Using Satellite Imagery to Count Nesting Albatross from Space

AN ABSTRACT OF THE THESIS OF

Jane Elizabeth Dolliver for the degree of Master of Science in Wildlife Science presented on June 3, 2019.

Title: Using Satellite Imagery to Count Nesting Albatross from Space.

Abstract approved:

______Robert Michael Suryan

One fundamental concern in conservation biology is species abundance. For many taxa, however, these data are costly to obtain via direct observation and thus limited in geographic or temporal scope. Very high-resolution satellite imagery provides a means to address these limitations and provide remotely-sensed counts of large, colonial species. We used very high resolution satellite imagery paired with field counts of three species of nesting albatrosses (Phoebastria immutabilis, P. nigripes, P. albatrus) at two sites (Torishima, Japan and Sand Island, Midway Atoll, Hawaii) in the Pacific Ocean to test the ability of satellite image-based counts to predict in- field counts with multiple image and habitat covariates. Albatross were identifiable on Torishima using both WorldView-2 and WorldView-3 platforms and on Sand Island using the WorldView-3 platform. Pan-fused images underestimate ground count by about 31%, when taking into account vegetation cover and sun elevation, and their availability is limited to WorldView-3 imagery. Panchromatic images more accurately model in-field count when taking into account platform, species and vegetation cover with errors of -40-25%. We applied the best-performing, panchromatic model to estimate an inaccessible colony of P. albatrus breeding in the Senkaku Islands, from a single satellite image in 2015. We show the colony has expanded to a minimum of 166 adult birds. We demonstrate that with sufficient

calibration, robust, multi-species models can be developed to expand the use of very high-resolution satellite imagery to satisfy monitoring objectives constrained by time, funds, or accessibility.

Using Satellite Imagery to Count Nesting Albatross from Space

by Jane Elizabeth Dolliver

A THESIS

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Master of Science

Presented June 3, 2019 Commencement June 2019

Master of Science thesis of Jane Elizabeth Dolliver presented on June 3, 2019

APPROVED:

Major Professor, representing Wildlife Science

Head of the Department of Fisheries and Wildlife

Dean of the Graduate School

I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.

Jane Elizabeth Dolliver, Author

ACKNOWLEDGEMENTS

My sincere appreciation goes out to the U.S. Fish and Wildlife Service – Endangered Species Division - Alaska, particularly Ellen Wilt, Erin Knoll, and Leah Kenney; and the National Fish and Wildlife Foundation-Pacific Seabird Program, administered by Scott Hall; for funding this research. I am grateful for awards from the Department of Fisheries and Wildlife – Coombs-Simpson Memorial Scholarship, the College of Agricultural Sciences – Savery Outstanding Masters Student, and the Graduate School – Provost’s Graduate Scholarship Match Award.

I cannot thank my major advisor, Dr. Robert Suryan enough. Rob, you have supported my graduate experience in every way – you have motivated and inspired me, challenged and broadened my analytical skills, and infused me with confidence. I have become a better leader, mentor, and researcher under your mentorship.

To my committee members Dr. Taal Levi and Dr. Anne Nolin, thank you for offering insight into data analysis and imagery interpretation roadblocks. I am in awe of the contributions you have made to your respective fields and your zest for making your science matter to the masses. I also extend my gratitude to Dr. Brian Sidlauskas for serving as my Departmental Reviewer and Dr. Ling Jin for serving as my Graduate Council Representative - thank you for your flexibility and willingness to assist in the first and final stage of my degree.

Special thanks goes out to six technical assistants: Adam Duarte, Christopher Noyles, Paulo Murillo, Michael Olsen, Chase Simpson and Steven Whitlock who have provided strategic and timely advice and feedback regarding image acquisition, image processing, GPS acquisition, and statistical analyses. Chris, thank you for serving as liaison to Digital Globe and for providing imagery and technical resources through the Civil Applications Committee during the project’s early stages. Penelope Chilton, Megan Dalton, Beth Flint, Hiroshi Hasegawa, Jenny Johnson, Eric VanderWerf, and Lindsay Young – your help and expertise in the field were invaluable.

I owe a debt of gratitude to the Seabird Oceanography lab, especially Don Lyons and Rachael Orben, who have served as informal committee members and provided me the opportunity to be involved with long-term seabird research on the Oregon Coast for three summers. Through this, I have had the chance to work with a truly exceptional group of biologists – Stephanie Loredo, Jess Porquez, and Amanda Gladics – and a set of top-notch undergraduates and interns: Ana Medina, Alayna Lawson, Denisse Silva, Isabel Justiniano, Christian Cortez, Melanie Birch, Makenzie Weber, Alyssa Nelson, Ally Melendez, and Jason Piasecki.

To my Levi lab-mates, thank you for schooling me in basic mammology, and for adding a healthy dose of scientific inspiration to my week. Jen Allen, Cara Appel, Brent Barry, Jennifer Van Brocklin, Emily Dziedzic, Charlotte Eriksson, Aimee Massey, Jenny Urbina, Joel Ruprecht, and Marie Tosa – I have tried to repay all of you with baked goods and coffee, but my debt is probably still outstanding.

Members of the Roby lab who have taken me in as their own, I am grateful to you: Olivia Bailey, Kirsten Bixler, Tim Lawes, Adam Peck-Richardson, Ethan Schniedermeyer, Sam Stark, and Yasuko Suzuki. Thank you for fostering such a fun and supportive seabird research community. Olivia and Kirsten, especially, you have provided some sage advice and encouragement.

To those who shared a 9am-5pm with me in the office or sometimes a 5pm- 9am – Niki Diogou, Chris Malachowski, Stan Piotrowski, Thaddaeus Buser, Virni Budi Arifanti – thanks for help with R and statistics coursework, and for being such enthusiastic recipients of my culinary creations.

My outdoor aficionados – Olivia Bailey, Carly Congdon, Andrea Kristof, Claire Revekant, Megan Zarzycki – have done their best to pull me away from work, where we occasionally hit a 2:1 dog-to-people ratio out on the trail. Leila Lemos, thank you for embarking on the graduate school journey with me. Two friends and colleagues in particular, Emily Runnells and Shannon Kawamura, pushed me to make the graduate school leap, and I would not be here without them.

Lastly, I give thanks to my parents and to my sister for their enduring support, guidance, and encouragement. My affection for all of you is immeasurable.

TABLE OF CONTENTS

Page

1 Introduction ………………………………………………………………………... 1

2 Methods ……………………………………………………………………………. 8

2.1 Study Areas ……………………………………………………………..... 8

2.2 Field Counts …………………………………………………………….. 10

2.3 Image Acquisition ………………………………………………………. 11

2.4 Image Processing ……………………………………………………….. 12

2.5 Data Analysis …………………………………………………………… 13

3 Results ……………………………………………………………………………. 15

3.1 Pan-fused pixel-based counts ….……………………………………..… 17

3.2 Panchromatic pixel-based counts …….…………………….…………... 20

3.3 Application to plots of unknown size …………………………….…….. 28

4 Discussion ………………………………………………………………………… 29

4.1 Implementation …….………………………………………………...…. 32

5. Conclusions …………………………………………………………………….... 34

6. Bibliography ……………………………………………………………………... 35

Appendices …………………………………………………………………………. 42

LIST OF FIGURES

Figure Page

1. Plumages of Phoebastria albatross …………………...…..……...…………….… 5

2. Study location …………………...……………………...………...……...…....….. 7

3. Colony photos ……………………………….………………………………...... 9

4. Comparison of WorldView-2 and WorldView-3 imagery ………………...... 16

5. Pan-fused model ……………………………………………..…………………... 20

6. Panchromatic model …………………………………………..………………..... 24

7. Panchromatic model by species & platform ……………………………………... 25

8. Seasonal attendance images from Sand Island …………..………………………. 26

9. Annual variation in attendance, Sand Island 2016-2017 …………………..…...... 27

10. 2015 Senkaku Islands image ………………………….……………………...... 29

LIST OF TABLES

Table Page

1. Size comparison for Phoebastria albatross …………...…………………….. 4

2. Summary statistics ………………………………………………………….. 14

3. Pan-fused model rankings ………………………………………..…………. 19

4. Pan-fused predictor estimates …………………………………………..…... 19

5. Panchromatic model rankings ………………..………………..…....………. 22

6. Panchromatic estimates …………………………………………….………. 23

7. Panchromatic model predictions ………...………………………………….. 23

LIST OF APPENDICES

Appendix Page

A. Imagery acquired (breeding season) ………………...…………...... 42

B. Nesting phenology for Phoebastria sp …………………………..……..... 44

C. Imagery acquired (non-breeding season) ………………………………… 45

D. Model variable descriptions …………………………………...... 46

E. Correlation table for pan-fused model …….…………….……....……..... 47

F. Correlation table for panchromatic model ….…...……………….……..... 48

Introduction

One of conservation biology’s fundamental concerns is species abundance and by

extrapolation, total population size (Gaston 1994, Caughley and Gunn 1996). For

many taxa, however, this information is difficult to obtain (Pierce and Guerra 1994,

Taylor et al. 2001, Seminoff and Shanker 2008). In the last decade, considerable

advances have occurred beyond direct observation techniques that increase the

frequency (e.g., camera traps, Rowcliffe et al. 2008), and/or geographic extent of count data (e.g., unmanned aerial surveys, Hodgson et al 2013). These advances simultaneously incur the benefits of reduced cost (Clare et al. 2015), minimized disturbance or observer effects (Kucera and Barrett 2011) and increased statistical power (Field et al. 2005).

Imagery is one complement to direct wildlife observation that has been used and improved on for decades across a multitude of platforms. Early naturalists and photographers captured aggregations of animals in landscapes that have changed substantially (Carter et al. 2001, Hentati-Sundberg and Olsson 2016, Cray 2018).

These images now provide a rigorous assessment of change superior to early field notes and anecdotes (Sparks 2007, Henatati-Sundberg and Olsson 2016). The desire

to encompass larger geographic segments, especially for large, migratory, or colonial

species, took cameras from the ground to the skies (Bowman 1955, Grzimek and

Grzimek 1960, Kadlec and Rury 1968). For many regions, aerial photographs, some

years apart, represent the only existing distribution and abundance data (Chase et al.

2016, Kirkman et al. 2012). The switch to digital imagery creates a snapshot in time

that simultaneously becomes both data and a permanent digital archive (Morgan et al.

2010). Like other technologies, digital imagery has advanced tremendously within the

last several years to the point that high definition panoramas can replicate in-field observers at large scales (Lynch et al. 2015). Remote cameras now record continuous foraging and habitat use patterns (Gaglio et al. 2017), species occurrence and behavior (Steenweg et al. 2017), or indices of abundance (Orben et al. 2019).

Digital imagery was propelled to space via the first satellites, nearly four decades ago. Early imagery provided the first vegetation and terrain data across the globe, especially for the world’s most remote locations (Sobur et al. 1978, Dregne and Tucker 1988, Mouginis-Mark et al. 1989). The uninterrupted, publicly-available satellite record of Landsat, in particular, continues to document long-term changes in land cover and land use (Wulder et al. 2008) with wide-ranging applications, including seagrass distribution (Lyons et al. 2012), flooding regimes (Díaz-Delgado et al. 2016), forest phenology (Elmore et al. 2012), and insect invasions (Goodwin et al. 2008). For wildlife biologists, use of satellite-derived data began as a means to assess landscape-level questions on habitat availability, suitability, change and preference (Lyon 1983, De Wulf et al. 1988, Hill and Kelly 1987). Beginning in

1999, commercial satellites started to provide significantly better, sub-meter spatial resolution, which enabled fine-scale classification, mapping, and habitat modeling techniques with global applications (Mumby and Edwards 2002, Everitt et al. 2005,

Levanoni et al. 2011, Nagendra et al. 2013).

Increased spatial resolution promoted the application of satellite imagery to the field of wildlife biology. Quantification of individual, large, terrestrial and marine

mammals first became possible using the WorldView-1 and QuickBird-2 platforms

(LaRue et al. 2011). In some cases, satellite imagery could serve as a proxy for aerial, plane-based wildlife surveys, which addressed safety and cost concerns associated with this method (Platonov et el. 2013, Stapleton et al. 2014). Improved resolution on

the next generation Worldview-3 platform allowed for the mapping of smaller

organisms, either by estimating the area of large aggregations, or by selecting large

species and backgrounds with high contrast in the visible light spectrum (Fretwell et al. 2014, Lynch and LaRue 2014, Yang et al. 2014, Fretwell et al. 2017, Phillips et al.

2018). These high-contrast applications, predominantly dark animals on ice, represent best-case scenarios for detection and enumeration. Further research is warranted to investigate how characteristics of imagery, habitat, timing, or species influence the

accuracy and precision of imagery-based counts, similar to analyses conducted with

aerial counts (Crete et al. 1986, Rodgers et al. 1995, Jachmann 2002).

As large, colonially-nesting species, the three north Pacific albatrosses - black-footed (Phoebastria nigripes), Laysan (Phoebastria immutabilis), short-tailed

(Phoebastria albatrus) - are ideal candidates to investigate the efficacy of satellite counts for several reasons. First, their body size is slightly larger than the pixel resolution of the highest resolution satellite imagery (Table 1). Second, their dorsal plumages (as viewed from a satellite) varies from a mix of entirely dark brown/gray to varying degrees of white on the head and rump (Figure 1). Third, they select generally open nesting habitat of lowland shrubs and grasses and bare sand or volcanic rock substrate on high relief volcanic islands and low-lying atolls (Hasegawa and Degange 1982, Arata et al. 2009, Young et al. 2009). Consistent monitoring of

breeding populations on these low-lying atolls is increasingly important given storm surge, severe weather events, and tsunamis that have killed birds and damaged or destroyed nesting habitat (Butchart et al. 2004, Baker et al. 2006, Arata et al. 2009,

Reynolds et al. 2015).

Table 1. Size comparison of Phoebastria albatross. Diomedea, the largest albatrosses (Southern Hemisphere) are included for comparison.

Length(cm) Wingspan (cm) Weight (kg)

Phoebastria nigripes1 68-82 193-220 2.6-4.3 Phoebastria immutabilis1 71-81 195-215 2.2-2.8 Phoebastria albatrus1,2 80-100 213-240 3.7-6.6

Diomedea exulans1 107-135 254-351 6.7-8.7 Diomedea sanfordi1 107-122 290-351 6.4-8.8

1 Handbook of the Birds of the World. https://www.hbw.com/species/ 2 USFWS 2008. Short-tailed Albatross Recovery Plan. https://www.st.nmfs.noaa.gov/Assets/nationalseabirdprogram/stal_recovery_plan.pdf

R. Suryan J. Dolliver a) b)

R. Suryan K. Ozaki c) d)

R. Suryan e) Figure 1. Plumages of Phoebastria albatross. a) P. nigripes adult (monomorphic) b) P. immutabilis adult (monomorphic) c) P. albatrus juvenile, d) P. albatrus sub-adult, e) P. albatrus adult.

In light of habitat loss and the compounding threats of fisheries bycatch,

plastics, and introduced species, determining trends in the abundance of breeding-age adult albatrosses is critical to monitoring the long-term survival of these populations

(Sievert and Sileo 1993, Wanless et al. 2009, Pardo et al. 2017). However, the bulk of these populations nest on remote islands that are difficult and costly to access

(Arata et al. 2009, Deguchi et al. 2012). At these remote colonies, ground counts within the colony have led to the collapse of burrows, degradation of nesting habitat

(McClelland et al. 2008) and disturbance to nesting albatross (Burger and Gochfeld

1999). The result is that frequently monitored colonies tend to be near human population centers where habitat is less fragile, and accessibility less limited (Young et al. 2009).

Two remote and inaccessible colonies of particular interest to managers and conservationists are the short-tailed albatrosses breeding on the Senkaku1 Islands:

Minami-kojima2 and Kita-kojima3 in the western North Pacific Ocean (25°N, 123°E

– Figure 2; USFWS 2008, USFWS 2014, BirdLife International 2018). Due to political instability arising from a territorial dispute between China and Japan (Pan

2007), this sub-population, approximately 15% of the global short-tailed albatross population, has not been counted since 2002 (USFWS 2014). Management actions under the U.S. Endangered Species Act for short-tailed albatross require knowledge of the population size and growth rate of the species on the Senkaku Islands (USFWS

1 Senkaku Islands is the Japanese reference, used here for simplicity. Also known as Diaoyudao Islands or Diaoyu Dao (China/People’s Republic of China), Diaoyutai Islands (Republic of China/Taiwan/Chinese Taipei), Pinnacle Islands (some Western scholars) 2 Chinese reference: Nan Xiaodao 3 Chinese reference: Bei Xiaodao

2008). Satellite remotely-sensed data provide an unobtrusive means to obtain this

critical information, as it has in other regions of instability and conflict (Asner et al.

2013, Jaafar et al. 2015).

Pacific Ocean

Torishima (Surveyed) Sand Island (Surveyed)

Senkakus (Inferred)

Figure 2. Study location within the Pacific Basin.

Our primary objective is to test the efficacy of very high-resolution satellite

imagery to census multiple albatross species, with varying plumages, across different islands, and nesting habitats. We sought to determine which environmental and

image-based factors influence the accuracy of satellite-based counts and under what

circumstances image processing methods may improve identification of individual

birds. We developed models to account for discrepancies between satellite and ground-based counts so that satellite imagery can be used to count albatrosses across

a wider range of species and habitats. Furthermore, we applied satellite imagery

calibration models to estimate the short-tailed albatross sub-population in the

Senkaku Islands and determine if and how this sub population has expanded over the

15-year period since it was last visited and counted in 2002.

Methods

Study areas

We used two nesting sites for model development – Torishima, Japan and Sand

Island, Midway Atoll, Hawaii – and one site for model application – Senkaku Islands,

Japan in the central and western North Pacific Ocean (Figure 2). Paired ground counts and satellite image counts were obtained for short-tailed albatrosses on Torishima, and for Laysan and black-footed albatrosses on Sand Island.

Torishima (30°28′N, 140°18′E) is a 4.6 km2 uninhabited volcanic island in the

Izu archipelago, 491km south of the Japanese main island of Honshu. Torishima

supports 80-85% of the global breeding population of short-tailed albatross (1011

breeding pairs on Torishima during the 2018-2019 breeding season, H. Hasegawa

unpubl. data) and colony counts to monitor this population occur annually, multiple

times per week during peak egg laying, late November to mid-December (Hasegawa

and Degange 1982, USFWS 2014). Short-tailed albatrosses nest in two main areas –

Tsubamezaki, a steep volcanic scree slope on the southwest (403-563 breeding pairs

in 2011-2018), Hatsunezaki, a flat, vegetated area to the northwest (102-389 pairs)

Figure 3a, 3b), and a small cliff-top area (7-59 pairs).

H. Hasegawa H. Hasegawa (a) (b)

J. Johnson H. Hasegawa

Figure 3. Ground-based colony photos of a) Tsubamezaki, Torishima; b) Hatsunezaki , Torishima; c) Interior, Sand Island; d) Senkaku Islands.

Sand Island (28°12′N 177°21′W) is a low-lying, 6.2 km2 island part of

Midway Atoll in the Northwestern Hawaiian Islands in the central North Pacific

Ocean, 1,900 km northwest of Kauai. The island supports globally significant

populations of Laysan albatross (590,000 pairs, approximately 69% of the global

population) and black-footed albatross (61,000 pairs, approximately 35% of the

global population). Albatrosses nest on multiple islands within the atoll and our study

plots were on Sand Island where one whole-island census occurs annually during incubation (late December-January). Nests are broadly dispersed across the entire island’s upland habitat characterized by low vegetation and white-sand beaches

(Arata et al. 2009, Figure 3c).

Minami-kojima/ (25°45′N 123°36′E) and Kita-kojima (25°45′N 123°36′E) are two islands within the Senkaku Islands group (0.5km2 and 0.3km2 respectively) that

supported 32 nests at Minami-kojima and 1 nest at Kita-Kojima during the last census

in 2002 (Figure 3d; USFWS 2014). The current population is estimated to be 190

breeding pairs from model projections based on colony growth from counts up to

2002 and observed growth of the Torishima colonies over the same time period

(USFWS 2014, Sievert and Hasegawa unpubl. data). Three nesting areas, two on

Minami-kojima and one on Kita-kojima, were identified in 2002 (USFWS 2014).

Field Counts

We obtained field counts from both Torishima and Sand Island for three species

(black-footed, Laysan and short-tailed albatross) during two breeding seasons

(November-February) to pair with satellite image counts (Appendix A). Although

species co-occur at each island, nesting areas are segregated by species so that only a

single species occurred within plot boundaries that were identifiable both on island

and via satellite. For each island, a field team of 1-4 people sampled 3-5 plots 0.002-

0.06km2 birds (mean = 0.010km2), in less than one hour. Counts were performed on

foot, either by walking a series of adjoining transects, or by conducting point

estimates from just outside the plot perimeter. Due to the difficulty of tracking and

possibly double-counting non-stationary birds within plots, only birds sitting on nests

were included in ground counts, with the knowledge that non-breeding birds visiting

the colony account for an additional 2.3 to 38.4% (Poncet et al. 2006, Fisher and

Fisher 1969). Ground counts were not adjusted for non-incubating albatrosses

(“walkers”) or dual nest attendance because these numbers vary widely by

environmental conditions, colony size, growth and location, and time of day

(Pickering 1988, Hedd and Gales 2005, Stahl and Sagar 2006, Powell et al. 2008,

Young et al. 2009). Age for short-tailed albatrosses was determined visually by

dominant dorsal plumage (Figure 1; Hasegawa and Degange 1982).

Image Acquisition

We retrieved a total of 13 beta 1-b level WorldView-2 and 16 beta 1-b level

WorldView-3 November 2011 - January 2018 for use in the calibration model

(DigitalGlobe 2014, DigitalGlobe 2016, Appendix A). For model use, image

acquisition was constrained to the part of the breeding season for all species when at

least one adult albatross would be attending the nest, incubation through chick

brooding (Appendix B). An additional nine images from Sand Island were retrieved

to assess seasonal attendance patterns (Appendix C). Two images outside of the incubation season at Torishima and the Senkaku Islands were used to verify that adult albatrosses identified in satellite images were indeed absent when these species are generally not attending the colonies (Appendix C).

Only one WorldView-3 image within the incubation period in 2015 was obtained from the Senkaku Islands (Appendix A). We tasked the WorldView-3 satellite for images of the Senkakus though collaboration with the U.S. Department of the Interior Civil Applications Committee and the Director of U.S. Federal Civilian

Government Agency Programs for DigitalGlobe, November 2016-December 2017.

Separately, we initiated a standing request through Oregon State University for the same area, June 2017-December 2017 and received no images during incubation.

Obtaining images for the Senkaku Islands was compromised because satellites passing this part of the globe were heavily tasked with other, higher priority missions of national and international interests.

Image Processing

Paired panchromatic and 8-band multispectral images included relative radiometric correction, referenced via rigorous projection model to UTM WGS-84 (DigitalGlobe

2014), and as National Imagery Transmission Format (NITF) 2.1 raster files. For counts including multispectral bands, we applied ERDAS Imagine version 16.0

(Hexagon Geospatial 2016) High Pass Filter (HPF) Resolution Merge based on a published review of pan fusion applications to count penguin colonies (Witharana

2016). A single observer performed blind counts on all images using ERDAS

Imagine feature count (see Data analysis). Three replicate counts were conducted on

randomly ordered plots to assess error around original counts.

Data analysis

We developed two, poisson-based generalized linear mixed models using two

imagery datasets (pan-fused, panchromatic) to determine the degree to which image

count, image characteristics, and the physical characteristics of plots describe the

count of adult albatross on the ground. We included an observation-level random

effect term to account for slight over-dispersion in count data (Hilbe and Greene

2007, Zuur et al. 2009, Harrison 2014).

Twelve explanatory variables were explored to assess how habitat and image variables affect the relationship between ground and image-based counts (Table 2,

Appendix D). WorldView-3 satellite is sun-synchronous with pointable camera measured by three angles – in-track, cross-track, off-nadir – that affect the spatial resolution and physical distortion of the imagery (DigitalGlobe 2014). In addition to camera angles, we included sun and satellite position – both elevation (altitude) and azimuth (cardinal direction) – to describe how objects in photos were illuminated differentially or in combination with camera angle to decrease or improve visibility of objects (Crespi and De Vendictis 2009, Poli and Caravaggi 2013). Three variables describing the physical attributes of the targets and surroundings - vegetation percent cover, presence of slope, albatross species - were also included. Finally, elapsed days between satellite and ground count were included to assess whether seasonal timing had an effect.

All continuous variables were z-scaled to improve model convergence (Zuur et al., 2009). Image count data were loge transformed to improve model fit. Strongly

correlated explanatory variables (Spearman rank coefficients >0.7) were excluded from the same model (Akoglu 2018, Appendix E, Appendix F). Models were built using the lme4 package in R 3.5.1 (R Core Team 2018). We assessed model fit and complexity using model weights and AICc. Top models were within two AICc units of the best performing model. All models included image count as an explanatory variable, satellite platform (panchromatic only), and additional variables for which p(>|z|) was less than 0.05. All models were validated using partial residual plots.

Table 2. Summary of data and explanatory variables used to develop the two models. Values are median (range) for elapsed days and pixel resolution, and mean (range) for all other variables.

Panchromatic Pan-Fused Data n images 29 10 n islands 2 2 n plots 12 9 n plot counts 147 42 Explanatory Variables elapsed days 17 (0-3861) 11 (0-3531) pixel resolution (cm) 0.49 (0.34-0.67) 144 (128-152) off-nadir angle (degrees) 23 (2-39) 19 (4-28) in-track angle (degrees) -7 (-27-35) -13 (-25-14) cross-track angle (degrees) 11 (-29-33) 10 (-11-14) sun azimuth (degrees) 163 (152-174) 163 (158-167) sun elevation (degrees) 38 (34-47) 37 (35-38) satellite azimuth (degrees) 154 (102-357) 155 (145-213) satellite elevation (degrees) 64 (47-88) 69 (59-85) vegetation cover (%) 86 (1-99) 95 (1-99) Random Variables Observation level random effect 147 42 1 Counts of a single plot on Sand Island only occurred in 2017

Results

Albatrosses were visually detected in WorldView-2 and Worldview-3 panchromatic images, but only WorldView-3 images were sufficient for pan-fused detection (Figure

4). The pan-fused model includes plots from 10 images, and the panchromatic model was built from 29 images (Appendix A, Table 2), with the panchromatic model applied to a single image of short-tailed albatross visible on the Senkaku Islands in

2015 (Appendix A).

(a) (b)

Figure 4. a) WorldView-2 panchromatic image for Tsubamezaki West plot, 18 December 2012 b) WorldView-3 panchromatic image for Tsubamezaki West plot, 18 December 2016 c) Same WorldView-2 image in 1a, pan-fused d) Same WorldView-3 in 1b, pan-fused. Select adult short-tailed albatross are indicated with yellow arrows on all plots.

Pan-fused pixel-based counts

The top-performing pan-fused model included sun elevation and vegetation cover

(ln(pan fused image count) β = 0.71±0.07, p<0.001, sun elevation β = 0.23±0.06,

p<0.001, vegetation cover β = 0.21±0.07, p<0.001; Table 3; Table 4; Figure 5). Nine

additional covariates explored did not improve model performance (Table 3). The

observation-level random effect accounts for extra-Poission model variance of 0.11

(standard deviation 0.34). Compared to the panchromatic model, the effect size of

image count within the pan-fused model is weaker (ln(Pan Fused Image Count) β =

0.71±0.07, p<0.001; ln(Pan Image Count) β = 0.83±0.04 p<0.001; Table 4). For a plot at the mean count, 107 albatrosses, at mean vegetation cover, and mean sun elevation, a mean of 155 (95% CI 82-293) birds are counted on the ground. Pan-fused counts underestimate ground count by 31%. The plots with the highest discrepancies between ground and satellite counts include two plots in the interior of Sand Island with a high percent vegetation cover and where the density of adult nesting birds was highest, 0.3-0.6 birds/m2.

We conducted two exploratory analyses to test whether there was a significant

advantage in using pan-fused counts to describe ground count and whether

randomized replicate counts were significantly different. These analyses revealed a)

the pan-fused method term was not significant in the combined pan-fused- panchromatic model (β = 0.23±0.13 p=.07) and b) models using a median count

based on three random replicates were not significantly different (ΔAICc 0.57,

ln(Single Image Count) β = 0.83±0.04 p<0.001; ln(Median Image Count) β =

0.70±0.07 p<0.001). Intra-observer variation was small and non-significant so we proceeded to stop replicate counts, abandoned additional pan-fused counts and continued adding data (counts, species, plots) to the panchromatic model.

Table 3. Poisson Generalized Linear Mixed Model model selection with pan-fused images to predict ground count based on AICc and statistical weight. ΔAICc is the difference between the specified and top model.

Model ΔAICc Weight ln(Image Count) + Sun Elevation + Vegetation Cover 0.00 0.68 ln(Image Count) + Sun Elevation + Vegetation Cover + Off-Nadir Angle 2.33 0.21 ln(Image Count) + Sun Elevation 4.41 0.07 ln(Image Count) + Sun Azimuth + Off-Nadir Angle + Vegetation Cover 7.69 0.01 ln(Image Count) + Sun Azimuth + Vegetation Cover 8.03 0.01 ln(Image Count) + Sun Azimuth 9.98 0 ln(Image Count) + Vegetation Cover 13.30 0 ln(Image Count) + Off-Nadir Angle 17.43 0 ln(Image Count) + Satellite Elevation 17.48 0 ln(Image Count) + Elapsed Days 17.87 0 ln(Image Count) + Spatial Resolution 17.98 0 ln(Image Count) + Cross-Track Angle 18.41 0 ln(Image Count) + Slope 20.28 0 ln(Image Count) 21.76 0 ln(Image Count) + Satellite Azimuth 21.76 0 ln(Image Count) + In-Track Angle 24.20 0

Table 4. Predictor estimates from the best performing Poisson mixed effects model for pan-fused images with ground count as the predictor variable.

Standard Confidence Estimate Error Interval p-value Intercept 1.74531 0.31013 (1.4, 2.1) <0.001 ln(Image Count) 0.70639 0.06963 (0.64, 0.78) <0.001 Sun Elevation 0.23219 0.06137 (0.17, 0.29) <0.001 Vegetation Cover 0.20808 0.0732 (0.13, 0.29) <0.001

Figure 5. Pan-fused model. Poisson-based generalized linear mixed effects model predictions (black line) with standard error (gray lines).

Panchromatic pixel-based counts

Image count, species, vegetation cover and platform were all significant predictors of ground count in the panchromatic model (Table 5, Table 6). Nine additional covariates explored did not improve model performance (Table 5). The observation- level random effect accounts for extra-Poisson model variance of 0.18 (standard deviation 0.43). For a plot at the mean count, 142 albatrosses, at mean vegetation cover, a mean of 158 adult albatross are counted on the ground, dependent on species and satellite platform (Table 7). Panchromatic image counts on the WorldView-3 platform have a margin of error -40% to +25%, depending on species (Table 7).

Black-footed and Laysan albatrosses were not reliably detected on WorldView-2

imagery. For the same plot, imaged one day apart in January 2017, 32 Laysan

albatrosses are counted on the WorldView-2 image, 230 are counted on the

WorldView-3 image. Our model similarly suggests if counts had proceeded, they

would be significant underestimates of ground count (Figure 6). On the WorldView-3

platform, at mean image count for each species, counts of black-footed and Laysan

albatrosses were underestimated (-12% and -40%, respectively) while counts of short- tailed albatrosses were overestimated (+25%) (Figure 6, Figure 7a-c). At mean image count, short-tailed albatross adult counts were consistently lower via the WorldView-

2 platform (-39%) (Figure 6, Figure 7d; Table 7). WorldView-2 provides a minimum estimate of adult attendance and WorldView-3 provides a better estimate of adult attendance, which is still an underestimate of total attendance (adult and non-adult plumaged birds, Figure 1c-1e).

Outside of incubation-constrained images for the panchromatic model, adult albatross are notably absent from images on Sand Island during the non-breeding season in summer 2017 (Figure 8, Appendix C). Seasonal variability in attendance is evident on Sand Island with counts sharply decreasing after January and remaining low through September, then increasing again by December (Figure 9). While the attendance trend is similar among plots, there is variation within plot and species

(Figure 9).

Table 5. Poisson Generalized Linear Mixed Model model selection with panchromatic images to predict ground count based on AICc and statistical weight. ΔAICc is the difference between the specified and top models.

Model ΔAICc Weight ln(Image Count) + Platform + Species + Vegetation Cover 0.00 0.92 ln(Image Count) + Platform + Species 4.95 0.08 ln(Image Count) + Platform + Vegetation Cover 13.79 0 ln(Image Count) + Platform + Sun Elevation 21.96 0 ln(Image Count) + Platform 25.89 0 ln(Image Count) + Platform + Slope 26.55 0 ln(Image Count) + Platform + Satellite Azimuth 26.92 0 ln(Image Count) + Platform + Sun Azimuth 27.46 0 ln(Image Count) + Platform + Cross-track Angle 27.81 0 ln(Image Count) + Platform + Elapsed Days 27.94 0 ln(Image Count) + Platform + In-track Angle 28.03 0

Table 6. Predictor estimates from the best performing Poisson mixed effects model for panchromatic images with ground count as the predictor variable.

Standard Confidence Estimate Error Interval p-value Intercept 1.42392 0.24305 (1.2, 1.7) <.001 ln(Image Count) 0.82989 0.03479 (0.8, 0.9) <.001 Species - Laysan 0.20342 0.14091 (0.06, 0.3) 0.1 Species – short-tailed -0.41162 0.18229 (-0.6, -0.2) <.05 Vegetation Cover 0.12365 0.04619 (.08, .2) <.01 Platform - WV3 -0.42573 0.15180 (-0.6, -0.3) <.01

Table 7. Panchromatic model estimates at mean vegetation cover by platform and species.

Species Count Platform Estimate Confidence Interval Black-foot NA WorldView-2 NA NA Black-foot 181 WorldView-3 203 (114-361) Laysan NA WorldView-2 NA NA Laysan 160 WorldView-3 224 (110-458) Short-tailed 56 WorldView-2 78 (44-137) Short-tailed 170 WorldView-3 128 (60-272)

Figure 6. Panchromatic model. Poisson-based generalized linear mixed effects model predictions, combined species and platforms. Top to bottom: Laysan albatross, WorldView-2 (blue); black-footed albatross, WorldView-2 (purple); Laysan albatross, WorldView-3 (green), black-footed albatross, WorldView-3 (red); short-tailed albatross WorldView-2 (yellow); short-tailed albatross WorldView-3 (gray).

(a) (b)

(c) (d) Figure 7. Panchromatic model. Poisson-based generalized linear mixed effects model predictions with standard errors for a) black- footed albatross, WorldView-3; b) Laysan albatross, WorldView-3; c) short-tailed albatross, WorldView-3; d) short-tailed albatross, WorldView-2.

Figure 8. Seasonal attendance images from Sand Island a) black-footed albatross, December 2016 b) same location, June 2017.

Figure 9. Annual variation in attendance of Laysan and black-footed albatross for the 2016-2017 breeding season on Sand Island via satellite counts (teal) compared to single annual ground census (red).

Application to plots of unknown size

Short-tailed albatross were identified on both Minami-kojima and Kita-Kojima using a single image from 23 November 2015. All of Kita-Kojima was visible in 2015, with

albatross visible at one site identified in 2002, and new locations, spread across the

north, eastern and southern edges on the main clifftop (Figure 10). One location

appears to be albatross co-nesting within and adjacent to a guano-stained area

occupied by a smaller species. Only the western tip of Minami-kojima is visible in

2015, but albatross are visible on this section, adjacent to the birds identified on the

top slope in 2002. On Kita-kojima we counted a mean of 45 adult-plumaged birds

from the single image in 2015. Image resolution for the 2015 Senkaku Islands

population was not sufficient to conduct pan-fused counts, so we used coefficients of

the panchromatic model (Table 6) to determine that the estimated number of adult-

plumaged albatross on Kita-kojima is 42, (95% CI 21-87). The estimated number of

adult-plumaged breeding albatross visible on the small section of Minami-kojima

(approximately 3% of available nesting habitat) is 9 birds, and the model predicts 11

(95% CI 6-21). Combined, our models predict a mean of 51 adult-plumaged short-

tailed albatross across Kita-Kojima and Minami-Kojima, with only a very partial

view of Minami-Kojima. If the obscured portion of Minami-Kojima is similarly

attended, this would result in a minimum of 200 adult-plumaged albatrosses on

Minami-Kojima and 21 on Kita-Kojima (total of 221). For a gross estimate of breeding pairs (i.e., adult-plumaged albatrosses attending a nest) we adjusted the number down by 25% (Poncet et al. 2006, Fisher and Fisher 1969), to a minimum of

166 breeding pairs, a result that supports the current population projection of 145 breeding pairs (Sievert and Hasegawa unpubl. data).

Figure 10. Senaku Islands image including original location of one chick and 4 immature birds on Kita-kojima in 2002 (orange circle), current range of detections for Kita-kojima and Minami-kojima in 2015 (light orange), and a guano-stained nesting area of a non-albatross species not present in August 2018 image (blue)

Discussion

We demonstrate that the potential for satellite imagery-based counts to estimate abundance of individual albatrosses on nesting colonies for three species, after accounting for important landscape and image acquisition variables. With

WorldView-3 imagery, we were able to enumerate all three bird species against a

variety of background substrates and vegetation types from 617 km above the earth’s

surface. We were successful with WorldView-2 imagery for the larger species, short-

tailed albatross, against a strongly contrasting background. Use of either satellite

platform, however, relies on acquisition of images with low cloud cover and at

satellite camera angles that result in sufficient resolution, especially for the two

smaller Phoebastria species (Table 1, Table 7). For instance, none of the images

received from the Kaena Point colony on Oahu, Hawaii, within the study timeframe,

had a panchromatic resolution of less than 4m, so these images could not be used in

this study.

Fretwell et al. 2017 found that imagery counts tend to overestimate nests, due

to the presence of non-breeding birds and errors of commission (e.g., pale rocks

counted as birds) by about 20.1%. For our study area, WorldView-2 imagery tended

to underestimate ground counts, whereas WorldView-3 imagery tended to

underestimate ground count for smaller species and overestimate ground count for

large species. This overestimation for large species is about 32% (Table 7). These

findings are important and reflect two major advances in our modeling approach,

which tested and accounted for vegetation cover and species.

Discrepancies in satellite image and ground counts in our study also could have occurred because of non-incubating albatrosses (“walkers”) within a plot that were not included in ground counts of incubating birds. It is possible that for large albatrosses, more walkers can be identified and counted in the higher resolution

WorldView-3 images, and indeed can include up to 36% for Laysan albatross

(unpubl. data), the high estimate of overestimation for WorldView-3 image counts.

The abundance of walkers in a plot changes frequently and it would only be possible to include them in the calibration counts if image acquisition was closely timed with the ground counts, which is not feasible. Furthermore, it would be difficult to quantitatively adjust for walkers because their abundance is based on factors difficult to quantify (e.g., attractiveness of nesting habitat) and known factors (e.g., time of day, hourly wind speed) that we could not examine given satellite orbit constraints.

Calibration of any remote-sensing method (aerial, drone, or satellite-based) that relies on human detection is essential to provide timely and accurate abundance data with the technology and resources available. The acceptable combined commission and omission error range for remote sensing platforms range 5-33%- plane-based (Johnson and Krohn 2001), 6-12%-drone-based (Lyons et al. 2019). We explicitly test these indices against a known population of breeding birds on colony and found combined rates are -40 to 25%, but error is highly dependent on image processing method used, and significant variables in our models. Still, this error falls within the range of some aerial surveys which census much larger wildlife from much smaller altitudes and is expectedly, higher on average, than drone-based surveys.

Furthermore, for purposes of tracking population change over seasons or years, the overall population trend is of primary concern and satellite counts allow us to detect these trends (Figure 9).

Implementation

We demonstrate the use of a multiple-site and multiple-species model for estimating the colony size of a single species on an inaccessible island. Model-derived estimates from satellite imagery of the Senkaku sub-population provided evidence that the breeding population is increasing as expected under logistic growth scenarios within the Recovery Plan (USFWS 2014) and expanding beyond aggregations last identified in 2002 (USFWS 2008). While monitoring this currently inaccessible colony via satellite image counts appears to be feasible, it is unlikely that management actions would be taken without ground-based confirmation. We caution against developing management actions based on models applied to unvalidated counts, however, as long as validation occurs, even infrequently, this method provides managers and conservationists with a tool to monitor population trends of remote species where ground-based counts are infrequent due to logistical constraints, safety concerns, or disturbance to sympatrically breeding species.

This new tool is especially useful for albatross in particular, given their dispersed nesting locations, susceptibility to climate-induced habitat loss, and fidelity to natal colonies. For many seabird colonies, attendance counts are performed once annually due to logistical and budgetary restraints of accessing disparate colonies more frequently (Hatch et al. 1993, Mallory et al. 2009). With satellite imagery, measures of attendance are available throughout the year (Figure 9) and for previous years via archives (Appendix A). As such other images and counts provide a robust baseline of adult attendance and habitat changes that may affect attendance such as storms (Arata et al. 2009), rising sea level (Baker et al. 2006, Reynolds et al. 2015) or

the benefit of restoration efforts aimed at increasing nesting habitat (Young et al.

2009, Duffy 2010).

On a smaller scale, satellite imagery detects larger albatross species over

smaller species (Table 6), but does not detect individuals of smaller, non-albatross

species in multi-species colonies (Figure 10). The spatial resolution of the

WorldView-3 imagery is smaller than the body length of these species, however we

can confirm their presence via guano stains that appear only during the breeding

season (Figure 10). Depending on the extent of guano in the area, counting albatross

within these areas should be avoided or conducted carefully because guano

effectively decreases vegetation cover to 0%. We did not test species detection in

areas where albatross species co-nest in large numbers (e.g., on Sand Island, plots

with 22% black-footed albatross, 88% Laysan albatross, E. Flint unpubl. data)

because in general, albatrosses nest in single-specific aggregations (median, Sand

Island, 0.6% black-footed albatross, 95% Laysan albatross, E. Flint unpubl. data). For single species aggregations, our models suggest detection differences between the two smaller albatross species (black-footed, Laysan) is similar (Table 6).

One set of images from the Oahu colony were not of sufficient resolution to perform a manual count on either the panchromatic or pan-fused images. We successfully orthorectified and georectified these images to a resolution above the underlying coarse digital elevation map to overlay field-collected albatross locations.

After several attempts to convert top-of-atmosphere radiance to reflectance values using ENVI’s atmospheric correction module, FLAASH (Excelis Visual Information

Solutions), we proceeded with radiance values. We explored several methods to

distinguish albatross pixels from surrounding colony pixels including multi-spectral

band thresholding, supervised learning via the random forest algorithm, and linear

spectral unmixing with radiance values. None of these methods yielded convincing

results for albatross detection but were nominally effective at identifying preferred

nesting habitat. We suspect our small training dataset (79 pixels) simply contained

too much variability to render results from the Oahu colony images. Our other two

calibrations sites (Sand Island, Torishima) may have yielded better results with

reflectance values, or with digital number (Fretwell et al. 2014) or radiance values,

and highly accurate field-collected nest locations (obtained for Oahu only).

The ability to automatically detect albatross from images, whether visible to

eye or not, is an obvious extension of this work and deserves attention. Even when

albatross are visible on images their multispectral and panchromatic pixel values are

not significantly different from the surrounding pixels. Any potential detection

algorithms must integrate across a larger pixel neighborhood. For automatic or

machine-learning detection applications, pan-fused images or false color composites

may be more preferred, even though they did not improve manual detection in this

study.

Conclusion

We confirm there is a positive, linear relationship between satellite counts and ground

counts for two disparate islands, multiple breeding seasons, and three species of

albatross. One previous study documents the use of WorldView-3 imagery to count

albatross (Fretwell et al. 2017), but does so with a species that is 52-67% larger than the species in our study (Table 1) and assessed accuracy with 1-2 colony images

during the breeding season. Across both models, which span multiple platforms and multiple images per colony within the breeding season, we could accurately estimate ground count when accounting for image count and vegetation cover (Table 3, Table

5). With fewer days and species sampled, including sun elevation angle is justified

(Table 2, Table 3). With the inclusion of more sampling days, islands, species and platforms, building platform and species into the model is justified, and sun elevation angle is not included in the top model (Table 2, Table 5).

This proof-of-concept study provides strong justification for expanding our dataset to additional locations in the Pacific. Locations with at least annual counts

(e.g., Laysan, French Frigate Shoals) could contribute to the development of a more broadly validated and transferrable model. Given global concern for albatross populations worldwide (Croxall 2012), our study responds to the need for more frequent assessment of trends, with a cost-effective method for monitoring breeding populations, given adequate calibration.

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APPENDICES

Appendix A. All images tasked and received through USGS EarthExplorer archives and paired with ground counts.

Pan Pan-fused Ground count date(s) Island/platform Date Image ID model model Sand Island 12/12/2016 WV320161212224853P00 U U 11-31 Dec 2016; 2 Jan 2017 WorldView-3 12/12/2016 WV320161212224853P01 U U 11-31 Dec 2016; 2 Jan 2017 12/31/2016 WV320161231225507P00 U U 30-31 Dec 2016; 2 Jan 2017, 23 Dec 2017 12/31/2016 WV320161231225520P00 U U 30-31 Dec 2016; 2 Jan 2017, 23 Dec 2017 1/12/2017 WV320170112224922P00 U U 30-31 Dec 2016; 2 Jan 2017, 23 Dec 2017 1/12/2017 WV320170112224935P00 U U 30-31 Dec 2016; 2 Jan 2017, 23 Dec 2017 12/28/2017 WV320171228231256P00 U NU 30-31 Dec 2016; 2 Jan 2017, 30-31 Dec 2017 12/28/2017 WV320171228231256P01 U NU 30-31 Dec 2016; 2 Jan 2017, 30-31 Dec 2017 1/9/2018 WV320180109230524P00 U NU 30-31 Dec 2016; 23 Dec 2017; 9 Jan 2018 1/9/2018 WV320180109230524P01 U NU 30-31 Dec 2016; 23 Dec 2017; 9 Jan 2018 1/20/2018 WV320180120224120P00 U NU 23-30 Dec 2017; 14 Jan 2018 1/20/2018 WV320180120224120P01 U NU 23-30 Dec 2017; 14 Jan 2018 Torishima 11/13/2011 WV220111113013100P00 U NSR1 28 Nov 2011 WorldView-2 11/23/2011 WV220111123020351P00 U NSR1 28 Nov 2011 1 12/10/2011 WV220111210013842P00 U NSR 28 Nov 2011 1 12/10/2011 WV220111221013416P00 U NSR 28 Nov 2011 1 2/24/2012 WV220120224014243P00 U NSR 6 Dec 2011 1 12/5/2012 WV220121205014014P00 U NSR 5 Dec 2012 1 12/18/2012 WV220121218020100P00 U NSR 7-13 Dec 2012 1 1/12/2013 WV220130112013935P00 U NSR 7-13 Dec 2012 1 1/10/2016 WV220160110013930P00 U NSR 6 Dec 2015

Pan Pan-fused Ground count date(s) Island/platform Date Image ID model model Torishima 11/17/2016 WV220161117012844P01 U NSR1 19 Nov 2016 WorldView-2 11/25/2016 WV220161125013242P00 U NSR1 25 Noc 2016 1 2/9/2017 WV220170209012742P00 U NSR 1 Dec 2016 2/22/2017 WV220170222014746P00 U NSR1 1 Dec 2016 Torishima 1/15/2016 WV320160115013243P00 U U 6 Dec 2015 WorldView-3 12/12/2016 WV320161212014615P00 U U 1 Dec 2016 12/18/2016 WV320161218014324P00 U U 1-2 Dec 2016 1/21/2018 WV320180121015504P00 U U 7 Dec 2017 Senkakus 11/23/2015 WV320151123023500P00 A A None WorldView-3

U=Used, NU=Not used A=Applied 1 Not sufficient resolution for identifying albatross from fused images, from this platform.

Appendix B. Incubation timing for black-footed (Phoebastria nigripes), Laysan albatross (Phoebastria immutabilis), and short-tailed albatross (Phoebastria albatrus).

Sand Island Sand Island Torishima Senkakus black-footed Laysan short-tailed short-tailed albatross albatross albatross albatross 1-15 Oct P P/I 15-31 Oct P P/I P/I 1-15 Nov P/I P P/I I 15-30 Nov P/I P/I I I 1-15 Dec I P/I I I/C 15-31 Dec I I I/C I/C 1-15 Jan I I I/C C 15-31 Jan I/C I/C C 1-15 Feb I/C I/C 15-28 Feb C C

A = adult (1-2/nest), I = incubation (adult + egg), C= chick (alone, not attended by adult) ______

Appendix C. Images retrieved from the USGS EarthExplorer archives outside the 2016-2017 incubation period season.

Island/platform Date Image ID Sand Island 4/22/2017 WV320170422230121P00 WorldView-3 4/22/2017 WV320170422230123P00 6/4/2017 WV320171228231256P00 6/4/2017 WV320171228231256P01 6/10/2017 WV320180109230524P00 6/10/2017 WV320180109230524P01 7/30/2017 WV320170730225310P00 9/24/2017 WV320180120224120P00 9/24/2017 WV320180120224120P01 Torishima 3/15/2017 WV320170315014704P00 WorldView-3 Senkaku Islands 8/15/2017 WV320170815024713P00 WorldView-3

Appendix D. Descriptions of variables used in model development.

Source Explored Description Source unit for Effect type Factor? Image spatial resolution in Spatial Resolution degrees Image Metadata image Pan/PanF Fixed N Lens angle parallel to direction Mean In-Track Angle of motion Image Metadata image Pan/PanF Fixed N Lens angle perpendicular to Mean Cross-Track Angle direction of motion Image Metadata image Pan/PanF Fixed N Lens angle relative to directly Mean Off-Nadir Angle below the lens (0° = nadir) Image Metadata image Pan/PanF Fixed N Mean Sun Azimuth Sun cardinal angle (0° = north) Image Metadata image Pan/PanF Fixed N Mean Sun Elevation Sun altitude (0° = horizon) Image Metadata image Pan/PanF Fixed N Satellite cardinal angle (0° = Mean Satellite Azimuth north) Image Metadata image Pan/PanF Fixed N Mean Satellite Elevation Satellite altitude (0° = horizon) Image Metadata image Pan/PanF Fixed N Percent of vegetation within Vegetation Cover plot Image Calculation image*plot Pan/PanF Fixed N Adult, breeding birds manually Visual Ln(Image Count) counted from imagery Determination image*plot Pan/PanF Fixed N Absolute difference between ground count and satellite Elapsed Days overpass date Data Calculation plot Pan/PanF Fixed N Visual Slope Grade of terrain Determination plot Pan/PanF Fixed Y Target species (black-footed, Visual Species Laysan short-tailed albatross) determination image Pan Fixed Y Satellite platform (WorldView- Platform 2, WorldView-3) Image Metadata image Pan Fixed Y Unique identifier for Observation count*plot*date NA NA Pan Random Y

Appendix E. Correlation table for Pan-fused model.

In- Cross- Off- Image Elapsed Spatial Track Track Nadir Sun Satellite Satellite Vegetation Count Days Resolution Angle Angle Angle Azimuth Azimuth Elevation Cover Image Count 1 Elapsed Days -0.43 1 Spatial Resolution 0.33 -0.29 1 In-Track Angle -0.10 0.09 -0.71 1 Cross-Track Angle -0.14 -0.14 0.16 -0.09 1 Off-Nadir Angle 0.07 -0.08 0.82 -0.83 0.31 1 Sun Azimuth 0.61 -0.43 0.37 -0.22 -0.363 0.13 1 Sun Elevation 0.65 0.48 -0.37 0.19 0.316 -0.05 -0.92 Satellite Azimuth 0.35 -0.09 0.30 -0.31 -0.78 0.04 0.54 1 Satellite Elevation -0.107 0.08 -0.82 0.83 -0.31 -1 -0.13 -0.04 1 Vegetation Cover 0.2 -0.58 0.16 -0.04 0.50 -0.19 -0.07 -0.30 -0.19 1

Appendix F. Correlation table for Panchromatic model.

In- Cross- Off- Image Elapsed Spatial Track Track Nadir Sun Sun Satellite Satellite Vegetation Count Days Resolution Angle Angle Angle Azimuth Elevation Azimuth Elevation Cover Image Count 1 Elapsed Days 0.15 1 Spatial Resolution -0.39 0.22 1 In-Track Angle -0.07 -0.17 0.11 1 Cross-Track Angle 0.04 0.15 0.11 0.45 1 Off-Nadir Angle -0.04 0.30 0.35 -0.29 0.05 1 Sun Azimuth -0.07 -0.06 -0.07 -0.15 -0.40 0.24 1 Sun Elevation -0.08 0.03 0.34 0.15 0.04 0.38 -0.21 1 Satellite Azimuth 0.00 -0.06 -0.07 -0.29 -0.75 0.35 0.57 0.05 1 Satellite Elevation 0.04 -0.3 -0.36 0.28 -0.05 -1 -0.24 -0.39 -0.34 1 Vegetation Cover 0.10 -0.52 -0.58 -0.15 0.07 -0.11 -0.06 -0.20 -0.04 0.12 1