請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93314完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 韓玉山 | zh_TW |
| dc.contributor.advisor | Yu-San Han | en |
| dc.contributor.author | 陳楊文 | zh_TW |
| dc.contributor.author | Vincent Y. Chen | en |
| dc.date.accessioned | 2024-07-29T16:12:21Z | - |
| dc.date.available | 2024-07-30 | - |
| dc.date.copyright | 2024-07-29 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-06-19 | - |
| dc.identifier.citation | Addessi, L., 1994. Human disturbance and long-term changes on a rocky intertidal community. Ecol. Appl. 4: 786-797.
Álvarez-Ellacuría, A., Palmer, M., Catalán, A. I., Lisani, J.-L., 2020. Image-based, unsupervised estimation of fish size from commercial landings using deep learning. ICES Journal of Marine Science, 77:330–1339. Australian Institute of Marine Science, 2020. Ozfish dataset - machine learning dataset for baited remote underwater video stations. Badawy, M., Direkoglu, C., 2020. Sea turtle detection using Faster R-CNN for Conservation Purpose. R. A. Aliev et al. (Eds.): ICSCCW 2019, AISC 1095, pp. 535–541, https://doi.org/10.1007/978-3-030-35249-3_68, Springer Nature Switzerland. Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., Silliman, B.R., 2011. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81 (2): 169e193. Beyan C., Browman H. I., 2020. Setting the stage for the machine intelligence era in marine science. ICES Journal of Marine Science. 77: 1267–1273. Bhattacharya, P., Dash, A. K., 2021.Determinants of blue economy in Asia-Pacific island countries: A study of tourism and fisheries sectors. Ocean Coastal Manag. 211: 105774. Bochkovskiy A., Wang, C.-Y., Liao ,M. H.-Y., 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv:2004.10934 https://doi.org/10.48550/arXiv.2004.10934. Bonney, R., J.L. Shirk, J.L., Phillips, T.B., Wiggins, A., Ballard, H.L., Miller-Rushing, A.J., Parrish, J.K., 2014. Next steps for citizen science. Science, 243 (2014), pp. 1427-1436 Burgess, H. K., Debey, L. B., Froehlich, H. E., Schmidt, N., Theobald, E. J., Ettinger, AK, Hillerislambers, J, Tewksbury, J and Parrish, JK., 2017. The science of citizen science: Exploring barriers to use as a primary research tool. Biological Conservation, 208: 113–120. DOI: https://doi.org/10.1016/j.biocon.2016.05.014 Chaloupka, M., Bjorndal, K. A., Balazs, G. H., Bolten, A.B., Ehrhart, L. M., Limpus,C. J., Suganuma, H., Troëng, S., Yamaguchi, M., 2008. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Global Ecology and Biogeography 17: 297-30. Chen V. Y., 2011. The Precious Secrets of a Tidal Pool. Flâneur Culture Lab Publishers , Taipei Taiwan Chen, V. Y., Wu, Y.-W., Hu, C.-W., Han, Y.-S., 2024a. Enhancing green sea turtle (Chelonia mydas) conservation for tourists at Little Liuqiu island, Taiwan: Application of deep learning algorithms Ocean Coast Manag. 262: 107111. Chen, V. Y., Lu, D.-J., Han, Y.-S., 2024b. Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan. Sustainability 16(1): 454; https://doi.org/10.3390/su16010454 Chiu, C.-C., Liao, C.-P. Kuo, T.-C., Huang, H.-W., 2020. Using citizen science to investigate the spatial-temporal distribution of floating marine litter in the waters around Taiwan. Marine Pollution Bulletin, Volume 157, 111301 Cigliano, J. A., Meyer, R., Ballard, H. L., Freitag, A., Phillips, T. B., Wasser, A., 2015. Making marine and coastal citizen science matter. Ocean Coast. Manage. 115, 77-87. (doi:10.1016/j.ocecoaman.2015.06.012) Cigliano, J. A., Kliman R.M., 2014. Density, age structure, and length of queen conch (Strombus gigas) in shallow-water aggregations in the Sapodilla Cayes Marine Reserve, Belize. Caribb. J. Sci., 48 (1), pp. 18-30 Cisneros-Montemayor, A. M., Townsel, A., Gonzales, C. M., Haas, A. R., Navarro-Holm, E. E., Salorio-Zuñiga, T., Johnson, A.F., 2020. Nature-based marine tourism in the Gulf of California and Baja California Peninsula: economic benefits and key species. Nat. Resour. Forum, 44 (2) (2020), pp. 111-128 CITES (https://cites.org/eng/gallery/species/reptile/green_turtle.html) Retrieved on 1st Dec. 2022 Coast Guard Director Office, Taiwan (https://www.facebook.com/CGA4U) Retrieved on 8th March 2022. Cranswick, A. S., Constantine, R., Hendriks, H., Carroll, E., 2022. Social media and citizen science records are important for the management of rarely sighted whales. Ocean Coast Manag. Vol. 226, 106271 Curtin, S., 2003. Whale-watching in Kaikoura: sustainable destination development? J. Ecotourism 2 (3), 173e195. Danielsen, F., Burgess, N. D., Jensen, P. M., Pirhofer-Walzl, K., 2010. Environmental monitoring: the scale and speed of implementation varies according to the degree of people's involvement. J. Appl. Ecol. 47, 1166-1168. Davidson, I. C., Crook, A. C., Barnes, D. K. A., 2004. Quantifying Spatial Patterns of Intertidal Biodiversity: Is Movement Important? Marine Ecology. Vol 25-1 p15-34. Dellermann, D., Ebel, P., Söllner, M., 2019. Hybrid Intelligence. Bus Inf Syst Eng 61, 637–643 (2019). https://doi.org/10.1007/s12599-019-00595-2 Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., Ebel, P., 2021. The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems. arXiv:2105.03354 https://doi.org/10.48550/arXiv.2105.03354 Deng, J., Dong, W., Socher, R.,Li, L.-J., Li, K., Li, F.-F., 2009. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848. Earp, H., Vye, S., Bohn, K., Burrows, M., Chenery, J., Dickens, S., Foster, C., Grist, H., Lamont, P., Long, S., Morrall, Z., Pocklington, J., Scott, A., Watson, G., West, V., Jenkins, S., Delany, J. and Sugden, H., 2022. Do You See What I See? Quantifying Inter-Observer Variability in an Intertidal Marine Citizen Science Experiment. Citizen Science: Theory and Practice, 7(1), p.12. DOI: http://doi.org/10.5334/cstp.483 Elqadi, M. M., Dorin, A., Dyer, A., Burd, M., Bukovac, Z., Shrestha, M., 2017. Mapping species distributions with social media geo-tagged images: case studies of bees and flowering plants in Australia. Ecol. Inf., 39 (2017), pp. 23-31 Ergin, A., Williams, A.T., Micallef, A., 2006. Coastal scenery: appreciation and evaluation. J. Coast. Res. 22 (4), 958e964. Everingham, M.,Eslami, S. M. A., Van Gool, L., Williams,C., Winn, J., Zisserman, A. 2010. The PASCAL Visual Object Classes (VOC) Challenge. Int J Comput Vis 88, 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4. French, G., Mackiewicz, M., Fisher, M., Holah, H., Kilburn, R.,Campbell, N., and Needle, C., 2020. Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. ICES Journal of Marine Science, 77:1340–1353. Fukushima, K., 1980. "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biological Cybernetics. 36 (4): 193–202. doi:10.1007/bf00344251. PMID 7370364. S2CID 206775608. Ghazanshahi, J., Huchel, T. D., Devinny, J. S., 1983. Alternation of Southern California rocky shore ecosystems by public recreational use. J. Environ. Manag., 16 (1983), pp. 379-394 Girshick, R., 2015. Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV) pp. 1440-1448. Girshick, R., Donahue J., Darrell T., Malik, J., 2014. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587, doi: 10.1109/CVPR.2014.81. Glenn, J., 2022. YOLOv5 release v6.1 https://github.com/ultralytics/yolov5/release/tag/v6.1 Goodfellow, I. J., Shlens, J., Szegedy, C., 2015. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572. Goodwin, M., Halvorsen K. T., Jiao L., Knausgård K. M., Martin A. H., Moyano M., Oomen R. A., Rasmussen J. H., Sørdalen T. K., Thorbjørnsen S. H., 2022. Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES Journal of Marine Science. 79:319–336. Guidino, C., Campbell, E., Alcorta, B., Gonzalez, V., Mangel, J.C., Pacheco, A.S., Silva, S., Alfaro-Shigueto, J., 2020. Whale watching in northern Peru: an economic boom? Tourism Mar. Environ., 15 (1) (2020), pp. 1-10 He, K., Gkioxari G., Dollár P., Girshick R., 2017. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. Hidalgo-Ruz, V., Thiel, M., 2013. Distribution and abundance of small plastic debris on beaches in the SE Pacific (Chile): a study supported by a citizen science project. Mar. Environ. Res., 87–88 (2013), pp. 12-18 Hsu, Y.-C., Huang, T.-H. K., Verma, H., Mauri, A.,Nourbakhsh, I., Bozzon, A., 2022. Empowering local communities using artificial intelligence. Patterns. Vol.3(3), 100449. Hsu,Y.-C., Huang,T.-H. K., Verma, H., Mauri, A., Nourbakhsh, I., Bozzon, A., 2022. Empowering local communities using artificial intelligence, Patterns,Vol. 3, Issue 3, 100449 Huff, T.M., 2011. Effects of human trampling on macro- and meiofauna communities associated with intertidal algal turfs and implications for management of protected areas on rocky shores (Southern California). Mar. Ecol., 32 (2011), pp. 335-345 Hyder, K., Townhill, B., Anderson L. G., Delany J, Pinnegar J. K.. 2015. Can citizen science contribute to the evidence-base that underpins marine policy? Marine Policy 59, 112-120. Jadidi, N., Varmazyar, M., 2023. A Survey of Cyber-physical Systems Applications (2017–2022). In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-97940-9_145 Jarvis, R. M., Bollard Breen, B., Krägeloh, C. U., Billington, D. R., 2015. Citizen science and the power of public participation in marine spatial planning. Mar. Policy 57, 21–26. Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B., 2022.A Review of Yolo Algorithm Developments. Procedia Computer Science Volume 199, 2022, Pages 1066-1073 Journal of Marine Science. 77(7-8), 2882-2889. Doi:10.093/icesmms/fsaa161. Jie,W.B., Gan,B.Q., Chen, V.Y., Kuo, S.H., 2016. Pseudoceros magangensis: a new species of pseudocerotid flatworm (Platyhelminthes: Polycladida) from Taiwan. Platax 13, 3 Kelly, R., Fleming, A., Pecl, G. T., von Gönner J., Aletta Bonn, A., 2020. Citizen science and marine conservation: a global review. Vol. 375 Issue 1814. https://doi.org/10.1098/rstb.2019.0461 Kido, J. S., Murray,S. N., 2003.Variation in owl limpet Lottia gigantea population structures, growth rates, and gonadal production on southern California rocky shored. Mar. Ecol. Prog. Ser., 257 (2003), pp. 111-124 Kieslinger, B., Schäfer, T., Heigl, F., Dörler, D., Richter, A., Bonn, A., 2018. Evaluating citizen science - Towards an open framework. In: Hekler, S., Haklay, M., Bowser, A., Vogel, J., Bonn, A. (Eds.), Citizen Science-Innovation in Open Science, Society and Policy. UCL Press, London, pp. 81–95. Kosmala, M, Wiggins, A, Swanson, A and Simmons, B., 2016. Assessing data quality in citizen science. Frontiers in Ecology and the Environment, 14(10): 551–560. DOI: https://doi.org/10.1002/fee.1436 Lamb, G., 2019. Spectacular sea turtles: Circuits of a wildlife ecotourism discourse in Hawaii. Applied Linguistics Review, vol. 12, no. 1, 2021, pp. 93-121. https://doi.org/10.1515/applirev-2019-0104. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 86 (11): 2278–2324. doi:10.1109/5.726791. Lin, T.Y., 2014. Microsoft COCO: Common Objects in Context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham. https://doi.org/10.1007/978-3-319-10602-1_48 Liuqion Township Office, Tourist statistic report (https://www.pthg.gov.tw/liuchiu/News_Content.aspx?n=D783F419458419EE&sms=D090B100E72FF603&s=6AE21CC577A19E59) Retrieved on 14th Oct. 2022. Loke, L. H., Todd,P. A., 2016. Structural complexity and component type increase intertidal biodiversity independently of area. Ecology sea. Vol 97-2. P383-393. Lotfian, M., Ingensand, J., Brovelli, M. A., 2021. The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality. Sustainability 2021, 13(14), 8087; https://doi.org/10.3390/su13148087 Lubchenco, J., Menge, B. A., 1978. Community development and persistence in a low rocky intertidal Zone. Ecologicial Monographs. Vol. 48 issue 1 p67-94. Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Kendrick, G., 2020. Automatic detection of Western rock lobster using synthetic data, ICES Journal of Marine Science, 77:1308–1317. Majidi, B., Hemmati, O., Baniardalan, F., Farahmand, H., Hajitabar, A., Sharafi, S., Aghajani, K., Esmaeili, A., Manzuri, M.T., 2020. Geo-spatiotemporal intelligence for smart agricultural and environmental eco-Cyber-Physical systems, Enabling AI Applications in Data Science. Springer, pp. 471-491. Malde, K., Handegard, N. O., Eikvil, L., and Salberg, A.-B., 2019. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science, 77: 1274–1285. Matear, L., Robbins, J. R., Hale, M., Potts, J., 2019. Cetacean biodiversity in the Bay of Biscay: suggestions for environmental protection derived from citizen science data. Mar. Policy. 109, 103672. McClure, E. C., Sievers, M., Brown, C. J., Buelow, C. A., Ditria E. M., Hayes, M. A., Pearson R. M., Tulloch, V. J. D., Unsworth, R. K. F. , Connolly, R. M., 2020. Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring. Patterns, Vol.1(7), 100109 McKinley, D.C., Miller-Rushing, A. J., Ballard, H. L., Bonney, R., Brown, H., Cook-Patton, s. C., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., Ryan, S. F., Shanley, L. A., Shirk,J. L., Stepenuck, K. F., Weltzin, J. F. , Wiggins, A., Boyle, O. D., Briggs, R. D., Chapin, S. F., Hewitt, D. A., Preuss, P. W.,Soukup, M. A., 2017. Citizen science can improve conservation science, natural resource management and environmental protection. Biol. Conserv. 208, 15-28. Miller-Rushing, A., Primack, R., Bonney, R., 2012. The history of public participation in ecological research Front. Ecol. Environ., 10 (6), pp. 285-290 Muksit, A., Hasan, F., Emon, F. B., Haque, R., Anwary, A. R., Shatabd, S., 2022. YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Ecological Informatics 72(2022) 101847. Murray, S. N., Denis, T. G., Kido, J. S., Smith, J. R., 1999. Human visitation and the frequency and potential effects of collecting on rocky intertidal populations in southern California marine reserves. Rep. California Cooper. Ocean. Fish. Investig., 40 (1999), pp. 100-106 National Oceanic and Atmospheric Administration, Legislative History of the National Marine Sanctuaries Act (https://sanctuaries.noaa.gov/about/legislation/leg_history.html), Retrieved on 20th Sept. 2023. Nazerdeylami, A., Majidi, B., Movaghar, A., 2021. Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems. Ocean and Coastal Management, Volume 200 105478 Neumann, B., Vafeidis, A.T., Zimmermann, J., Nicholls, R.J., 2015. Future coastal population growth and exposure to sea level rise and coastal flooding - a global assessment. PLoS One 10 (3), e0118571. Nursey-Bray, M., Palmer, R., Pecl, G. T., 2018. Spot, log, map: assessing a marine virtual citizen science program against Reed's best practice for stakeholder participation in environmental management. Ocean Coast. Manage. 15, 1-9. Ocean Affairs Council, Taiwan. 2019. https://www.oac.gov.tw/ch/home.jsp?id=67&parentpath=0,6&mcustomize=bulletin_view.jsp&dataserno=201912030004, Retrieved on 12th July. 2022. Ocean Conservation Administration. Ocean Affairs Council, Taiwan. 2021. Survey report. https://www.cna.com.tw/news/ahel/202107100081.aspx Retrieved on 1th Dec. 2022. Orams, M.B., 2002. Feeding wildlife as a tourism attraction: a review of issues and impacts. Tour. Manag. 23 (3), 281e293. Own-Bar, E.,and Trivedi, M. M., 2017. Multi-scale volumes for deep object detection and localization. Pattern Recognition, 61: 557-572. Ozbulak, G., Aytar, Y., and Ekenel, H. K., 2016. How Transferable Are CNN-Based Features for Age and Gender Classification? In 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1-6. IEEE. Pattengill-Semmens, C., Semmens, B., 2003. Conservation and management applications of the REEF volunteer fish monitoring program. Environ. Monit. Assess., 81(2003), pp.43-50 Polunin, N. V. C., Raffaelli, D. G., Williams, PJle, B., 2005. Marine Ecology: Processes, Systems and Impacts. Oxford University Press, Oxford. Rafner, J., Gajdacz, M., Kragh, G., Hjorth, A., Gander, A., Palfi, B., Berditchevskaia, A., Grey, F., Gale, K., Segal, A., Walmsley, M., Miller,J.A., Dellerman, D., Haklay, M., Michelucci, P., Sherson J., 2021. Revisiting Citizen Science Through the Lens of Hybrid Intelligence. arXiv:2104.14961 [cs.HC] https://doi.org/10.48550/arXiv.2104.14961 Rangel-Buitrago, N., 2023. Human epoch - Human responsibility: Rethinking coastal zone management in the Anthropocene. Ocean Coast Manag., 244(2023), 106801 Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You Only Look Once: Unified, Real-Time Object Detection. arXiv:1506.02640. Redmon, J., Farhadi, A., 2018. YOLOv3: An Incremental Improvement. arXiv:1804.02767. Ren, S., He, K., Girshick, R., Sun, J., 2016. Faster R-CNN: towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28 (NIPS 2015) https://arxiv.org/pdf/1506.01497.pdf Rick van Essen, R., Mencarelli, A., van Helmond, A., Nguyen, L., Batsleer, J., Poos, J.-J., and Kootstra, G., 2021. Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review. ICES Journal of Marine Science, 78: 3834–3846. Rosenthal, I. S., Byrnes, J. E., Cavanaugh, K. C., Bell, T. W., Harder, B., Haupt, A. J., Rassweiler, A. T. W., Perez-Matus, A., Assis, J., Swanson, A., Boyer, A., McMaster, A. Trouille, L., 2018. Floating forests: Quantitative validation of citizen science data generated from consensus classifications. ArXiv Preprint, 1801.08522 Roy, H. E., Pocock, M. J. O., Preston, C. D., Roy, D., Savage, J., Tweddle, J. C., & Robinson, L. D., 2012. Understanding Citizen Science and Environmental Monitoring, final report on behalf of UK Environmental Observation Framework. Roy, K., Collins, A. G., Becker, B. J., Begovic, E., Engle, J. M., 2003. Anthropogenic impacts and historical decline in body size of rocky intertidal gastropods in southern California. Ecol. Lett., 6 (2003), pp. 205-211 Saleh, A., Laradji, I. H., Konovalov, D. A., Bradley, M., Vazquez, D., Sheaves, M., 2020. A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Sci. Rep. 10 (1), 1–10. Salman, A., Siddiqui, S. A., Shafait, F., Mian, A., Shortis, M. R., Khurshid, K., Ulges, A., Schwanecke, U., 2020. Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES Journal of Marine Science, 77 (4), 1295–1307. Schiel, D. R., Taylor, D.I., 1999. Effects of trampling on a rocky intertidal algal assemblage in southern New Zealand. J. Exp. Mar. Biol. Ecol., 235 (1999), pp. 213-235 Schneider S., Taylor G. W., Linquist S., Kremer S. C., 2019. Past, present and future approaches using computer vision for animal re-identification from camera trap data. Methods in Ecology and Evolution, 10: 1151–1155. Scuderi, A., Sanabria, J. G., Vighi, M., Merino, L., Martins, F. C., 2022. Insights into sustainable tourism policy: Identikit of the whale watchers and their economic contribution in Tarifa (Strait of Gibraltar). Ocean Coastal Manag. 227, 106293. Smith, J. R., Fong, P., Ambrose, R. F., 2006. Long-term change in mussel (Mytilus californianus Conrad) populations along the wave-exposed coast of southern California. Mar. Biol., 149 (2006), pp. 537-545 Smith, J. R., Murray, S.N., 2005. The effects of experimental bait collection and trampling on a Mytilus californianus mussel bed in southern California. Mar. Biol., 147 (2005), pp. 699-706 Stoeckl, N., Birtles, A., Farr, M., Mangott, A., Curnock, M., Valentine, P., 2010. Live-aboard dive boats in the Great Barrier Reef: regional economic impact and the relative values of their target marine species. Tour. Econ. 16 (4), 995e1018. Tapsuwan, S., Asafu-Adjaye, J., 2008. Estimating the economic benefit of SCUBA diving in the Similan Islands, Thailand. Coast. Manag. 36 (5), 431e442. Tisdell, C. and Wilson, C., 2005. Perceived Impacts of Ecotourism on Environmental Learning and Conservation: Turtle Watching as a Case Study. Environment, Development and Sustainability volume 7, pp.291–302 Springer. Toivonen, T., Heikinheimo, V., Fink, C., Hausmann, A., Hiippala, T., Järv, O., Tenkanen, H., Di Minin, E., 2019. Social media data for conservation science: a methodological overview. Biol. Conserv. 233(2019), pp. 298-315. Tseng, C.-H., and Kuo, Y.-F., 2020. Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks. ICES Journal of Marine Science, 77: 1367–1378. Turrini, T., Dörler, D., Richter, A., Heigl, F., Bonn, A., 2018 The threefold potential of environmental citizen science—generating knowledge, creating learning opportunities and enabling civic participation. Biol. Conserv. 225, 178-186. van der Velde, T, Milton, D. A., Lawson, T. J., Wilcox, C, Lansdell, M., Davis, G., Perkins, G., Hardesty, B. D., 2017. Comparison of marine debris data collected by researchers and citizen scientists: Is citizen science data worth the effort?. Biological Conservation, 208: 127–138. DOI: https://doi.org/10.1016/j.biocon.2016.05.025 van Essen, R., Mencarelli, A., van Helmond, A., Nguyen, L., Batsleer, J., Poos, J.-J., and Kootstra, G., 2021. Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review. ICES Journal of Marine Science, 78: 3834–3846. van Helmond, A., Chen, C., and Poos, J.-J., 2017. Using electronic monitoring to record catches of sole (Solea solea) in a bottom trawl fishery. ICES Journal of Marine Science, 74: 1421–1427. Velando, A., Munilla, I., 2011. Disturbance to a foraging seabird by sea-based tourism: implications for reserve management in marine protected areas. Biol. Conserv. 144 (3), 1167e1174. Wang, C.-Y., Bochkovskiy, A., Liao,M. H.-Y. 2022. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv:2207.02696 Ward, E.J., Marshall, K.N., Ross, T., Sedgley, A., Hass, T., Pearson, S.F., Joyce, G., Hamel, N.J., Hodum, P.J., Faucett, R., 2015. Using citizen-science data to identify local hotspots of seabird occurrence PeerJ, 3, p. e704 Warner, K. A., Lowell, B., Timme, W., Shaftel, E., Hanner, R. H., 2019 Seafood sleuthing: how citizen science contributed to the largest market study of seafood mislabeling in the US and informed policy. Marine Policy 99, 304-311 Weaver, D. B., & Lawton, L. J. 2007. Twenty years on: The state of contemporary ecotourism research. Tourism Management, 28(5), 1168–1179. https://doi.org/10.1016/j.tourman.2007.03.004. Wei, X.-S., Xie, C.-W., Wu, J., and Shen, C., 2018. Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognition, 76: 704-714. Weinstein B. G., 2018. A computer vision for animal ecology. Journal of Animal Ecology, 87: 533–545. Wildlife Conservation Act, Taiwan (https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=M0120001) Retrieved on 14th Feb. 2023. Windrim, L., Ramakrishnan, R., Melkumyan, A., and Murphy, R., 2016. Hyperspectral CNN Classification with Limited Training Samples.arXiv preprint arXiv:1611.09007. Witherington, B., and Witherington, D., 2015. Our Sea Turtles: A Practical Guide for the Atlantic and Gulf, from Canada to Mexico. Pineapple Press, Florida USA. Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., Jackson, J. B. C., Lotze, H. K., Micheli, F., Palumbi, S. R., Sala, E., Selkoe, K.A ., Stachowicz, J. J., Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787e790. Zettler, E. R., Takada H., Monteleone B., Mallos N., Eriksen M., Amaral-Zettler L. A., 2017. Incorporating citizen science to study plastics in the environment. Anal. Methods 9, 1392-1403. Zhang, J., Chen, S., Cheng, C., Liu, Y., Jennerjahn, T. C., 2023. Citizen science to support coastal research and management: Insights from a seagrass monitoring case study in Hainan, China. Ocean Coast Manag., 231(2023), 106403. Zhang, Y., Kang, B., Hooi, B., Yan,S., Feng, J., 2021. Deep Long-Tailed Learning: A Survey. arXiv:2110.04596 https://doi.org/10.48550/arXiv.2110.04596. Zhao, Z., Liu, Y., Sun, X., Liu, J., Yang, X., & Zhou, C., 2021. Composited FishNet: Fish Detection and Species Recognition From Low-Quality Underwater Videos. IEEE Transactions on Image Processing, 30, 4719-4734. 屏東縣琉球鄉公所（2022）《111年屏東縣琉球鄉觀光人數統計通報》（https://www.pthg.gov.tw/liuchiu/News_Content.aspx?n=D783F419458419EE&sms=D090B100E72FF603&s=E01CC4FD16376000） 海巡署署長辦公室臉書（2022）(https://www.facebook.com/CGA4U) Retrieved on 8th March 2022. 海洋保育署（2019）海洋保護區https://www.oac.gov.tw/ch/home.jsp?id=67&parentpath=0,6&mcustomize=bulletin_view.jsp&dataserno=201912030004, Retrieved on 12th July. 2022. 海洋保育署（2021）《109年度岩礁生態系調查計畫》海洋事務委員會。https://www.oca.gov.tw/ch/home.jsp?id=220&parentpath=0&mcustomize=research_view.jsp&dataserno=202101280023 野澤洋耕、馮加伶、何芷蔚、陳芃諭、蘇淮（2119）。108 年度臺灣周邊海龜族群調查計畫成果報告書。海洋委員會海洋保育署。 陳楊文（2011）《一個潮池的秘密》臺北市：行人文化出版社。 陳楊文（2023）《AI人工智慧辨識應用》新北市：台科大圖書有限公司。 臉書《海龜點點名 TurtleSpot Taiwan》（2022）https://www.facebook.com/groups/470808019930309 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93314 | - |
| dc.description.abstract | 人類對近海資源的利用，由傳統漁業發展至蓬勃的藍色經濟模式，尤其在沿海休閒活動方面特別顯著。然而，當代如人口增長、都市化和氣候變化等環境衝擊已造成潮間帶生態系統顯著的生態影響，需要從科學、社會、經濟、法律等，多元方法進行海岸生態資源的永續管理。在日益增加的旅遊壓力下，小琉球和三貂角馬崗的海岸，面臨了經濟利益與環境保護之間的衝突。因應的海洋保育法尚在進行立法過程，現實存在管理上的窘境，也凸顯了社區自發參與保育倡議的迫切性。本研究探究如何運用AI與人之間的協力合作下，善用人工智慧視覺識別技術在海洋資源管理中的應用，重點關注綠蠵龜保育和通過結合公民科學和人工智慧協同的混合智慧（HI）框架，促進公民參與，希望達到教育遊客改變遊憩行為模式。在AI辨識綠蠵龜研究中，發現影像多樣化的小型訓練集，即可以訓練出高效能的YOLOv5s模型，提供未來運用AI調查野生動物的資料管理需求。三貂角混合智慧研究中，在AI的識別輔助下，不僅能促成公民科學參與者對科學數據的集體調查，且能利用機器學習結果來衡量與增進公民科學調查數據的品質，因而能評估調查協議策略的優缺點，且產生一個交互的、持續的、迭代增強混合智慧學習環境，在同時兼顧聯合國永續發展指標中的「良好工作與經濟發展」、「負責任的消費與生產」與「維護海洋資源」，對於未來海洋保護區的設定和管理提供社會與科技（Socio-technology）綜合模式、確保在海岸生物多樣性資源保護的效能和永續經營。 | zh_TW |
| dc.description.abstract | The utilization of littoral zone resources by human populations has evolved from traditional fisheries to a burgeoning blue economy paradigm, notably in coastal recreation. However, contemporary challenges such as population growth, urbanization, and climate change have precipitated notable ecological impacts on intertidal ecosystems, necessitating multifaceted approaches to ensure sustainable management. Studies in Little Liuqiu and Cape Santiago illustrate this dynamic, showcasing the tension between economic imperatives and environmental stewardship in the face of increasing tourist pressures. Despite legislative efforts, regulatory gaps persist, highlighting the importance of community-led conservation initiatives. This study aims to explore the integration of visual recognition AI technologies in marine resource management, focusing on green sea turtle conservation and citizen participation through a hybrid intelligence (HI) framework combining citizen science (CS) and AI-assisted learning methodologies. Through this experimentation, the streamlined YOLOv5s model consistently eclipsed its more complex counterparts in performance. The Santiago HI initiative not only streamlines the collective gathering and AI-assisted analysis of critical data but also utilizes machine-learning outputs to assess data quality, informing subsequent data collection and refinement strategies. This process fosters a mutual and continuous HI learning environment through collective and iterative enhancement. Our HI model plays a crucial role in promoting community engagement and public participation in CS efforts, developing the skills needed to document changes in rocky intertidal biodiversity. These efforts are essential for guiding the design and socio-technology governance of future Marine Protected Areas (MPAs), ensuring their effectiveness and sustainability in alignment with SDGs 8, 12, and 14 in marine conservation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-29T16:12:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-29T16:12:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要與關鍵字 iii Abstract and Keywords iv 中英文專有名詞與簡寫 v 目次 vi 表次 xi 圖次 xv 第1章 緒論 1 1.1人類世對海洋資源的衝擊 1 1.2 藍色經濟與永續目標 3 1.3 海洋公民科學 4 1.4 海岸資源保育 6 1.5人工智慧應用在海洋保育 7 1.6 研究動機與目的 9 第2章 用於永續生態旅遊之綠蠵龜圖像AI模型之生成訓練與效能評估 13 2.1. 前言 13 2.2 材料與方法 16 2.2.1 調查地點與影像資料蒐集 16 2.2.2 AI模型選擇與訓練方方法 17 2.2.3 AI模型效能評估 18 2.2.4 AI模型最佳化實驗 19 2.2.5 網路部署人工智慧模型提供辨識服務 21 2.3. 結果 22 2.3.1 蒐集資料註記結果 22 2.3.2 資料集數量對不同模型效應 23 2.3.3資料集標示方式對不同模型效應 23 2.3.4 YOLO模型性能比較分析 24 2.3.5 部署應用 25 2.4. 討論 26 2.4.1 訓練資料集數量與品質的影響 26 2.4.2 不同YOLO版本性能比較 26 2.4.3 AI服務與使用者體驗 30 2.4.4 使用者與AI互動 31 2.4.5 AI辨識對綠蠵龜保育的影響 32 第3章 應用人機協同於海洋保護區倡議 34 3.1 前言 34 3.2 材料與方法 37 3.2.1 研究區域與公民科學調查計畫 37 3.2.2 訓練AI模型和效能評估 38 3.2.3 公民科學和AI的混合智慧協同性標準 39 3.3 結果 41 3.3.1 調查協議（Survey Protocol）內容與規範 41 3.3.2 建議調查協議（PSP）和AI模型訓練結果 43 3.3.3 調適調查協議（ASP）和AI模型訓練結果 44 3.3.4部署人工智慧提供辨識服務 44 3.4 討論 45 3.4.1 改善海洋生物多樣性調查方法 45 3.4.2 公民科學與AI協力學習系統 47 3.4.3 發展混合智慧的過程 49 3.4.4 社會與科技的融合 51 3.4.5 混合智慧在設定海洋保護區中所扮演的角色 55 第4章 結論與未來研究方向 58 參考文獻 62 附表 87 附圖 92 附錄：本論文已發表之學術文章 108 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 應用混合智慧於海洋生物多樣性資源之管理研究 | zh_TW |
| dc.title | The Study of Hybrid Intelligence on the Management of Marine Biodiversity Resource | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳正虔;盧道杰;楊恩誠;張俊偉 | zh_TW |
| dc.contributor.oralexamcommittee | Tsen-Chien Chen;Day-Jye Lu;En-Cheng Yang;Chun-Wei Chang | en |
| dc.subject.keyword | 綠蠵龜,三貂角,YOLO,公民科學,人工智慧,混合智慧,岩礁潮間帶生物多樣性,永續發展指標, | zh_TW |
| dc.subject.keyword | Green Sea Turtle,Biodiversity,Conservation initiative,Citizen Science,Marine Protect Areas,Rocky Intertidal Ecosystem,Hybrid Intelligence,YOLO,SDGs, | en |
| dc.relation.page | 159 | - |
| dc.identifier.doi | 10.6342/NTU202401236 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-06-20 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 漁業科學研究所 | - |
| 顯示於系所單位： | 漁業科學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 23.29 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。