Abstract
Bathymetry is described as Sea and Ocean depth measurements, and performed by many methods. Traditional methods, which are still used from the past to the present, have been replaced by modern methods with the development of technology. Sonar systems, LIDAR and remote sensing systems are listed as examples of these modern methods. The use of acoustic systems or LIDAR, are not economical in terms of both time and cost. In this study, remote sensing methods are investigated in order to minimize the time and cost. It is aimed to extract the information about bathymetry with use of free of charge satellite images. The method data used includes Sentinel-2 satellite images taken at different wavelengths and reference bathymetry values. Later, regression analyzes were made by using these data in band ratio and multi-band methods. By using the coefficients obtained by the regression analysis, the bathymetry estimation was made in places with unknown depth using the above methods without the need for reference depth.Band ratio and multi-band methods are used, and the results were evaluated. Bathymetric maps obtained from two methods were analyzed with the ground-truth values of the region and the amount of error was calculated. The highest accuracy was obtained from the ratio of blue band to green band. It has been observed that the red band has a disruptive effect.
Keywords
Supporting Institution
Harita Genel Müdürlüğü
Project Number
92904297-112.01.02-E.682029
Thanks
This work has been supported by General Directorate of Mapping Turkey. Authors acknowledge the datasets provided by Sentinel Copernicus and TCARTA.
References
- Agency, European Space. (2015). Sentinel-2 User Handbook. Paris: ESA.
- Bailly du Bois, P. (2011). Automatic calculation of bathymetry for coastal hydrodynamic models. Computers & Geosciences, 1303-1310.
- Bramante, J. F., Raju, D. K., & Sin, T. M. (2012). Multispectral derivation of bathymetry in Singapore's shallow, turbid waters. International Journal of Remote Sensing, 34, 2070-2088.
- Brando, V., Anstee, J., Wettle, M., Dekker, A., Phinn, S., & Roelfsema, C. (2009). A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment, 113(4), 755-770.
- Brock, J., Wright, C., Clayton, T., & Nayegandhi , A. (2004). LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs, 23(1), 48-59.
- Caballero, I., & Stumpf, P. R. (2019). Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters. Estuarine, Coastal and Shelf Science, 226, 106-277.
- Chen, B., Yang, Y., Xu, D., & Huang, E. (2019). A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 1-13.
- Costa, B., Battista, T., & Pittman, S. (2009). Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment, 113(5), 1082-1100.
- Dartnell, P., & Gardner, J. (2004). Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogrammetric Engineering and Remote Sensing, 70(9), 1081-1091.
- Gao, J. (2009). Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Progsress in Physical Geography, 103-116.
- Gitelson, A., Kaufman, Y., & Merzlyak, M. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289-298.
- Jawak, S. D., & Luis, A. J. (2015). Spectral Information Analysis for the Semiautomatic Derivation of Shallow Lake Bathymetry Using High-resolution Multispectral Imagery: A Case Study of Antarctic Coastal Oasis. Aquatic Procedia, 4, 1331-1338.
- 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.
- Kumari, P., & Ramesh, H. (2020). Remote sensing image based nearshore bathymetry extraction of Mangaluru coast for planning coastal reservoir. Sustainable Water Resource Development Using Coastal Reservoirs, 247-265.
- Lee, Z., Carder, K., Mobley, C., Steward, R., & Patch, J. (1999). Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied Optics, 38(18), 3831-3843.
- Liu, D., & Wang, Y. (2013). Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns. Progress in Oceanography, 116, 154-166.
- Liu, S., Gao, Y., Zheng, W., & Li, X. (2015). Performance of two neural network models in bathymetry. Remote Sensing Letters, 6(4), 321-330.
- Ma, M., Wang, X., & Veroustraete, F. (2007). Change in area of Ebinur Lake during the 1998–2005 period. International Journal of Remote Sensing, 28(24), 5523-5533.
- Maritorena, S., Morel, A., & Gentili, B. (1994). Diffuse reflectance of oceanic shallow waters: Influence of water depth and bottom albedo. Limnology and Oceanography, 39(7), 1689-1703.
- Misra, A., & Ramakrishnan, B. (2020). Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry. Continental Shelf Research, 207.
- Pacheco, A., Horta, J., Loureiro, C., & Ferreira, Ó. (2015). Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sensing of Environment, 102-116.
- Philpot, W. (1989). Bathymetric mapping with passive multispectral imagery. Applied Optics, 28(8), 1569-1578.
- Stumpf, P. R., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variablebottom types. Limnol. Oceanog, 48(1), 547-556.
- Wilson, M. F., O'Connell, B., Brown, C., Guinan, J., & Grehan, A. (2006). Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Marine Geodesy, 3-35.
Abstract
Batimetri, Deniz ve Okyanus derinlik ölçümleri olarak tanımlanmakta ve birçok yöntemle yapılmaktadır. Geçmişten günümüze kadar hala kullanılan geleneksel yöntemler, teknolojinin gelişmesiyle yerini modern yöntemlere bırakmıştır. Sonar sistemleri, LIDAR ve uzaktan algılama sistemleri bu modern yöntemlerin örnekleri olarak listelenmiştir. Akustik sistemlerin veya LIDAR'ın kullanımı hem zaman hem de maliyet açısından ekonomik değildir. Bu çalışmada zaman ve maliyeti en aza indirmek için uzaktan algılama yöntemleri araştırılmıştır. Batimetri ile ilgili bilgilerin ücretsiz uydu görüntüleri kullanılarak çıkarılması amaçlanmaktadır. Kullanılan yöntem verileri, farklı dalga boylarında alınan Sentinel-2 uydu görüntülerini ve referans batimetri değerlerini içerir. Daha sonra bu veriler bant oranı ve çok bantlı yöntemlerde kullanılarak regresyon analizleri yapılmıştır. Regresyon analizi ile elde edilen katsayılar ile, referans derinliğe ihtiyaç duyulmadan derinliği bilinmeyen yerlerde yukarıdaki yöntemler kullanılarak batimetri tahmini yapılmıştır. Bant oranı ve çok bantlı yöntemler kullanılmış ve sonuçlar değerlendirilmiştir. Her iki yöntemden elde edilen batimetrik haritalar bölgenin gerçek derinlik değerleri ile analiz edilmiş ve hata miktarı hesaplanmıştır. En yüksek doğruluk, mavi bandın yeşil banda oranından elde edilmiştir. Kırmızı bandın bozucu bir etkiye sahip olduğu görülmüştür
.
Keywords
Project Number
92904297-112.01.02-E.682029
References
- Agency, European Space. (2015). Sentinel-2 User Handbook. Paris: ESA.
- Bailly du Bois, P. (2011). Automatic calculation of bathymetry for coastal hydrodynamic models. Computers & Geosciences, 1303-1310.
- Bramante, J. F., Raju, D. K., & Sin, T. M. (2012). Multispectral derivation of bathymetry in Singapore's shallow, turbid waters. International Journal of Remote Sensing, 34, 2070-2088.
- Brando, V., Anstee, J., Wettle, M., Dekker, A., Phinn, S., & Roelfsema, C. (2009). A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment, 113(4), 755-770.
- Brock, J., Wright, C., Clayton, T., & Nayegandhi , A. (2004). LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs, 23(1), 48-59.
- Caballero, I., & Stumpf, P. R. (2019). Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters. Estuarine, Coastal and Shelf Science, 226, 106-277.
- Chen, B., Yang, Y., Xu, D., & Huang, E. (2019). A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 1-13.
- Costa, B., Battista, T., & Pittman, S. (2009). Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment, 113(5), 1082-1100.
- Dartnell, P., & Gardner, J. (2004). Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogrammetric Engineering and Remote Sensing, 70(9), 1081-1091.
- Gao, J. (2009). Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Progsress in Physical Geography, 103-116.
- Gitelson, A., Kaufman, Y., & Merzlyak, M. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289-298.
- Jawak, S. D., & Luis, A. J. (2015). Spectral Information Analysis for the Semiautomatic Derivation of Shallow Lake Bathymetry Using High-resolution Multispectral Imagery: A Case Study of Antarctic Coastal Oasis. Aquatic Procedia, 4, 1331-1338.
- 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.
- Kumari, P., & Ramesh, H. (2020). Remote sensing image based nearshore bathymetry extraction of Mangaluru coast for planning coastal reservoir. Sustainable Water Resource Development Using Coastal Reservoirs, 247-265.
- Lee, Z., Carder, K., Mobley, C., Steward, R., & Patch, J. (1999). Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied Optics, 38(18), 3831-3843.
- Liu, D., & Wang, Y. (2013). Trends of satellite derived chlorophyll-a (1997–2011) in the Bohai and Yellow Seas, China: Effects of bathymetry on seasonal and inter-annual patterns. Progress in Oceanography, 116, 154-166.
- Liu, S., Gao, Y., Zheng, W., & Li, X. (2015). Performance of two neural network models in bathymetry. Remote Sensing Letters, 6(4), 321-330.
- Ma, M., Wang, X., & Veroustraete, F. (2007). Change in area of Ebinur Lake during the 1998–2005 period. International Journal of Remote Sensing, 28(24), 5523-5533.
- Maritorena, S., Morel, A., & Gentili, B. (1994). Diffuse reflectance of oceanic shallow waters: Influence of water depth and bottom albedo. Limnology and Oceanography, 39(7), 1689-1703.
- Misra, A., & Ramakrishnan, B. (2020). Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry. Continental Shelf Research, 207.
- Pacheco, A., Horta, J., Loureiro, C., & Ferreira, Ó. (2015). Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters. Remote Sensing of Environment, 102-116.
- Philpot, W. (1989). Bathymetric mapping with passive multispectral imagery. Applied Optics, 28(8), 1569-1578.
- Stumpf, P. R., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variablebottom types. Limnol. Oceanog, 48(1), 547-556.
- Wilson, M. F., O'Connell, B., Brown, C., Guinan, J., & Grehan, A. (2006). Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Marine Geodesy, 3-35.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Project Number | 92904297-112.01.02-E.682029 |
| Publication Date | June 15, 2021 |
| Acceptance Date | March 23, 2021 |
| Published in Issue | Year 2021 Volume: 3 Issue: 1 |
