A Remote Sensing Analysis of Ad61ie Penguin Rookeries

REMOTE SENS. ENVIRON. 28:199-206 (1989)

Mathew R. Schwaller SAIC, Washington, DC Charles E. Olson Jr., Zhenqui Ma, and Zhiliang Zhu University of Michigan, Ann Arbor Paul Dahmer ERIM

The Ad~lie penguin (Pygoscelis adeliae) makes penguin rookeries, and the imagery may provide a up the vast majority of bird biomass in the means of developing new population estimation Antarctic. As a major consumer of krill, these birds methods for Antarctic ornithology. play an important role in the Antarctic food web, and they have been proposed as an indicator INTRODUCTION species of the vitality of the Southern Ocean ecosystem. This study explores the terrestrial habi- This paper describes satellite remote sensing meth- tat of the Ad~lie penguin as a target for remote ods used to estimate the size of Adblie penguin sensing reconnaissance. Laboratory and ground- (Pygoscelis adeliae) nesting sites. Preliminary lab- level reflectance measurements of Antarctic mate- oratory investigations studied the unique spectral rials found in and around penguin rookeries were characteristics of Ad~lie penguins and penguin examined in detail. These analyses suggested data guano (Schwaller et al., 1984). Furthermore, image transformations which helped separate penguin analysis indicated that guano plus other debris rookeries fiom surrounding areas in Landsat The- from Ad~lie nesting sites created a unique organic matic Mapper imagery. The physical extent of soil that was distinguishable from surrounding ar- penguin rookeries on Ross and Beaufort Islands, eas by radiance recorded in satellite imagery Antarctica, was estimated fiom the satellite data (Schwaller et al., 1987). This study builds on previ- and compared to published estimates of penguin ous work by examining laboratory reflectance mea- populations. The results suggest that TM imagery surements of Antarctic materials found in and may be used to identify previously undiscovered around penguin rookeries, and by evaluating ground-level radiometric measurements. These analyses suggested data transformations which Address Correspondence to Dr. Mathew R. SehwaUer, SAIC, helped separate penguin rookeries from surround- 400 Virginia Ave., S.W., Washington, DC 20024 ing areas in Landsat Thematic Mapper (TM) im- Received 25 August 198& revised 16 February 1989. oo34-4257 / 89 /s3.~o ©Elsevier Science Publishing Co. Inc., 1989 655 Avenue of the Americas, New York, NY 10010 199 200 Schwaller et al. agery. Finally, the physical extent of penguin though some birds will not return to any rookery colonies on Ross Island, Antarctica, was deter- in a given year, the breeding population found on mined from satellite data and compared to pub- rookeries and the true population should be highly lished estimates of penguin populations. correlated. Fluctuation in the actual population The interest in Ad61ie penguins was motivated should be reflected on the rookery population in by the role these birds play in the Antarctic terres- any given year. Furthermore, knowledge of the trial and Southern Ocean ecosystems. Ad61ie pen- breeding population is often more important than guins nest within rookeries on snow and ice-free an estimate of the total for assessing population beaches during the Austral summer, from late dynamics. October to mid-February (Taylor, 1962). Rookeries Ad61ie penguin rookeries make appealing tar- vary in size from assemblages of a few nests to gets for remote sensing reconnaissance. A rookery's rookeries occupied by over 200,000 nests, equiva- physical extent is well defined, and the penguins lent to nearly 1 million adult birds and chicks maintain a relatively constant packing density (Wilson and Taylor, 1984; Croxall and Kirkwood, within the individual colonies that make up a 1979). rookery (Penney, 1968). The objective of the re- The Southern Ocean ecosystem is unique be- search reported here was to better understand the came a single species of krill (Euphausia superba) reflectance properties of penguin rookeries, and to is, either directly or indirectly, the main prey propose methods by which these attributes may be species of numerous species of whales, seals, squid, exploited for penguin-counting techniques with fish, and seabirds, including penguins. Because of satellite remote sensing. the critical role played by kriU, the Southern Ocean ecosystem may be vulnerable to human interfer- METHODS ence and to commercial exploitation of its living resources (E1-Sayed, 1985). This is especially true Laboratory spectrophotometric measurements since a krill fishery has recently begun in the were made of four ground cover categories col- Southern Ocean, with the prospect that modem lected at Cape Crozier on Ross Island, Antarctica: methods could harvest immense quantities. Bird Ad~lie penguin guano, volcanic tufts, basalt, and populations fluctuate with the availability of their andesite. Nine spectra were measured of each of prey species, and populations can drop precipi- the four groups. Measurements were made with a tously when prey species are over-fished (Schaefer, Beckman DK-2A spectrophotometer, using barium 1970). Croxall and Prince (1979) suggested moni- sulfate as a reference standard. The spectropho- toring changes in the breeding numbers of krill- tometer measures reflectance from a small sample feeding seabirds, such as the Ad6lie penguin, may (about 2 cm2), and records reflectance in the wave- provide an index of the distribution and abun- lengths from 0.4 to 2.7 #m. The reflectance data dance of krill stocks. Such an index may be useful were subset and digitized into band reflectance for managing catch limits, and it may also serve as values corresponding to the six TM reflectance an index of the vitality of the Southern Ocean channels by numerical integration. Thus, the final ecosystem (SCAR, 1979). data set consisted of nine replicate measurements Current baseline information on the distribu- of four groups (guano, tufts, basalt, and andesite) tion and continent-wide population of Ad6lie pen- measured in six variables (TM Bands 1-5 and 7; gnins is sketchy. International programs provide see Table 1 for TM band wavelengths). relatively accurate estimates of breeding popula- Radiometric sampling was conducted in situ tions in certain segments of Antarctica, most no- at Cape Crozier on Ross Island, Antarctica. Mea- tably within areas of the Antarctic Peninsula and Ross Sea (Croxall and Kirkwood, 1979). Accurate estimates beyond these areas are unavailable, and Table 1. TM Spectral Bands recently quoted continent-wide estimates vary by up to an order of magnitude (Mougin and Pr~vost, Band Wavelength (~m) 1980; Wilson and Taylor, 1984; Stonehouse, 1985). TM 1 0.45-0.52 TM 2 0.52-0.60 A large fraction of the total Ad~lie population TM 3 0.63-0.69 returns to established rookeries to breed during the TM 4 0.76-0.90 Austral summer. Thus penguin rookeries make ap- TM 5 1.55-1.75 pealing targets for estimating populations. A1- TM 7 2.08-2.35 Remote Sensing of Penguin Rookeries 201 surements were made with a NASA three-band fore, no subtractions were conducted prior to im- radiometer (Tucker et al., 1981) filtered to simulate age analysis. TM Bands 3, 4, and 5. A halon panel was used as a A variation of the ratio technique helped re- reflectance standard. Four ground targets were duce the topographic effect and to enhance the chosen for radiometric measurements: areas within separation of penguin rookeries from other compo- Ad~lie penguin rookeries (sample size, n = 40, with nents in the Antarctic environment. The method sampling both within and between individual pen- involved data transformation from rectangular to guin colonies within the rookery), areas of rocky spherical coordinates. The transformations for basalt (n = 16) and tufts (n = 26), plus snow fields computing spherical coordinates are common (n = 30). Measurements were made along tran- knowledge (for example, Salas and Hille, 1974, pp. sects between 0845 and 1045 local time on 14- 381-382). Using these transformations, angular 16 January 1986. (Data collection was timed to measures psi, phi, and theta were computed from approximately match the Landsat-5 daytime over- TM Bands 3, 4, 5, and 7. These are ratiolike pass, which occurs at about 0900 local time.) Due parameters, since they were computed from the to constraints on time and logistics, radiometric TM data by dividing bands. measurements were not made of andesite expo- Linear discriminant analysis tested for statisti- sures. After omitting outliers, the final ground-level cally significant separation among ground targets. reflectance data set consisted of the following sam- In the case of the laboratory measurements, where ple sizes: rookery (n = 38), tufts (n = 25), snow significant overlap between two groups of ground fields (n = 29), and basalt (n = 16), with three targets was observed in bivariate plots, pooled variables (reflectance in TM Bands 3, 4, and 5) covariance matrices were used to estimate the measured in each group. discriminant function. Pooling the covariance ma- Analysis of Landsat TM data was conducted trices allowed estimation of the number of within- on a scene of the Ross Island area collected on group standard deviations separating group mean 26 January 1985. Targets of interest (rookeries, vectorsmwhich defines the statistical separability snow fields, exposures of tufts and basalt) were of any two groups (Kendall, 1980). Separate identified based on ground reconnaissance and map within-group covariance matrices were used to es- location, and pixel values from selected areas were timate the discriminant functions for the radiomet- chosen for analysis. To increase statistical indepen- ric and TM data (both raw and transformed), with dence among samples, 80% of the pixels were the assumption of equal prior probabilities for the dropped from selected areas with a 1-in-5 system- ground cover groups. Using Kendall's (1980) atic sampling. The tufts data were not subsampled, method, the accuracy of classifying ground targets however, because of the relatively small number of based on the linear discriminant function was esti- samples available over known tufts exposures. Pix- mated after reapplication of the function to the els were sampled from eight different classes of same sample data that derived the discriminant ground targets: the west Ad61ie penguin rookery at function. Kendall's method increases the variance Cape Crozier (number of pixels sample for use in estimates to account for the problem of using the the statistical analysis, n = 90); the east rookery at same data for training and testing the discriminant Cape Crozier (n = 21); the north rookery at Cape function. Bird (n = 22); snow fields and areas of sea ice All statistical analyses were conducted on SAS (n = 500); open ocean (n = 200); areas of basalt VMS Version 5 (SAS, 1985). Standard analyses exposure (n = 132); and areas of tufts exposure were conducted using existing SAS functions; some (n = 40). For the linear discriminant analysis de- of the analyses required custom programing with scribed below, the three penguin rookeries were SAS MATRIX. pooled into a single rookery class (n = 133). Image analysis was conducted using the Land RESULTS AND DISCUSSION Analysis System at the NASA Goddard Space Flight Center Laboratory for Terrestrial Physics. Identification and classification of penguin rook- Dark target subtraction was attempted as a means eries in satellite imagery is based on the assump- for atmospheric correction of the satellite imagery. tion that these targets possess relatively unique However, a minimum digital value of 0 was found and consistent spectral qualities. Measurements in each of the six reflective bands in the subscenes were examined to determine whether such quali- employed in the analyses described below. There- ties were evident in the spectra of pure samples 202 SchwaUer et al.

TM5 - Percent Reflectance

60 x X 55 _-- ANDESITE 50 X 45 X ¢0 X_~~X-~-X~- ~ TUFFS 55

10 "~1--BASALT

5 10 15 20 25 30 35 40 45 50 55 60 65

TM4 - Percent Reflectcmce Figure 1. Bivariate plots (90% confidence region) of laboratory reflectance in TM Bands 4 and 5 common Antarctic materials, with actual sample reflectance points as indicated. measured in the laboratory, and in radiometric identical targets were andesite and tufts, which measurements of well-defined field sites. These data were separated by 12.1 standard deviations. As- were also used to suggest simple transformations to suming that the linear discriminant function was enhance the separability of penguin rookeries from normal, the mean vectors were statistically separa- other targets in TM imagery. ble at greater than the 99.99% level of confidence. Laboratory reflectance data in TM Bands 3, 4, All groups were perfectly separated when the dis- and 5 were plotted for visual analysis. These plots criminant function was applied to the sample data. included illustration of the 90% confidence ellip- Visual analysis of in situ, hand-held radiometer soids (Cooley and Lohnes, 1971, pp. 266-267) data displayed in bivariate plots suggested that drawn around four groups of ground targets: guano, basalt, tufts, and penguin colonies are readily sepa- andesite, basalt, and tufts (Fig. 1). Visual analysis rable by their reflectance in TM Bands 3, 4, and 5 of these graphs indicated relatively good separa- (a subset of these plots is illustrated in Fig. 2). tion of the groups, although there was some over- Generally, basalt is darkest in these three re- lap, especially between andesite and tufts. The flectance bands, tufts has an intermediate re- bivariate plots were somewhat limited, however, flectance, and the colony areas are the brightest of because they are projections from three-dimen- the three targets. Snow has a unique reflectance; it sional to two-dimensional space. Groups can is highly reflective in TM Bands 3 (reflectance overlap significantly when projected into two di- > 83%) and 4 (> 71%), but it is nearly black in mensions and still be completely distinct in three- TM Band 5 (< 5%). Visual examination of the dimensions. Linear discriminant analysis estimated bivariate plots also indicated a much greater vari- the number of within-group standard deviations ability in the in situ measurements, compared to separating group mean vectors in the three-dimen- the laboratory spectrophotometric data. This was sional space of TM 3, 4, and 5. The most nearly especially true of the measurements collected Remote Sensing of Penguin Rookeries 203

SNOW

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

TM4 Figure 2. Bivariate plots (90% confidence region) of ground reflectance in TM Bands 4 and 5 for targets in and around the Ad&lie penguin rookeries on Cape Crozier. within the penguin rookeries. The coefficient of strongly influenced by the slope and aspect of variation for TM Band 5, for example, was 38.50 ground targets. Assuming a clear atmosphere, slope for radiometric data collected within the rookery, and aspect of the terrain will have much less compared to 9.67 for laboratory spectrophotomet- influence on the angular distance from the axes to ric measurements of guano sampled within the a given datum. Inspection of Figs. 1 and 2 sug- same band. Linear discriminant analysis confirmed gested that classification of TM imagery would the visual analysis; as with the laboratory spec- benefit from a transformation which preserved the trophotometric data, all groups were perfectly sep- unique spectral features of Antarctic targets while arated when the discriminant function was applied minimizing the absolute brightness. The transfor- to sample data. mations used here converted rectangular coordi- Penguin rookeries were readily separated from nates of TM band reflectance into spherical coor- other Antarctic materials based on laboratory and dinates. field reflectance measurements. One interpretation Linear discriminant analysis gauged the sepa- of Figs. 1 and 2 suggests that there are two ele- rability of five ground targets (penguin rookeries, ments which contribute to this separability: abso- snow, ocean, basalt, and tuffs) based on measure- lute target brightness (defined here are the radial ments recorded in Bands 3, 4, 5, and 7 of Landsat distance from the origin to a data point) and target TM imagery. Only one sample from the tuffs group spectral features (angular distance from the axes to was confused with the penguin rookery group a data point). The radiance recorded in satellite when raw TM data were used in the analysis, and imagery can be similarly interpreted. However, the all other groups were perfectly separated. When absolute brightness recorded in satellite imagery is the transformed data were evaluated, all groups 204 Schwaller et al.

(penguin rookery, snow, ocean, basalt, andesite, appearance than when displayed in any combina- and tufts) were perfectly separated by the discrim- tion of TM Bands 3, 4, 5, and 7. inant function. Analysis revealed that the transformation in- Discriminant analysis suggested that penguin creased homogeneity of data collected over pen- rookeries are relatively easily identified in both the guin colonies in other ways. An examination of raw and transformed TM Data. Although data Fig. 3 indicated that the average brightness transformation does not provide any clear advan- recorded by the TM sensor of the north Cape Bird tages in separating targets, the transformation Ad61ie penguin rookery was lower than the Cape appears to have some advantages when the quali- Crozier rookeries, and the TM band measurements tative aspects of image enhancement and interpre- collected over the west Cape Crozier rookery gen- tation are considered. First, the transformation erally have a greater variability than those col- lowered the coefficient of variation (c.v.) of the lected on Cape Crozier east. These differences can data in the TM bands examined. For the raw data, be attributed to slope and aspect of the terrains on the c.v. for data collected over penguin rookeries which the rookeries are found. The aspect of the in TM Bands 3, 4, 5, and 7 were 20.5, 23.3, 26.7, Cape Bird rookery pointed away from direct solar and 25.4, respectively. For the transformed data, illumination at the time the imagery was collected, the c.v. for psi, phi, and theta were 1.70, 6.03, and while the Cape Crozier rookeries pointed toward 3.26, respectively. These results suggested that, the sun. Thus the TM sensor recorded a generally when displayed as a psi, phi, theta image, penguin lower band radiance over the Cape Bird rookery rookeries will have a more uniform, homogeneous compared to the Cape Crozier rookeries, even

Figure 3. Bivariateplots (90% confidenceregion) of TM data in Bands 4 and 5 sampled from three penguin rookeries on Cape Crozier.

7M5

130

120

110 CROZIER WEST

100

CROZIER EAST 7o

3O CAPE BIRD

l- 10 10 20 30 ¢0 50 60 70 80 90 100 110 120 130

TM4 Remote Sensing of Penguin Rookeries 205

THETA

095

09 C

085

075

CROZIER WEST 07

0 65 CAPE BIRD

055

0 45 06 065 07 075 0.8 085 09 095 I I 05 I I 1 ~5 12

PHI Figure 4. Bivariate plots (90% confidence region) of transformed TM data (theta and phi) sampled from three penguin rookeries on Cape Crozier.

though the rookery substrates are essentially iden- Table 2. Estimates of Rookery Area tical. Even with similar aspects, the west rookery Number Population Date of POP. on Cape Crozier covers a much more topographi- Location of Pixels (Breeding Pairs) Estimate cally varied terrain than the Cape Crozier east Cape Crozier W. 660 84,937 1966 rookery, which explains the greater variability in Cape Crozier E. 126 12,970 1966 data recorded by the TM sensor over the west Cape Bird N. 131 23,600 1978 Cape Bird S. 55 10,000 1978 rookery. Visual analysis of graphs which plotted Beaufort Is. 66 21,000 1966 the transformed data indicated an increase in ho- Source: Population estimates compiled by Wilson and Taylor mogeneity when compared to raw data (a subset 0984). of transformed data plots is illustrated in Fig. 4). The centroids of the bivariate predictions for lineated, rookery areas were estimated by counting transformed TM data collected over the three pixels (Table 2). rookeries were in close proximity, and the contours A positive correlation (r 2= 0.97) exists be- of the 90% confidence ellipsoids overlap much tween population estimates compiled by Wilson more closely than those of the raw data (Figs. 3 and Taylor (1984) and rookery areas (Table 2). and 4). This correlation is indicative of a strong linear TM Bands 3, 4, 5, and 7 were transformed to relationship, but should be regarded with caution. spherical coordinates, and the resulting three angu- The relationship needs to be tested huther: with a lar coordinates were displayed as images which larger sample size, with current population data, were used to delineate the five Ad~lie penguin and in areas where rookery populations are rapidly rookeries on Ross and Beaufort Islands. Once de- rising or falling. 206 Schwaller et al.

The lack of current population data for the five Croxall, J. P., and Kirkwood, E. D. (1979), The Breeding study sites is indicative of the current state of Distribution of Penguins on the Antarctic Peninsula and Islands of the Scotia Sea, British Antarctic Survey, knowledge for all living resources in Antarctica. Cambridge. Regional surveys are sketchy, and continent-wide Croxall, J. P., and Prince, P. A. (1979), Antarctic seabird and estimates of species populations rely primarily on seal monitoring studies, Polar Record 19:573-595. guesswork--none are based on rigorous statistical E1-Sayed, S. Z. (19853, Phytoplankton of the Antarctic seas, in survey design. If validated in more extensive tests, Key Environments--Antarctica (W. N. Bonner and a linear relationship between rookery and popula- D. W. H. Walton, Eds.) Pergamon, New York. tion could serve as the basis for a continent-wide Kendall, M. G. (1980), Multivariate Analysis, 2nd ed., survey. Ratio estimates, for example, take advan- MacMillan, New York, 210 pp. tage of the correlation between an auxiliary vari- Mougin, J. L., and Pr6vost, J. (1980), l~volution annuelle des able which is relatively easy to measure (such as ~ffectifs et des biomasses des oiseaux antarctiques, Terre rookery area from satellite imagery) and a variable Vie R~v. Ecol. 34:101-133. of interest which is more difficult to measure Permey, R. L. (1968), Territorial and social behavior of the (population). Typically, the efficiency and preci- Ad61ie penguin, in Antarctic Bird Studies (O. L. Austin, sion of the estimate is increased when an auxiliary Ed.), Antarctic Research Series Vol. 12, American Geo- variable is included in the estimate of a population physical Union, Washington, DC. total if the linear correlation between the two Salas, S. L., and Hille, E. (1974), Calculus, Xerox, Lexington, variables is greater than about +0.5 (Sheaffer MA. et al., 1979). SAS (19853, SAS User's Guide, SAS Institute, Cary, NC, Continent-wide estimates of penguin popula- 2 vols. tions are currently hampered because large SCAR (1979), Fifteenth Meeting of SCAR. Charmonix, 16-26 stretches of the Antarctic coastline remain unex- May 1978. APPendix A. Working Group on Biology, Polar plored. The regions for Marie Byrd Land to the Rec. 19:304-312. Antarctic Peninsula, the Trinity Coastline of the Schaefer, M. B. (1970), Men, birds and anchovies in the Peru Antarctic Peninsula, and the South Shetland Is- current--dynamic interactions, Trans. Am. Fisheries Soc. lands of the Scotia Sea have been suggested as 99:461-467. locales where large undiscovered rookeries may lie Scheaffer, R. L., Mendenhall, W., and Ott, L. (1979), Elemen- (J. CroxaU, personal communication). Based on tary Survey Sampling, Duxbury Press, Boston, MA. reflectance properties, penguin rookeries were readily separated from other elements of the SchwaUer, M. R., Benninghoff, W. S., and Olson, C. E., Jr. (1984), Prospects for satellite remote sensing of Ad61ie Antarctic landscape. These results, and the meth- penguin rookeries, Int. I. Remote Seas. 5:849-853. ods used to obtain them, hold promise for expand- ing the scope of Antarctic ornithology: for identify- Schwaller, M. R., Olson, C. E., Jr., Benninghoff, W. S., and Whenes, K. M. (1987), Preliminary report on satellite ing previously undiscovered penguin rookeries and remote sensing of Ad61ie penguin rookeries, Antarct. 1. for developing continent-wide estimates of pen- U.S. 21:205-206. guin populations. Stonehouse, B. (19853, Birds and mammals--penguins, in Key Environments--Antarctica (W. N. Bonner and This work was supported by the National Aeronautics and D. W. H. Walton, Eds.), Pergamon, New York. Space Administration Contract NAS 5-28755 and by National Science Foundation Grant DPP 85-07483. We would also like Taylor, R. H. (1962), The Ad61ie penguin (Pygoscelis adeliae) to acknowledge the contributions of Kathy Whenes, William at Cape Royds, lbis 104:176-204. Benninghoff, and David Parmellee. Tucker, C. J., Jones, w. H., Kley, W. A., and Sundstrom, G. J. (1981), A three-band hand-held radiometer for field use, Science 211:281-283. REFERENCES Wilson, G. J., and Taylor, R. H. (1984), Distribution and Cooley, W. W., and Lohnes, P. R. (1971), Multivariate Data abundance of penguins in the Ross Sea sector of Antarc- Analysis, Wiley, New York. tica, N.Z. Antaret. Ree. 6:1-7.