Operational estimation of Kadıköy Dam surface water extent from Sentinel-2 multispectral imagery using OTSU method between 2015 and 2023

Year 2024, , 254 - 271, 26.09.2024

Suphi Öztürk , Ali Levent Yağcı

https://doi.org/10.48123/rsgis.1508139

Abstract

The objective of this study is to automatically estimate the surface water extent of Kadıköy Dam between 2015 and 2023 using Sentinel-2 satellite imagery. In this regard, widely used indices such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were employed. The shortwave infrared band (SWIR-1) in 20m spatial resolution was upscaled to 10m resolution using a convolutional neural network method to eliminate the resolution discrepancy in NDWI and MNDWI results. A fixed (MNDWI_0) and OTSU dynamic thresholding (MNDWI_OTSU) scheme applied to MNDWI results to delineate water surface from land surfaces. The results obtained from MNDWI_0 and MNDWI_OTSU methods were compared to the observations downloaded from Global Water Watch (GWW) website and the counting of the water pixels in the scene classification layer (SCL) during the days when cloud cover is below 1%. The OTSU thresholding scheme applied to NDWI maps to derive the water extent estimates on the GWW website. The results indicated that even though the lowest average relative error was observed between MNDWI_0 and MNDWI_OTSU methods, the lowest median relative error was observed between GWW and MNDWI_OTSU water extent results because several physically impossible sudden changes or outliers seen in GWW water extent time series inflated the average relative error of GWW results.

Keywords

MNDWI, OTSU, Surface water extent, Freshwater management

2015-2023 yılları arasında Kadıköy Barajı su yüzey alanının OTSU yöntemiyle Sentinel-2 multispektral görüntülerinden operasyonel olarak belirlenmesi

Abstract

Bu çalışmada, Kadıköy Barajı'nın 2015-2023 yılları arasındaki su yüzey alanı değişimleri, Sentinel-2 uydu görüntüleri kullanılarak otomatik bir şekilde belirlenmesi amaçlanmıştır. Çalışma kapsamında, yaygın olarak kullanılan Normalleştirilmiş Fark Su İndeksi (NDWI) ve Modifiye Edilmiş Normalleştirilmiş Fark Su İndeksi (MNDWI) kullanılmıştır. NDWI ve MNDWI sonuçlarındaki mekânsal çözünürlük farkını ortadan kaldırmak için 20m çözünürlüğündeki kısa dalga kızılötesi bandı (SWIR-1), evrişimli sinir ağları yöntemiyle 10m çözünürlüğe yükseltilmiştir. Su alanlarını diğer alanlardan ayırmak için MNDWI ile hem sabit (MNDWI_0) hem de OTSU (MNDWI_OTSU) dinamik eşikleme yöntemleri kullanılmıştır. Daha sonra, elde edilen sonuçlar, Kadıköy Barajını NDWI OTSU dinamik eşikleme yöntemi ile operasyonel olarak takip eden Global Water Watch (GWW) gözlemleri ve Level-2 Sentinel-2 sınıflandırma katmanındaki (SCL) su olarak etiketlenen piksellerden hesaplanan baraj alanı ile bulutluluk oranının %1’in altında olduğu günlerde karşılaştırılmıştır. Sonuçlara göre, en düşük bağıl hata MNDWI_OTSU ile MNDWI_0 yöntemleri arasında görülmesine rağmen, MNDWI_OTSU ile GWW yöntemleri arasında en düşük ortanca bağıl hata görülmüştür. Bunun nedeni, GWW gözlemlerinde bazı günlerde fiziksel olarak mümkün olmayan ani değişimler ortalama bağıl hatayı yükseltmiştir.

Keywords

MNDWI, OTSU, Su yüzey alanı, Su kaynakları yönetimi

References

• Aggarwal, R., Kaushal, M., Kaur, S., & Farmaha, B. (2009). Water resource management for sustainable agriculture in Punjab, India. Water Science and Technology, 60(11), 2905-2911. https://doi.org/10.2166/wst.2009.348
• Albarqouni, M. M. Y., 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), Article 407. https://doi.org/10.3390/ijgi11070407
• Ati̇z, Ö. F., Alkan, T., & Durduran, S. S. (2023). Google Earth Engine Based Spatio-Temporal Changes of Bafa Lake from 1984 to 2022. International Journal of Environment and Geoinformatics, 10(3), 116-123. https://doi.org/10.30897/ijegeo.1257413
• Bai, J., Chen, X., Li, J., Yang, L., & Fang, H. (2011). Changes in the area of inland lakes in arid regions of central Asia during the past 30 years. Environmental Monitoring and Assessment, 178(1), 247-256. https://doi.org/10.1007/s10661-010-1686-y
• Copernicus Data Space Ecosystem. (2024). OpenSearch Catalog web service. 16 Mayıs 2024’de https://documentation.dataspace.copernicus.eu/APIs/OpenSearch.html adresinden alındı.
• Davranche, A., Lefebvre, G., & Poulin, B. (2010). Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sensing of Environment, 114(3), 552-562. https://doi.org/10.1016/j.rse.2009.10.009
• Deltares (2024). Global Water Watch. 08 Mayıs 2024’de https://www.globalwaterwatch.earth/reservoir/80987 adresinden alındı.
• Doğa Koruma ve Milli Parklar Genel Müdürlüğü. (2023). Ulusal Sulak Alan Envanteri Yönetim Bilgi Sistemi. 08 Mayıs 2024’de https://saybis.tarimorman.gov.tr/ adresinden alındı.
• Donchyts, G., Winsemius, H., Baart, F., Dahm, R., Schellekens, J., Gorelick, N., … Schmeier, S. (2022). High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs. Scientific Reports, 12(1), Article 13776. https://doi.org/10.1038/s41598-022-17074-6
• Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., … & Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. https://doi.org/10.1016/j.rse.2011.11.026
• Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). 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 Sensing, 8(4), Article 354. https://doi.org/10.3390/rs8040354
• Duan, Z., & Bastiaanssen, W. G. M. (2013). Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sensing of Environment, 134, 403-416.
• 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
• Filipponi, F. (2019). Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires. Remote Sensing, 11(6), Article 622. https://doi.org/10.3390/rs11060622
• Fiorio, C., & Gustedt, J. (1996). Two linear time Union-Find strategies for image processing. Theoretical Computer Science, 154(2), 165-181. https://doi.org/10.1016/0304-3975(94)00262-2
• Firatli, E., Dervisoglu, A., Yagmur, N., Musaoglu, N., & Tanik, A. (2022). Spatio-temporal assessment of natural lakes in Turkey. Earth Science Informatics, 15(2), 951-964. https://doi.org/10.1007/s12145-022-00778-8
• Fuentes, I., Padarian, J., van Ogtrop, F., & Vervoort, R. W. (2019). Comparison of Surface Water Volume Estimation Methodologies That Couple Surface Reflectance Data and Digital Terrain Models. Water, 11(4), Article 780. https://doi.org/10.3390/w11040780
• Huang, S., Chen, X., Ma, X., Fang, H., Liu, T., Kurban, A., … Van de Voorde, T. (2023). Monitoring Surface Water Area Changes in the Aral Sea Basin Using the Google Earth Engine Cloud Platform. Water, 15(9), Article 1729. https://doi.org/10.3390/w15091729
• Huang, Z., Xu, J., & Zheng, L. (2023). Long-Term Change of Lake Water Storage and Its Response to Climate Change for Typical Lakes in Arid Xinjiang, China. Water, 15(8), Article 1444. https://doi.org/10.3390/w15081444
• Hui, F., Xu, B., Huang, H., Yu, Q., & Gong, P. (2008). Modelling spatial‐temporal change of Poyang Lake using multitemporal Landsat imagery. International Journal of Remote Sensing, 29(20), 5767-5784. https://doi.org/10.1080/01431160802060912
• HydroSHEDS (2024). HydroLAKES. 31 Ekim 2023’de https://www.hydrosheds.org/products/hydrolakes adresinden alındı.
• 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. https://doi.org/10.14358/PERS.75.11.1307
• Karaman, M., & Özelkan, E. (2022). Comparative assessment of remote sensing–based water dynamic in a dam lake using a combination of Sentinel-2 data and digital elevation model. Environmental Monitoring and Assessment, 194(2), Article 92. https://doi.org/10.1007/s10661-021-09703-w
• Katusiime, J., & Schütt, B. (2020). Integrated Water Resources Management Approaches to Improve Water Resources Governance. Water, 12(12), Article 3424. https://doi.org/10.3390/w12123424
• Khattab, M. F. O., Abo, R. K., Al-Muqdadi, S. W., & Merkel, B. J. (2017). Generate Reservoir Depths Mapping by Using Digital Elevation Model: A Case Study of Mosul Dam Lake, Northern Iraq. Advances in Remote Sensing, 6(3), 161-174. https://doi.org/10.4236/ars.2017.63012
• Lanaras, C. (2018) 2024. “lanha/DSen2”. Jupyter Notebook. https://github.com/lanha/DSen2.
• Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319. https://doi.org/10.1016/j.isprsjprs.2018.09.018
• Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., … Zhang, X. (2013). A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI. Remote Sensing, 5(11), 5530-5549. https://doi.org/10.3390/rs5115530
• Liu, C., Hu, R., Wang, Y., Lin, H., Zeng, H., Wu, D., … Shao, C. (2022). Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google Earth Engine. Journal of Hydro-environment Research, 44, 53-64. https://doi.org/10.1016/j.jher.2022.07.005
• Lu, L., & Sun, H. (2023). Dynamic monitoring of surface water areas of nine plateau lakes in Yunnan Province using long time-series Landsat imagery based on the Google Earth Engine platform. Geocarto International, 38(1), Article 2253196. https://doi.org/10.1080/10106049.2023.2253196
• Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., & Gascon, F. (2017). Sen2Cor for Sentinel-2. Image and Signal Processing for Remote Sensing XXIII, 10427, 37-48. SPIE. https://doi.org/10.1117/12.2278218
• 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. https://doi.org/10.1080/01431169608948714
• Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), Article 13603. https://doi.org/10.1038/ncomms13603
• OECD. (2010). Sustainable Management of Water Resources in Agriculture. OECD. https://doi.org/10.1787/9789264083578-en
• Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. https://doi.org/10.1109/TSMC.1979.4310076
• Özelkan, E. (2019). Uzaktan Algılama ile Belirlenen Baraj Gölü Alanının Zamansal Değişiminin Meteorolojik Kuraklık ile Değerlendirilmesi: Atikhisar Barajı (Çanakkale) Örneği. Türk Tarım ve Doğa Bilimleri Dergisi, 6(4), 904-916.
• Öztürk, M. Z., Çeti̇Nkaya, G., & Aydin, S. (2017). Köppen-Geiger İklim Sınıflandırmasına Göre Türkiye’nin İklim Tipleri. Journal of Geography, 35, 17-27. https://doi.org/10.26650/JGEOG295515
• Pan, F., Xi, X., & Wang, C. (2020). A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery. Remote Sensing, 12(10), Article 1611. https://doi.org/10.3390/rs12101611
• 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
• Qiu, S., Zhu, Z., & He, B. (2019). Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery. Remote Sensing of Environment, 231, Article 111205. https://doi.org/10.1016/j.rse.2019.05.024
• Segarra, J., Buchaillot, M. L., Araus, J. L., & Kefauver, S. C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10(5), Article 641. https://doi.org/10.3390/agronomy10050641
• Senay, G. B., Velpuri, N. M., Bohms, S., Budde, M., Young, C., Rowland, J., & Verdin, J. P. (2015). Chapter 9 - Drought Monitoring and Assessment: Remote Sensing and Modeling Approaches for the Famine Early Warning Systems Network. In J. F. Shroder, P. Paron, & G. D. Baldassarre (Eds.), Hydro-Meteorological Hazards, Risks and Disasters (pp. 233-262). Boston: Elsevier. https://doi.org/10.1016/B978-0-12-394846-5.00009-6
• Tottrup, C., Druce, D., Meyer, R. P., Christensen, M., Riffler, M., Dulleck, B., … Paganini, M. (2022). Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection. Remote Sensing, 14(10), Article 2410. https://doi.org/10.3390/rs14102410
• Vasilakos, C., Kavroudakis, D., & Georganta, A. (2020). Machine Learning Classification Ensemble of Multitemporal Sentinel-2 Images: The Case of a Mixed Mediterranean Ecosystem. Remote Sensing, 12(12), Article 2005. https://doi.org/10.3390/rs12122005
• Walt, S. van der, Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., … Yu, T. (2014). scikit-image: Image processing in Python. PeerJ, 2, Article e453. https://doi.org/10.7717/peerj.453
• Wu, K., Otoo, E., & Shoshani, A. (2005). Optimizing connected component labeling algorithms. https://escholarship.org/uc/item/7jg5d1zn
• 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.
• Yağmur, N., Tanık, A., Tuzcu, A., Musaoğlu, N., Erten, E., & Bilgilioglu, B. (2020). Opportunities provided by remote sensing data for watershed management: Example of Konya Closed Basin. International Journal of Engineering and Geosciences, 5(3), 120-129. https://doi.org/10.26833/ijeg.638669
• Yilmaz, O. S. (2023). Uzaktan algılama teknikleri ile su yüzeylerinin tespit edilmesinde kullanılan su çıkarma indekslerinin performans analizi. Türk Uzaktan Algılama ve CBS Dergisi, 4(2), 242-261. https://doi.org/10.48123/rsgis.1256092
• Yue, H., & Liu, Y. (2019). Variations in the lake area, water level, and water volume of Hongjiannao Lake during 1986–2018 based on Landsat and ASTER GDEM data. Environmental Monitoring and Assessment, 191(10), Article 606. https://doi.org/10.1007/s10661-019-7715-6
• Zhou, H., Liu, S., Hu, S., & Mo, X. (2021). Retrieving dynamics of the surface water extent in the upper reach of Yellow River. Science of The Total Environment, 800, Article 149348. https://doi.org/10.1016/j.scitotenv.2021.149348