Automatic detection of water surfaces using K-means++ clustering algorithm with Landsat-9 and Sentinel-2 images on the Google Earth Engine Platform

Year 2023, Volume: 7 Issue: 2, 105 - 111, 30.09.2023

Osman Salih Yılmaz

https://doi.org/10.30516/bilgesci.1262550

Abstract

Water is the most essential requirement for sustaining the life cycle on Earth. These resources are constantly dynamic due to anthropogenic and climatological effects. Therefore, management and consistent water policies are necessary to be followed for the proper management of water resources. Monitoring water resources is possible by accurately determining the water surface boundaries and determining the change in water surface areas. In this context, the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were computed using JavaScript on the Google Earth Engine through Landsat-9 and Sentinel-2 satellite images. Water pixels were extracted d from other details using the K-means++ cluster algorithm based on the calculated indices. The water surfaces were determined using the Otsu thresholding method, which is the most preferred method for the NDWI and MNDWI indices calculated from the Sentinel images and was used as verification data. The K-means++ clustering algorithm yielded successful results in detecting water surfaces. In the two indices used, the NDWI index was found to be more successful than the MNDWI index. For Landsat-9 images, OA, Kappa, and F1-scores in the NDWI index were calculated as 99.72%, 0.994, and 99.57%, respectively. The OA, Kappa, and F1-scores in the NDWI index for Sentinel-2 images were calculated as 99.39%, 0.986, and 99.04%, respectively. This study demonstrated that clustering algorithms can be successfully applied to automatically detect water surfaces.

Keywords

Remote sensing, Google Earth engine, NDWI, MNDWI, K-means++, Terkos Lake

References

• Agarwal, S., Yadav, S., & Singh, K. (2012). Notice of Violation of IEEE Publication Principles: K-means versus k-means++ clustering technique. In 2012 Students Conference on Engineering and Systems, 1–6.
• Arthur, D., & Vassilvitskii, S. (2007). K-means++: The advantages of careful seeding In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SODA’07, Society for Industrial and Applied Mathematics, 1027–1035, Philadelphia, PA, USA.
• Bayram, B., Seker, D. Z., Acar, U., Yuksel, Y., Guner, H. A. A., & Cetin, I. (2013). An integrated approach to temporal monitoring of the shoreline and basin of Terkos Lake. Journal of Coastal Research, 29(6), 1427–1435. https://doi.org/10.2112/JCOASTRES-D-12-00084.1
• Bouslihim, Y., Kharrou, M. H., Miftah, A., Attou, T., Bouchaou, L., & Chehbouni, A. (2022). Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers. Journal of Geovisualization and Spatial Analysis, 6(2), 35.
• Cordeiro, M. C. R., Martinez, J. M., & Peña-Luque, S. (2021). Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors. Remote Sensing of Environment, 253(November 2020). https://doi.org/10.1016/j.rse.2020.112209
• Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., & van de Giesen, N. (2016). A 30 m resolution surfacewater mask including estimation of positional and thematic differences using landsat 8, SRTM and OPenStreetMap: A case study in the Murray-Darling basin, Australia. Remote Sensing, 8(5). https://doi.org/10.3390/rs8050386
• Elachi, C., & Van Zyl, J. J. (2021). Introduction to the physics and techniques of remote sensing. John Wiley & Sons.
• Feng, M., Sexton, 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. https://doi.org/10.1080/17538947.2015.1026420
• 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. https://doi.org/10.1016/j.rse.2013.08.029
• 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. https://doi.org/10.1016/S0034-4257(96)00067-3
• Gao, H., Birkett, C., & Lettenmaier, D. P. (2012). Global monitoring of large reservoir storage from satellite remote sensing. Water Resources Research, 48(9), 1–12. https://doi.org/10.1029/2012WR012063
• Govender, M., Chetty, K., & Bulcock, H. (2007). A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water Sa, 33(2), 145–151.
• Gu, Z., Zhang, Y., & Fan, H. (2021). Mapping inter- and intra-annual dynamics in water surface area of the Tonle Sap Lake with Landsat time-series and water level data. Journal of Hydrology, 601(July), 126644. https://doi.org/10.1016/j.jhydrol.2021.126644
• Hu, Q., Li, C., Wang, Z., Liu, Y., & Liu, W. (2022). Continuous Monitoring of the Surface Water Area in the Yellow River Basin during 1986–2019 Using Available Landsat Imagery and the Google Earth Engine. ISPRS International Journal of Geo-Information, 11(5), 305. https://doi.org/10.3390/ijgi11050305
• Ji, L., Zhang, L., & Wylie, B. (2009). Problems of Dynamic NDWI Threshold and Objectives of the Study The NDWI data derived from Landsat MSS, TM, and ETM (Jain et al. Photogrammetric Engineering & Remote Sensing, 75(11), 1307–1317. https://doi.org/10.14358/PERS.75.11.1307
• Kaya, H., Ertek, T. A., & Gazioğlu, C. (2019). Geomorphological Features of Terkos Lake and Surroundings. International Journal of Environment and Geoinformatics (IJEGEO), 6(2), 192–205. https://doi.org/10.30897/ijegeo
• Khalid, H. W., Khalil, R. M. Z., & Qureshi, M. A. (2021). Evaluating spectral indices for water bodies extraction in western Tibetan Plateau. Egyptian Journal of Remote Sensing and Space Science, 24(3), 619–634. https://doi.org/10.1016/j.ejrs.2021.09.003
• Khan, F. (2012). An initial seed selection algorithm for k-means clustering of georeferenced data to improve replicability of cluster assignments for mapping application. Applied Soft Computing Journal, 12(11), 3698–3700. https://doi.org/10.1016/j.asoc.2012.07.021
• Liu, C., Shi, J., Liu, X., Shi, Z., & Zhu, J. (2020). Subpixel mapping of surfacewater in the Tibetan Plateau with MODIS data. Remote Sensing, 12(7), 1–20. https://doi.org/10.3390/rs12071154
• Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. MacQuuen, J. B. (1967). Some methods for classification and analysis of multivariate observation. Proceedings of the 5th Berkley Symposium on Mathematical Statistics and Probability, 281–297. Mahdianpari, M., Salehi, B., Mohammadimanesh, F., & Motagh, M. (2017). Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 13–31. https://doi.org/10.1016/j.isprsjprs.2017.05.010
• Maktav, D., Sunar Erbek, F., & Kabdasli, S. (2002). Monitoring coastal erosion at the Black Sea coasts in Turkey using satellite data: A case study at the Lake Terkos, North-west Istanbul. International Journal of Remote Sensing, 23(19), 4115–4124. https://doi.org/10.1080/01431160110115979
• Mansaray, L. R., Wang, F., Huang, J., & Yang, L. (2019). Accuracies of support vector machine (SVM) and random forest (RF) in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto International, 0(0), 1–17. https://doi.org/10.1080/10106049.2019.1568586
• McFeeters. (1996). The use of the Normalized Difference Water IndexMcFeeters. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
• McFeeters, S. K. (2013). Using the normalized difference water index (ndwi) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561. https://doi.org/10.3390/rs5073544
• 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), 1–12. https://doi.org/10.1007/s10661-019-7355-x
• Owusu, C. (2022). PyGEE-SWToolbox : A Python Jupyter Notebook Toolbox for Interactive Surface Water Mapping and Analysis Using Google Earth Engine. Sustainability, 14, 2557.
• Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
• Qiao, C., Luo, J., Sheng, Y., Shen, Z., Zhu, Z., & Ming, D. (2012). An Adaptive Water Extraction Method from Remote Sensing Image Based on NDWI. Journal of the Indian Society of Remote Sensing, 40(3), 421–433. https://doi.org/10.1007/s12524-011-0162-7
• Rad, A. M., Kreitler, J., & Sadegh, M. (2021). Augmented Normalized Difference Water Index for improved surface water monitoring. Environmental Modelling and Software, 140(March), 105030. https://doi.org/10.1016/j.envsoft.2021.105030
• Reis, S., & Yilmaz, H. M. (2008). Temporal monitoring of water level changes in Seyfe Lake using remote sensing. Hydrological Processes, 22(22), 4448–4454. https://doi.org/10.1002/hyp.7047
• Sekertekin, A. (2021). A Survey on Global Thresholding Methods for Mapping Open Water Body Using Sentinel-2 Satellite Imagery and Normalized Difference Water Index. Archives of Computational Methods in Engineering, 28(3), 1335–1347. https://doi.org/10.1007/s11831-020-09416-2
• Tang, H., Lu, S., Baig, M. H. A., Li, M., Fang, C., & Wang, Y. (2022). Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images. Water (Switzerland), 14(9). https://doi.org/10.3390/w14091454
• 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. https://doi.org/10.1080/01431160600589179
• Yang, X., Zhao, S., Qin, X., Zhao, N., & Liang, L. (2017). Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sensing, 9(6), 1–19. https://doi.org/10.3390/rs9060596
• Yilmaz, O. S. (2023). Spatiotemporal statistical analysis of water area changes with climatic variables using Google Earth Engine for Lakes Region in Türkiye. Environmental Monitoring and Assessment, 195(6), 735. https://doi.org/10.1007/s10661-023-11327-1
• Yilmaz, O. S., Gulgen, F., Balik Sanli, F., & Ates, A. M. (2023). The Performance Analysis of Different Water Indices and Algorithms Using Sentinel-2 and Landsat-8 Images in Determining Water Surface: Demirkopru Dam Case Study. Arabian Journal for Science and Engineering, 48, 7883–7903. https://doi.org/10.1007/s13369-022-07583-x
• Zhang, Y., Liu, X., Zhang, Y., Ling, X., & Huang, X. (2018). Automatic and unsupervised water body extraction based on spectral-spatial features using GF-1 satellite imagery. IEEE Geoscience and Remote Sensing Letters, 16(6), 927–931.