Over the last two decades there has been a revolution in our ability to map and monitor large areas of subaerial topography using technologies such as radar and near-infrared Light Detection and Ranging. The Multispectral Remote Sensing (RS) Satellite ‘WorldView-2’ imagery has the ability to measure water depth up to 25m. Studies have been conducted based on the band ratio algorithm to determine water depth in the study area the Ganges River in Bangladesh. This method is able to generate accurate depth measurements at points or along transects, and also offer more flexible, efficient and cost-effective means of mapping bathymetry over broad areas. There are two methods are available to derive bathymetry from remote sensing imagery which are “linear method” and “ratio method”. The linear method is depended upon bottom type albedo. While different bottom types at the same depth would be incorrectly calculated for one of these two substrates. The accuracy of the retrieved bathymetry varies with water depth, with the accuracy substantially lower at a depth beyond 12 m. Other influential factors and challenges include water turbidity and bottom materials, as well as image properties.
| Published in | International Journal of Atmospheric and Oceanic Sciences (Volume 1, Issue 1) |
| DOI | 10.11648/j.ijaos.20170101.12 |
| Page(s) | 7-13 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Remote Sensing, Bathymetry, Ratio Algorithm, Worldview-2, Linear Regression
| [1] | Bagheri, S., Stein, M. and Dios, R. 1998: Utility of hyperspectral data for bathymetric mapping in a turbid estuary. International Journal of Remote Sensing 19, 1179–88. |
| [2] | Bierwirth, P. N., Lee, T. J. and Burne, R. V. 1993: Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogrammetric Engineering and Remote Sensing 59, 331–38. |
| [3] | Baban, S. M. J. 1993: The evaluation of different algorithms for bathymetric charting of lakes using Landsat imagery. International Journal of Remote Sensing 14, 2263–73. |
| [4] | Camacho, M. A., (2006). Depth analysis of Midway Atoll using Quickbird multi-spectral imaging over variable substrates. M. S. Thesis, Dept. of Space Systems Operations, the Naval Postgraduate School. |
| [5] | Cracknell, A. P., Ibrahim, M. and McManus, J. 1987: Use of satellite and aircraft data for bathymetry studies. Advances in digital image processing. In Proceedings of the RSS 13th Annual Conference, Nottingham, Remote Sensing Society, University of Nottingham, 391–402. |
| [6] | D. Zhongwei, J. Minhe, Z. Zhihua (2008). Mapping Bathymetry from Multi-Source Remote Sensing Images: A Case Study in The Beilun Estuary, Guangxi, China. The International Archives of the Photogrammetric, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing. |
| [7] | Dierssen, H. M., Zimmerman, R. C., Leathers, R. A., Downes, T. V. and Davis, C. O. 2003: Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery. Limnology and Oceanography 48, 444–55. |
| [8] | Globe, D. (2009). the benefits of the 8 Spectral Bands of WorldView-2. |
| [9] | J. Jensen. (2000). Remote sensing of the environment, Pearson Education. |
| [10] | J, Michael. J, Loomis. (2009). Depth derivation from the worldview-2 satellite using hyperspectral imagery. M. S. Thesis, Naval Postgraduate School, Monterey, California. |
| [11] | Ji, W., Civco, D. L. and Kennard, W. C. 1992: Satellite remote bathymetry: a new mechanisms for modeling. Photogrammetric Engineering and Remote Sensing 58, 545–49. |
| [12] | Lafon, V., Froidefond, J. M., Lahet, F. and Castaing, P. 2002: SPOT shallow water bathymetry of a moderately turbid tidal inlet based on fi eld measurements. Remote Sensing of Environment 81, 136–48. |
| [13] | L. Hongxing, B. Richard (no date). Bathymetric mapping using multispectral satellite imagery. Department of Physics and Geosciences, Texas A & M University At Kingsville. |
| [14] | Naif, (2012). The Potential of Using Worldview-2 Imagery for Shallow Water Depth Mapping. Thesis, Department of Geomatics Engineering Calgary, Alberta. |
| [15] | Stumpf, R., K. Holderied and M. Sinclair (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types, the American Society of Limnology and Oceanography, Inc. 48 (1): 547-556. |
| [16] | Kumar, V. K., Palit, A. and Bhan, S. K. 1997: Bathymetric mapping in Rupnarayan–Hooghly river confl uence using IRS data. International Journal of Remote Sensing 18, 2269–70. |
| [17] | George, D. G. 1997: Bathymetric mapping using a compact airborne spectrographic imager (CASI). International Journal of Remote Sensing 18, 2067–71. |
| [18] | Siegal, B. S. and Gillespie, A. R. 1980: Remote sensing in geology. New York: Wiley. |
| [19] | Lyzenga, D. R., Malinas, N. P. and Tanis, F. J. 2006: Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing 44, 2251–59. |
| [20] | Mishra, D., Narumalani, S., Lawson, M. and Rundquist, D. 2004: Bathymetric mapping using IKONOS multispectral data. GIScience and Remote Sensing 41, 301–21. |
| [21] | Muslim, A. M. and Foody, G. M. 2008: DEM and bathymetry estimation for mapping a tidecoordinated shoreline from fine spatial resolution satellite sensor imagery. International Journal of Remote Sensing 29, 4515–36. |
| [22] | Nordman, M. E., Wood, L., Michalek, J. L. and Christy, J. L. 1990: Water depth extraction from Landsat-5 imagery. In Proceedings of the Twentythird International Symposium on Remote Sensing of Environment, 1129–39. |
| [23] | Tripathi, N. K. and Rao, A. M. 2002: Bathymetric mapping in Kakinada Bay, India, using IRS-1D LISSIII data. International Journal of Remote Sensing 23, 1013–25. |
APA Style
Md. Shafiqul Islam Khondoker, Md. Zahid Hasan Siddiquee, Md. Ashraful Islam. (2017). The Challenges of River Bathymetry Survey Using Space Borne Remote Sensing in Bangladesh. International Journal of Atmospheric and Oceanic Sciences, 1(1), 7-13. https://doi.org/10.11648/j.ijaos.20170101.12
ACS Style
Md. Shafiqul Islam Khondoker; Md. Zahid Hasan Siddiquee; Md. Ashraful Islam. The Challenges of River Bathymetry Survey Using Space Borne Remote Sensing in Bangladesh. Int. J. Atmos. Oceanic Sci. 2017, 1(1), 7-13. doi: 10.11648/j.ijaos.20170101.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijaos.20170101.12,
author = {Md. Shafiqul Islam Khondoker and Md. Zahid Hasan Siddiquee and Md. Ashraful Islam},
title = {The Challenges of River Bathymetry Survey Using Space Borne Remote Sensing in Bangladesh},
journal = {International Journal of Atmospheric and Oceanic Sciences},
volume = {1},
number = {1},
pages = {7-13},
doi = {10.11648/j.ijaos.20170101.12},
url = {https://doi.org/10.11648/j.ijaos.20170101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijaos.20170101.12},
abstract = {Over the last two decades there has been a revolution in our ability to map and monitor large areas of subaerial topography using technologies such as radar and near-infrared Light Detection and Ranging. The Multispectral Remote Sensing (RS) Satellite ‘WorldView-2’ imagery has the ability to measure water depth up to 25m. Studies have been conducted based on the band ratio algorithm to determine water depth in the study area the Ganges River in Bangladesh. This method is able to generate accurate depth measurements at points or along transects, and also offer more flexible, efficient and cost-effective means of mapping bathymetry over broad areas. There are two methods are available to derive bathymetry from remote sensing imagery which are “linear method” and “ratio method”. The linear method is depended upon bottom type albedo. While different bottom types at the same depth would be incorrectly calculated for one of these two substrates. The accuracy of the retrieved bathymetry varies with water depth, with the accuracy substantially lower at a depth beyond 12 m. Other influential factors and challenges include water turbidity and bottom materials, as well as image properties.},
year = {2017}
}
TY - JOUR T1 - The Challenges of River Bathymetry Survey Using Space Borne Remote Sensing in Bangladesh AU - Md. Shafiqul Islam Khondoker AU - Md. Zahid Hasan Siddiquee AU - Md. Ashraful Islam Y1 - 2017/01/16 PY - 2017 N1 - https://doi.org/10.11648/j.ijaos.20170101.12 DO - 10.11648/j.ijaos.20170101.12 T2 - International Journal of Atmospheric and Oceanic Sciences JF - International Journal of Atmospheric and Oceanic Sciences JO - International Journal of Atmospheric and Oceanic Sciences SP - 7 EP - 13 PB - Science Publishing Group SN - 2640-1150 UR - https://doi.org/10.11648/j.ijaos.20170101.12 AB - Over the last two decades there has been a revolution in our ability to map and monitor large areas of subaerial topography using technologies such as radar and near-infrared Light Detection and Ranging. The Multispectral Remote Sensing (RS) Satellite ‘WorldView-2’ imagery has the ability to measure water depth up to 25m. Studies have been conducted based on the band ratio algorithm to determine water depth in the study area the Ganges River in Bangladesh. This method is able to generate accurate depth measurements at points or along transects, and also offer more flexible, efficient and cost-effective means of mapping bathymetry over broad areas. There are two methods are available to derive bathymetry from remote sensing imagery which are “linear method” and “ratio method”. The linear method is depended upon bottom type albedo. While different bottom types at the same depth would be incorrectly calculated for one of these two substrates. The accuracy of the retrieved bathymetry varies with water depth, with the accuracy substantially lower at a depth beyond 12 m. Other influential factors and challenges include water turbidity and bottom materials, as well as image properties. VL - 1 IS - 1 ER -
Information
Email: