Drought monitoring in Burdur Lake using Sentinel-2 images

Year 2024, Volume: 13 Issue: 3, 882 - 891, 15.07.2024

Ümit Haluk Atasever , Hussein Hadi Abbas

https://doi.org/10.28948/ngumuh.1411803

Cited By: 2

Abstract

This study presents an automated workflow for drought monitoring in Burdur Lake, Turkey, utilizing Sentinel-2 satellite data and K-means clustering. Five Sentinel-2 images from 2019 to 2023 were processed to derive spectral water indices. A water mask was generated by thresholding the indices, allowing for the distinction of water bodies. K-means clustering quantified changes in the lake area over time. The results reveal a decreasing trend in water extent from August 2019 to August 2023. In August 2019, the water extent was approximately 18.53% (138.9456 Km2), which declined to around 16.64% (124.7500 Km2) by August 2023, signifying an approximately 10.3% reduction in water extent between the start and end years. This approach demonstrates a valuable framework for the integration of freely available satellite data and machine learning algorithms in operational drought monitoring.

Keywords

Drought Monitoring , Water Mask , Sentinel-2 Imagery , K-Means Clustering , Burdur Lake Türkiye

Sentinel-2 görüntüleri kullanılarak Burdur Gölü'nde kuraklık izleme

Abstract

Bu çalışma, Burdur Gölü'nde kuraklık izlemesi için Sentinel-2 uydu verileri ve K-means kümeleme kullanılarak otomatik bir iş akışı sunmaktadır. 2019'dan 2023'e kadar beş Sentinel-2 görüntüsü, spektral su indekslerini üretmek için işlenmiştir. Su indekslerinin eşiklenmesiyle bir su maskesi oluşturulmuş, böylece su kütlelerinin ayırt edilmesi sağlanmıştır. K-means kümeleme, zaman içinde göl alanındaki değişiklikleri ölçümlenmiştir. Sonuçlar, Ağustos 2019'dan Ağustos 2023'e kadar su genişliğinde azalan bir eğilim ortaya koymaktadır. Ağustos 2019'da su genişliği yaklaşık %18.53 (138.9456 Km2) iken, Ağustos 2023'e kadar yaklaşık %16.64'e (124.7500 Km2) düşmüş, başlangıç ve bitiş yılları arasında yaklaşık %10.3'lük bir su genişliği azalmasına işaret etmektedir. Bu yaklaşım, serbestçe erişilebilir uydu verileri ve makine öğrenmesi algoritmalarının operasyonel kuraklık izlemeye entegrasyonu için değerli bir çerçeve sunmaktadır.

Keywords

Kuraklık İzleme , Su Maskesi , Sentinel-2 Görüntüleme , K-Means Kümeleme , Burdur Gölü Türkiye

References

• Y. O. Ouma and R. Tateishi, A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data, Int J Remote Sens, vol. 27, no. 15, pp. 3153–3181, Aug. 2006, doi: 10.1080/01431160500309934.
• C. Giardino, M. Bresciani, P. Villa, and A. Martinelli, Application of Remote Sensing in Water Resource Management: The Case Study of Lake Trasimeno, Italy, Water Resources Management, vol. 24, no. 14, pp. 3885–3899, 2010, doi: 10.1007/s11269-010-9639-3.
• J. E. Pardo-Pascual, J. Almonacid-Caballer, L. A. Ruiz, and J. Palomar-Vázquez, Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision, Remote Sens Environ, vol. 123, pp. 1–11, Aug. 2012, doi: 10.1016/J.RSE.2012.02.024.
• J. H. Ryu, J. S. Won, and K. D. Min, Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea, Remote Sens Environ, vol. 83, no. 3, pp. 442–456, Dec. 2002, doi: 10.1016/S0034-4257(02)00059-7.
• G. Sarp and M. Ozcelik, Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey, Journal of Taibah University for Science, vol. 11, no. 3, pp. 381–391, 2017, doi: 10.1016/j.jtusci.2016.04.005.
• S. K. McFEETERS, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int J Remote Sens, vol. 17, no. 7, pp. 1425–1432, May 1996, doi: 10.1080/01431169608948714.
• H. Xu, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int J Remote Sens, vol. 27, no. 14, pp. 3025–3033, Jul. 2006, doi: 10.1080/01431160600589179.
• K. Rokni, A. Ahmad, A. Selamat, and S. Hazini, Water feature extraction and change detection using multitemporal landsat imagery, Remote Sens (Basel), vol. 6, no. 5, pp. 4173–4189, 2014, doi: 10.3390/rs6054173.
• H. Gao, C. Birkett, and D. P. Lettenmaier, Global monitoring of large reservoir storage from satellite remote sensing, Water Resour Res, vol. 48, no. 9, 2012, doi: 10.1029/2012WR012063.
• W. Sun, B. Du, and S. Xiong, Quantifying sub-pixel surface water coverage in urban environments using low-albedo fraction from Landsat Imagery, Remote Sens (Basel), vol. 9, no. 5, May 2017, doi: 10.3390/rs9050428.
• E. Firatli, A. Dervisoglu, N. Yagmur, N. Musaoglu, and A. Tanik, Spatio-temporal assessment of natural lakes in Turkey, Earth Sci Inform, vol. 15, no. 2, pp. 951–964, Jun. 2022, doi: 10.1007/s12145-022-00778-8.
• R. R. Colditz, C. Troche Souza, B. Vazquez, A. J. Wickel, and R. Ressl, Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico, International Journal of Applied Earth Observation and Geoinformation, vol. 70, pp. 13–24, Aug. 2018, doi: 10.1016/j.jag.2018.03.008.
• G. L. Feyisa, H. Meilby, R. Fensholt, and S. R. Proud, Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery, Remote Sens Environ, vol. 140, pp. 23–35, Jan. 2014, doi: 10.1016/j.rse.2013.08.029.
• W. Li et al., A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI, Remote Sens (Basel), vol. 5, no. 11, pp. 5530–5549, 2013, doi: 10.3390/rs5115530.
• Y. Han et al., Water distribution based on SAR and optical data to improve hazard mapping, Environ Res, vol. 235, Oct. 2023, doi: 10.1016/j.envres.2023.116694.
• Y. Du, Y. Zhang, F. Ling, Q. Wang, W. Li, and X. Li, Water bodies’ mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band, Remote Sens (Basel), vol. 8, no. 4, 2016, doi: 10.3390/rs8040354.
• A. A. Davrawalska, Drought Monitoring with Sentinel-2 Case study: Western Cape Province, 2015-2020 training KIT-HYDR03, Nov. 2021. [Online]. Available: https://rus-copernicus.eu/portal/the-rus-library/learn-
• U. H. Atasever, A novel unsupervised change detection approach based on reconstruction independent component analysis and ABC-Kmeans clustering for environmental monitoring, Environ Monit Assess, vol. 191, no. 7, Jul. 2019, doi: 10.1007/s10661-019-7591-0.