Google Earth Engine Based Spatio-Temporal Changes of Bafa Lake from 1984 to 2022

Year 2023, Volume: 10 Issue: 3, 116 - 123, 30.09.2023

Ömer Faruk Atiz , Tansu Alkan , Süleyman Savaş Durduran

https://doi.org/10.30897/ijegeo.1257413

Cited By: 1

Abstract

The water resource management is crucial to protect environment and ecological cycle. The detection of temporal and spatial changes in the lake's water extent is important for sustainable land planning. Therefore, the areal changes over the wetlands must be well monitored. Bafa Lake is an essential downstream water in the Büyük Menderes Basin which is the largest river basin of the Aegean Region. Google Earth Engine (GEE) is an easy-to-use online remote sensing data processing platform based on cloud computing. In this study, the long-term spatio-temporal changes of Bafa Lake between 1984-2022 have been analyzed using Landsat-5/8 satellite images on the GEE platform. A total of 1093 Landsat images were processed. The annual water areas were computed through composite images per year. The water area extraction was done using the normalized water difference index (NDWI). The minimum and maximum lake water areas in 38 years were detected as 5474 ha and 6789 ha in 1990 and 2006, respectively. In the accuracy assessment according to random sampling points, the Overall Accuracy (OA) was calculated as 98% and the kappa coefficient as 0.96. The water surface area was increased by 3.9% from 1984 to 2022. Between 2015-2022, the maximum increase or decrease in the lake area compared to the previous year observed as less than 1%. Therefore, there has not been a notable variation in the water area of Bafa Lake in the past few years.

Keywords

Google Earth Engine, Landsat, NDWI, Remote Sensing, Water Resources

Thanks

The authors would like to thank Google for providing the Google Earth Engine platform.

References

• Albarqouni, M. M., Yagmur, N., Bektas Balcik, F., Sekertekin, A. (2022). Assessment of spatio-temporal changes in water surface extents and lake surface temperatures using Google Earth Engine for lakes region, Türkiye. ISPRS International Journal of Geo-Information, 11(7), 407.
• Arikan, C., Tumer, I. N., Aksoy, S., Sertel, E. (2022). Determination of burned areas using Sentinel-2A imagery and machine learning classification algorithms. 4th Intercontinental Geoinformation Days (IGD), 43-46, Tabriz, Iran.
• Asokan, A., Anitha, J. (2019). Change detection techniques for remote sensing applications: A survey. Earth Science Informatics, 12, 143-160.
• Ates, A. M., Yilmaz, O. S., Gulgen, F. (2020). Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface. Sustainable Energy Technologies and Assessments, 41, 100799.
• Bahsi, K., Ustaoglu, B., Aksoy, S., Sertel, E. (2022). Estimation of emissions from crop residue burning in Türkiye using remotely sensed data and the Google Earth Engine platform. Geocarto International, 2157052.
• Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., White, J. C. (2014). Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing, 40(5), 362-384.
• Bar, S., Parida, B. R., Pandey, A. C. (2020). Landsat-8 and Sentinel-2 based forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324.
• Buma, W. G., Lee, S. I., Seo, J. Y. (2018). Recent surface water extent of lake Chad from multispectral sensors and GRACE. Sensors, 18(7), 2082.
• Cao, W., Zhou, Y., Li, R., Li, X., Zhang, H. (2021). Monitoring long-term annual urban expansion (1986–2017) in the largest archipelago of China. Science of The Total Environment, 776, 146015.
• Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., Sohlberg, R. A. (2009). A new global raster water mask at 250 m resolution. International Journal of Digital Earth, 2(4), 291-308.
• Çelik, O. I., Çelik, S. Gazioğlu, C. (2022). Evaluation on 2002-2021 CHL-A concentrations in the Sea of Marmara with GEE enhancement of satellite data. International Journal of Environment and Geoinformatics, 9(4), 68-77.
• De Diego, I. M., Redondo, A. R., Fernández, R. R., Navarro, J., Moguerza, J. M. (2022). General performance score for classification problems. Applied Intelligence, 52(10), 12049-12063.
• DEHA (2012). Deniz Haber Ajansı, Bafa Lake breathes with "Rubber Dam" (Bafa Gölü "Şişme Savak" ile nefes alıyor). Retrieved 24 February 2023 from https://www.denizhaber.net/
• SHW (2007). General Directorate of State Hydraulic Works 2007 Activity Report (Devlet Su İşleri Genel Müdürlüğü 2007 Yılı Faaliyet Raporu).
• Erdogan, S. (2011). A chemical reaction to a physical impact: Lake Bafa wetland ecosystem (Turkey) case. Ankara Üniversitesi Çevrebilimleri Dergisi, 3(1), 1-8.
• Feng, M., Sfexton, J. O., Channan, S., Townshend, J. R. (2016). A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic–spectral classification algorithm. International Journal of Digital Earth, 9(2), 113-133.
• 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.
• Firatli, E., Dervisoglu, A., Yagmur, N., Musaoglu, N., Tanik, A. (2022). Spatio-temporal assessment of natural lakes in Turkey. Earth Science Informatics, 15, 951-964.
• Gardner, R.C., Finlayson, M. (2018). Global wetland outlook: State of the world’s wetlands and their services to people 2018. Secretariat of the Ramsar Convention.
• GDWM (2017). General Directorate of Water Management, Lakes and Wetlands Action Plan 2017-2023 (Su Yönetimi Genel Müdürlüğü, Göller ve Sulak Alanlar Eylem Planı 2017-2023).
• GEE (2023a). Google Earth Engine Data Catalog.
• GEE (2023b). Google Earth Engine API Reference.
• Ghasempour, F., Sekertekin, A., Kutoglu, S. H. (2021). Google earth engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing. Journal of Cleaner Production, 319, 128599.
• 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.
• Hürriyet (2007). 1.3 million hectares of wetland lost (1.3 milyon hektarlık sulak alan kaybedildi). Retrieved 24 February 2023 from https://www.hurriyet.com.tr
• Kale, S. Acarlı, D. (2019). Spatial and temporal change monitoring in water surface area of Atikhisar Reservoir (Çanakkale, Turkey) by using remote sensing and geographic information system techniques. Alinteri Journal of Agriculture Science, 34(1), 47-56.
• Kandekar, V. U., Pande, C. B., Rajesh, J., Atre, A. A., Gorantiwar, S. D., Kadam, S. A., Gavit, B. (2021). Surface water dynamics analysis based on sentinel imagery and Google Earth Engine Platform: a case study of Jayakwadi dam. Sustainable Water Resources Management, 7(3), 44.
• Karaca, M., Yagmur, N. Balcik, F. (2022). Determination of the temporal variation of Istanbul Terkos Lake using Google Earth Engine (İstanbul Terkos Gölü zamansal değişiminin Google Earth Engine kullanılarak belirlenmesi). Geomatik, 7(3), 235-242.
• Kaya, Z., Dervisoglu, A. (2023). Determination of urban areas using Google Earth Engine and spectral indices; Esenyurt case study. International Journal of Environment and Geoinformatics, 10(1), 1-8.
• Khanal, N., Uddin, K., Matin, M. A., Tenneson, K. (2019). Automatic detection of spatiotemporal urban expansion patterns by fusing OSM and Landsat data in Kathmandu. Remote Sensing, 11(19), 2296.
• Kucuksumbul, A. (2018). Hydrogeological study of Söke Plain and Lake Bafa surroundings: Geothermal potential, soil and water contamination (Söke Ovası ve Bafa Gölü çevresinin hidrojeolojik incelenmesi: Jeotermal potansiyeli, toprak ve su kirliliği) (Master Thesis). Dokuz Eylül University the Graduate School of Natural and Applied Sciences, Izmir, Turkey.
• LANDSAT (2023). NASA Landsat Technical Details.
• Li, D., Gao, Z., Wang, Y. (2022). Research on the long-term relationship between green tide and chlorophyll-a concentration in the Yellow Sea based on Google Earth Engine. Marine Pollution Bulletin, 177, 113574.
• Loukika, K. N., Keesara, V. R., Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13(24), 13758.
• 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.
• Mitsch, W. J., Gosselink, J. G. (2015). Wetlands. New Jersey: John Wiley & Sons. Notarnicola, C. (2020). Hotspots of snow cover changes in global mountain regions over 2000–2018. Remote Sensing of Environment, 243, 111781.
• Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
• Ormeci, C., Ekercin, S. (2007). An assessment of water reserve changes in Salt Lake, Turkey, through multi‐temporal Landsat imagery and real‐time ground surveys. Hydrological Processes: An International Journal, 21(11), 1424-1435.
• Pande, C. B. (2022). Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the Google Earth Engine and machine learning approach. Geocarto International, 1-21.
• Peker, E. A. (2019). Spatio-temporal changes of lake water extents in lakes region (Turkey) using remote sensing (Master's thesis), Middle East Technical University, Ankara, Turkey.
• Sahtiyanci, O. H. (2014). Environmental targets and measures program under the water framework directive: The Case of the Büyük Menderes Basin (Su çerçeve direktifi kapsamında çevresel hedefler ve önlemler programı: Büyük Menderes Havzası örneği) (Specialist thesis). General Directorate of Water Management, Ankara, Turkey.
• Temiz, F., Durduran, S. S. (2016). Monitoring coastline change using remote sensing and GIS technology: a case study of Acıgöl Lake, Turkey. In IOP Conference Series: Earth and Environmental Science (Vol. 44, No. 4, p. 042033). IOP Publishing.
• Topcu, H. Atatanir, L. (2021). Determination of temporal variation in Bafa and Azap lake surface areas (Bafa ve Azap göl yüzey alanlarındaki zamansal değişimin belirlenmesi). Akademik Ziraat Dergisi, 10(1), 115-122.
• Tuna, M. (2015). GEKA Project Report: Determination of community supported ecotourism activities in Lake Bafa Basin (Bafa Gölü havzasında toplum destekli ekoturizm faaliyetlerinin belirlenmesi).
• Verpoorter, C., Kutser, T., Seekell, D. A., Tranvik, L. J. (2014). A global inventory of lakes based on high‐resolution satellite imagery. Geophysical Research Letters, 41(18), 6396-6402.
• WWF (2020). Annual Report 2020 Turkey (WWF Türkiye 2020 Faaliyet Raporu).
• Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., ... Meng, Q. (2019). Changes in water surface area during 1989–2017 in the Huai River Basin using Landsat data and Google Earth Engine. Remote Sensing, 11(15), 1824.
• 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.
• Xu, X., Jiang, B., Tan, Y., Costanza, R., Yang, G. (2018). Lake-wetland ecosystem services modeling and valuation: Progress, gaps and future directions. Ecosystem Services, 33, 19-28.
• Yagmur, N., Bilgilioglu, B. B., Dervisoglu, A., Musaoglu, N., Tanik, A. (2021). Long and short‐term assessment of surface area changes in saline and freshwater lakes via remote sensing. Water and Environment Journal, 35(1), 107-122.
• Yilmaz, O. S., Acar, U., Sanli, F. B., Gulgen, F., Ates, A. M. (2023). Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics, 1-20.