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Determining Atikhisar Reservoir’s Bathymetry from Landsat-5 TM Satellite Images Using the Stumpf Algorithm

Yıl 2022, , 97 - 110, 30.12.2022
https://doi.org/10.26650/JGEOG2022-1099122

Öz

Determining the bathymetry of shallow waters is important for managing coastal areas, river basins, and water resources. However, economic and practical difficulties in collecting bathymetric data cause disruptions in bathymetric studies. To overcome these challenges, a recent focus has involved the use of remote sensing technology as an alternative approach to the bathymetric mapping of shallow waters. This study uses Landsat-5 TM satellite imagery, which is free and open data, to determine the digital bathymetric model (DBM) of Atikhisar Reservoir. The study also uses the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to determine the reservoir’s surface area and the Stumpf algorithm to perform the bathymetric mapping. Satellite image-based DBMs were obtained using the linear regression equations created from the blue/green log-ratio values from the Landsat-5 TM satellite image and the values obtained from a 1/5000 scale digital bathymetric map for five different training reference point sets. The root mean square error (RMSE) values were calculated by comparing the DBMs with the test data. The model with the best results showed the regression determination coefficient (R2 ) to be 0.701 and the RMSE to be 2.1 m. These results reveal the potential of low-cost bathymetric map production for preliminary investigation and general evaluation in reservoirs with easy data processing from Landsat images.

Kaynakça

  • Akgül, M. A., Dağdeviren, M. ve Biroğlu, İ. (2018). Çok zamanlı uydu görüntüleri kullanılarak uydu-kaynaklı batimetri. DSİ Teknik Bülteni, 127, 14-27. google scholar
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  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903. http://dx.doi.org/10.1016/j.rse.2009.01.007 google scholar
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  • Dietrich, J. T. (2017). Bathymetric structure from motion: extracting shallow stream bathymetry from multiview stereo photogrammetry. Earth Surface Processes and Landforms, 42(2), 355-364. http:// dx.doi.org/10.1002/esp.4060 google scholar
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Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi

Yıl 2022, , 97 - 110, 30.12.2022
https://doi.org/10.26650/JGEOG2022-1099122

Öz

Sığ sular için batimetrinin belirlenmesi; kıyı bölgeleri, akarsu havzaları ve su kaynaklarının yönetimi açısından önemlidir. Ancak batimetrik verilerin toplanmasındaki ekonomik ve uygulama zorlukları batimetriye dayalı çalışmaları da zorlaştırmaktadır. Bu zorlukların üstesinden gelmek için son yıllarda sığ sular için batimetrik haritalamada alternatif bir yaklaşım olarak uzaktan algılama teknolojisinin kullanımı üzerinde çalışmalar yoğunlaşmaktadır. Bu çalışmada Atikhisar Baraj Gölünün Sayısal Batimetrik Modelinin (SBM) belirlenmesinde ücretsiz ve açık bir veri olan Landsat-5 TM uydu görüntüsü kullanılmıştır. Baraj göl alanının belirlenmesinde NDWI (Normalleştirilmiş Fark Su İndeksi) ve MNDWI (Modifiye Edilmiş Normalleştirilmiş Fark Su İndeksi) su indeksleri, batimetrik haritalamada Stumpf algoritması uygulanmıştır. Beş farklı alıştırma referans nokta kümesi için Landsat-5 TM uydu görüntüsünün Mavi/Yeşil log-oran değerleri ve 1/5000 ölçekli sayısal batimetrik haritadan elde edilen değerler kullanılarak oluşturulan doğrusal regresyon denklemleri ile uydu görüntüsü tabanlı SBM’ler elde edilmiştir. SBM’lerin test verileriyle karşılaştırılması sonucunda karesel ortalama hata (KOH) değerleri hesaplanmıştır. En iyi sonuç veren model için regresyon belirleme katsayısı (R2 ) 0,701 ve KOH 2,1 m olarak belirlenmiştir. Sonuçlar, Landsat görüntülerinden düşük maliyet ve kolay veri işleme ile baraj göllerinde ön inceleme ve genel değerlendirme amaçlı batimetrik harita üretim potansiyelini ortaya koymuştur.

Kaynakça

  • Akgül, M. A., Dağdeviren, M. ve Biroğlu, İ. (2018). Çok zamanlı uydu görüntüleri kullanılarak uydu-kaynaklı batimetri. DSİ Teknik Bülteni, 127, 14-27. google scholar
  • Bierwirth, P. N., Lee, T. J., & Burne, R. V. (1993). Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogrammetric Engineering and Remote Sensing, 59(3), 331-338. google scholar
  • Brander, R. W., & Cowell, P. J. (2003). A trend-surface technique for discrimination of surf-zone morphology: Rip current channels. Earth Surface Processes and Landforms, 28(8), 905-918. http:// dx.doi.org/10.1002/esp.489 google scholar
  • Bruce, C. M., & Hilbert, D. W. (2006). Pre-processing methodology for application to Landsat TM/ETM+ imagery of the wet tropics. Research Report, Cooperative Research Centre for Tropical Rainforest Ecology and Management, James Cook University, Australia, 38 p. google scholar
  • Caballero, I., & Stumpf, R. P. (2019). Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters. Estuarine, Coastal and ShelfScience, 226(6), 106277. http://dx.doi. org/10.1016/j.ecss.2019.106277 google scholar
  • Chander, G., & Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2674-2677. google scholar
  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903. http://dx.doi.org/10.1016/j.rse.2009.01.007 google scholar
  • Chavez, P. S. (1996). Image-based atmospheric corrections - Revisited and improved. Photogrammetric Engineering Remote Sensing, 62(9), 1025-1036. google scholar
  • Cetin, M., Musaoglu, N., & Kocal, O. H. (2017). A comparison of atmospheric correction methods on hyperion imagery in forest areas. Uludag University Journal of The Faculty of Engineering, 22(1), 103-114. http://dx.doi.org/10.17482/uumfd.308630 google scholar
  • Çelik, H. E., Şengönül, K., Akyüz, F., Altunel, O., Dağcı, M. ve Esin, A. İ. (2012). İstanbul’un içme suyu barajlarının sedimantasyon problemi ve çözüm önerileri: Alibey Barajı örneği. Journal of the Faculty ofForestry Istanbul University, 62(2), 113-127. google scholar
  • Di Kaichang, D. Q., Wei, C., & Wenyu, C. (1999). Shallow water depth extraction and chart production from TM images in Nansha Islands and nearby sea area. Remote Sensingfor Land & Resources, 11(3), 59-64. google scholar
  • Dietrich, J. T. (2017). Bathymetric structure from motion: extracting shallow stream bathymetry from multiview stereo photogrammetry. Earth Surface Processes and Landforms, 42(2), 355-364. http:// dx.doi.org/10.1002/esp.4060 google scholar
  • DSİ (2010). DSİ Mühendislik Meslek Eğitimi, Cilt 2, Ankara. google scholar
  • Ehses, J. S., & Rooney, J. J. (June 2015). Depth Derivation Using Multispectral Worldview-2 Satellite Imagery. NOAA Technical Memorandum NMFS-PIFSC-46. google scholar
  • Elhassan I. (2015). Development of bathymetric techniques. FIG Working Week 2015, From the Wisdom ofthe Ages to the Challenges of the Modern World, Sofia, Bulgaria, 17-21 May 2015. google scholar
  • ESA, (2015). Sentinel-2 User Handbook. Retrieved from https:// sentinel.esa.int/documents/247904/685211/sentinel-2_user_ handbook (Last accessed: 15.06.2022) google scholar
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  • Everitt, J. H., Yang, C., Sriharan, S., & Judd, F. W. (2008). Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast. Journal of Coastal Research, 24(6), 1582-1586. http:// dx.doi.org/ 10.2112/07-0987.1 google scholar
  • Flener, C., Wang, Y., Laamanen, L., Kasvi, E., Vesakoski, J. M., & Alho, P. (2015). Empirical modeling of spatial 3D flow characteristics using a remote-controlled ADCP system: Monitoring a spring flood. Water, 7(1), 217-247. http://dx.doi.org/10.3390/w7010217 google scholar
  • Forfinski-Sarkozi, N. A., & Parrish, C. E. (2016). Analysis of MABEL bathymetry in Keweenaw bay and implications for ICESat-2 ATLAS. Remote Sensing, 8(9), 772. http://dx.doi.org/10.3390/rs8090772 google scholar
  • Geyman, E. C., & Maloof, A. C. (2019). A simple method for extracting water depth from multispectral satellite imagery in regions of variable bottom type. Earth and Space Science, 6(3), 527-537. http://dx.doi.org/10.1029/2018EA000539 google scholar
  • Ghebreamlak, A. Z., Tanakamaru, H., Tada, A., Ahmed Adam, B. M., & Elamin, K. A. (2018). Satellite-based mapping of cultivated area in Gash Delta Spate irrigation system, Sudan. Remote Sensing, 10(2), 186. http://dx.doi.org/10.3390/rs10020186 google scholar
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  • Hell, B., Broman, B., Jakobsson, L., Jakobsson, M., Magnusson, A., & Wiberg, P. (2012). The use of bathymetric data in society and science: A review from the Baltic Sea. Ambio, 41(2), 138-150. http://dx.doi.org/10.1007/s13280-011-0192-y google scholar
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Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Derya Öztürk 0000-0002-0684-3127

Yayımlanma Tarihi 30 Aralık 2022
Gönderilme Tarihi 5 Nisan 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Öztürk, D. (2022). Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Journal of Geography(45), 97-110. https://doi.org/10.26650/JGEOG2022-1099122
AMA Öztürk D. Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Journal of Geography. Aralık 2022;(45):97-110. doi:10.26650/JGEOG2022-1099122
Chicago Öztürk, Derya. “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”. Journal of Geography, sy. 45 (Aralık 2022): 97-110. https://doi.org/10.26650/JGEOG2022-1099122.
EndNote Öztürk D (01 Aralık 2022) Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Journal of Geography 45 97–110.
IEEE D. Öztürk, “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”, Journal of Geography, sy. 45, ss. 97–110, Aralık 2022, doi: 10.26650/JGEOG2022-1099122.
ISNAD Öztürk, Derya. “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”. Journal of Geography 45 (Aralık 2022), 97-110. https://doi.org/10.26650/JGEOG2022-1099122.
JAMA Öztürk D. Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Journal of Geography. 2022;:97–110.
MLA Öztürk, Derya. “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”. Journal of Geography, sy. 45, 2022, ss. 97-110, doi:10.26650/JGEOG2022-1099122.
Vancouver Öztürk D. Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Journal of Geography. 2022(45):97-110.