Using remote-controlled vehicles (ROV) as tools for sea cucumber conservation: A review

Authors

  • Ummi Nurain Akma Baharin Centre of Research for Sustainable Uses of Natural Resources, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, 84600 Muar, Johor Darul Tazim, Malaysia
  • Kamarul Rahim Kamarudin Centre of Research for Sustainable Uses of Natural Resources, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, 84600 Muar, Johor Darul Tazim, Malaysia

DOI:

https://doi.org/10.33175/mtr.2025.274346

Keywords:

Remote controlled vehicles; ROVs; Sea cucumber; Orthomosaics; Mapping

Abstract

The use of Remotely Operated Vehicles (ROVs) in marine research and conservation has become increasingly popular due to their versatility and ability to operate in challenging environments. This technology shows great potential in the conservation of marine species such as sea cucumbers, which are vital for ecosystem balance but are threatened by overfishing and habitat degradation. This review explores the application of ROVs in sea cucumber population monitoring, highlighting their role in providing real-time data on population dynamics, habitat conditions, and human impacts. The integration of ROV data with aerial orthomosaics to improve the accuracy and reliability of habitat assessments is discussed. The versatility of ROVs extends beyond sea cucumber conservation, with applications in mapping coral reefs and monitoring fish nurseries, showcasing their broad utility in marine ecosystem protection. As these technologies continue to advance, ROVs offer immense potential for developing informed and effective conservation strategies. This review underscores the critical role of ROVs in marine conservation, emphasizing their capacity to enhance our understanding of sea cucumber ecology and to support sustainable management practices.

Highlights

  • UAV and ROV integration enables precise sea cucumber habitat mapping and monitoring.
  • OBIA and machine learning improved species identification in complex marine habitats.
  • ROVs’ high-resolution imagery supports accurate population density and habitat surveys.
  • UAV limitations include battery life, environmental challenges, and species detection.
  • Future research focuses on enhanced algorithms and improved validation for data accuracy.

References

Álvarez-González, M., Suarez-Bregua, P., Pierce, G. J., & Saavedra, C. (2023). Unmanned aerial vehicles (UAVs) in marine mammal research: A review of current applications and challenges. Drones, 7(11), 667. https://doi.org/10.3390/drones7110667

Aguzzi, J., Thomsen, L., Flögel, S., Robinson, N. J., Picardi, G., Chatzievangelou, D., Bahamon, N., Stefanni, S., Grinyó, J., Fanelli, E., Corinaldesi, C., Fernandez, J. D. R., Calisti, M., Mienis, F., Chatzidouros, E., Costa, C., Violino, S., Tangherlini, M., & Danovaro, R. (2024). New technologies for monitoring and upscaling marine ecosystem restoration in deep-sea environments. Engineering, 34, 195-211. https://doi.org/10.1016/j.eng.2023.10.012

Aguzzi, J., Chatzievangelou, D., Company, J. B., Thomsen, L., Marini, S., Bonofiglio, F., Juanes, F., Rountree, R., Berry, A., Chumbinho, R., Lordan, C., Doyle, J., Rio, J. D., Navarro, J., Leo, F. C. D., Bahamon, N., García, J. A., Danovaro, P. R., Francescangeli, M., Lopez-Vazquez, V., & Gaughan, P. (2020). The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES Journal of Marine Science, 77(7-8), 2396-2410. https://doi.org/10.1093/icesjms/fsaa169

Anderson, S. C., Flemming, J., Watson, R., & Lotze, H. (2011). Serial exploitation of global sea cucumber fisheries. Fish and Fisheries, 12(3), 317-339. https://doi.org/10.1111/j.1467-2979.2010.00397.x

Audenhaege, L. V., Broad, E., Hendry, K., & Huvenne, V. (2021). High-resolution vertical habitat mapping of a deep-sea cliff offshore Greenland. Scientific Reports, 11, 4879. https://doi.org/10.3389/fmars.2021.669372

Bagheri, H., Vardy, A., & Bachmayer, R. (2011). Strategies for filtering incorrect matches in seabed mosaicking (pp. 1-5). In Proceedings of the IEEE KONA OCEANS'11 MTS. Waikoloa, USA. https://doi.org/10.23919/OCEANS.2011.6107147

Barreto, J., Cajaiba, L., Teixeira, J. B., Nascimento, L., Giacomo, A., Barcelos, N., Fettermann, T., & Martins, A. (2021). Drone-monitoring: Improving the detectability of threatened marine megafauna. Drones, 5(1), 14. https://doi.org/10.3390/drones5010014

Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R. Q., Van der Meer, F., van der Werff, H., Coillie, F. M. B. V., & Tiede, D. (2014). Geographic object-based image analysis: Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191. https://doi.org/10.1016/j.isprsjprs.2013.09.014

Brito, R., Hernandez-Lopez, D., Lopez-Garcia, P., & Leon, P. (2019). Comparative study of fixed-wing and multi-copter UAVs for 2D mapping applications. Journal of Environmental Monitoring, 21(3), 149-159.

Brown, C., Mitchell, A., Limpenny, D., Robertson, M., & Golding, N. (2005). Mapping seabed habitats in the Firth of Lorn off the west coast of Scotland: Evaluation and comparison of habitat maps produced using the acoustic ground-discrimination system, RoxAnn, and sidescan sonar. Marine Technology Society Journal, 39(1), 58-75. https://doi.org/10.1016/j.icesjms.2004.10.008

Brown, A., Smith, J., & Kim, L. (2021). Conventional monitoring techniques such as remote underwater vehicles. Journal of Marine Technology, 34(2), 123-135.

Brown, P., & Kim, H. (2021). Advanced imaging technologies in ROVs for marine research. Journal of Marine Technology, 18(2), 112-125.

Buscher, E., Mathews, D. L., Bryce, C., Bryce, K., Joseph, D., & Ban, N. C. (2020). Applying a low-cost, mini remotely operated vehicle (ROV) to assess an ecological baseline of an indigenous seascape in Canada. Frontiers in Marine Science, 7, 669. https://doi.org/10.3389/fmars.2020.00669

Butcher, P. A., Colefax, A. P., Gorkin III, R. A., Kajiura, S. M., López, N. A., Mourier, J., Purcell, C. R., Skomal, G. B., Tucker, J. P., Walsh, A. J., Williamson, J. E., & Raoult, V. (2021). The drone revolution of shark science: A review. Drones, 5(1), 8. https://doi.org/10.3390/drones5010008

Campbell, J. W., Connors, P. G., Kirchoff, V., & Swanson, J. R. (2015). UAV-based aerial surveys for ecosystem monitoring. Ecological Applications, 25(7), 1906-1914.

Chang, Y. (2019). A drone IFF and tracking algorithm with the relay drone and the beacon system. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 7, 199-203.

Chimienti, G., Angeletti, L., Rizzo, L., Tursi, A., & Mastrototaro, F. (2018). ROV vs trawling approaches in the study of benthic communities: The case of Pennatula rubra (Cnidaria: Pennatulacea). Journal of the Marine Biological Association of the United Kingdom, 98(8), 1859-1869. https://doi.org/10.1017/S0025315418000851

Clark, C., & Kelaher, B. (2019). Development and trials of a 6000m class ROV for marine scientific research. In Proceedings of the IEEE Kobe Techno-Oceans OCEANS.

Colefax, A., Butcher, P., Pagendam, D., & Kelaher, B. (2019). Reliability of marine faunal detections in drone-based monitoring. Marine Technology Society Journal, 53(4), 92-105. https://doi.org/10.1016/j.ocecoaman.2019.03.008

Colefax, A. P., Butcher, P. A., & Kelaher, B. P. (2018). The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft. ICES Journal of Marine Science, 75(1), 1-8. https://doi.org/10.1093/icesjms/fsx100

Crutsinger, G. M., Short, J., & Short, J. R. (2016). Aerial survey technologies for ecosystem monitoring: Challenges and opportunities. Journal of Ecosystem Sciences, 33(1), 45-52.

Dines, B. (2018). History of benthic mapping. Journal of Marine History, 45(2), 123-145.

Davie, A., Hartmann, K., Timms, G., de Groot, M., & McCulloch, J. (2008). Benthic habitat mapping with autonomous underwater vehicles. International Journal of Remote Sensing, 29(4), 1165-1182. https://doi.org/10.1109/OCEANS.2008.5151927

Davis, R., Thompson, P., & Clark, M. (2018). Aerial perspective: UAVs survey shallow coastal regions. Marine Ecology Progress Series, 48(4), 201-215.

Dodge, K. L., Kukulya, A., Burke, E., & Baumgartner, M. (2018). TurtleCam: A “smart” autonomous underwater vehicle for investigating behaviors and habitats of sea turtles. Marine Technology Society Journal, 52(4), 91-104. https://doi.org/10.3389/fmars.2018.00090

Duffy, J. P. (2018). Coastal eye: Monitoring coastal environments using lightweight drones. University of Exeter.

Duffy, J. P., Pratt, L., Anderson, K., Land, P. E., & Shutler, J. D. (2018). Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science, 200, 169-180. https://doi.org/10.1016/j.ecss.2017.11.001

Dujon, A. M., & Schofield, C. J. (2019). Automated detection of marine species using UAVs and machine learning algorithms. Remote Sensing of Environment, 235, 111-121.

Durden, J. M., Bett, B. J., & Ruhl, H. A. (2016). A comparison of megafaunal populations in sedimented regions of the deep-sea floor: ROV and towed camera surveys. Deep Sea Research Part I: Oceanographic Research Papers, 112, 25-38.

Fallati, L., Saponari, L., Savini, A., Marchese, F., Corselli, C., & Galli, P. (2020). Multi-temporal UAV data and object-based image analysis (OBIA) for estimation of substrate changes in a post-bleaching scenario on a Maldivian reef. Remote Sensing, 12(13), 2093. https://doi.org/10.3390/rs12132093

Feidantsis, K., Gkafas, G. A., Exadactylos, A., Michaelidis, B., Staikou, A., Hatziioannou, M., Apostologamvrou, C., Sarantopoulou, J., & Vafidis, D. (2022). Different interspecies demographic histories within the same locality: A case study of sea cucumbers, cuttlefish, and clams in Greek waters. Sustainability, 14(21), 14380. https://doi.org/10.3390/su142114380

Félix, P. M., Pombo, A., Azevedo e Silva, F., Simões, T., Marques, T. A., Melo, R., Rocha, C., Sousa, J., Venâncio, E., Costa, J. L., & Brito, A. C. (2021). Modelling the distribution of a commercial NE-Atlantic sea cucumber, Holothuria mammata: Demographic and abundance spatio-temporal patterns. Frontiers in Marine Science, 8, 675330. https://doi.org/10.3389/fmars.2021.675330

Ferrini, V., Singh, H., Clarke, M., Wakefield, W., & York, K. (2006). Computer-assisted analysis of near-bottom photos for benthic habitat studies. Marine Technology Society Journal, 40(3), 19-30. https://doi.org/10.1109/OCEANS.2006.306899

Gajdosechova, Z., Palmer, C. H., Dave, D., Jiao, G., Zhao, Y., Tan, Z., Chisholm, J., Zhang, J., Stefanova, R., Hossain, A., & Mester, Z. (2020). Arsenic speciation in sea cucumbers: Identification and quantitation of water-extractable species. Environmental Pollution, 266(2), 115190. https://doi.org/10.1016/j.envpol.2020.115190

Garcia, R., Lopez, A., & Rodriguez, M. (2018). Habitat assessment using ROVs. Marine Ecology Journal, 13(1), 98-110.

Gerringer, M. E., Ismail, Y., Cannon, K. A., Camilo A. H., Gonzales F. P., Bohen, R., Cartwright, J. C., Feasley, A., Fregosi, L., Lehman, H., Niles, H., Quay, J., Sherpa, N., Woodworth, B. H., & Cantwell, K. (2023). Deep-sea biology in undergraduate classrooms: Open access data from remotely operated vehicles provide impactful research experiences. Frontiers in Marine Science, 9, 1033274. https://doi.org/10.3389/fmars.2022.1033274

Green, A., Chadwick, M. A., & Jones, P. J. (2018). Variability of UK seagrass sediment carbon: Implications for blue carbon estimates and marine conservation management. PLoS One, 13(4), e0195521. https://doi.org/10.1371/journal.pone.0204431

Harris, K., Ford, J. K., & Zaleha, M. (2021). Improving deep-sea ecological data through the application of autonomous underwater vehicles (AUVs). Marine Ecology Progress Series, 676, 109-123.

Hilborn, R., & Walters, C. (1992). Quantitative fisheries stock assessment: Choice, dynamics and uncertainty. Springer. https://doi.org/10.1007/978-1-4615-3598-0

Hernandez, C., Hunt, J., & Dutton, I. (2022). Remote observations of marine life using aerial drones: From deployment to data analysis. Marine Biology Research, 18(1), 41-55.

Hodgson, A. J., Peel, D., & Kelly, N. (2018). UAV-based surveys: Implications for wildlife monitoring. Conservation Biology, 32(4), 1014-1024.

Jaiswal, K., Subbiah, S., & Siva, S. K. (2020). Monitoring and mapping seaweed farming using unmanned aerial vehicle-based remote sensing technology (pp. 127-134). Agricultural Remote Sensing and Analytics.

Jiang, J., Xu, T., Liu, Y., & Zhang, X. (2020). New ways of detecting benthic macrofauna communities using unmanned aerial vehicles. Frontiers in Marine Science, 7, 551.

Jin, Z., Xu, J., Zheng, Q., & Pan, L. (2021). Machine learning for autonomous sea cucumber identification from underwater images. Ecological Informatics, 61, 101219. https://doi.org/10.1016/j.ecoinf.2021.101219

Johnson, L., & Wang, X. (2021). Automated benthic mapping with underwater cameras. Marine Research Journal, 22(4), 345-356.

Jones, M., & Lee, H. (2019). UAV application in marine ecology: Potential to furnish density estimates for shallow water invertebrate species. Ecological Applications, 29(3), 45-58.

Jørgensen, H. B., & Villadsen, J. (2021). Mapping Arctic benthic communities with unmanned aerial vehicles: Challenges and opportunities. Marine Ecology Progress Series, 669, 121-133. https://doi.org/10.3354/meps13873

Joyce, K. E., & Kelly, N. M. (2019). Assessing the utility of UAVs for marine species monitoring. Marine Ecology Progress Series, 615, 103-116.

Kampmann, P., Christensen, L., Fritsche, M., Gaudig, C., Hanff, H., Hildebrandt, M., & Kirchner, F. (2018). Robotics support for marine mining: Innovations in underwater manipulation. In Proceedings of the IEEE on OCEANS 2018. Houston, Texas, USA. https://doi.org/10.4043/29069-MS

Kelaher, B. P., Colefax, A. P., & Raoult, V. (2020). Research utilizing wind power: The potential of UAVs in marine surveys. In Proceedings of the OCEANS 2017. Aberdeen.

Kilfoil, J. P., Jones, J. W., & Widdicombe, A. C. (2017). UAV technology for shallow-water marine mapping. Geographical Journal, 183(2), 240-249.

Kim, D. H., Cho, K. Y., & Choi, K. S. (2019). Deep-sea sea cucumber detection in underwater ROV images using machine learning methods. Journal of Ocean Technology, 14(4), 75-89.

Laliberte, A. S., & Rango, A. (2011). Image classification in OBIA: A comparison of supervised and unsupervised methods. Remote Sensing, 3(4), 608-629. https://doi.org/10.2747/1548-1603.48.1.4

Lee, S., Chen, Y., & Park, J. (2019). Cost-effectiveness of ROVs in marine conservation. Oceanography Research Journal, 20(3), 201-215.

Liao, K., Liu, X., Wu, Q., & Wang, M. (2021). Real-time underwater object tracking with unmanned underwater vehicles for marine research and monitoring. Sensors, 21(4), 1090.

Liu, Y., Yang, Y., Li, Z., & Li, M. (2021). Object detection in underwater imagery using deep learning with convolutional neural networks. Remote Sensing, 13(2), 256. https://doi.org/10.3390/rs13020256

López-Merino, J., Reyes-Bonilla, H., Moreno-López, M., & Villanueva, M. (2020). The use of drones in marine ecological research: Challenges and opportunities. Frontiers in Marine Science, 7, 732.

Lyons, M. B., Smith, A. M., & Clark, G. F. (2019). Machine learning techniques for UAV image analysis in marine conservation. Environmental Conservation, 46(3), 222-234.

Miller, T., & Thompson, D. (2020). Accessibility and deployment of ROVs in marine research. Marine Technology Review, 14(4), 223-235.

Macreadie, P. I., McLean, D. L., Thomson, P. G., Partridge, J. C., Jones, D. O., Gates, A. R., Benfield, M. C., Collin, S. P., Booth, D. J., Smith, L. L., Techera, E., Skropeta, D., Horton, T., Pattiaratchi, C., Bond, T., & Fowler, A. M. (2018). Eyes in the sea: unlocking the mysteries of the ocean using industrial, remotely operated vehicles (ROVs). Science of the Total Environment, 634, 1077-1091. https://doi.org/10.1016/j.scitotenv.2018.04.049

Mazzeo, A., Aguzzi, J., Calisti, M., Canese, S., Angiolillo, M., Allcock, A. L., Vecchi, F., Stefanni, S., & Controzzi, M. (2022). Marine robotics for deep-sea specimen collection: A taxonomy of underwater manipulative actions. Sensors, 22(4), 1471. https://doi.org/10.3390/s22041471

Matsumoto, T., Hara, T., & Sato, T. (2021). Development of a marine drone for seagrass and reef monitoring. Journal of Ocean Technology, 16(3), 87-101.

McDonald, R. B., Vasquez, M., & Caudill, C. (2022). Evaluating habitat diversity in coral reefs with unmanned aerial systems. Marine Biology Research, 18(5), 508-520.

McLean, D. L., Parsons, M. J., Gates, A. R., Benfield, M. C., Bond, T., Booth, D. J., Bunce, M., Fowler, A. M., Harvey, E. S., Macreadie, P. I., Pattiaratchi, C. B., Rouse, S., Partridge, J. C., Thomson, P. G., Todd, V. L. G., & Jones, D. O. B. (2020). Enhancing the scientific value of industry remotely operated vehicles (ROVs) in our oceans. Frontiers in Marine Science, 7, 220. https://doi.org/10.3389/fmars.2020.00220

Muller, Y., Oshiro, S., Nakagawa, S., & Wada, T. (2022). Underwater GPS system for autonomous underwater wireless drone control. International Journal of Computer Science and Network Security, 22(4), 817. https://doi.org/10.22937/IJCSNS.2022.22.4.98

O'Donnell, C., Reilly, N., & McCormack, M. (2021). Analyzing marine ecosystems with high-resolution drone imagery: A case study of Kelp forests in Scotland. Marine Ecology Progress Series, 668, 35-49.

Ozbulk, C., Thompson, M., & Barnes, M. A. (2016). Improving machine learning performance for ecological applications. Ecological Modelling, 325, 113-121.

Peel, A., Waller, C., & Allen, D. (2019). UAV monitoring of marine flora and fauna: A review of recent advancements and applications. Ocean & Coastal Management, 179, 104814. https://doi.org/10.1016/j.ocecoaman.2019.104814

Pettit, A., Neff, D., & Slocum, C. (2021). Enhancing fish population surveys with autonomous underwater vehicles. ICES Journal of Marine Science, 78(6), 2289-2300.

Piazzolla, D., Scanu, S., Mancuso, F. P., Bosch-Belmar, M., Bonamano, S., Madonia, A., Scagnoli, E., Tantillo, M. F., Russi, M., Savini, A., Fersini, G., Sarà, G., Coppini, G., Marcelli, M., & Piermattei, V. (2024). An integrated approach for benthic habitat mapping based on innovative surveying technologies and ecosystem functioning measurements. Scientific Reports, 14(1), 5888. https://doi.org/10.1038/s41598-024-56662-6

Poole, A., Saxton, M., & Adams, P. (2017). UAV applications in marine research: Enhancing data collection for marine biology studies. Marine Technology Society Journal, 51(4), 77-87.

Ramos, M., Brito, A., Andrade, S., & Martínez, L. (2020). Monitoring coastal marine environments with low-cost drone technology. Journal of Coastal Research, 36(5), 1079-1090.

Raoult, V., Colefax, A. P., Allan, B. M., Cagnazzi, D., Castelblanco-Martínez, N., Ierodiaconou, D., Johnston, D. W., Landeo-Yauri, S., Lyons, M., Pirotta, V., Schofield, G., & Butcher, P. A. (2020). Operational protocols for the use of drones in marine animal research. Drones, 4(4), 64. https://doi.org/10.3390/drones4040064

Rosenberg, A., Munro, P., & Robinson, J. (2020). Investigating marine habitats using autonomous underwater vehicles and drones. Marine Ecology Progress Series, 632, 1-11.

Sánchez-Lizaso, J. L., Pérez-Ruzafa, A., & Aguzzi, J. (2021). Drone-based surveillance of seagrass meadows: Comparing methods and efficacy. Journal of Marine Systems, 213, 103453.

Santos, R., Ribeiro, M., & Sousa, C. (2023). High-resolution underwater imagery analysis for assessing marine biodiversity. Drones, 7(10), 769.

Schjølberg, I., & Utne, I. B. (2015). Towards autonomy in ROV operations. IFAC-PapersOnLine, 48(2), 183-188. https://doi.org/10.1016/j.ifacol.2015.06.030

Shi, X., Zhu, C., & Lu, P. (2022). Design and control of underwater robot system for sea cucumber fishing. International Journal of Advanced Robotic Systems, 19(1), 17298806221077625. https://doi.org/10.1177/17298806221077625

Silva, R., Chou, H., & Marques, J. (2021). New methods for tracking deep-sea organisms with underwater drones. Journal of Experimental Marine Biology and Ecology, 541, 151-163.

Slater, M., & Chen, J. (2015). Sea cucumber biology and ecology. In Brown, N. P., & Eddy, S. D. (Eds.). Holothuroidea: Marine biodiversity. John Wiley & Sons. https://doi.org/10.1002/9781119005810.ch3

Sloan, N. A., & von Bodungen, B. (1980). Sea cucumber population dynamics in relation to sediment structure. Journal of Marine Ecology, 5(1), 25-34.

Smith, D., Gonzalez, R., & Patel, S. (2020). Diverse applications and significant contributions of ROV technology to marine biology and ecosystem management. Marine Biology Research, 27(1), 89-101

Smith, J., & Lee, M. (2020). Technological advancements in benthic mapping. Marine Technology Review, 78(4), 233-257.

Smith, J., Doe, A., & Roe, P. (2022). Autonomous ROVs for marine organism capture. Journal of Marine Robotics, 17(3), 156-167.

Smith, R., & Mason, G. (2022). A comparison of UAV and traditional survey methods for marine habitat mapping. Marine Technology Society Journal, 56(2), 50-65. https://doi.org/10.4031/MTSJ.56.3.11

Smith, R., & Waller, C. (2021). Advancements in marine drone technology: Applications for environmental monitoring and research. Remote Sensing, 13(12), 2448.

Tanaka, S., Yu, H., & Hirano, Y. (2021). Real-time underwater monitoring of marine fauna using autonomous vehicles. Sensors and Actuators B: Chemical, 329, 129196.

Teague, J., Allen, M., & Scott, T. B. (2018). The potential of low-cost ROV for use in deep-sea mineral, ore prospecting and monitoring. Ocean Engineering, 147, 333-339. https://doi.org/10.1016/j.oceaneng.2017.10.046.

Tello, J., Fernández, J., & García, C. (2020). Mapping seaweed distribution using UAV technology: Applications in marine ecology. Estuarine, Coastal and Shelf Science, 233, 106478.

Tursi, A., Nocita, E., & Nardone, G. (2022). Underwater drones for deep-sea benthic habitat mapping. Journal of Marine Science and Engineering, 10(6), 881.

Uthicke, S., & Karez, R. (1999). The ecology of sea cucumbers: Environmental impacts on population distribution. Marine Biology Review, 75(2), 67-78.

Vizzini, S., Zuccarello, G., & Preti, M. (2022). Sea cucumber monitoring using unmanned aerial vehicles: A case study in the Mediterranean Sea. Journal of Sea Research, 184, 102358.

Wisesa, M. W., Bakti, D., & Fadhilah, A. (2018). Abundance of sea cucumbers on the ecosystem of seagrasses Inunggeh island, Tapanuli Tengah Regency North Sumatera Province. IOP Conference Series: Earth and Environmental Science, 122, 012107. https://doi.org/10.1088/1755-1315/122/1/012107

Xiang, X., Wu, D., Yang, M., & Xu, H. (2021). Drone-based image acquisition and analysis for marine conservation efforts. Remote Sensing, 13(4), 740.

Zhao, H., Wu, H., & Wang, Z. (2022). Detection of marine biodiversity with underwater drones: Challenges and future directions. Marine Ecology Progress Series, 686, 13-23.

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2024-12-01