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Uzaktan Algılama Teknikleri ile Su Yüzeylerinin Tespit Edilmesinde Kullanılan Su Çıkarma İndekslerinin Performans Analizi

Yıl 2023, , 242 - 261, 28.09.2023
https://doi.org/10.48123/rsgis.1256092

Öz

Bu çalışmada Türkiye’de bulunan farklı karakteristik özelliklere sahip Küçükçekmece, Köyceğiz, Burdur, Hazar ve Çıldır gölleri üzerinde sekiz farklı su çıkarma indeksi test edilmiş ve bu indekslerin performansları karşılaştırılmıştır. Su yüzeylerinin belirlenmesi için yapılan çalışmalarda en çok kullanılan NDVI, NDWI1, NDWI2, MNDWI, AWEInsh, AWEIsh, NDMI ve NWI indeksleri kullanılmıştır. Bu indeksler Google Earth Engine platformunda JavaScript kodları ile Sentinel-2 görüntüleri kullanılarak hesaplanmışlardır. Elde edilen indeksler üzerinde su ve su olamayan alanları belirlemek için ise otomatik eşikleme yapabilen Otsu yöntemi kullanılmıştır. Yapılan çalışmanın doğruluk değerlendirmesi için Google Earth Pro tarafından sağlanan WorldView-1/2/3, GeoEye-1 ve Airbus’ın Pleiades yüksek çözünürlüklü görüntüler kullanılmıştır. Değerlendirme, genel doğruluk, Kappa istatistiği ve F1-skor hesaplanarak gerçekleştirilmiştir. Kullanılan indekslerin su yüzeylerini tespit etmedeki başarılarının istatistiksel olarak anlamlı olup olmadığı McNemar testi ile değerlendirilmiştir. Çalışmada en iyi performans gösteren NDW1 indeksi genel doğruluk (GD) değeri minimum %98.00, maksimum %98.94, Kappa istatistiği minimum 0.958, maksimum 0.996 ve F1-skor minimum %97.46, maksimum %98.84 olarak hesaplanmıştır. En kötü performans gösteren indeks olan NDMI için, GD değeri minimum %48.57, maksimum %89.60, Kappa istatistiği minimum 0.047, maksimum 0.703 ve F1-skor minimum %30.77, maksimum %76.77 hesaplanmıştır. Yapılan genel değerlendirme sonucu incelenen sekiz indeks arasında NDWI1 en başarılı, NDMI ise en başarısız çıkmıştır.

Kaynakça

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  • Acharya, T. D., Subedi, A., Yang, I. T., & Lee, D. H. (2017). Combining water indices for water and background threshold in Landsat image. Multidisciplinary Digital Publishing Institute Proceedings, 2(3), 143. doi:10.3390/ecsa-4-04902.
  • Acharya, T. D., Subedi, A. & Lee, D. H. (2018). Evaluation of water indices for surface water extraction in a landsat 8 scene of Nepal. Sensors, 18(8), 2580. doi:10.3390/s18082580.
  • Akar, İ., Maktav, D., & Günal, N. (2012). Göl Yüzeyi Değişimlerinin belirlenmesinde farklı dijital görüntü işleme tekniklerinin kullanılması. Havacılık ve Uzay Teknolojileri Dergisi, 5(4), 35-51.
  • Ashraf, M., & Nawaz, R. (2015). A Comparison of change detection analyses using different band algebras for baraila wetland with nasa’s multi-temporal landsat dataset. Journal of Geographic Information System, 07(01), 1-19.
  • Aydıngün, Ş., & Aydıngül, H. (2020). İstanbul Küçükçekmece göl havzası’nın tarih öncesi (Paleolitik-Erken tunç çağları). Amisos, 5(8), 7-30.
  • Aykır, D., & Fıçıcı, M. (2022). Çıldır Gölü Havzasında erozyon risk analizi. Jeomorfolojik Araştırmalar Dergisi, 9, 38-49.
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  • Çölkesen, İ., & Yomralıoğlu, T. (2014). Arazi örtüsü ve kullanımının haritalanmasında WorldView-2 uydu görüntüsü ve yardımcı verilerin kullanımı. Harita Dergisi, 152(2), 12-24.
  • 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, 112209. doi: 10.1016/j.rse.2020.112209.
  • Ding, F. (2009). Study on information extraction of water body with a new water index (NWI). Science of Surveying and Mapping, 34(4), 155-158.
  • 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), 386. doi: 10.3390/rs8050386.
  • Efe, E., & Algancı, U. (2022). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34.
  • Elsahabi, M., Negm, A., & M.H. El Tahan, A. H. (2016). Performances Evaluation of Surface Water Areas Extraction Techniques Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL). Procedia Technology, 22, 1205-1212.
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  • 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.
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  • Li, J., Ma, R., Cao, Z., Xue, K., Xiong, J., Hu, M., & Feng, X. (2022). Satellite detection of surface water extent: A review of methodology. Water, 14(7), 1148. doi:10.3390/w14071148.
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Performance Analysis of Water Extraction Indices Used in Detection of Water Surfaces with Remote Sensing Techniques

Yıl 2023, , 242 - 261, 28.09.2023
https://doi.org/10.48123/rsgis.1256092

Öz

In this study, eight different water extraction indices were tested in Küçükçekmece, Köyceğiz, Burdur, Hazar, and Çıldır lakes in Türkiye, and the performances of these indices were compared. To determine water surfaces, NDVI, NDWI1, NDWI2, MNDWI, AWEInsh, AWEIsh, NDMI, and NWI indices were utilized. These indices were computed using Sentinel-2 images on the Google Earth Engine platform. The Otsu method, capable of performing automatic thresholding, was employed to delineate water and non-water areas on the indices. For accuracy assessment, images from WorldView-1/2/3, GeoEye-1, and Airbus' Pleiades, provided by Google Earth Pro, were utilized. The evaluation was conducted by calculating overall accuracy (OA), Kappa statistic, and F1-score. The statistical significance of the performance of the utilized indices was assessed using McNemar's test. The best-performing NDW1 index had an OA value of 98% to 99%, a Kappa of 0.96 to 0.99, and an F1-score of 97% to 98%. The worst-performing NDMI had an OA value of 49% to 89%, a Kappa of 0.05 to 0.70, and an F1-score of 31% to 77%. As a result of the general evaluation, NDWI1 was the most successful and NDMI was the most unsuccessful among the eight indexes examined.

Kaynakça

  • Acar, U., Bayram, B., Şanli, F. B., Abdİkan, S., & Üstüner, M. (2012). Sar görüntülerinden kiyi şeridi belirleme algoritması. IV. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 2012. Proocedings. (pp. 1-8). UZAL-CBS.
  • Acharya, T. D., Subedi, A., Yang, I. T., & Lee, D. H. (2017). Combining water indices for water and background threshold in Landsat image. Multidisciplinary Digital Publishing Institute Proceedings, 2(3), 143. doi:10.3390/ecsa-4-04902.
  • Acharya, T. D., Subedi, A. & Lee, D. H. (2018). Evaluation of water indices for surface water extraction in a landsat 8 scene of Nepal. Sensors, 18(8), 2580. doi:10.3390/s18082580.
  • Akar, İ., Maktav, D., & Günal, N. (2012). Göl Yüzeyi Değişimlerinin belirlenmesinde farklı dijital görüntü işleme tekniklerinin kullanılması. Havacılık ve Uzay Teknolojileri Dergisi, 5(4), 35-51.
  • Ashraf, M., & Nawaz, R. (2015). A Comparison of change detection analyses using different band algebras for baraila wetland with nasa’s multi-temporal landsat dataset. Journal of Geographic Information System, 07(01), 1-19.
  • Aydıngün, Ş., & Aydıngül, H. (2020). İstanbul Küçükçekmece göl havzası’nın tarih öncesi (Paleolitik-Erken tunç çağları). Amisos, 5(8), 7-30.
  • Aykır, D., & Fıçıcı, M. (2022). Çıldır Gölü Havzasında erozyon risk analizi. Jeomorfolojik Araştırmalar Dergisi, 9, 38-49.
  • Bolanos, S., Stiff, D., Brisco, B., & Pietroniro, A. (2016). Operational surface water detection and monitoring using Radarsat 2. Remote Sensing, 8(4), 285. doi: 10.3390/rs8040285.
  • Çölkesen, İ., & Yomralıoğlu, T. (2014). Arazi örtüsü ve kullanımının haritalanmasında WorldView-2 uydu görüntüsü ve yardımcı verilerin kullanımı. Harita Dergisi, 152(2), 12-24.
  • 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, 112209. doi: 10.1016/j.rse.2020.112209.
  • Ding, F. (2009). Study on information extraction of water body with a new water index (NWI). Science of Surveying and Mapping, 34(4), 155-158.
  • 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), 386. doi: 10.3390/rs8050386.
  • Efe, E., & Algancı, U. (2022). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34.
  • Elsahabi, M., Negm, A., & M.H. El Tahan, A. H. (2016). Performances Evaluation of Surface Water Areas Extraction Techniques Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL). Procedia Technology, 22, 1205-1212.
  • Feng, S., Liu, S., Zhou, G., Gao, C., Sheng, D., Yan, W., Wu, Y., Gao, H., Jia, J., Wang, Z., Ning, Y., Ren, D., & Liu, M. (2022). Long-term dense Landsat observations reveal detailed waterbody dynamics and temporal changes of the size-abundance relationship. Journal of Hydrology: Regional Studies, 41, 101111. doi: 10.1016/j.ejrh.2022.101111.
  • Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated water extraction ındex: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23-35.
  • Foody, G. M. (2004). Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70(5), 627-633.
  • 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.
  • 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, 126644. doi: 10.1016/j.jhydrol.2021.126644.
  • Güneş, C., & Uyguçgil, H. (2022). Investigation of 6-year land use change of Sakarya River around İnhisar (Bilecik) using remote sensing. Turkish Journal of Remote Sensing and GIS, 3(2), 112-125.
  • 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.
  • Jin, H., Huang, C., Lang, M. W., Yeo, I. Y., & Stehman, S. V. (2017). Monitoring of wetland inundation dynamics in the Delmarva Peninsula using Landsat time-series imagery from 1985 to 2011. Remote Sensing of Environment, 190, 26-41.
  • Kaplan, A. (2019). Burdur Gölü ve çevresinin peyzaj değerleri açısından turizm potansiyelinin belirlenmesi. Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD), 4(2), 105-121.
  • Kaya, Ç. M. (2022). Methods used in flood susceptibility mapping. Turkish Journal of Remote Sensing and GIS, 3(2), 191-209.
  • Kaya, L. G., Yücedağ, C., & Duruşkan, Ö. (2015). Burdur Gölü havzasının çevresel açıdan irdelenmesi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 6-10.
  • Kaya, Ö. (2019). Küçükçekmece Göl havzası (Bathonea) kazılarında bulunan kandiller (Yüksek Lisans Tezi). Kocaeli Üniversitesi, Sosyal Bilimler Enstitüsü, Kocaeli, Türkiye.
  • 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.
  • Li, J., Ma, R., Cao, Z., Xue, K., Xiong, J., Hu, M., & Feng, X. (2022). Satellite detection of surface water extent: A review of methodology. Water, 14(7), 1148. doi:10.3390/w14071148.
  • Lothspeich, A. C., & Knight, J. F. (2022). The applicability of landtrendr to surface water dynamics : A case study of Minnesota from 1984 to 2019 using google earth engine. Remote Sensing, 14, 2662. doi: 10.3390/rs14112662.
  • Ma, M., Wang, X., Veroustraete, F., & Dong, L. (2007). Change in area of Ebinur Lake during the 1998-2005 period. International Journal of Remote Sensing, 28(24), 5523-5533.
  • Mansaray, L. R., Wang, F., Huang, J., Yang, L., & Kanu, A. S. (2020). Accuracies of support vector machine (SVM) and random forest (RF) in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto International, 35(10), 1088–1108.
  • 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.
  • Naik, B. C., & Anuradha, B. (2018). Extraction of water-body area from high-resolution Landsat imagery. International Journal of Electrical and Computer Engineering, 8(6), 4111. doi: 10.11591/ijece.v8i6.pp4111-4119.
  • Nandi, D., Chowdhury, R., Mohapatra, J., Mohanta, K., & Ray, D. (2018). Automatic delineation of water bodies using multiple spectral ındices. International Journal of Scientific Research in Science, Engineering and Technology, 4(4), 498-512.
  • Owusu, C. (2022). PyGEE-SWToolbox : A python jupyter notebook toolbox for ınteractive surface water mapping and analysis using google earth engine. Sustainability, 14, 2557. doi: 10.3390/su14052557.
  • Özdemir, S. (2013). Hazar Gölü’nde (Elazığ) Pleyistosen-Holosen dönemi yüksek çözünürlü iklim ve su seviyesi değişimleri (Yüksek Lisans Tezi). Fırat Üniversitesi, Elazığ, Türkiye.
  • 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.
  • Rad, A. M., Kreitler, J., & Sadegh, M. (2021). Augmented Normalized Difference Water Index for improved surface water monitoring. Environmental Modelling and Software, 140, 105030. doi: 10.1016/j.envsoft.2021.105030.
  • Reis, L. G. de M., Souza, W. de O., Ribeiro Neto, A., Fragoso, C. R., Ruiz-Armenteros, A. M., Cabral, J. J. da S. P., & Montenegro, S. M. G. L. (2021). Uncertainties involved in the use of thresholds for the detection of water bodies in multitemporal analysis from landsat-8 and sentinel-2 images. Sensors, 21(22), 7494. doi: 10.3390/s21227494.
  • Rogers, A. S., & Kearney, M. S. (2004). Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. International Journal of Remote Sensing, 25(12), 2317-2335.
  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. In 3rd ERTS Symposium, 1974. Proocedings. (pp. 309-317). NASA.
  • 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.
  • Selim, S., Çoşlu, M., Sönmez, N. K., & Karakuş, N. (2016). Köyceğiz Gölü ve Dalyan kanallarında kıyı kenar çizgisinin UA ve CBS Teknikleri ile belirlenmesi, Alanda Karşılaşılan Sorunlar. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 20(2), 254-260.
  • Song, S., Cao, Z., Wu, Z., & Chuai, X. (2022). Spatial and temporal dynamics of surface water in China from the 1980s to 2015 Based on remote sensing monitoring. Chinese Geographical Science, 32(1), 174-188.
  • Türedi, M. (2006). Köyceğiz Gölü (Limnolojik Etüt) (Yüksek Lisans Tezi). Marmara Üniversitesi, İstanbul, Türkiye.
  • Wang, R., Pan, L., Niu, W., Li, R., Zhao, X., Bian, X., Yu, C., Xia, H., & Chen, T. (2021). Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine. Open Geosciences, 13(1), 1290-1302.
  • Wang, R., Xia, H., Qin, Y., Niu, W., Pan, L., Li, R., Zhao, X., Bian, X., & Fu, P. (2020). Dynamic monitoring of surface water area during 1989-2019 in the hetao plain using landsat data in google earth engine. Water, 12(11), 3010. doi:10.3390/w12113010.
  • Worden, J., & de Beurs, K. M. (2020). Surface water detection in the Caucasus. International Journal of Applied Earth Observation and Geoinformation, 91, 102159. doi: 10.1016/j.jag.2020.102159.
  • 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.
  • Yang, X., Qin, Q., Grussenmeyer, P., & Koehl, M. (2018). Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing of Environment, 219, 259-270.
  • Yilmaz, C. S. (2022). Improving the land cover mapping accuracy of the Sentinel-2 imagery on Google Earth Engine. Turkish Journal of Remote Sensing and GIS, 3(2), 150-159.
  • Yilmaz, O. S., Gulgen, F., Balik Sanli, F., & Ates, A. M. (2023). The performance analysis of different water ındices and algorithms using Sentinel-2 and Landsat-8 images in determining water surface: Demirkopru Dam case study. Arabian Journal for Science and Engineering, 48(6), 7883-7903.
  • Zengin, M., Yerli, S. V, Dağtekin, M., & Akpınar, İ. Ö. (2012). Çıldır Gölü balıkçılığında son yirmi yılda meydana gelen değişimler. Süleyman Demirel Üniversitesi Eğirdir Su Ürünleri Fakültesi Dergisi, 8(2), 10-24.
  • Zhai, K., Wu, X., Qin, Y., & Du, P. (2015). 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.
  • Zhao, Q., Dong, X., Li, G., Jin, Y., Yang, X., & Qu, Y. (2022). Classification and Regression Tree Models for Remote Recognition of Black and Odorous Water Bodies Based on Sensor Networks. Scientific Programming, 2022, 7390098. doi: 10.1155/2022/7390098.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Osman Salih Yılmaz 0000-0003-4632-9349

Erken Görünüm Tarihi 26 Eylül 2023
Yayımlanma Tarihi 28 Eylül 2023
Gönderilme Tarihi 24 Şubat 2023
Kabul Tarihi 16 Mayıs 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yılmaz, O. S. (2023). Uzaktan Algılama Teknikleri ile Su Yüzeylerinin Tespit Edilmesinde Kullanılan Su Çıkarma İndekslerinin Performans Analizi. Türk Uzaktan Algılama Ve CBS Dergisi, 4(2), 242-261. https://doi.org/10.48123/rsgis.1256092

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Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.