1932

Abstract

Advancements in space-based ocean observation and computational data processing techniques have demonstrated transformative value for managing living resources, biodiversity, and ecosystems of the ocean. We synthesize advancements in leveraging satellite-derived insights to better understand and manage fishing, an emerging revolution of marine industrialization, ocean hazards, sea surface dynamics, benthic ecosystems, wildlife via electronic tracking, and direct observations of ocean megafauna. We consider how diverse space-based data sources can be better coupled to modernize and improve ocean management. We also highlight examples of how data from space can be developed into tools that can aid marine decision-makers managing subjects from whales to algae. Thoughtful and prospective engagement with such technologies from those inside and outside the marine remote sensing community is, however, essential to ensure that these tools meet their full potential to strengthen the effectiveness of ocean management.

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2025-01-16
2025-04-19
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Literature Cited

  1. Abrahms B, Welch H, Brodie S, Jacox MG, Becker EA, et al. 2019.. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. . Divers. Distrib. 25:(8):118293
    [Crossref] [Google Scholar]
  2. Adilov N, Alexander P, Cunningham B, Albertson N. 2022.. An analysis of launch cost reductions for low Earth orbit satellites. . Econ. Bull. 42:(3):156174
    [Google Scholar]
  3. Albright A, Glennie C. 2021.. Nearshore bathymetry from fusion of Sentinel-2 and ICESat-2 observations. . IEEE Geosci. Remote Sens. Lett. 18:(5):9004
    [Crossref] [Google Scholar]
  4. Anderson CR, Kudela RM, Kahru M, Chao Y, Rosenfeld LK, et al. 2016.. Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system. . Harmful Algae 59::43118
    [Crossref] [Google Scholar]
  5. Anderson CR, Moore SK, Tomlinson MC, Silke J, Cusack CK. 2014.. Living with harmful algal blooms in a changing world: strategies for modeling and mitigating their effects in coastal marine ecosystems. . In Coastal and Marine Hazards, Risks, and Disasters, ed. JF Shroder, JT Ellis, DJ Sherman , pp. 495561. Amsterdam:: Elsevier
    [Google Scholar]
  6. Anderson DM, Fensin E, Gobler CJ, Hoeglund AE, Hubbard KA, et al. 2021.. Marine harmful algal blooms (HABs) in the United States: history, current status and future trends. . Harmful Algae 102::101975
    [Crossref] [Google Scholar]
  7. Andréfouët S, Muller-Karger F, Robinson J, Kranenburg C, Torres-Pulliza D, et al. 2006.. Global assessment of modern coral reef extent and diversity for regional science and management applications: a view from space. . In Proceedings of the 10th International Coral Reef Symposium, pp. 173245. Kochi, Jpn:.: Jpn. Coral Reef Soc.
    [Google Scholar]
  8. Andrzejaczek S, Mikles CS, Dale JJ, Castleton M, Block BA. 2023.. Seasonal and diel habitat use of blue marlin Makaira nigricans in the North Atlantic Ocean. . ICES J. Mar. Sci. 80:(4):100215
    [Crossref] [Google Scholar]
  9. Ashphaq M, Srivastava PK, Mitra D. 2021.. Review of near-shore satellite derived bathymetry: classification and account of five decades of coastal bathymetry research. . J. Ocean Eng. Sci. 6:(4):34059
    [Crossref] [Google Scholar]
  10. Asner GP, Vaughn NR, Balzotti C, Brodrick PG, Heckler J. 2020.. High-resolution reef bathymetry and coral habitat complexity from airborne imaging spectroscopy. . Remote Sens. 12:(2):310
    [Crossref] [Google Scholar]
  11. Asner GP, Vaughn NR, Martin RE, Foo SA, Heckler J, et al. 2022.. Mapped coral mortality and refugia in an archipelago-scale marine heat wave. . PNAS 119:(19):e2123331119
    [Crossref] [Google Scholar]
  12. Atwood TB, Romanou A, DeVries T, Lerner PE, Mayorga JS, et al. 2024.. Atmospheric CO2 emissions and ocean acidification from bottom-trawling. . Front. Mar. Sci. 10::1125137
    [Crossref] [Google Scholar]
  13. Bannari A, Ali TS, Abahussain A. 2022.. The capabilities of Sentinel-MSI (2A/2B) and Landsat-OLI (8/9) in seagrass and algae species differentiation using spectral reflectance. . Ocean Sci. 18:(2):36188
    [Crossref] [Google Scholar]
  14. Bar-On YM, Phillips R, Milo R. 2018.. The biomass distribution on Earth. . PNAS 115:(25):650611
    [Crossref] [Google Scholar]
  15. Bedriñana-Romano L, Hucke-Gaete R, Viddi FA, Johnson D, Zerbini AN, et al. 2021.. Defining priority areas for blue whale conservation and investigating overlap with vessel traffic in Chilean Patagonia, using a fast-fitting movement model. . Sci. Rep. 11:(1):2709
    [Crossref] [Google Scholar]
  16. Behrenfeld MJ, Hu Y, Hostetler CA, Dall'Olmo G, Rodier SD, et al. 2013.. Space-based lidar measurements of global ocean carbon stocks. . Geophys. Res. Lett. 40:(16):435560
    [Crossref] [Google Scholar]
  17. Behrenfeld MJ, Hu Y, O'Malley RT, Boss ES, Hostetler CA, et al. 2017.. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. . Nat. Geosci. 10:(2):11822
    [Crossref] [Google Scholar]
  18. Belanger AM, Sherbo BA, Roth JD, Watt CA. 2024.. Use of satellite imagery to estimate distribution and abundance of Cumberland Sound beluga whales reveals frequent use of a glacial river estuary. . Front. Mar. Sci. 10::1305536
    [Crossref] [Google Scholar]
  19. Bell TW, Allen JG, Cavanaugh KC, Siegel DA. 2020.. Three decades of variability in California's giant kelp forests from the Landsat satellites. . Remote Sens. Environ. 238::110811
    [Crossref] [Google Scholar]
  20. Bell TW, Cavanaugh KC, Saccomanno VR, Cavanaugh KC, Houskeeper HF, et al. 2023.. Kelpwatch: a new visualization and analysis tool to explore kelp canopy dynamics reveals variable response to and recovery from marine heatwaves. . PLOS ONE 18:(3):e0271477
    [Crossref] [Google Scholar]
  21. Beukema P, Bastani F, Wolters P, Herzog H, Ferdinando J. 2023.. Satellite imagery and AI: a new era in ocean conservation, from research to deployment and impact. . arXiv:2312.03207 [cs.CV]
  22. Block BA, Dewar H, Farwell C, Prince ED. 1998.. A new satellite technology for tracking the movements of Atlantic bluefin tuna. . PNAS 95:(16):938489
    [Crossref] [Google Scholar]
  23. Block BA, Jonsen ID, Jorgensen SJ, Winship AJ, Shaffer SA, et al. 2011.. Tracking apex marine predator movements in a dynamic ocean. . Nature 475:(7354):8690
    [Crossref] [Google Scholar]
  24. Block BA, Teo SLH, Walli A, Boustany A, Stokesbury MJW, et al. 2005.. Electronic tagging and population structure of Atlantic bluefin tuna. . Nature 434:(7037):112127
    [Crossref] [Google Scholar]
  25. Bowler E, Fretwell PT, French G, Mackiewicz M. 2020.. Using deep learning to count albatrosses from space: assessing results in light of ground truth uncertainty. . Remote Sens. 12:(12):2026
    [Crossref] [Google Scholar]
  26. Braun CD, Arostegui MC, Farchadi N, Alexander M, Afonso P, et al. 2023.. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. . Ecol. Appl. 33::e2893
    [Crossref] [Google Scholar]
  27. Breece MW, Oliver MJ, Fox DA, Hale EA, Haulsee DE, et al. 2021.. A satellite-based mobile warning system to reduce interactions with an endangered species. . Ecol. Appl. 31:(6):e02358
    [Crossref] [Google Scholar]
  28. Brodie S, Pozo Buil M, Welch H, Bograd SJ, Hazen EL, et al. 2023.. Ecological forecasts for marine resource management during climate extremes. . Nat. Commun. 14:(1):7701
    [Crossref] [Google Scholar]
  29. Brodie S, Smith JA, Muhling BA, Barnett LAK, Carroll G, et al. 2022.. Recommendations for quantifying and reducing uncertainty in climate projections of species distributions. . Glob. Change Biol. 28:(22):6586601
    [Crossref] [Google Scholar]
  30. Bunting P, Rosenqvist A, Hilarides L, Lucas RM, Thomas N, et al. 2022.. Global mangrove extent change 1996–2020: Global Mangrove Watch version 3.0. . Remote Sens. 14:(15):3657
    [Crossref] [Google Scholar]
  31. Butler C, Lucieer V, Wotherspoon S, Johnson C. 2020.. Multi-decadal decline in cover of giant kelp Macrocystis pyrifera at the southern limit of its Australian range. . Mar. Ecol. Prog. Ser. 653::118
    [Crossref] [Google Scholar]
  32. Carlson RR, Evans LJ, Foo SA, Grady BW, Li J, et al. 2021.. Synergistic benefits of conserving land-sea ecosystems. . Glob. Ecol. Conserv. 28::e01684
    [Google Scholar]
  33. Castagna A, Dierssen HM, Devriese LI, Everaert G, Knaeps E, Sterckx S. 2023.. Evaluation of historic and new detection algorithms for different types of plastics over land and water from hyperspectral data and imagery. . Remote Sens. Environ. 298::113834
    [Crossref] [Google Scholar]
  34. Cavanaugh KC, Bell T, Costa M, Eddy NE, Gendall L, et al. 2021.. A review of the opportunities and challenges for using remote sensing for management of surface-canopy forming kelps. . Front. Mar. Sci. 8::753531
    [Crossref] [Google Scholar]
  35. Cavanaugh KC, Cavanaugh KC, Pawlak CC, Bell TW, Saccomanno VR. 2023.. CubeSats show persistence of bull kelp refugia amidst a regional collapse in California. . Remote Sens. Environ. 290::113521
    [Crossref] [Google Scholar]
  36. Cawse-Nicholson K, Townsend PA, Schimel D, Assiri AM, Blake PL, et al. 2021.. NASA's surface biology and geology designated observable: a perspective on surface imaging algorithms. . Remote Sens. Environ. 257::112349
    [Crossref] [Google Scholar]
  37. Cetinić I, Rousseaux CS, Carroll IT, Chase AP, Kramer SJ, et al. 2024.. Phytoplankton composition from sPACE: requirements, opportunities, and challenges. . Remote Sens. Environ. 302::113964
    [Crossref] [Google Scholar]
  38. Chan Y-C, Brugge M, Tibbitts TL, Dekinga A, Porter R, et al. 2016.. Testing an attachment method for solar-powered tracking devices on a long-distance migrating shorebird. . J. Ornithol. 157:(1):27787
    [Crossref] [Google Scholar]
  39. Charry B, Tissier E, Iacozza J, Marcoux M, Watt CA. 2021.. Mapping Arctic cetaceans from space: a case study for beluga and narwhal. . PLOS ONE 16:(8):e0254380
    [Crossref] [Google Scholar]
  40. Claisse JT, Pondella DJ, Love M, Zahn LA, Williams CM, et al. 2014.. Oil platforms off California are among the most productive marine fish habitats globally. . PNAS 111:(43):1546267
    [Crossref] [Google Scholar]
  41. Clarke PJ, Cubaynes HC, Stockin KA, Olavarría C, de Vos A, et al. 2021.. Cetacean strandings from space: challenges and opportunities of very high resolution satellites for the remote monitoring of cetacean mass strandings. . Front. Mar. Sci. 8::650735
    [Crossref] [Google Scholar]
  42. Cubaynes HC. 2020.. Whales from space: assessing the feasibility of using satellite imagery to monitor whales. PhD Thesis , Univ. Cambridge, Cambridge, UK:
    [Google Scholar]
  43. Cubaynes HC, Fretwell PT, Bamford C, Gerrish L, Jackson JA. 2019.. Whales from space: four mysticete species described using new VHR satellite imagery. . Mar. Mamm. Sci. 35:(2):46691
    [Crossref] [Google Scholar]
  44. Dai Y, Yang S, Zhao D, Hu C, Xu W, et al. 2023.. Coastal phytoplankton blooms expand and intensify in the 21st century. . Nature 615:(7951):28084
    [Crossref] [Google Scholar]
  45. Davies TE, Carneiro APB, Tarzia M, Wakefield E, Hennicke JC, et al. 2021.. Multispecies tracking reveals a major seabird hotspot in the North Atlantic. . Conserv. Lett. 14:(5):e12824
    [Crossref] [Google Scholar]
  46. Dokter AM, Oosterbeek K, Baptist MJ, Desmet P, van der Kolk H-J, et al. 2023.. O_BALGZAND – Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) wintering on Balgzand (the Netherlands). . Zenodo 10053932. https://doi.org/10.5281/zenodo.10053932
  47. Doney SC, Ruckelshaus M, Duffy JE, Barry JP, Chan F, et al. 2012.. Climate change impacts on marine ecosystems. . Annu. Rev. Mar. Sci. 4::1137
    [Crossref] [Google Scholar]
  48. Dong Y, Liu Y, Hu C, MacDonald IR, Lu Y. 2022.. Chronic oiling in global oceans. . Science 376:(6599):13004
    [Crossref] [Google Scholar]
  49. Duarte CM, Chapuis L, Collin SP, Costa DP, Devassy RP, et al. 2021.. The soundscape of the Anthropocene ocean. . Science 371:(6529):eaba4658
    [Crossref] [Google Scholar]
  50. Duarte CM, Middelburg JJ, Caraco N. 2005.. Major role of marine vegetation on the oceanic carbon cycle. . Biogeosciences 2:(1):18
    [Crossref] [Google Scholar]
  51. Dunn DC, Jablonicky C, Crespo GO, McCauley DJ, Kroodsma DA, et al. 2018.. Empowering high seas governance with satellite vessel tracking data. . Fish Fish. 19:(4):72939
    [Crossref] [Google Scholar]
  52. Eckert SA, Stewart BS. 2001.. Telemetry and satellite tracking of whale sharks, Rhincodon typus, in the Sea of Cortez, Mexico, and the north Pacific Ocean. . In The Behavior and Sensory Biology of Elasmobranch Fishes: An Anthology in Memory of Donald Richard Nelson, ed. TC Tricas, SH Gruber , pp. 299308. Dordrecht, Neth:.: Springer
    [Google Scholar]
  53. Elith J, Leathwick JR. 2009.. Species distribution models: ecological explanation and prediction across space and time. . Annu. Rev. Ecol. Evol. Syst. 40::67797
    [Crossref] [Google Scholar]
  54. FAO (Food Agric. Organ. UN). 2022.. The state of world fisheries and aquaculture 2022: towards blue transformation. Rep. , FAO, Rome:
    [Google Scholar]
  55. Fischbach AS, Douglas DC. 2021.. Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout. . Remote Sens. 13:(21):4266
    [Crossref] [Google Scholar]
  56. Fretwell PT, Cubaynes HC, Shpak OV. 2023.. Satellite image survey of beluga whales in the southern Kara Sea. . Mar. Mamm. Sci. 39:(4):120414
    [Crossref] [Google Scholar]
  57. Fretwell PT, Trathan PN. 2009.. Penguins from space: faecal stains reveal the location of emperor penguin colonies. . Glob. Ecol. Biogeogr. 18:(5):54352
    [Crossref] [Google Scholar]
  58. Friess DA, Yando ES, Abuchahla GMO, Adams JB, Cannicci S, et al. 2020.. Mangroves give cause for conservation optimism, for now. . Curr. Biol. 30:(4):R15354
    [Crossref] [Google Scholar]
  59. Fu Y, Deng J, Wang H, Comber A, Yang W, et al. 2021.. A new satellite-derived dataset for marine aquaculture areas in China's coastal region. . Earth Syst. Sci. Data. 13:(5):182942
    [Crossref] [Google Scholar]
  60. Fudala K, Bialik RJ. 2022.. Seals from outer space—population census of southern elephant seals using VHR satellite imagery. . Remote Sens. Appl. Soc. Environ. 28::100836
    [Google Scholar]
  61. Garthe S, Schwemmer P, Paiva VH, Corman A-M, Fock HO, et al. 2016.. Terrestrial and marine foraging strategies of an opportunistic seabird species breeding in the Wadden Sea. . PLOS ONE 11:(8):e0159630
    [Crossref] [Google Scholar]
  62. Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, et al. 2011.. Status and distribution of mangrove forests of the world using earth observation satellite data. . Glob. Ecol. Biogeogr. 20:(1):15459
    [Crossref] [Google Scholar]
  63. Golden CD, Allison EH, Cheung WWL, Dey MM, Halpern BS, et al. 2016.. Nutrition: fall in fish catch threatens human health. . Nature 534:(7607):31720
    [Crossref] [Google Scholar]
  64. Green KM, Virdee MK, Cubaynes HC, Aviles-Rivero AI, Fretwell PT, et al. 2023.. Gray whale detection in satellite imagery using deep learning. . Remote Sens. Ecol. Conserv. 9:(6):82940
    [Crossref] [Google Scholar]
  65. Harper S, Kleiber D, Appiah S, Atkins M, Bradford K, et al. 2023.. Towards gender inclusivity and equality in small-scale fisheries. . In Illuminating Hidden Harvests: The Contributions of Small-Scale Fisheries to Sustainable Development, pp. 12744. Rome:: Food Agric. Organ. UN, Duke Univ., and WorldFish
    [Google Scholar]
  66. Hazen EL, Jorgensen S, Rykaczewski RR, Bograd SJ, Foley DG, et al. 2013.. Predicted habitat shifts of Pacific top predators in a changing climate. . Nat. Clim. Change 3:(3):23438
    [Crossref] [Google Scholar]
  67. Hazen EL, Palacios DM, Forney KA, Howell EA, Becker E, et al. 2017.. WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current. . J. Appl. Ecol. 54:(5):141528
    [Crossref] [Google Scholar]
  68. Hazen EL, Scales KL, Maxwell SM, Briscoe DK, Welch H, et al. 2018.. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. . Sci. Adv. 4:(5):eaar3001
    [Crossref] [Google Scholar]
  69. Hedley JD, Roelfsema C, Brando V, Giardino C, Kutser T, et al. 2018.. Coral reef applications of Sentinel-2: coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8. . Remote Sens. Environ. 216::598614
    [Crossref] [Google Scholar]
  70. Hieronymi M, Bi S, Müller D, Schütt EM, Behr D, et al. 2023.. Ocean color atmospheric correction methods in view of usability for different optical water types. . Front. Mar. Sci. 10::1129876
    [Crossref] [Google Scholar]
  71. Hobday AJ, Hartog JR, Spillman CM, Alves O. 2011.. Seasonal forecasting of tuna habitat for dynamic spatial management. . Can. J. Fish. Aquat. Sci. 68:(5):898911
    [Crossref] [Google Scholar]
  72. Hobday AJ, Spillman CM, Paige Eveson J, Hartog JR. 2016.. Seasonal forecasting for decision support in marine fisheries and aquaculture. . Fish. Oceanogr. 25:(S1):4556
    [Crossref] [Google Scholar]
  73. Holman R, Haller MC. 2013.. Remote sensing of the nearshore. . Annu. Rev. Mar. Sci. 5::95113
    [Crossref] [Google Scholar]
  74. Holton MD, Wilson RP, Teilmann J, Siebert U. 2021.. Animal tag technology keeps coming of age: an engineering perspective. . Philos. Trans. R. Soc. B 376:(1831):20200229
    [Crossref] [Google Scholar]
  75. Hostetler CA, Behrenfeld MJ, Hu Y, Hair JW, Schulien JA. 2018.. Spaceborne lidar in the study of marine systems. . Annu. Rev. Mar. Sci. 10::12147
    [Crossref] [Google Scholar]
  76. Houskeeper HF, Rosenthal IS, Cavanaugh KC, Pawlak C, Trouille L, et al. 2022.. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). . PLOS ONE 17:(1):e0257933
    [Crossref] [Google Scholar]
  77. Hsu F-C, Elvidge CD, Baugh K, Zhizhin M, Ghosh T, et al. 2019.. Cross-matching VIIRS boat detections with vessel monitoring system tracks in Indonesia. . Remote Sens. 11:(9):995
    [Crossref] [Google Scholar]
  78. Hu C. 2021.. Remote detection of marine debris using satellite observations in the visible and near infrared spectral range: challenges and potentials. . Remote Sens. Environ. 259::112414
    [Crossref] [Google Scholar]
  79. Hu C, Lu Y, Sun S, Liu Y. 2021.. Optical remote sensing of oil spills in the ocean: What is really possible?. J. Remote Sens. 2021::9141902
    [Crossref] [Google Scholar]
  80. Hu C, Murch B, Barnes B, Wang M, Maréchal J-P, et al. 2016.. Sargassum Watch warns of incoming seaweed. . Eos 97:. https://doi.org/10.1029/2016EO058355
    [Crossref] [Google Scholar]
  81. Hu C, Zhang S, Barnes BB, Xie Y, Wang M, et al. 2023.. Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi-band medium-resolution satellite data and deep learning. . Remote Sens. Environ. 289::113515
    [Crossref] [Google Scholar]
  82. Hu T, Zhang Y, Su Y, Zheng Y, Lin G, Guo Q. 2020.. Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. . Remote Sens. 12:(10):1690
    [Crossref] [Google Scholar]
  83. Jacox MG, Buil MP, Brodie S, Alexander MA, Amaya DJ, et al. 2023.. Downscaled seasonal forecasts for the California Current System: skill assessment and prospects for living marine resource applications. . PLOS Clim. 2:(10):e0000245
    [Crossref] [Google Scholar]
  84. Jane SF, Hansen GJA, Kraemer BM, Leavitt PR, Mincer JL, et al. 2021.. Widespread deoxygenation of temperate lakes. . Nature 594:(7861):6670
    [Crossref] [Google Scholar]
  85. Jiao J, Lu Y, Hu C. 2023.. Optical interpretation of oil emulsions in the ocean—part III: a three-dimensional unmixing model to quantify oil concentration. . Remote Sens. Environ. 296::113719
    [Crossref] [Google Scholar]
  86. Joshua M, Salvaggio K, Keremedjiev M, Roth K, Foughty E. 2023.. Planet's upcoming VIS-SWIR hyperspectral satellites. . In Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), pap. HM3C.5 . Washington, DC:: Optica Publ. Group
    [Google Scholar]
  87. Jouffray J-B, Barbour FP, Blasiak R, Feine J, Gallagher L, et al. 2023.. Ocean sand: putting sand on the ocean sustainability agenda. Rep. , Ocean Risk Resil. Action Alliance, Washington, DC:
    [Google Scholar]
  88. Jouffray J-B, Blasiak R, Norström AV, Österblom H, Nyström M. 2020.. The blue acceleration: the trajectory of human expansion into the ocean. . One Earth 2:(1):4354
    [Crossref] [Google Scholar]
  89. Kavanaugh MT, Bell T, Catlett D, Cimino MA, Doney SC, et al. 2021.. Satellite remote sensing and the marine biodiversity observation network: current science and future steps. . Oceanography 34:(2):6279
    [Crossref] [Google Scholar]
  90. Kavanaugh MT, Oliver MJ, Chavez FP, Letelier RM, Muller-Karger FE, Doney SC. 2016.. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. . ICES J. Mar. Sci. 73:(7):183950
    [Crossref] [Google Scholar]
  91. Khan CB, Goetz KT, Cubaynes HC, Robinson C, Murnane E, et al. 2023.. A biologist's guide to the galaxy: leveraging artificial intelligence and very high-resolution satellite imagery to monitor marine mammals from space. . J. Mar. Sci. Eng. 11:(3):595
    [Crossref] [Google Scholar]
  92. Kramer SJ, Siegel DA, Maritorena S, Catlett D. 2022.. Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales. . Remote Sens. Environ. 270::112879
    [Crossref] [Google Scholar]
  93. Kroodsma DA, Hochberg T, Davis PB, Paolo FS, Joo R, Wong BA. 2022.. Revealing the global longline fleet with satellite radar. . Sci. Rep. 12:(1):21004
    [Crossref] [Google Scholar]
  94. Kroodsma DA, Mayorga J, Hochberg T, Miller NA, Boerder K, et al. 2018.. Tracking the global footprint of fisheries. . Science 359:(6378):9048
    [Crossref] [Google Scholar]
  95. Kroodsma DA, Turner J, Luck C, Hochberg T, Miller N, et al. 2023.. Global prevalence of setting longlines at dawn highlights bycatch risk for threatened albatross. . Biol. Conserv. 283::110026
    [Crossref] [Google Scholar]
  96. Laborie J, Authier M, Chaigne A, Delord K, Weimerskirch H, Guinet C. 2023.. Estimation of total population size of southern elephant seals (Mirounga leonina) on Kerguelen and Crozet Archipelagos using very high-resolution satellite imagery. . Front. Mar. Sci. 10::1149100
    [Crossref] [Google Scholar]
  97. LaRue MA, Ainley DG, Pennycook J, Stamatiou K, Salas L, et al. 2020.. Engaging “the crowd” in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica. . Remote Sens. Ecol. Conserv. 6:(1):7078
    [Crossref] [Google Scholar]
  98. LaRue MA, Lynch HJ, Lyver POB, Barton K, Ainley DG, et al. 2014.. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. . Polar Biol. 37:(4):50717
    [Crossref] [Google Scholar]
  99. LaRue MA, Salas L, Nur N, Ainley D, Stammerjohn S, et al. 2021.. Insights from the first global population estimate of Weddell seals in Antarctica. . Sci. Adv. 7:(39):eabh3674
    [Crossref] [Google Scholar]
  100. LaRue MA, Stapleton S. 2018.. Estimating the abundance of polar bears on Wrangel Island during late summer using high-resolution satellite imagery: a pilot study. . Polar Biol. 41:(12):262126
    [Crossref] [Google Scholar]
  101. LaRue MA, Stapleton S, Anderson M. 2017.. Feasibility of using high-resolution satellite imagery to assess vertebrate wildlife populations. . Conserv. Biol. 31:(1):21320
    [Crossref] [Google Scholar]
  102. Lezama-Ochoa N, Brodie S, Welch H, Jacox MG, Pozo Buil M, et al. 2024.. Divergent responses of highly migratory species to climate change in the California Current. . Divers. Distrib. 30:(2):e13800
    [Crossref] [Google Scholar]
  103. Li J, Asner GP. 2023.. Global analysis of benthic complexity in shallow coral reefs. . Environ. Res. Lett. 18:(2):024038
    [Crossref] [Google Scholar]
  104. Li J, Knapp DE, Fabina NS, Kennedy EV, Larsen K, et al. 2020.. A global coral reef probability map generated using convolutional neural networks. . Coral Reefs 39:(6):180515
    [Crossref] [Google Scholar]
  105. Li J, Knapp DE, Lyons M, Roelfsema C, Phinn S, et al. 2021.. Automated global shallow water bathymetry mapping using Google Earth Engine. . Remote Sens. 13:(8):1469
    [Crossref] [Google Scholar]
  106. Li J, Knapp DE, Schill SR, Roelfsema C, Phinn S, et al. 2019.. Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites. . Remote Sens. Environ. 232::111302
    [Crossref] [Google Scholar]
  107. Li Y, Robinson SVJ, Nguyen LH, Liu J. 2023.. Satellite prediction of coastal hypoxia in the northern Gulf of Mexico. . Remote Sens. Environ. 284::113346
    [Crossref] [Google Scholar]
  108. Liu Y, Pu Y, Hu X, Dong Y, Wu W, et al. 2023.. Global declines of offshore gas flaring inadequate to meet the 2030 goal. . Nat. Sustain. 6:(9):1095102
    [Crossref] [Google Scholar]
  109. Lynch HJ. 2023.. Satellite remote sensing for wildlife research in the polar regions. . Mar. Technol. Soc. J. 57:(3):4350
    [Crossref] [Google Scholar]
  110. Lynch HJ, Schwaller MR. 2014.. Mapping the abundance and distribution of Adélie penguins using Landsat-7: first steps towards an integrated multi-sensor pipeline for tracking populations at the continental scale. . PLOS ONE 9:(11):e113301
    [Crossref] [Google Scholar]
  111. Lyons M, Roelfsema C, Kennedy E, Kovacs E, Borrego Acevedo R, et al. 2020.. Mapping the world's coral reefs using a global multiscale earth observation framework. . Remote Sens. Ecol. Conserv. 6:(4):55768
    [Crossref] [Google Scholar]
  112. Ma Y, Xu N, Liu Z, Yang B, Yang F, et al. 2020.. Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets. . Remote Sens. Environ. 250::112047
    [Crossref] [Google Scholar]
  113. Maekawa T, Ohara K, Zhang Y, Fukutomi M, Matsumoto S, et al. 2020.. Deep learning-assisted comparative analysis of animal trajectories with DeepHL. . Nat. Commun. 11:(1):5316
    [Crossref] [Google Scholar]
  114. Marlier ME, Resetar SA, Lachman BE, Anania K, Adams K. 2022.. Remote sensing for natural disaster recovery: lessons learned from Hurricanes Irma and Maria in Puerto Rico. . Environ. Sci. Policy 132::15359
    [Crossref] [Google Scholar]
  115. Maxwell SM, Gjerde KM, Conners MG, Crowder LB. 2020.. Mobile protected areas for biodiversity on the high seas. . Science 367:(6475):25254
    [Crossref] [Google Scholar]
  116. McCauley DJ. 2023.. The future of whales in our Anthropocene ocean. . Sci. Adv. 9:(25):eadi7604
    [Crossref] [Google Scholar]
  117. McCauley DJ, Jablonicky C, Allison EH, Golden CD, Joyce FH, et al. 2018.. Wealthy countries dominate industrial fishing. . Sci. Adv. 4:(8):eaau2161
    [Crossref] [Google Scholar]
  118. McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR. 2015.. Marine defaunation: animal loss in the global ocean. . Science 347:(6219):1255641
    [Crossref] [Google Scholar]
  119. McCauley DJ, Woods P, Sullivan B, Bergman B, Jablonicky C, et al. 2016.. Ending hide and seek at sea. . Science 351:(6278):114850
    [Crossref] [Google Scholar]
  120. McDonald GG, Costello C, Bone J, Cabral RB, Farabee V, et al. 2021.. Satellites can reveal global extent of forced labor in the world's fishing fleet. . PNAS 118:(3):e2016238117
    [Crossref] [Google Scholar]
  121. McKenzie LJ, Nordlund LM, Jones BL, Cullen-Unsworth LC, Roelfsema C, Unsworth RKF. 2020.. The global distribution of seagrass meadows. . Environ. Res. Lett. 15:(7):074041
    [Crossref] [Google Scholar]
  122. Mcowen C, Weatherdon L, Bochove J-W, Sullivan E, Blyth S, et al. 2017.. A global map of saltmarshes. . Biodivers. Data J. 5::e11764
    [Crossref] [Google Scholar]
  123. Michelot T, Blackwell PG. 2019.. State-switching continuous-time correlated random walks. . Methods Ecol. Evol. 10:(5):63749
    [Crossref] [Google Scholar]
  124. Mills KE, Pershing AJ, Hernández CM. 2017.. Forecasting the seasonal timing of Maine's lobster fishery. . Front. Mar. Sci. 4::337
    [Crossref] [Google Scholar]
  125. Mobley CD, Werdell J, Franz B, Ahmad Z, Bailey S. 2016.. Atmospheric correction for satellite ocean color radiometry. Rep. NASA/TM–2016-217551 , Goddard Space Flight Cent., Natl. Aeronaut. Space Adm., Greenbelt, MD:
    [Google Scholar]
  126. Mora A, Capsey A, Friedlander A, Palacios Subiabre M, Brewin P, et al. 2021.. One of the least disturbed marine coastal ecosystems on Earth: spatial and temporal persistence of Darwin's sub-Antarctic giant kelp forests. . J. Biogeogr. 48:(10):256277
    [Crossref] [Google Scholar]
  127. Mouw CB, Greb S, Aurin D, DiGiacomo PM, Lee Z, et al. 2015.. Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions. . Remote Sens. Environ. 160::1530
    [Crossref] [Google Scholar]
  128. Mouw CB, Hardman-Mountford NJ, Alvain S, Bracher A, Brewin RJW, et al. 2017.. A consumer's guide to satellite remote sensing of multiple phytoplankton groups in the global ocean. . Front. Mar. Sci. 4::41
    [Crossref] [Google Scholar]
  129. Paolo FS, Kroodsma D, Raynor J, Hochberg T, Davis P, et al. 2024.. Satellite mapping reveals extensive industrial activity at sea. . Nature 625:(7993):8591
    [Crossref] [Google Scholar]
  130. Park J, Lee J, Seto K, Hochberg T, Wong BA, et al. 2020.. Illuminating dark fishing fleets in North Korea. . Sci. Adv. 6:(30):eabb1197
    [Crossref] [Google Scholar]
  131. Parrish CE, Magruder LA, Neuenschwander AL, Forfinski-Sarkozi N, Alonzo M, Jasinski M. 2019.. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS's bathymetric mapping performance. . Remote Sens. 11:(14):1634
    [Crossref] [Google Scholar]
  132. Pauly D, Zeller D. 2016.. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. . Nat. Commun. 7:(1):10244
    [Crossref] [Google Scholar]
  133. Pham TD, Xia J, Ha NT, Bui DT, Le NN, Tekeuchi W. 2019.. A review of remote sensing approaches for monitoring blue carbon ecosystems: mangroves, seagrasses and salt marshes during 2010–2018. . Sensors 19:(8):1933
    [Crossref] [Google Scholar]
  134. Phillips OM. 1988.. Remote sensing of the sea surface. . Annu. Rev. Fluid Mech. 20::89109
    [Crossref] [Google Scholar]
  135. Pinsky ML, Reygondeau G, Caddell R, Palacios-Abrantes J, Spijkers J, Cheung WWL. 2018.. Preparing ocean governance for species on the move. . Science 360:(6394):118991
    [Crossref] [Google Scholar]
  136. Posner SM, Fenichel EP, McCauley DJ, Biedenweg K, Brumbaugh RD, et al. 2020.. Boundary spanning among research and policy communities to address the emerging industrial revolution in the ocean. . Environ. Sci. Policy. 104::7381
    [Crossref] [Google Scholar]
  137. Priede IG. 1984.. A basking shark (Cetorhinus maximus) tracked by satellite together with simultaneous remote sensing. . Fish. Res. 2:(3):20116
    [Crossref] [Google Scholar]
  138. Purkis S, Chirayath V. 2022.. Remote sensing the ocean biosphere. . Annu. Rev. Environ. Resour. 47::82347
    [Crossref] [Google Scholar]
  139. Qi L, Hu C, Barnes BB, Lapointe BE, Chen Y, et al. 2022.. Climate and anthropogenic controls of seaweed expansions in the East China Sea and Yellow Sea. . Geophys. Res. Lett. 49:(19):e2022GL098185
    [Crossref] [Google Scholar]
  140. Ramos E, Santoya L, Verde J, Walker Z, Castelblanco-Martínez N, et al. 2022.. Lords of the rings: mud ring feeding by bottlenose dolphins in a Caribbean estuary revealed from sea, air, and space. . Mar. Mamm. Sci. 38:(1):36473
    [Crossref] [Google Scholar]
  141. Raymond B, Lea M-A, Patterson T, Andrews-Goff V, Sharples R, et al. 2015.. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. . Ecography 38:(2):12129
    [Crossref] [Google Scholar]
  142. Rodríguez-Martínez RE, Medina-Valmaseda AE, Blanchon P, Monroy-Velázquez LV, Almazán-Becerril A, et al. 2019.. Faunal mortality associated with massive beaching and decomposition of pelagic Sargassum. . Mar. Pollut. Bull. 146::2015
    [Crossref] [Google Scholar]
  143. Roelfsema C, Kovacs E, Ortiz JC, Wolff NH, Callaghan D, et al. 2018.. Coral reef habitat mapping: a combination of object-based image analysis and ecological modelling. . Remote Sens. Environ. 208::2741
    [Crossref] [Google Scholar]
  144. Roquet F, Boehme L, Fedak M, Block B, Charrassin J-B, et al. 2017.. Ocean observations using tagged animals. . Oceanography 30:(2):139
    [Crossref] [Google Scholar]
  145. Rußwurm M, Venkatesa SJ, Tuia D. 2023.. Large-scale detection of marine debris in coastal areas with Sentinel-2. . iScience 26:(12):108402
    [Crossref] [Google Scholar]
  146. Samhouri JF, Feist BE, Jacox M, Liu OR, Richerson K, et al. 2024.. Stay or go? Geographic variation in risks due to climate change for fishing fleets that adapt in-place or adapt on-the-move. . PLOS Clim. 3:(2):e0000285
    [Crossref] [Google Scholar]
  147. Sathyendranath S, Brewin RJW, Ciavatta S, Jackson T, Kulk G, et al. 2023.. Ocean biology studied from space. . Surv. Geophys. 44:(5):1287308
    [Crossref] [Google Scholar]
  148. SatVu. 2024.. SatVu tasking. . SatVu. https://www.satellitevu.com/satvu-tasking
    [Google Scholar]
  149. Scales K, Hazen E, Jacox M, Edwards C, Boustany A, et al. 2017.. Scale of inference: on the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. . Ecography 40:(1):21020
    [Crossref] [Google Scholar]
  150. Scarpignato A, Harrison A-L, Newstead D, Niles L, Porter R, et al. 2016.. Field-testing a new miniaturized GPS-Argos satellite transmitter (3.5 g) on migratory shorebirds. . Wader Study 123:(3):24046
    [Crossref] [Google Scholar]
  151. Schaeffer BA, Whitman P, Conmy R, Salls W, Coffer M, et al. 2022.. Potential for commercial PlanetScope satellites in oil response monitoring. . Mar. Pollut. Bull. 183::114077
    [Crossref] [Google Scholar]
  152. Schneider GIC. 2020.. Marine diamond mining in the Benguela Current Large Marine Ecosystem: the case of Namibia. . Environ. Dev. 36::100579
    [Crossref] [Google Scholar]
  153. Schofield G, Dimadi A, Fossette S, Katselidis KA, Koutsoubas D, et al. 2013.. Satellite tracking large numbers of individuals to infer population level dispersal and core areas for the protection of an endangered species. . Divers. Distrib. 19:(7):83444
    [Crossref] [Google Scholar]
  154. Seto KL, Miller NA, Kroodsma D, Hanich Q, Miyahara M, et al. 2023.. Fishing through the cracks: the unregulated nature of global squid fisheries. . Sci. Adv. 9:(10):eadd8125
    [Crossref] [Google Scholar]
  155. Simard M, Fatoyinbo L, Smetanka C, Rivera-Monroy V, Thomas N, et al. 2019.. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. . Nat. Geosci. 12::4045
    [Crossref] [Google Scholar]
  156. Skubel RA, Wilson K, Papastamatiou YP, Verkamp HJ, Sulikowski JA, et al. 2020.. A scalable, satellite-transmitted data product for monitoring high-activity events in mobile aquatic animals. . Anim. Biotelem. 8:(1):34
    [Crossref] [Google Scholar]
  157. Smith MD, Asche F, Bennear LS, Oglend A. 2014.. Spatial-dynamics of hypoxia and fisheries: the case of Gulf of Mexico brown shrimp. . Mar. Resour. Econ. 29:(2):11131
    [Crossref] [Google Scholar]
  158. Stienen EWM, Buijs R-J, de Visser J, Fijn R, Lilipaly S, et al. 2023.. DELTATRACK – herring gulls (Larus argentatus, Laridae) and lesser black-backed gulls (Larus fuscus, Laridae) breeding at Neeltje Jans (Netherlands). . Zenodo 10209521. https://doi.org/10.5281/zenodo.10209520
  159. Stumpf RP, Li Y, Kirkpatrick B, Litaker RW, Hubbard KA, et al. 2022.. Quantifying Karenia brevis bloom severity and respiratory irritation impact along the shoreline of Southwest Florida. . PLOS ONE 17:(1):e0260755
    [Crossref] [Google Scholar]
  160. Taconet M, Kroodsma D, Fernandes JA. 2019.. Global Atlas of AIS-Based Fishing Activity: Challenges and Opportunities. Rome:: Food Agric. Organ. UN
    [Google Scholar]
  161. Tait L, Thoral F, Pinkerton M, Thomsen M, Schiel D. 2021.. Loss of giant kelp, Macrocystis pyrifera, driven by marine heatwaves and exacerbated by poor water clarity in New Zealand. . Front. Mar. Sci. 8::721087
    [Crossref] [Google Scholar]
  162. Teo SLH, Boustany A, Blackwell S, Walli A, Weng KC, Block BA. 2004.. Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. . Mar. Ecol. Prog. Ser. 283::8198
    [Crossref] [Google Scholar]
  163. Thomas N, Pertiwi AP, Traganos D, Lagomasino D, Poursanidis D, et al. 2021.. Space-borne cloud-native satellite-derived bathymetry (SDB) models using ICESat-2 and Sentinel-2. . Geophys. Res. Lett. 48:(6):e2020GL092170
    [Crossref] [Google Scholar]
  164. UCSB (Univ. Calif. Santa Barbara). 2024.. Deep Sea Mining Watch. . UCSB. https://deepseaminingwatch.msi.ucsb.edu
    [Google Scholar]
  165. UN Off. Outer Space Aff. 2024.. Online index of objects launched into outer space. . United Nations Office for Outer Space Affairs. https://www.unoosa.org/oosa/osoindex/search-ng.jspx
    [Google Scholar]
  166. UNEP (UN Environ. Programme), GRID-Geneva (Global Resource Information Database Geneva). 2024.. Marine Sand Watch. . UNEP/GRID-Geneva. https://unepgrid.ch/en/marinesandwatch
    [Google Scholar]
  167. UNEP-WCMC (UN Environ. Programme World Conserv. Monit. Cent.), WorldFish, World Resour. Inst., Nat. Conserv. 2021.. Global distribution of coral reefs. Dataset, UNEP-WCMC, WorldFish, World Resour. Inst., Nat. Conserv:. https://doi.org/10.34892/t2wk-5t34
    [Google Scholar]
  168. Valiela I, Bowen JL, York JK. 2001.. Mangrove forests: one of the world's threatened major tropical environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. . BioScience 51:(10):80715
    [Crossref] [Google Scholar]
  169. Wang M, Hu C, Barnes BB, Mitchum G, Lapointe B, Montoya JP. 2019.. The great Atlantic Sargassum belt. . Science 365:(6448):8387
    [Crossref] [Google Scholar]
  170. Warwick-Evans V, Atkinson PW, Walkington I, Green JA. 2018.. Predicting the impacts of wind farms on seabirds: an individual-based model. . J. Appl. Ecol. 55:(2):50315
    [Crossref] [Google Scholar]
  171. Watanabe YY, Papastamatiou YP. 2023.. Biologging and biotelemetry: tools for understanding the lives and environments of marine animals. . Annu. Rev. Anim. Biosci. 11::24767
    [Crossref] [Google Scholar]
  172. Welch H, Clavelle T, White TD, Cimino MA, Van Osdel J, et al. 2022.. Hot spots of unseen fishing vessels. . Sci. Adv. 8:(44):eabq2109
    [Crossref] [Google Scholar]
  173. Welch H, Hazen EL, Bograd SJ, Jacox MG, Brodie S, et al. 2019.. Practical considerations for operationalizing dynamic management tools. . J. Appl. Ecol. 56:(2):45969
    [Crossref] [Google Scholar]
  174. Welch H, Liu OR, Riekkola L, Abrahms B, Hazen EL, Samhouri JF. 2023.. Selection of planning unit size in dynamic management strategies to reduce human-wildlife conflict. . Conserv. Biol. 38:(3):e14201
    [Crossref] [Google Scholar]
  175. Wernberg T, Krumhansl K, Filbee-Dexter K, Pedersen M. 2019.. Status and trends for the world's kelp forests. . In World Seas: An Environmental Evaluation, ed. C Sheppard , pp. 5778. London:: Academic. , 2nd ed..
    [Google Scholar]
  176. White TD, Carlisle AB, Kroodsma DA, Block BA, Casagrandi R, et al. 2017.. Assessing the effectiveness of a large marine protected area for reef shark conservation. . Biol. Conserv. 207::6471
    [Crossref] [Google Scholar]
  177. Womersley FC, Humphries NE, Queiroz N, Vedor M, da Costa I, et al. 2022.. Global collision-risk hotspots of marine traffic and the world's largest fish, the whale shark. . PNAS 119:(20):e2117440119
    [Crossref] [Google Scholar]
  178. Xi H, Losa SN, Mangin A, Soppa MA, Garnesson P, et al. 2020.. Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data. . Remote Sens. Environ. 240::111704
    [Crossref] [Google Scholar]
  179. Yao Y, Hu C, Cannizzaro JP, Barnes BB, English DC, et al. 2023.. Detection of Karenia brevis red tides on the West Florida Shelf using VIIRS observations: accounting for spatial coherence with artificial intelligence. . Remote Sens. Environ. 298::113833
    [Crossref] [Google Scholar]
  180. Yao Y, Hu C, Cannizzaro JP, Zhang S, Barnes BB, et al. 2024.. Detecting cyanobacterial blooms in the Caloosahatchee River and Estuary using PlanetScope imagery and deep learning. . IEEE Trans. Geosci. Remote Sens. 62::4202513
    [Google Scholar]
  181. Zhao J, Temimi M, Ghedira H. 2017.. Remotely sensed sea surface salinity in the hyper-saline Arabian Gulf: application to Landsat 8 OLI data. . Estuar. Coast. Shelf Sci. 187::16877
    [Crossref] [Google Scholar]
  182. Zhou M-J, Liu D-Y, Anderson DM, Valiela I. 2015.. Introduction to the special issue on green tides in the Yellow Sea. . Estuar. Coast. Shelf Sci. 163::38
    [Crossref] [Google Scholar]
  183. Zhou Q, Ke Y, Wang X, Bai J, Zhou D, Li X. 2022.. Developing seagrass index for long term monitoring of Zostera japonica seagrass bed: a case study in Yellow River Delta, China. . ISPRS J. Photogramm. Remote Sens. 194::286301
    [Crossref] [Google Scholar]
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