Aquaculture pond mapping in Sungai Udang, Penang, using Google Earth engine

Arvinth Rajandran

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Type :	article
Subject :	G Geography (General)
Main Author :	Arvinth Rajandran
Additional Authors :	Tan, Mou Leong Narimah Samat Chan, Ngai Weng
Title :	Aquaculture pond mapping in Sungai Udang, Penang, using Google Earth engine

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains Kemanusiaan
Year of Publication :	2021
Corporate Name :	Universiti Pendidikan Sultan Idris
PDF Full Text :	Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris

Aquaculture has a vital function in ecology, environment, and economy. Without adequate monitoring and management, aquaculture might have negative environmental repercussions. In terms of managing and design the industry's long-term operations, it is necessary to map the distribution of aquaculture ponds. Aquaculture ponds can now be detected and mapped using remote sensing. A large-scale mapping can be performed fast due to the recent advancements in cloud computing and big data. In this study, 10 m Sentinel 2 images were used to classify aquaculture in Sungai Udang, Pulau Pinang. This study aims to compare three machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Classification and Regression Tree (CART) that available in the Google Earth Engine (GEE) cloud computing platform in mapping aquaculture ponds. From 2016 to 2020, the SVM, CART, and RF generated 97.35%, 93.86%, and 93.48% overall accuracy, respectively. In general, SVM was the most accurate among the three machine learning classifier algorithms in classifying the three classes (aquaculture, vegetation, and urban). The area of the aquaculture pond derived from Google Earth Pro is nearly identical to the classified image's region. This study shows that GEE is useful in mapping aquaculture ponds on a small scale using a cloud-based and free platform. The result of this study can be used by a variety of organisations to manage and monitor aquaculture pond fish production and environment degradation.

References

Ahmed, M., & Lorica, M. H. (2002). Improving developing country food security through aquaculture development—lessons from Asia. Food Policy, 27(1652), 125–141.

Aksoy, S., Koperski, K., Tusk, C., Marchisio, G., Tilton, J. C., & Member, S. (2005). Learning Bayesian classifiers for scene classification with a visual grammar. IEEE Transactions on Geoscience And Remote Sensing, 43(3), 581–589.

Allison, E. H. (2011). Aquaculture, Fisheries, Poverty and Food Security (65).

Azra, M. N., Azman, N., Othman, R., Afiz, G., Ruslan, G., Mazelan, S., Bin, Z., & Sar, G. (2021). Impact of COVID-19 on aquaculture sector in Malaysia: Findings from the first national survey. Aquaculture Reports, 19. https://doi.org/10.1016/j.aqrep.2020.100568

Bannari, A., Taylor, P., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(June 2013), 95–120. https://doi.org/http://dx.doi.org/10.1080/02757259509532298

Barbosa, C. C. D. A., Atkinson, P. M., & Dearing, J. A. (2015). Remote sensing of ecosystem services : A systematic review. Ecological Indicators, 52, 430–443. https://doi.org/10.1016/j.ecolind.2015.01.007

Belgiu, M., & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions gut. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Beveridge, M. C. M., Thilsted, S. H., Phillip, M. J., Metian, M., Troell, M., & Hall, S. J. (2013). Meeting the food and nutrition needs of the poor : the role of fish and the opportunities and challenges emerging from the rise of aquaculture. Journal of Fish Biology, 83, 1067–1084. https://doi.org/10.1111/jfb.12187

Breiman, L. E. O. (2001). Random Forests. 5–32.

Breiman, L, Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman and Hall/CRC.

Breiman, Leo. (1996). Bagging Predictors. Machine learning, 24, 123–140.

Campbell, B., & Pauly, D. (2013). Mariculture: A global analysis of production trends since 1950. Marine Policy, 39, 94–100. https://doi.org/10.1016/j.marpol.2012.10.009

Cao, L., Wang, W., Yang, Y., Yang, C., Yuan, Z., Xiong, S., & Diana, J. (2007). Environmental impact of aquaculture and countermeasures to aquaculture pollution in China. Environmental Science Pollution Research, 14(7), 452–462. https://doi.org/http://dx.doi.org/10.1065/espr2007.05.426

DOF. (2020). Malaysian Fishing Industry Scenario. Department of Fisheries Malaysia. https://www.dof.gov.my/index.php/pages/view/42

Duan, Y, Li, X., Zhang, L., Liu, W., Liu, S., Chen, D., & Ji, H. (2020). Detecting spatiotemporal changes of large-scale aquaculture ponds regions over 1988–2018 in Jiangsu Province, China using Google Earth Engine. Ocean and Coastal Management, 188. https://doi.org/10.1016/j.ocecoaman.2020.105144

Duan, Yuanqiang, Li, X., Zhang, L., Chen, D., Liu, S., & Ji, H. (2019). Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone. Aquaculture, 520(November 2019), 734666.

https://doi.org/10.1016/j.aquaculture.2019.734666

FAO. (2002). CWP Handbook of Fishery Statistical Standards. Section J: AQUACULTURE. CWP Data Collection [WWW Document]. FAO Fish. Aquac. Dep. http://www.fao.org/fishery/cwp/handbook/J/en

FAO. (2014). The State of World Fisheries and Aquaculture 2014.

FAO. (2016). The state of world fisheries and aquaculture 2016. Contributing to food security and nutrition for all.

FAO. (2020). The State of World Fisheries and Aquaculture 2020. Sustainability in action. https://doi.org/https://doi.org/10.4060/ca9229en

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine : Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2016. https://doi.org/10.1016/j.rse.2017.06.031

He, Z., Cheng, X., Kyzas, G. Z., & Fu, J. (2016). Pharmaceuticals pollution of aquaculture and its management in China. Journal of Molecular Liquids, 223, 781–789. https://doi.org/10.1016/j.molliq.2016.09.005

Herbeck, L. S., Unger, D., Wu, Y., & Jennerjahn, T. C. (2013). Effluent , nutrient and organic matter export from shrimp and fish ponds causing eutrophication in coastal and back-reef waters of NE Hainan , tropical China. Continental Shelf Research, 57, 92–104. https://doi.org/10.1016/j.csr.2012.05.006

Holmstrom, K., Graslund, S., Wahlstrom, A., Poungshompoo, S., Bengtsson, B.-E., & Kautsky, N. (2003). Antibiotic use in shrimp farming and implications for environmental impacts and human health. International Journal of Food Science and Technology, 38, 255–266.

Hossain, M. S., Uddin, M. J., & Fakhruddin, A. N. M. (2013). Impacts of shrimp farming on the coastal environment of Bangladesh and approach for management. Reviews Environmental Science and Bio/technology, 12, 313–332. https://doi.org/10.1007/s11157-013-9311-5

Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725–749. https://doi.org/10.1080/01431160110040323

Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, 42(2), 513–529.

Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., Reiche, J., & Ryan, C. M. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8(70), 1–23. https://doi.org/10.3390/rs8010070

Keesing, J. K., Liu, D., Fearns, P., & Garcia, R. (2011). Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007 – 2009 , their origin and relationship to the expansion of coastal seaweed aquaculture in China. Marine Pollution Bulletin, 62, 1169–1182. https://doi.org/10.1016/j.marpolbul.2011.03.040

Kumar, L., & Mutanga, O. (2018). Google Earth Engine Applications Since Inception :

Usage, Trends, and Potential. Remote Sensing, 10(1509), 1–15. https://doi.org/10.3390/rs10101509

Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3–10. https://doi.org/10.1016/j.gsf.2015.07.003

Lee, J. H., Pang, I., Moon, I., & Ryu, J. (2011). On physical factors that controlled the massive green tide occurrence along the southern coast of the Shandong Peninsula in 2008 : A numerical study using a particle-tracking experiment. Journal of Geophysical Research, 116, 1–12. https://doi.org/10.1029/2011JC007512

Liang, P., Wu, S., Zhang, J., Cao, Y., Yu, S., & Wong, M.-H. (2016). The effects of mariculture on heavy metal distribution in sediments and cultured fi sh around the Pearl River Delta region , south China. Chemosphere, 148, 171–177. https://doi.org/10.1016/j.chemosphere.2015.10.110

Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., & Jie, W. (2015). Remote sensing big data computing : Challenges and opportunities. Future Generation Computer Systems, 51, 47–60. https://doi.org/10.1016/j.future.2014.10.029

Mantero, P., Moser, G., & Serpico, S. B. (2005). Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 559–570.

Marini, Y., Emiyati, P. T., Hanzah, R., & Hasyim, B. (2013). Fishpond aquaculture inventory in Maros Regency of south sulawesi province. Int. J. Remote Sens, 10, 25–35.

Natale, F., Hofherr, J., Fiore, G., & Virtanen, J. (2013). Interactions between aquaculture and fisheries. Marine Policy, 38, 205–213. https://doi.org/10.1016/j.marpol.2012.05.037

Naylor, R. L., Goldburg, R. J., Primavera, J. H., Kautsky, N., Beveridge, M. C. M., Clay, J., Folke, C., Lubchenco, J., Mooney, H., & Troell, M. (2000). Effect of aquaculture on world fish supplies. Nature, 405, 1017–1024.

Nery, T., Sadler, R., Solis-aulestia, M., White, B., Polyakov, M., & Chalak, M. (2016). Comparing supervised algorithms in land use and land cover classification of a landsat time-series. IGARSS 2016, 5165–5168.

Ottinger, M., Clauss, K., & Kuenzer, C. (2016). Aquaculture : Relevance , distribution , impacts and spatial assessments - A review. Ocean and Coastal Management, 119(2016), 244–266. https://doi.org/10.1016/j.ocecoaman.2015.10.015

Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007–1011. https://doi.org/10.1080/01431160512331314083

Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, 554–565. https://doi.org/10.1016/S0034-4257(03)00132-9

Pal, M., & Mather, P. M. (2004). Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20, 1215–1225. https://doi.org/10.1016/j.future.2003.11.011

Pattanaik, C., & Prasad, S. N. (2011). Assessment of aquaculture impact on mangroves of Mahanadi delta ( Orissa ), East coast of India using remote sensing and GIS. Ocean

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