geospatial). The top of atmosphere correction is a necessary step before analyzing the radiances of surfaces and objects on the Earth. Without this correction, a satellite image will show the overall radiance value of the surfaces and the particles contained in the atmosphere (Rees 2013). Then, we extracted the four radiances for each pixel of whale-like objects, water and nonwhale objects from the satellite image.   The radiance of whale-like objects was collected only for "end members." In remote sensing "end members" refers to a group of pure pixels (e.g., pixels filled with whale only) as opposed to mixed pixels (e.g., pixels filled with water and whale; Rees 2013). In this study, the "end members" were chosen among the "definite" individuals. The whales showing most of their body length were selected. Following this, we identified the pure pixels of every selected "definite" whales by describing every pixel of the selected "definite" whales with one, or a combination of the characteristics listed in Appendix S2. Some pixels were made of both water and submerged whale, or submerged whale and nonsubmerged whale (i.e., mixed pixels). We removed these mixed pixels from the analysis, as they could bias the spectral analysis, and only retained the purest "whale pixels" (i.e., only submerged whale, or only nonsubmerged whale).
The radiance of the surrounding environment of whales (i.e., water) was assessed by manually selecting 100 pure pixels of water for each location, in clusters of five pixels. None of these pixels contained either white caps, or shallow water (i.e., where the seabed was visible and the sea was lighter in color than the surrounding waters).
For nonwhale objects, we separately measured the radiance of boats and planes. For each type of nonwhale object (i.e., boats or planes), 100 pure pixels were selected manually in clusters of five among all the counted objects.
For the acquisition of the radiance of whale-like objects, surrounding environments, and nonwhale objects, we used ArcGIS 10.4 ESRI 2017. The radiance was averaged for each whale species, and per location, for each type of nonwhale object and the surrounding waters. To allow for quantitative comparisons to be made between species, between whalelike and nonwhale objects, and between whale-like objects and their surrounding waters, the standard error of the mean was also calculated for each pixel set.

Whale Morphology and Behavior
A total of 211 whale-like objects were observed across the four studied areas, covering approximately 5,440 km 2 ( Table 1). Individuals of each of the four species were detected within their respective satellite images by manual scanning (Fig. 2). Most of the individuals identified were adults with the exception of gray whale calves (two "definite," four "probable," and two "possible") and one "possible" southern right whale calf. In terms of confidence in identification of an object as a whale, fin whales had the highest proportion of "definite" individuals, followed by gray whales, with humpback and southern right whales having the lowest proportion ( Table 1). Comparison of the classification system between observers showed consensus in terms of whale identification when more subjective parameters (e.g., body color) were down weighted (Appendix S1). There was no change in definite identifications for humpback whales and fin whales between the consensus CUBAYNES ET AL.: WHALES FROM SPACE identification (i.e., three observers) and the single observer identification. For gray whales and southern right whales, there were 10% less definite identifications when the consensus identification was used, than when a single observer was used (Appendix S1).
The total number of whale-like objects (categorized as "definite,", "probable," and "possible" whales) for fin whales included three individuals that were likely observed twice, as three of the four satellite images acquired for the Pelagos Sanctuary were taken on the same day with intervals of <30 s. These three whales were observed in the overlap region of these images in slightly different locations, suggesting movement. Among the 25 "possible" humpback whales, seven were included in this category based solely on the presence of whale signs, including flukeprints (Levy et al. 2011), that were not associated with any other whales recorded in the image. These seven whale signs were estimated to be too far apart from any detected whales to be associated with them, and it is possible that they were associated with whales that dived too far below the surface to be visible on the satellite images.
The length and width measurements of the body were acquired for all the surveyed species. However, other body measurements such as flipper length or fluke width could not be measured for all individuals. No flukes were observed on any of the humpback whales, nor flippers for southern right whales (Table 1).
Some distinct body characteristics known to be unique features for each species were observed on some individuals, such as long flippers for the humpback whales; these were observed on five of the counted individuals. White head callosities, a characteristic specific to right whales, were positively identified on four of the recorded "definite" southern right whales (Table 1).
Along with body features, other evidence of whale presence was observed (Table 2). Some are related to surface water disturbance: afterbreach, flukeprint, wake, and contour (see Table 2 for a description). There were also other signs associated with near surface disturbances: blow and defecation. Fluke prints and contours were observed for each surveyed species. Wake and blow were seen for three out of the four species. After-breach splashing was only detected for the humpback whale, likewise with defecation for the southern right whale.

Spectral Characteristics of Whales
The purest "whale pixels" that were retained for the spectral analyses were of submerged whales, as there were no pure pixels of whales above the sea surface for fin whales, gray whales, and humpback whales. There were six pure "whale pixels" of southern right whales below the sea surface, seven of humpback whales, 26 of gray whales, and 34 of fin whales. The spectral signatures of the four candidate species were overall similar in shape (Fig. 3). Comparatively, gray whales had the highest radiance values (i.e., they were the lightest), followed closely by fin whales, then southern right whales and humpback whales, which are much darker (Fig. 3).
Comparisons of whale with their environment showed two main results: (1) the radiance values for gray whales and fin whales distinguished them from the surrounding waters of the location where they were studied, as well as the waters of the other study locations (Fig. 4); and (2) southern right whales and humpback whales had very similar radiance values relative to the surrounding waters of the location where they were studied, but were distinct relative to waters from other locations (Pelagos and San Ignacio; Fig. 4). Overall none of the spectral signatures of the four candidate species differed greatly from the spectral signatures of their environment (Fig. 4).

Nonwhale Objects
In all the analyzed satellite images, except the one from Península Valdés, nonwhale objects were observed and clearly discernible from   whale-like objects. Boats and planes were the only types of nonwhale objects that were detected (Fig. 5). Various types and sizes of boats were observed across the three locations (i.e., the Pelagos Sanctuary, Laguna San Ignacio, and Maui Nui) such as ferries, fishing boats, cargo and sail boats. Planes, (i.e., passenger and smaller aircraft) were detected in the Pelagos Sanctuary and Maui Nui. Among all the nonwhale objects that were observed, the ones of smaller or similar size to whales were confidently identified (and discriminated from whales) due to their recognizable shape and, sometimes, due to the presence of other features such as fishing gear (Fig. 5).
The spectral analysis of nonwhale objects demonstrated differences from whales in radiance values that could be used in an automated whale detection system. Boats and planes displayed higher radiance values than the gray, fin, and humpback whales (Fig. 6). The spectral signatures of the planes detected in the Pelagos Sanctuary showed a concave-down negative slope, compared to the fin whale spectral signature, which was comparatively straight (Fig. 6b). On the satellite image of Maui Nui, the planes exhibited a spectral signature similar to the humpback whales except for a slight plateau between the green and red bands (Fig. 6c). In comparison, boats were the only nonwhale object detected on the satellite image of Laguna San Ignacio (Fig. 6a). They showed a similar spectral signature to the gray whales, but could again be confidently discriminated due to their features.

DISCUSSION
The four study species were detected on WorldView-3 satellite images, which are the first whale observations for this satellite system, and the first satellite-based detections of fin and gray whales. While earlier work detected right whales and probable humpback whales using Worldview-2 and IKONOS 2 satellite imagery, respectively (Abileah 2002, Fretwell et al. 2014, the higher spatial resolution of WorldView-3 made it possible to characterize each of the four surveyed species, and generate more confident observations. Characterizing each of these species was also possible due to the careful selection of time and location of acquisition of the imagery. Each image was taken when and where only one whale species was present, and near their respective peak abundance period. Several characteristics helped identify objects as whales, including size, coloration, and specific features (e.g., white head callosities, fluke). The size of the observed objects (i.e., length and width) was the first indication that an object could be a whale when compared to the known body size range (Shirihai andJarrett 2006, Jefferson et al. 2015). In this study, gray whales were the only species found within their size range; all other species appeared smaller than expected. Adult southern right whales and humpback whales were close to the lower limit of their documented size range. The average body length of fin whales was below its known size range (Shirihai andJarrett 2006, Jefferson et al. 2015). This discrepancy in body length compared to known size range is likely due to ascending or descending whales positioned diagonally to the sea surface. Additionally, flukes were rarely detected in the images of fin whales, southern right whales and humpback whales, which would lead to underestimates of body length. If there were doubts about the size of a whale-like object, other characteristics could be used to help identify whether it was a whale (e.g., white head callosities). For instance, sea surface water disturbance such as flukeprints (Levy et al. 2011) were observed for all the studied species. Smaller details (i.e., body features) also helped identify the observed objects as whales. Fluke and flippers were some of the main body features that could be observed among the four candidate species. Species-specific features were also observed and helped identify objects as whales, such as white head callosities which are distinctive of right whales for example. These smaller features, as well as body length and shape were, however, not equally seen for each species.
Identifying an object as a whale was more challenging for some species due to specific behaviors, which affect their detectability in the water. The "definite" observations were made when a whale was positioned parallel to the sea surface, which whales tend to do when traveling. In this position, body features such as fluke, flippers, as well as the general shape of the animal were visible. For example, the streamlined body shape could clearly be noticed for some fin whales and for gray whales with their more robust body. In contrast, the humpback whales were not as confidently identified. Their well-documented acrobatic nature on the breeding grounds (Helweg and Herman 1994, Frankel et al. 1995, Clapham and Mead 1999 hindered identification on the satellite image, as whale-specific characteristics such as body shape, flippers, or fluke were indistinct. A strong contrast between a whale and its surrounding environments is required to detect whales on satellite imagery (LaRue et al. 2017). In comparison to humpback and southern right whales, fin and gray whales contrasted more strongly with their surrounding waters, with their light body coloration appearing a useful feature assisting identification. While Maui humpback whales and Península Valdés right whales did not show strong contrast in this study, more confident identifications may be made where they occur in lighter toned habitats, areas where the surrounding water is lighter in color. All species were clearly spectrally and visually distinct from nonwhale objects (e.g., boats and planes), even though sizes were sometimes similar. Boats and planes, the only types of nonwhale objects observed on the satellite images, had clear specific outlines, different from whales. This dissimilarity was also seen in the spectral analysis with the different radiance values.
Although our study demonstrates that different species of large whales can be detected and counted using satellite images, manual scanning is time demanding. To reduce the time spent manually scanning satellite images, an automated system should be developed. The visual and spectral characterizations of the four study species could be used to inform and develop automated systems to detect them. Various methods currently exist to automatically identify specific objects (e.g., Rees 2013, Fretwell et al. 2014, Maire et al. 2015. A common method used to analyze satellite images is based on a purely pixel analysis (i.e., only the spectral characteristics) of a given object. Our results from the spectral analysis show that such a method is not likely to prove useful, as the four candidate species had similar spectral signatures with their habitat. However, other methods, such as an object-based image analysis (e.g., Groom et al. 2011, Yang et al. 2014 or a deep learning approach (e.g., Maire et al. 2015), may be more useful because these research techniques include the shape and texture of the object in addition to the spectral characteristics. The whale characteristics used to identify whalelike objects could be useful, particularly body shape and surface or near surface disturbances associated with a whale.
While a reliable automated detection method is under development, another way to reduce the time required to manually scan satellite images is to implement crowdsourcing projects (Supriadi andPrihatmanto 2016, Rey et al. 2017), requiring citizen scientists to scan the images and manually count the whales. One example of this approach is being taken for Weddell seals (Leptonychotes weddellii) on the Tomnod. com platform. However, for identification of whale signs, experienced marine mammal observers may be required, necessitating the careful set up of such a project. To maximize the utility of this approach, we recommend the following parameters to be considered when developing a satellite imagery based whale study (see Appendix S3 for more details): (1) behavior: e.g., traveling or resting, which means the animal full body length will likely be parallel to the surface; (2) coloration relative to surrounding waters: e.g., if observing a whale in deep water, lighter colors should be more easily discernible; (3) size: animal above 10 m in total length; (4) sea surface: e.g., species found in calm coastal water compared to open ocean might be easier to detect due to a potentially lower swell; and (5) cooccurrence of similar species, e.g., potential challenge for misidentification of species and potential for a positive bias in species-specific counts.
The constraint of animal size when using VHR satellite images to detect whales must be improved for broader applications. Spatial resolution of satellite images has improved since Abileah (2002), yet it does not appear to be high enough to detect smaller cetacean species or whale calves. In our study, two of the images were acquired during calving season, one for gray whales in Laguna San Ignacio (Jones andSwartz 1984, Urbán et al. 2003) and one for southern right whales in Península Valdés (Crespo et al. 2014, Cooke et al. 2015. Therefore, the presence of calves, which have an approximate length of 5 m for both species, was expected. However, few calves were observed on the satellite images and fewer with high confidence. This is likely explained by their 18 bodies being too small to clearly identify major anatomical features. Some of their behaviors, such as riding on the back of their mother, would also make it difficult to discern calves on VHR satellite images (Smultea et al. 2017).
As with the traditional survey methods, surface presence is an issue when surveying whale populations (Marsh and Sinclair 1989, Buckland and Turnock 1992, Buckland et al. 2001). Deep-diving species provide future challenges (to growth and density estimates) due to relatively lower sightings. Comparative studies between aerial and satellite-based methods are needed to assess the utility of satellite imagery for estimating density relative to aerial surveys. A better understanding of how deep below the sea surface a whale is likely to be visible on satellite images is also required. As suggested by Fretwell et al. (2014), large reflectance panels could be installed underwater in key habitats, to assist with calibrating the depth at which whales are visible. Another idea would be to install artificial whale models at various depths, similar to what Pollock et al. (2006) and Robbins et al. (2014) did with artificial dugong (Dugong dugon) and shark models to estimate the detectability of these animals from aircraft.
Per unit area, VHR satellite imagery has the potential to provide a cheaper and safer means of studying wildlife in remote places compared to traditional surveys (Fretwell et al. 2014, LaRue et al. 2011. The cost of acquiring VHR satellite imagery has reduced in the past decade (Fretwell et al. 2014, LaRue et al. 2017, with discounts available for the nonprofit sector, particularly education (LaRue et al. 2017). The personnel and analysis time are roughly comparable between VHR satellite imagery and traditional surveys, although less personnel are usually required for satellite imagery analysis. However compared to the main cost of most traditional surveys (i.e., fuel and charter of the survey platform), satellite imagery can be substantially cheaper, particularly for remote areas. A considerable advantage of using satellite imagery is that no time-consuming logistics and permitting are involved in this approach.

Conclusion
This survey is the first to address detection and species description (both visually and spectrally) of whales with WorldView-3 satellite imagery, suggesting that great whales can be detected on VHR satellite imagery. However, some species such as humpback whales and southern right whales were more difficult to detect on satellite images, although they are easily identifiable from boat or aerial surveys. This is due to either their body coloration being similar to their environment or to their behavior, which can make it difficult to discern body shape from above. The opposite is true for species with less acrobatic behavior at the surface, or lighter body coloration, such as fin and gray whales, which appear more easily discernible on satellite images in our study. VHR satellite technology could, therefore, be useful to monitor some whale species, especially over large areas of the ocean. Next steps in development of this approach will be (1) to automate or semiautomate CUBAYNES ET AL.: WHALES FROM SPACE the detection of each species following methods by Fretwell et al. (2014), and using an object-based image analysis approach which is likely to be more appropriate based on this study findings; and (2) to compare sightings data from the VHR satellite imagery approach with data collected from traditional survey methods, to test the efficacy of high resolution satellite imagery for measuring population density. Downstream, this technique has the potential to be used to measure whale densities and assessment density changes through time, following a thorough review of how satellite imagery analysis compares with traditional surveys for making such measurements.

ACKNOWLEDGMENTS
This research was possible thanks to the MAVA Foundation for their financial support of the project "Studying Whales from Space" (16035). We are also thankful to DigitalGlobe for providing us with satellite imagery. This study represents a contribution to the Ecosystems Component of the British Antarctic Survey Polar Science for Planet Earth Programme, funded by the Natural Environment Research Council (NERC). We are grateful for the thoughtful comments and reviews of the three anonymous referees, which greatly improved the manuscript.

SUPPORTING INFORMATION
The following supporting information is available for this article online at http://onlinelibrary.wiley.com/doi/10.1111/mms.12544/suppinfo. Appendix S1. Classification method and validation. Table S1. List of parameters to identify whale-like objects on satellite images based on Jefferson et al. (2015) and Woodward et al. (2006). The minimum values for "body length range" corresponds to size of calves. The maximum values for "body length range" corresponds to the maximum length of an adult. Table S2. Classification score equation and categorization for the studied species: gray whale, southern right whale, humpback whale and fin whale. Some classification parameters (Table S1) were downweighted, if there was less than 75% consensus. Other parameters, characteristic of whales (i.e., flukeprint, fluke and flipper), where upweighted only if more than 75% consensus was reached. For fin whales, the flukeprint parameter had to be down-weighted, as it reached less than 75% consensus (Table S3). Table S3. Percentage of consensus reached for each parameter listed in Table S1 per species .  Table S4. Results of the classification score and categorization comparison between the three observers, including the consensus for the categorization. CUBAYNES ET AL.: WHALES FROM SPACE