Sains Malaysiana 51(2)(2022): 369-378

http://doi.org/10.17576/jsm-2022-5102-04

 

Comparison of Three Water Indices for Tropical Aquaculture Ponds Extraction using Google Earth Engine

(Perbandingan Tiga Indeks Air untuk Pengekstrakan Kolam Akuakultur Tropika menggunakan Google Earth Engine)

 

YI LIN TEW1, MOU LEONG TAN1, NARIMAH SAMAT1*, NGAI WENG CHAN1, MOHD AMIRUL MAHAMUD1, MUHAMMAD AZIZAN SABJAN2, LAI KUAN LEE3, KOK FONG SEE4 & SEOW TA WEE5

 

1GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia

 

2Philosophy and Civilization Section, School of Humanities, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia

 

3School of Industrial Technology, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia

 

4School of Distance Education, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia

 

5Faculty of Management Technology and Business, Universiti Tun Hussein Onn, 86400 Batu Pahat, Johor Darul Takzim, Malaysia

 

Received: 26 March 2021/Accepted: 12 June 2021

 

ABSTRACT

Information on the spatial distribution of aquaculture ponds, especially the inland brackish aquaculture, is crucial for effective and sustainable aquaculture management. Google Earth Engine (GEE) has been utilized to quickly map aquaculture ponds in different parts of the world, but the application is still limited in tropical regions. Selection of an optimal water index is essential to accurately map the aquaculture ponds from the Landsat 8 satellite images that are available in GEE. This study aims to evaluate the capability of three different water indices, namely Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI), in mapping of the aquaculture ponds in Sungai Udang, Pulau Pinang, Malaysia. The results show that MNDWI is the best index for aquaculture ponds extraction in Sungai Udang, with an accuracy of 81.87% and Kappa coefficient of 0.61. Meanwhile, the accuracy of NDWI and AWEI as compared to the digitized aquaculture ponds are 58.21 and 61.60%, and Kappa coefficient of 0.33 and 0.36, respectively. Then, MNDWI was applied to calculate the spatial changes of aquaculture ponds from 2014 to 2020. The result indicates that the area of aquaculture ponds has expanded by 26.16% since the past seven years.

 

Keywords: Aquaculture; Google Earth Engine; Landsat; Malaysia; tropical

 

ABSTRAK

Maklumat ruang kolam akuakultur terutamanya kolam akuakultur air payau pedalaman adalah penting dalam keberkesanan pengurusan akuakultur yang lestari. Google Earth Engine (GEE) telahpun dimanfaatkan dalam pemetaan kolam akuakultur di beberapa negara, namun aplikasinya di kawasan tropika masih kurang. Pemilihan indeks air yang sesuai boleh memetakan kolam akuakultur dengan tepat daripada imej Landsat 8 dengan menggunakan GEE. Kajian ini bertujuan untuk menilai kemampuan tiga jenis indeks air yang bernama Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) dan Automated Water Extraction Index (AWEI) dalam pemetaan kolam akuakultur di Sungai Udang, Pulau Pinang. Hasil daripada kajian ini, MNDWI menunjukkan ketepatan yang paling tinggi dalam memetakan kolam akuakultur di Sungai Udang, dengan ketepatan sebanyak 81.87% dan nilai pekali Kappa 0.61. Manakala bagi NDWI dan AWEI pula, ketepatan kedua-dua indeks air ini adalah 58.21 dan 61.60%, serta nilai pekali Kappa 0.33 dan 0.36 sahaja. Dengan ini, MNDWI telah digunakan untuk memperoleh perubahan ruang kawasan kolam-kolam akuakultur di Sungai Udang dari tahun 2014 sehingga 2020. Hasilnya menunjukkan kawasan kolam-kolam ini telah berkembang sebanyak 26.16% dalam masa tujuh tahun.

 

Kata kunci: Akuakultur; Google Earth Engine; Landsat; Malaysia; tropika

 

REFERENCES

Amani, M., Ghorbanian, A., Ahmadi, S.A., Kakooei, M., Moghimi, A., Mirmazloumi, S.M., Moghaddam, S.H.A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q. & Brisco, B. 2020. Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal in Google Earth Engine for Remote Sensing 13: 5326-5350.

Aziz, F., Kusratmoko, E. & Mandini, M.D. 2020. Estimation of changes in the lake water level and area using remote sensing techniques (Case study: Lake Toba, North Sumatra). Proceedings of The International Conference of Science and Applied Geography - IOP Conference. Series: Earth and Environmental Science 561(012022). pp. 1-7.

Barsi, J.A., Lee, K., Kvaran, G., Markham, B.L. & Pedelty, J.A. 2014. The spectral response of the Landsat-8 operational land imager. Remote Sensing 6(10): 10232-10251.

Blondeau-Patissier, D., Gower, J.F.R., Dekker, A.G., Phinn, S.R. & Brando, V. 2014. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography 123: 123-144.

Chen, F., Chen, X., Tim, V.d.V., Roberts, D., Jiang, H. & Xu, W. 2020a. Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sensing of Environment 242(11706): 1-17.

Chen, J., Kang, T., Yang, S., Bu, J., Cao, K. & Gao, Y. 2020b. Open-surface water bodies dynamics analysis in the Tarim River Basin (North-Western China), based on Google Earth Engine cloud platform. Water 12(10): 2822-2848.

DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.W. & Lang, M.W. 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment 240(111664): 1-15.

Duan, Y., 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(734666): 1-10.

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(105144): 1-11.

FAO. 2020. The State of World Fisheries and Aquaculture: Sustainability in Action. Rome: Food and Agriculture Organization of the United Nations.

Feyisa, G.L., Meilby, H., Fensholt, R. & Proud, S.R. 2014. Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140: 23-35.

Gao, B.C. 1996. NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58(3): 257-266.

Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A. & Hasanlou, M. 2020. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing 167: 276-288.

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 202: 18-27.

Holben, B. & Justice, C. 1981. An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data. International Journal of Remote Sensing 2(2): 115-133.

Ji, L., Zhang, L. & Wylie, B. 2009. Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing 75(11): 1307-1317.

Khor, W., Fazhan, H., Ishak, S.D., Kasan, N.A., Liew, H.J., Norainy, M.H. & Ikhwanuddin, M. 2020. Potential impacts of COVID-19 on the aquaculture sector of Malaysia and its coping strategies. Aquaculture Reports 18(100450): 1-6.

Koskinen, J., Leinonen, U., Vollrath, A., Ortmann, A., Linquist, E., d'Annunzio, R., Pekkarinen, A. & Käyhkö, N. 2019. Participatory mapping of forest plantations with Open Foris and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing 148: 63-74.

Lim, I. 2015. Something fishy in Kampung Sungai Udang. Penang Monthly. https://penangmonthly.com/article/2826/something-fishy-in-kampung-sungai-udang-1.

McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7): 1425-1432.

Nguyen, U.N.T., Pham, L.T.H. & Dang, T.D. 2019. An automatic water detection approach using Landsat 8 OLI and Google Earth Engine cloud computing to map lakes and reservoirs in New Zealand. Environmental Monitoring and Assessment 191(4): 235-246.

Soja-Woźniaka, M., Laiolo, L., Baird, M.E., Matear, R. & Clementson, L. 2020. Effect of phytoplankton community size structure on remote-sensing reflectance and chlorophyll a products. Journal of Marine Systems 211(103400): 1-11.

Sun, Z., Luo, J., Yang, J., Yu, Q., Zhang, L., Xue, K. & Lu, L. 2020. Nation-scale mapping of coastal aquaculture ponds with Sentinel-1 SAR data using Google Earth Engine. Remote Sensing 12(18): 3086-3097.

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S. & Brisco, B. 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 164: 152-170.

Tew, Y.L., Tan, M.L., Narimah, S. & Yang, X. 2019. Urban expansion analysis using Landsat Images in Penang, Malaysia. Sains Malaysiana48(11): 2307-2315.

Vaghefi, N. 2017. Penang’s Aquaculture Industry Holds Great Economic Potential. https://penanginstitute.org/publications/issues/1005-penang-s-aquaculture-industry-holds-great-economic-potential/.

Wang, R., Xia, H., Qin, Y., Niu, W., Pan, L., Li, R., Zhao, X., Bian, X. & Fu, P. 2020a. Dynamic monitoring of surface water area during 1989–2019 in the Hetao Plain using Landsat data in Google Earth Engine. Water 12(11): 3010-3030.

Wang, Y., Li, Z., Zeng, C., Xia, G.S. & Shen, H. 2020b. An urban water extraction method combining deep learning and Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 768-781.

Worden, J. & de Beurs, K.M. 2020. Surface water detection in the Caucasus. International Journal of Applied Earth Observation and Geoinformation 91(102159): 1-16.

Xia, Z., Guo, X. & Chen, R. 2020. Automatic extraction of aquaculture ponds based on Google Earth Engine. Ocean and Coastal Management 198(105348): 1-10.

Xu, H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14): 3025-3033.

Yin, Z., Ling, F., Foody, G.M., Li, X. & Du, Y. 2020. Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep convolutional neural network. Remote Sensing Letters 11(12): 1181-1190.

You, N. & Dong, J. 2020. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing 161: 109-123.

Yu, Z., Di, L., Rahman, M.S. & Tang, J. 2020. Fishpond mapping by spectral and spatial-based filtering on Google Earth Engine: A case study in Singra Upazila of Bangladesh. Remote Sensing 12(17): 2692-2711.

Zhai, K., Wu, X., Qin, Y. & Du, P. 2020. Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-spatial Information Science 18(1): 32-42.

Zurqani, H.A., Post, C.J., Mikhailova, E.A., Schlautman, M.A. & Sharp, J.L. 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation 69: 175-185.

 

*Corresponding author; email: narimah@usm.my

     

 

 

previous