系統識別號 U0026-0401202001085200
論文名稱(中文) 整合遙測與機器學習於多時間尺度魚場預測
論文名稱(英文) Integration of Remote Sensing and Machine Learning for Multi-timescale Fishing Area Determination
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 108
學期 1
出版年 109
研究生(中文) 艾迪拉
研究生(英文) Adillah Alfatinah
電子信箱 adillah.alfatinah@gmail.com
學號 P66077026
學位類別 碩士
語文別 英文
論文頁數 127頁
口試委員 指導教授-朱宏杰
中文關鍵字 none 
英文關鍵字 Skipjack Tuna  Remote Sensing  Environmental Parameters  Decision Tree (DT)  Generalized Linear Model (GLM)  Catch Per Unit Effort (CPUE) 
中文摘要 none
英文摘要 Skipjack tuna was one of the most important and significant catches of fish in the world. That makes maintaining their existences critically important to human communities that rely on them for food and economic well-being, particularly at a time of global ocean change. The tuna distribution in broad geographic areas and high market value have gradually supported the political and economic importance of the various commercial activities related to this species group. Predicting a specific fish area for this species can help to optimize the catch and help make the monitoring more accessible. Studies about predicting fish area were commonly done by investigating the relation between skipjack tuna life preference with the marine environmental variables, such as Chlorophyll-a, Sea Surface Temperature, and so on. Satellite imagery often employed to obtain the value of these environmental variables due to its ability to monitor a large area and provide much more information than it would be possible to get solely from the surface.
This study used skipjack tuna catch data in the year 2017 and utilized Chlorophyll-a, Sea Surface Temperature (SST), and Sea Surface Height (SSH) as the environmental variables. Chlorophyll-a and SST was obtained from MODIS-Aqua Level 3 SMI while SSH was obtained from Global Ocean Analysis. Following the catch data, all the environmental data were from the year 2017. This study considered three multi-scale cases distinguished by temporal processing. The Case I processed with the yearly data in 2017, Case II processed seasonal data, and Case III processed weekly data. Two methods (Decision Tree, DT and Generalized Linear Model, GLM) were employed for predicting skipjack area. DT achieved mostly above 80% accuracy rate. Overall, based on the accuracy assessment, this study concluded that DT appears to perform better than GLM in predicting skipjack tuna fish area. Furthermore, this study also predicted the fish catch per unit effort (CPUE) using regression tree and GLM, both models were appropriate for prediction. Meanwhile, GLM (average RMSE is 11.452) performed slightly better than the regression tree (average RMSE is 10.878) in the monthly. Moreover, the most influenced environmental variable in both model construction was SST, which means the existence of skipjack tuna in one region is affected mostly by the regional temperature.
論文目次 ABSTRACT ii
2.1 Skipjack Tuna Presence Relation with Environmental Parameter 5
2.2 Utilization of Satellite Imagery for Fish Prediction 6
2.3 Related Works Regarding to Fishing Area Prediction 8
3.1 Study Area & Cases 11
3.1.1 Study Area 11
3.1.2 Study Case 12
3.2 Data Collection 13
3.2.1 Fishing Catch Points 14
3.2.2 Environmental Factors Satellite Imagery 19
3.3 Workflow 22
3.4 Fish Presence Area Prediction Processing 27
3.4.1 Fishing Catch Points Density Map 27
3.4.2 Training and Testing Samples Dataset Preparation 28
3.4.3 Fishing Area Prediction Model Construction 29
3.4.4 Accuracy Assessment 32
3.4.5 Prediction Model Selection for Weekly Processing 34
3.5 Catch Per Unit Efforts Prediction Processing 35
3.5.1 Catch Per Unit Efforts Calculation 35
3.5.2 Training and Testing Samples Dataset Preparation 36
3.5.3 CPUE Prediction Model Construction 36
3.5.4 Root Mean Square Error (RMSE) Calculation 38
4.1 Fishing Catch Points Density Maps 39
4.2 Fish Presence Probability Map 43
4.2.1 Fish Presence Probability Map Predicted by DT 44
4.2.2 Fish Presence Probability Map Predicted by GLM 54
4.3 Fish Presence Area Maps 61
4.3.1 Fish Presence Area Maps Predicted by DT 61
4.3.2 Fish Presence Area Maps Predicted by GLM 66
4.4 Accuracy Assessment 70
4.4.1 Accuracy Assessment for Case I 70
4.4.2 Accuracy Assessment for Case II 71
4.4.3 Accuracy Assessment for Case III 72
4.5 Model Prediction Selection Based on the Environmental Variables Correlation 74
4.6 CPUE Value Prediction 77
4.6.1 CPUE Prediction from Regression Tree 77
4.6.2 CPUE Prediction from GLM 83
4.6.3 RMSE Calculation 88
4.7 Discussion 96
4.7.1 Model Construction & Performances 96
4.7.2 Spatial Distribution of Predicted Skipjack Tuna Fish Area 99
4.7.3 Temporal Change in Environmental Parameter 107
5.1 Conclusion 114
5.2 Future Works 116
參考文獻 Abeare, S. (2009). Comparison of boosted regression tree, GLM and GAM performance in the standardization of yellowfin tuna catch-rate data from the gulf of Mexico Lonline fishery. Thesis. Louisiana State University and Agriculture and Mechanical College.. Lousiana State University.
Alexander, R. E. (2016). A Comparison of GLM , GAM , and GWR Modeling of Fish Distribution and Abundance in Lake Ontario. University of Southern Calfornia.
Baboo, D. S. S., &Devi, M. R. (2010). An Analysis of Different Resampling Methods in Coimbatore, District. Global Journal of Computer Science and Technology, 10(15), 61–66. Retrieved from http://globaljournals.org/GJCST_Volume10/10-An-Analysis-of-Different-Resampling-Methods-in-Coimbatore-District.pdf
Barkley, R. a, Neill, W. H., &Gooding, R. M. (1978). Skipjack Tuna, Katsuwonus Pelamzs, Habitat Based on Temperature and Oxygen Requirements. Fishery Bulletin, 76(3), 653–662.
Breiman, L., Friedman, J. H., Olshen, R. A., &Stone, C. J. (1984). Classification and regression trees. Classification and Regression Trees, pp. 1–358. https://doi.org/10.1201/9781315139470
Brewington, L., Frizzelle, B. G., Walsh, S. J., Mena, C, F., &Sampedro, C. (2014). The Galapagos Marine Reserve. The Galapagos Marine Reserve, Social and Interactions in the Galapagos Islands, 71–80. https://doi.org/10.1007/978-3-319-02769-2
Chai, T., &Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Daqamseh, S. T., Mansor, S., Pradhan, B., Billa, L., &Mahmud, A. R. (2013). Potential fish habitat mapping using MODIS-derived sea surface salinity, temperature and chlorophyll-a data: South China Sea Coastal areas, Malaysia. Geocarto International, 28(6), 546–560. https://doi.org/10.1080/10106049.2012.730065
deGaridel-Thoron, T., Rosenthal, Y., Bassinot, F., &Beaufort, L. (2005). Stable sea surface temperatures in the Western Pacific warm pool over the past 1.75 million years. Nature, 433, 294–298. https://doi.org/10.1038/nature03189
Dean, A. M., &Populus, J. (2007). Remote sensing and GIS integration. In Advances in geographic information systems and remote sensing for fisheries and aquaculture (pp. 147–189). Retrieved from ftp://ftp.fao.org/fi/Cdrom/T552/root/06.pdf
Dueri, S., Faugeras, B., &Maury, O. (2012). Modelling the skipjack tuna dynamics in the Indian Ocean with APECOSM-E: Part 1. Model formulation. Ecological Modelling, 245, 41–54. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2012.02.007
Elith, J., Leathwick, J. R., &Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Fitrianah, D., Praptono, N. H., Hidayanto, A. N., &Arymurthy, A. M. (2015). Feature Exploration for Prediction of Potential Tuna Fishing Zones. International Journal of Information and Electronics Engineering, 5(4), 270–274. https://doi.org/10.7763/ijiee.2015.v5.543
Fransen, B., Duke, S., Mcwethy, G., Walter, J., &Bilby, R. (2006). A Logistic Regression Model for Predicting the Upstream Extent of Fish Occurrence Based on Geographical Information Systems Data. North American Journal of Fisheries Management - NORTH AM J FISH MANAGE, 26, 960–975. https://doi.org/10.1577/M04-187.1
Froeschke, B. F., Tissot, P., Stunz, G. W., &Froeschke, J. T. (2013). Spatiotemporal Predictive Models for Juvenile Southern Flounder in Texas Estuaries. North American Journal of Fisheries Management, 33(4), 817–828. https://doi.org/10.1080/02755947.2013.811129
Galland, G., Rogers, A., &Nickson, A. (2016). Netting billions: A Global Valuation of Tuna. In The Pew Charitable Trusts. Retrieved from http://www.pewtrusts.org/en/research-and-analysis/reports/2016/05/netting-billions-a-global-valuation-of-tuna
Gao, F., Chen, X., Guan, W., &Li, G. (2016). A new model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea. Acta Oceanologica Sinica, 35(4), 74–81. https://doi.org/10.1007/s13131-015-0767-8
Gupta, A. K., Johnson, B. E., &Nagar, D. K. (2013). Testing Equality of Several Correlation Matrices. Revista Colombiana de Estadística, 36(2), 239–260. https://doi.org/10.2307/2985240
Hampton, J. (2010). Tuna Fisheries Status and Management in the Western and Central Pacific Ocean. 23.
Hunt, E. B. (1966). Experiments in induction / Earl B. Hunt, Janet Marin, Philip J. Stone (P. J.Stone &J.Marin, Eds.). New York: Academic Press.
IATCC. (2016). Tunas, billfishes and other pelagic species in the eastern Pacific Ocean in 2015. 02(August).
Indian Ocean Tuna Commission. (2005). Executive Summary Of The Status Of The Skipjack Tuna Resource.
Izenman, A. J. (2008). Modern Multivariates Statistical Techniques. London, UK: Springer.
Jagannathan, S., Samraj, A., &Rajavel, M. (2012). Potential fishing zone estimation by rough cluster predictions. Proceedings of International Conference on Computational Intelligence, Modelling and Simulation, 82–87. https://doi.org/10.1109/CIMSim.2012.34
Kitchell, J. F., Neill, W. H., Dizon, A. E., &Magnuson, J. J. (1978). Bioenergetic Spectra of Skipjack and Yellowfin Tunas. The Physiological Ecology of Tunas, 357–368. https://doi.org/10.1016/b978-0-12-639180-0.50030-6
Kitts, B. (2006). Regression Trees Lecture. Data Mining, 7. Retrieved from http://www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf
Laevastu, T., &Hayes, M. L. (1981). Fisheries Oceanography and Ecology. Fishing News Books Ltd.
Leclere, J., Oberdorff, T., Belliard, J., &Leprieur, F. (2011). A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system. Ecological Informatics, 6(5), 276–285. https://doi.org/10.1016/j.ecoinf.2011.05.001
Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., &Picaut, J. (1997). El Nino Southern Oscillation and tuna in the western Pacific. Nature, 389(6652), 715–718. https://doi.org/10.1038/39575
Lehodey, Patrick, Andre, J. M., Bertignac, M., Hampton, J., Stoens, A., Menkes, C., …Grima, N. (1998). Predicting skipjack tuna forage distributions in the equatorial Pacific using a coupled dynamical bio-geochemical model. Fisheries Oceanography, 7(3–4), 317–325. https://doi.org/10.1046/j.1365-2419.1998.00063.x
Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8
Mahaliyana, A. S., Jinadasa, B. K. K. K., Liyanage, N. P. P., Jayasinghe, G. D. T. M., &Jayamanne, S. C. (2015). Nutritional Composition of Skipjack Tuna (Katsuwonus pelamis) Caught from the Oceanic Waters around Sri Lankae. American Journal of Food and Nutrition, 3(4), 106–111. https://doi.org/10.12691/ajfn-3-4-3
Manel, S., Williams, H. C., &Ormerod, S. J. (2001). Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38(5), 921–931. https://doi.org/10.1046/j.1365-2664.2001.00647.x
Mansor, S., Tan, C. K., Ibrahim, H. M., &Shariff, A. R. M. (2001). Sattelite Fish Forecasting in South China Sea. 22nd Asian Conference on Remote Sensing, (November). Retrieved from http://www.crisp.nus.edu.sg/~acrs2001/pdf/015venka.pdf
Maunder, M. (2009). Updated indicators of stock status for skipjack tuna in the eastern Pacific Ocean.
McCluney, J. K., Anderson, C. M., &Anderson, J. L. (2019). The fishery performance indicators for global tuna fisheries. Nature Communications, 10(1), 1–9. https://doi.org/10.1038/s41467-019-09466-6
McKeen, S., Wilczak, J., Grell, G., Djalalova, I., Peckham, S., Hsie, E. Y., …Mathur, R. (2005). Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004. Journal of Geophysical Research Atmospheres, 110(21), 1–16. https://doi.org/10.1029/2005JD005858
McKenna Jr., J. E., &Castiglione, C. (2014). Model distribution of Silver Chub (Macrhybopsis storeriana) in western Lake Erie. American Midland Naturalist, 171(2), 301–310. https://doi.org/10.1674/0003-0031-171.2.301
Mfc, G. L. O., &Dombrowsky, E. (2011). For the GLOBAL Ocean Sea Physical Analysis and Forecasting Products GLOBAL _ ANALYSIS _ FORECAST _ PHYS _ 001 _ 001 _ c And GLOBAL _ ANALYSIS _ FORECAST _ PHYS _ 001 _ 001 _ d. (June), 1–26.
Miyake, M. P., Guillotreau, P., Sun, C.-H., &Ishimura, G. (2010). Recent developments in the tuna industry. Rome: FAO.
Morgan, J. N., &Messenger, R. C. (1973). THAID, a sequential analysis program for the analysis of nominal scale dependent variables,. Retrieved from http://lib.ugent.be/catalog/rug01:001027612
Morgan, J. N., &Sonquist, J. A. (1963). Problems in the Analysis of Survey Data, and a Proposal. Journal of the American Statistical Association, 58(302), 415–434.
Mugo, R., Saitoh, S. I., Nihira, A., &Kuroyama, T. (2010). Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective. Fisheries Oceanography, 19(5), 382–396. https://doi.org/10.1111/j.1365-2419.2010.00552.x
Murthy, S. K., Kasif, S., &Salzberg, S. (1994). A System for Induction of Oblique Decision Trees. Journal of Artificial Intelligence Research, 2, 1–32. https://doi.org/10.1613/jair.63
Nicol, S., Menkes, C., Jurando-Molina, J., Lehodey, P., Usu, T., Kumasi, B., …Briand, K. (2014). Oceanographic characterisation of the Pacific Ocean and potential impact of climate variability on tuna stocks and their fisheries. (pp. 1–10). pp. 1–10. Secretariat of the Pacific Community (SPC).
Pearce, A., &Pattiaratchi, C. (1997). Applications of satellite remote sensing to the marine environment in Western Australia. Journal of the Royal Society of Western Australia, 80(1), 1–14.
Pham-Gia, T., &Choulakian, V. (2014). Distribution of the Sample Correlation Matrix and Applications. Open Journal of Statistics, 04(05), 330–344. https://doi.org/10.4236/ojs.2014.45033
Physical Oceanography Distributed Active Archive Center (PO.DAAC). (2015). PO.DAAC MODIS LEVEL 3 DATA USER GUIDE. California.
Physical Oceanography Distributed Active Archive Center (PO.DAAC). (2017). Ocean Science Data Product Format Specification.
Quinlan, J R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
Quinlan, J Ross. (1993). C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria.
Savage, N. H., Agnew, P., Davis, L. S., Ordóñez, C., Thorpe, R., Johnson, C. E., …Dalvi, M. (2013). Air quality modelling using the Met Office Unified Model (AQUM OS24-26): Model description and initial evaluation. Geoscientific Model Development, 6(2), 353–372. https://doi.org/10.5194/gmd-6-353-2013
Scaillet, O. (2004). Density estimation using inverse and reciprocal inverse Gaussian kernels. Journal of Nonparametric Statistics, 16(1–2), 217–226. https://doi.org/10.1080/10485250310001624819
Sharma, R., Ghosh, A., &Joshi, P. K. (2013). Decision tree approach for classification of remotely sensed satellite data using open source support. Journal of Earth System Science, 122(5), 1237–1247. https://doi.org/10.1007/s12040-013-0339-2
Sobang, N. B. (2014). Access to fishing grounds and adaptive strategies. The Arctic University of Norway.
Solanki, H. U., Mankodi, P. C., Nayak, S. R., &Somvanshi, V. S. (2005). Evaluation of remote-sensing-based potential fishing zones (PFZs) forecast methodology. Continental Shelf Research, 25(18), 2163–2173. https://doi.org/https://doi.org/10.1016/j.csr.2005.08.025
Stéquert, B., &Marsac, F. (1989). Tropical tuna - surface fisheries in the Indian Ocean. Rome: FAO Fisheries Department.
Stoens, A., Menkes, C., Dandonneau, Y., &Memery, L. (1998). New production in the equatorial Pacific: A coupled dynamical-biogeochemical model. Fisheries Oceanography, 7(3–4), 311–316. https://doi.org/10.1046/j.1365-2419.1998.00079.x
Stuart, V., Platt, T., &Sathyendranath, S. (2011). The future of fisheries science in management: A remote-sensing perspective. ICES Journal of Marine Science, 68(4), 644–650. https://doi.org/10.1093/icesjms/fsq200
Thuiller, W., Araújo, M. B., &Lavorel, S. (2003). Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14(5), 669–680. https://doi.org/10.1111/j.1654-1103.2003.tb02199.x
Trinh, R. C., Fichot, C. G., Gierach, M. M., Holt, B., Malakar, N. K., Hulley, G., &Smith, J. (2017). Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality. Frontiers in Marine Science, 4, 329. https://doi.org/10.3389/fmars.2017.00329
Virdin, J. (2016). Tuna Fisheries. In Tuna Fisheries. https://doi.org/10.1596/28412
Wang, X., Tsokos, C. P., &Saghafi, A. (2018). Improved parameter estimation of Time Dependent Kernel Density by using Artificial Neural Networks. The Journal of Finance and Data Science, 4(3), 172–182. https://doi.org/https://doi.org/10.1016/j.jfds.2018.04.002
Weglarczyk, S. (2018). Kernel density estimation and its application. ITM Web of Conferences, 23, 37. https://doi.org/10.1051/itmconf/20182300037
Williams, P., Terawasi, P., &Reid, C. (2017). Overview of tuna fisheries in the Western and Central Pacific Ocean, including economic conditions - 2016. WCPFC Scientific Committee WCPFC-SC13-2017/GN-WP-01, (August).
Zainuddin, M., Nelwan, A., Farhum, S. A., N., Hajar, M. A. I., Kurnia, M., & S. (2013). Characterizing Potential Fishing Zone of Skipjack Tuna during the Southeast Monsoon in the Bone Bay-Flores Sea Using Remotely Sensed Oceanographic Data. International Journal of Geosciences, 04(01), 259–266. https://doi.org/10.4236/ijg.2013.41A023
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