Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2022, Sayı: 45, 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
  • 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
  • Eugenio, F., Marcello, J., & Martin, J. (2015). High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3539-3549. http://dx.doi. org/10.1109/TGRS.2014.2377300 google scholar
  • 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
  • Green, E., Mumby, P., Edwards, A., & Clark, C. (2000). Remote sensing: Handbook for tropical coastal management. United Nations Educational, Scientific and Cultural Organization (UNESCO). google scholar
  • 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
  • Huang, W. Q., Wu, D., Yang, Y., Liang, Z. C., & Zhang, Y. Y. (2013). Multi-spectral remote sensing water depth retrieval technique in shallow sea. Ocean Technology, 32(2), 43-46. google scholar
  • IHO (2020). International Hydrographic Organization Standards for Hydrographic Surveys (S-44 Edition 6.0.0). google scholar
  • Jagalingam, P., Akshaya, B. J., & Hegde, A. V. (2015). Bathymetry mapping using Landsat 8 satellite imagery. Procedia Engineering, 116, 560-566. http://dx.doi.org/10.1016/j.proeng.2015.08.326 google scholar
  • Jawak, S. D., Vadlamani, S. S., & Luis, A. J. (2015). A synoptic review on deriving bathymetry information using remote sensing technologies: models, methods and comparisons. Advances in Remote Sensing, 4(2), 147-162. http://dx.doi.org/10.4236/ars.2015.42013 google scholar
  • Kasvi, E., Salmela, J., Lotsari, E., Kumpula, T., & Lane, S. N. (2019). Comparison of remote sensing based approaches for mapping bathymetry of shallow, clear water rivers. Geomorphology, 333, 180-197. http://dx.doi.org/10.1016/j.geomorph.2019.02.017 google scholar
  • Kerr, J. M., & Purkis, S. (2018). An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data. Remote Sensing of Environment, 210, 307-324. http://dx.doi.org/10.1016/j.rse.2018.03.024 google scholar
  • Koca N. (2005). Atikhisar Barajı’nın (Çanakkale) çevresel ve ekonomik etkileri. Doğu Coğrafya Dergisi, 10(14), 209-233. google scholar
  • Liu, Q., & Trinder, J. C. (2018). Sub-pixel technique for time series analysis of shoreline changes based on multispectral satellite imagery. In M. Marghany (Ed.), Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. IntechOpen. http://dx.doi.org/10.5772/ intechopen.81789 google scholar
  • Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379-383. http://dx.doi.org/10.1364/AO.17.000379 google scholar
  • Lyzenga, D. R. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing, 6(1), 115-125. http://dx.doi.org/10.1080/01431168508948428 google scholar
  • Lyzenga, D. R., Malinas, N. P., & Tanis, F. J. (2006). Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44(8), 22512259. http://dx.doi.org/10.1109/TGRS.2006.872909 google scholar
  • Mancino, G., Nole, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. iForest, 7(2), 75-84. http://dx.doi.org/10.3832/ifor0909-007 google scholar
  • 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, 1425-1432. http:// dx.doi.org/10.1080/01431169608948714 google scholar
  • Minghelli-Roman, A., Goreac, A., Mathieu, S., Spigai, M., & Gouton, P. (2009). Comparison of bathymetric estimation using different satellite images in coastal sea waters. International Journal of Remote Sensing, 30(21), 5737-5750. http://dx.doi.org/10.1080/01431160902729580 google scholar
  • Mishra, D., Narumalanii S., Lawson, M., & Rundquist, D. (2004). Bathymetric mapping using IKONOS multispectral data. GIScience & Remote Sensing, 41(4), 301-321. http://dx.doi.org/10.2747/1548-1603.41.4.301 google scholar
  • Moore, D. S., Notz, W. I., & Flinger, M. A. (2013). The basic practice of statistics (6th edition). New York: W. H. Freeman and Company. google scholar
  • 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. google scholar
  • Özelkan, E. ve Karaman, M. (2018). Baraj göllerindeki meteorolojik ve hidrolojik kuraklığın etkisinin çok zamanlı uydu görüntüleri ile analizi: Atikhisar Barajı (Çanakkale) örneği. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 7(2), 1023-1037. google scholar
  • Parente, C., & Pepe, M. (2018). Bathymetry from WorldView-3 satellite data using radiometric band ratio. Acta Polytechnica, 58(2), 109117. http://dx.doi.org/10.14311/AP.2018.58.0109 google scholar
  • Radermacher, M., de Schipper, M. A., & Reniers, A. J. H. M. (2018). Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry. Coastal Engineering, 135, 66-76. http://dx.doi. org/10.1016/j.coastaleng.2018.01.007 google scholar
  • Renaud, O., & Victoria-Feser, M. P. (2010). A robust coefficient of determination for regression. Journal of Statistical Planning and Inference, 140(7), 1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008 google scholar
  • Rossi, L, Mammi, I, & Pelliccia, F. (2020). UAV-Derived Multispectral Bathymetry. Remote Sensing, 12(23), 3897. http://dx.doi. org/10.3390/rs12233897 google scholar
  • Rossi, L., Mammi, I., & Pranzini, E. (2018). A comparison between UAV and high-resolution multispectral satellite images for bathymetry estimation. In G. Chirici & M. Gianinetto (Eds.), Trends in Earth Observation: Earth Observation Advancements in a Changing World (Vol 1, pp.143-146). Firenze, Italy. google scholar
  • Saeed, R., Abdelrahman, S. M., & Negm, A. (2021). Satellite-derived bathymetry using Landsat-8 imagery for Safaga Coastal Zone, Egypt. Acta Marisiensis. Seria Technologica, 18(1), 8-15. google scholar
  • Setiawan, K. T., Adawiah, S. W., Marini, Y., & Winarso, G. (2016). Bathymetry data extraction analysis using Landsat 8 Data. International Journal of Remote Sensing and Earth Sciences, 13(2), 79-86. google scholar
  • Setiawan, K. T. (2013). Study of bathymetry map using Landsat ETM+ data - A case study at Menjangan Island, Bali (MSc Thesis, Udayana University, Indonesia). google scholar
  • Shah, A., Deshmukh, B., & Sinha, L. K. (2020). A review of approaches for water depth estimation with multispectral data. World Water Policy, 6, 152-167. http://dx.doi.org/10.1002/wwp2.12029 google scholar
  • Shintani, C., & Fonstad, M. A. (2017). Comparing remote-sensing techniques collecting bathymetric data from a gravel-bed river. International Journal of Remote Sensing, 38 (8-10), 2883-2902. http://dx.doi.org/10.1080/01431161.2017.1280636 google scholar
  • Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effect. Remote Sensing of Environment, 75, 230-244. http://dx.doi.org/10.1016/S0034-4257(00)00169-3 google scholar
  • Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1, part2), 547556. http://dx.doi.org/10.4319/lo.2003.48.1_part_2.0547 google scholar
  • Trimble, S. M., & Houser, C. (2014). Mapping bathymetry and rip channels with WorldView2 multispectral data. American Geophysical Union Fall Meeting (AGUFM) 2014, EP31B-3555. google scholar
  • Trimble, S. M., Houser, C., Brander, R., & Chirico, P. (2015). Mapping bathymetry in an active surf zone with the WorldView2 multispectral satellite. American Geophysical Union Fall Meeting (AGUFM) 2015, EP23B-0948. google scholar
  • Turoglu, H. (2019). Yapay kıyıların jeomorfolojik tanımlaması: Diliskelesi kıyıları örneği (Kocaeli, Türkiye). Cografya Dergisi, 39, 11-27. https://doi.org/10.26650/JGEOG2019-0015 google scholar
  • USGS 2022a. Earth Explorer. Retrieved from: https://earthexplorer. usgs.gov/ (Last accessed: 07.02.2022) google scholar
  • USGS 2022b. Landsat 5. Retrieved from: https://www.usgs.gov/ landsat-missions/landsat-5 (Last accessed: 31.03.2022) google scholar
  • Uzakara, H., & Demir, N. (2021). Bathymetry analysis with use of Sentinel-2 images. Turkish Journal of Remote Sensing, 3(1), 14-20. google scholar
  • Vargas, R., Wasserman, J. C. D. F. A., da Silva A. L., Tavares, T. L., Américo, C., & dos Santos, F. F. D. (2021). Satellite-derived bathymetry models from Sentinel-2A and 2B in the coastal clear waters of Arraial do Cabo, Rio de Janeiro, Brazil. Revista Brasileira de Geografia Física, 14(5), 3078-3095. http://dx.doi.org/10.26848/ rbgf.v14.5.p3078-3095 google scholar
  • Wang, Z., Thome, K., Lockwood, R., & Wenny, B. N. (2022). Absolute radiometric calibration of an imaging spectroradiometer using a laboratory detector-based approach. Remote Sensing, 14(9), 2245. https://doi.org/10.3390/rs14092245 google scholar
  • Wei, J., Wang, M., Lee, Z., Briceño, H. O., Yu, X., Jiang, L., Garcia, R., Wang, J., & Luis, K. (2020). Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data. Remote Sensing of Environment, 250, 112035. http:// dx.doi.org/10.1016/j.rse.2020.112035 google scholar
  • Wicaksono, P., & Hafizt, M. (2018). Dark target effectiveness for darkobject subtraction atmospheric correction method on mangrove above-ground carbon stock mapping. IET Image Processing, 12(4), 582-587. http://dx.doi.org/10.1049/iet-ipr.2017.0295 google scholar
  • 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, 3025-3033. http:// dx.doi.org/10.1080/01431160600589179 google scholar
  • Yunus, A. P., Dou, J., Song, X., & Avtar, R. (2019). Improved bathymetric mapping of coastal and lake environments using Sentinel-2 and Landsat-8 images. Sensors, 19(12), 2788. http://dx. doi.org/10.3390/s19122788 google scholar

Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi

Yıl 2022, Sayı: 45, 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
  • Eugenio, F., Marcello, J., & Martin, J. (2015). High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3539-3549. http://dx.doi. org/10.1109/TGRS.2014.2377300 google scholar
  • 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
  • Green, E., Mumby, P., Edwards, A., & Clark, C. (2000). Remote sensing: Handbook for tropical coastal management. United Nations Educational, Scientific and Cultural Organization (UNESCO). google scholar
  • 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
  • Huang, W. Q., Wu, D., Yang, Y., Liang, Z. C., & Zhang, Y. Y. (2013). Multi-spectral remote sensing water depth retrieval technique in shallow sea. Ocean Technology, 32(2), 43-46. google scholar
  • IHO (2020). International Hydrographic Organization Standards for Hydrographic Surveys (S-44 Edition 6.0.0). google scholar
  • Jagalingam, P., Akshaya, B. J., & Hegde, A. V. (2015). Bathymetry mapping using Landsat 8 satellite imagery. Procedia Engineering, 116, 560-566. http://dx.doi.org/10.1016/j.proeng.2015.08.326 google scholar
  • Jawak, S. D., Vadlamani, S. S., & Luis, A. J. (2015). A synoptic review on deriving bathymetry information using remote sensing technologies: models, methods and comparisons. Advances in Remote Sensing, 4(2), 147-162. http://dx.doi.org/10.4236/ars.2015.42013 google scholar
  • Kasvi, E., Salmela, J., Lotsari, E., Kumpula, T., & Lane, S. N. (2019). Comparison of remote sensing based approaches for mapping bathymetry of shallow, clear water rivers. Geomorphology, 333, 180-197. http://dx.doi.org/10.1016/j.geomorph.2019.02.017 google scholar
  • Kerr, J. M., & Purkis, S. (2018). An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data. Remote Sensing of Environment, 210, 307-324. http://dx.doi.org/10.1016/j.rse.2018.03.024 google scholar
  • Koca N. (2005). Atikhisar Barajı’nın (Çanakkale) çevresel ve ekonomik etkileri. Doğu Coğrafya Dergisi, 10(14), 209-233. google scholar
  • Liu, Q., & Trinder, J. C. (2018). Sub-pixel technique for time series analysis of shoreline changes based on multispectral satellite imagery. In M. Marghany (Ed.), Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. IntechOpen. http://dx.doi.org/10.5772/ intechopen.81789 google scholar
  • Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3), 379-383. http://dx.doi.org/10.1364/AO.17.000379 google scholar
  • Lyzenga, D. R. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing, 6(1), 115-125. http://dx.doi.org/10.1080/01431168508948428 google scholar
  • Lyzenga, D. R., Malinas, N. P., & Tanis, F. J. (2006). Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing, 44(8), 22512259. http://dx.doi.org/10.1109/TGRS.2006.872909 google scholar
  • Mancino, G., Nole, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. iForest, 7(2), 75-84. http://dx.doi.org/10.3832/ifor0909-007 google scholar
  • 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, 1425-1432. http:// dx.doi.org/10.1080/01431169608948714 google scholar
  • Minghelli-Roman, A., Goreac, A., Mathieu, S., Spigai, M., & Gouton, P. (2009). Comparison of bathymetric estimation using different satellite images in coastal sea waters. International Journal of Remote Sensing, 30(21), 5737-5750. http://dx.doi.org/10.1080/01431160902729580 google scholar
  • Mishra, D., Narumalanii S., Lawson, M., & Rundquist, D. (2004). Bathymetric mapping using IKONOS multispectral data. GIScience & Remote Sensing, 41(4), 301-321. http://dx.doi.org/10.2747/1548-1603.41.4.301 google scholar
  • Moore, D. S., Notz, W. I., & Flinger, M. A. (2013). The basic practice of statistics (6th edition). New York: W. H. Freeman and Company. google scholar
  • 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. google scholar
  • Özelkan, E. ve Karaman, M. (2018). Baraj göllerindeki meteorolojik ve hidrolojik kuraklığın etkisinin çok zamanlı uydu görüntüleri ile analizi: Atikhisar Barajı (Çanakkale) örneği. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 7(2), 1023-1037. google scholar
  • Parente, C., & Pepe, M. (2018). Bathymetry from WorldView-3 satellite data using radiometric band ratio. Acta Polytechnica, 58(2), 109117. http://dx.doi.org/10.14311/AP.2018.58.0109 google scholar
  • Radermacher, M., de Schipper, M. A., & Reniers, A. J. H. M. (2018). Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry. Coastal Engineering, 135, 66-76. http://dx.doi. org/10.1016/j.coastaleng.2018.01.007 google scholar
  • Renaud, O., & Victoria-Feser, M. P. (2010). A robust coefficient of determination for regression. Journal of Statistical Planning and Inference, 140(7), 1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008 google scholar
  • Rossi, L, Mammi, I, & Pelliccia, F. (2020). UAV-Derived Multispectral Bathymetry. Remote Sensing, 12(23), 3897. http://dx.doi. org/10.3390/rs12233897 google scholar
  • Rossi, L., Mammi, I., & Pranzini, E. (2018). A comparison between UAV and high-resolution multispectral satellite images for bathymetry estimation. In G. Chirici & M. Gianinetto (Eds.), Trends in Earth Observation: Earth Observation Advancements in a Changing World (Vol 1, pp.143-146). Firenze, Italy. google scholar
  • Saeed, R., Abdelrahman, S. M., & Negm, A. (2021). Satellite-derived bathymetry using Landsat-8 imagery for Safaga Coastal Zone, Egypt. Acta Marisiensis. Seria Technologica, 18(1), 8-15. google scholar
  • Setiawan, K. T., Adawiah, S. W., Marini, Y., & Winarso, G. (2016). Bathymetry data extraction analysis using Landsat 8 Data. International Journal of Remote Sensing and Earth Sciences, 13(2), 79-86. google scholar
  • Setiawan, K. T. (2013). Study of bathymetry map using Landsat ETM+ data - A case study at Menjangan Island, Bali (MSc Thesis, Udayana University, Indonesia). google scholar
  • Shah, A., Deshmukh, B., & Sinha, L. K. (2020). A review of approaches for water depth estimation with multispectral data. World Water Policy, 6, 152-167. http://dx.doi.org/10.1002/wwp2.12029 google scholar
  • Shintani, C., & Fonstad, M. A. (2017). Comparing remote-sensing techniques collecting bathymetric data from a gravel-bed river. International Journal of Remote Sensing, 38 (8-10), 2883-2902. http://dx.doi.org/10.1080/01431161.2017.1280636 google scholar
  • Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effect. Remote Sensing of Environment, 75, 230-244. http://dx.doi.org/10.1016/S0034-4257(00)00169-3 google scholar
  • Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1, part2), 547556. http://dx.doi.org/10.4319/lo.2003.48.1_part_2.0547 google scholar
  • Trimble, S. M., & Houser, C. (2014). Mapping bathymetry and rip channels with WorldView2 multispectral data. American Geophysical Union Fall Meeting (AGUFM) 2014, EP31B-3555. google scholar
  • Trimble, S. M., Houser, C., Brander, R., & Chirico, P. (2015). Mapping bathymetry in an active surf zone with the WorldView2 multispectral satellite. American Geophysical Union Fall Meeting (AGUFM) 2015, EP23B-0948. google scholar
  • Turoglu, H. (2019). Yapay kıyıların jeomorfolojik tanımlaması: Diliskelesi kıyıları örneği (Kocaeli, Türkiye). Cografya Dergisi, 39, 11-27. https://doi.org/10.26650/JGEOG2019-0015 google scholar
  • USGS 2022a. Earth Explorer. Retrieved from: https://earthexplorer. usgs.gov/ (Last accessed: 07.02.2022) google scholar
  • USGS 2022b. Landsat 5. Retrieved from: https://www.usgs.gov/ landsat-missions/landsat-5 (Last accessed: 31.03.2022) google scholar
  • Uzakara, H., & Demir, N. (2021). Bathymetry analysis with use of Sentinel-2 images. Turkish Journal of Remote Sensing, 3(1), 14-20. google scholar
  • Vargas, R., Wasserman, J. C. D. F. A., da Silva A. L., Tavares, T. L., Américo, C., & dos Santos, F. F. D. (2021). Satellite-derived bathymetry models from Sentinel-2A and 2B in the coastal clear waters of Arraial do Cabo, Rio de Janeiro, Brazil. Revista Brasileira de Geografia Física, 14(5), 3078-3095. http://dx.doi.org/10.26848/ rbgf.v14.5.p3078-3095 google scholar
  • Wang, Z., Thome, K., Lockwood, R., & Wenny, B. N. (2022). Absolute radiometric calibration of an imaging spectroradiometer using a laboratory detector-based approach. Remote Sensing, 14(9), 2245. https://doi.org/10.3390/rs14092245 google scholar
  • Wei, J., Wang, M., Lee, Z., Briceño, H. O., Yu, X., Jiang, L., Garcia, R., Wang, J., & Luis, K. (2020). Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data. Remote Sensing of Environment, 250, 112035. http:// dx.doi.org/10.1016/j.rse.2020.112035 google scholar
  • Wicaksono, P., & Hafizt, M. (2018). Dark target effectiveness for darkobject subtraction atmospheric correction method on mangrove above-ground carbon stock mapping. IET Image Processing, 12(4), 582-587. http://dx.doi.org/10.1049/iet-ipr.2017.0295 google scholar
  • 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, 3025-3033. http:// dx.doi.org/10.1080/01431160600589179 google scholar
  • Yunus, A. P., Dou, J., Song, X., & Avtar, R. (2019). Improved bathymetric mapping of coastal and lake environments using Sentinel-2 and Landsat-8 images. Sensors, 19(12), 2788. http://dx. doi.org/10.3390/s19122788 google scholar
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 Sayı: 45

Kaynak Göster

APA Öztürk, D. (2022). Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi. Coğrafya Dergisi(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. Coğrafya Dergisi. 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”. Coğrafya Dergisi, 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. Coğrafya Dergisi 45 97–110.
IEEE D. Öztürk, “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”, Coğrafya Dergisi, 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”. Coğrafya Dergisi 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. Coğrafya Dergisi. 2022;:97–110.
MLA Öztürk, Derya. “Atikhisar Baraj Gölü Batimetrisinin Landsat-5 TM Uydu Görüntüsünden Stumpf Algoritması Kullanılarak Belirlenmesi”. Coğrafya Dergisi, 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. Coğrafya Dergisi. 2022(45):97-110.