Volume 10, Issue 19 (5-2019)                   jwmr 2019, 10(19): 46-57 | Back to browse issues page

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Habibi Khaveh H, GHolami Sefidkouhi M A, Emadi A R. Bathymetry and Volume Estimation of Reservoirs using OLI Imagary. jwmr. 2019; 10 (19) :46-57
URL: http://jwmr.sanru.ac.ir/article-1-680-en.html
Sari Agricultural Sciences and Natural Resources University
Abstract:   (502 Views)

Nowadays using field depth measurement to estimate capacity of water reservoirs is a costly and time consuming method. In this regard, different multiple techniques of remote sensing method has been adopted. In this study, a radiation and reflection based model was introduced to estimating the bathymetry and water capacity of reservoir by images of OLI sensor. For this, at May 17, 2015 water depth was measured each 50 meters in Mahdasht, Sorbon and Dasht-e-naz water reservoirs using Sonar system. All water reservoirs located in Miandoroud plain between latitude of and longitude of in north east of Sari city, Mazandaran province, Iran. Then, the measurement continued for five more times; on May 26, Aug 5 of 2015 and again on May 28, June 29 and August 23 of 2016 to record depth and precise determination of water capacity. In order to estimate the reservoir capacity image values for blue, green and red band two times more than maximum depth, were classified equally (any given class represents 0.5 meter depth). Subsequently, total volume of reservoir was extracted by a sum of product of number of cells, cell depth index and cell area. Finally, the average relative error and estimated correlation coefficients of volume estimation for 3 mentioned reservoirs in 6 times in blue, green and red bands were 11.19, 4.95, 5.5 and 96, 97 and 98 percent, respectively. Consequently, the use of this method is proposed to measure the volume of reservoirs.

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Type of Study: Research | Subject: سنجش از دور و سامانه های اطلاعات جغرافيايی
Received: 2016/07/20 | Revised: 2019/07/31 | Accepted: 2017/12/31 | Published: 2019/08/3

1. Amani, M. 2014. Prioritization of Sub-Watersheds based on Morphometric Analysis, GIS and RS Techniques: Lohandar Watershed, Golestan Province. Journal of Watershed Management Research, 5(2): 1-15 (In Persian).
2. Bagheri, A. 2008. Pool management and it's role in storage and water supply for agricultural lands in north of Iran. 2th International Conference on Management of irrigation and drainage networks, 221-226 pp., Ahvaz, Chamran university, Iran (In Persian).
3. Bostater, C.R., T.S. Oney, T. Rotkiske, S. Aziz, C. Morrisette, K. Callahan and D. Mcallister. 2017. Hyperspectral signatures and WorldView-3 imagery of Indian River Lagoon and Banana River Estuarine water and bottom types. In Remote Sensing of the Ocean, Sea Ice, Coastal Waters and Large Water Regions. International Society for Optics and Photonics. (10422): 104220 pp [DOI:10.1117/12.2288511]
4. Li, C., B. Sun, K. Jia, S. Zhang, W. Li, X. Shi and L. Pereira. 2013. Multi-Band Remote Sensing Based Retrieval Model and 3D Analysis of Water Depth in Hulun Lake, China. Mathematical and Computer, 58(3-4): 771-781. [DOI:10.1016/j.mcm.2012.12.027]
5. Civco, D.L. and W.C. Kennard. 1992. Satellite remote bathymetry: a new mechanism for modeling, Photogramm. Engineering. Remote Sensing, 5(58): 545-549.
6. Deng, Z., J. Minhe and Z. Zhang. 2008. Mapping Bathymetry From Multi-S Ource Remote Sensing Images: A Case Study in the Beilun Estuary, Guangxi, China. The International Archives of the Photogr ammetry, Remote Sensing and Spatial Information Sciences, (XXXVII): 1321-1326.
7. Fonstad Mark, A. and W.A. Marcus. 2005. Remote Sensing of Stream Depths with Hydraulically Assisted Bathymetry (HAB) Models. Geomorphology, 72(1): 320-339. [DOI:10.1016/j.geomorph.2005.06.005]
8. Heidarian, K., S. Kaboodvandpur and J. Aminollahi. 2016. Evaluation of Wetlands International Zarivar deep changes using remote sensing and artificial neural network. Journal of Geographic Space, 53: 271-289 (In Persian).
9. Jagalingam, P. 2015. Bathymetry Mapping Using Landsat 8 Satellite Imagery. Procedia Engineering, 116: 560-566. [DOI:10.1016/j.proeng.2015.08.326]
10. Kowkabi, L., R. setayesh, A.R. Badri and A. Rajaee. 2013. The application of fuzzy multi-attribute group decision making to prioritize the landscapes with high ecological value: Khoshk River in Shiraz. International Journal of Envirnomental Research, 7(2): 423-434.
11. Majozi, N.P., M.S. Salama, S. Bernard, D.M. Harper and M.G. Habte. 2014. Remote Sensing of Environment Remote Sensing of Euphotic Depth in Shallow Tropical Inland Waters of Lake Naivasha Using MERIS Data. Remote Sensing of Environment, 148:178-89. [DOI:10.1016/j.rse.2014.03.025]
12. Manessa, M.D., M.M. Haidar, D. Hartuti and K. Kresnawati. 2018. Determination of the best methodology for bathymetry mapping using spot 6 imagery: A Study of 12 empirical algorithms. International Journal of Remote Sensing and Earth Sciences (IJReSES), 14(2): 127-136. [DOI:10.30536/j.ijreses.2017.v14.a2827]
13. Meskar, H. and R. Fazloula. 2013. Investigation of Sedimentation Pattern in the Shahid Rajaee ReservoirUsing GSTAR3.0 Numerical Model. Journal of Watershed Management Research, 4(7): 16-29 (In Persian).
14. Misra, A., Z. Vojinovic, B. Ramakrishnan, A. Luijendijk and R. Ranasinghe. 2018. Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. International Journal of Remote Sensing, 1-20 pp. [DOI:10.1080/01431161.2017.1421796]
15. Oh, C.Y., K. Ahn, J. Park and S.W. Park. 2017. Coastal Shallow-Water Bathymetry Survey through a Drone and Optical Remote Sensors. Journal of Korean Society of Coastal and Ocean Engineers, 29(3): 162-168. [DOI:10.9765/KSCOE.2017.29.3.162]
16. Özçelik, C. and A. Yalçın. 2010. Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science, 89: 89-96. [DOI:10.1016/j.ecss.2010.05.015]
17. Sichangi, A.W. and G.O. Makokha. 2017. Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993-2016. Modeling Earth Systems and Environment, 1-6 pp. [DOI:10.1007/s40808-017-0311-2]
18. Solaimani Sarood, F., S. Soltani Kopaii and A. Salajeghe. 2013. Selection of Appropriate Flooding Potential Index by Using RainfallRunoff (HEC-HMS) Model and RS & GIS Techniques in Jiroft Dam Basin. Journal of Watershed Management Research, 4(8): 90-105 (In Persian).
19. Sternberg, T. and P. Paillou. 2015. Mapping Potential Shallow Groundwater in the Gobi Desert Using Remote Sensing: Lake Ulaan Nuur. Journal of Arid Environments, 118: 21-27. [DOI:10.1016/j.jaridenv.2015.02.020]
20. Stumpf, R.P. and K. Holderied. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Liminology and Oceanography, 48(1): 547-556. [DOI:10.4319/lo.2003.48.1_part_2.0547]
21. Sylvain J. and M. Guillaume. 2014. Remote Sensing of Environment A Novel Maximum Likelihood Based Method for Mapping Depth and Water Quality from Hyperspectral Remote-Sensing Data. Remote Sensing of Environment, 147: 121-32. [DOI:10.1016/j.rse.2014.01.026]
22. Tian, Q.J., J. Wang and X. Du. 2007. Study on Water Depth Extraction from Remote Sensing Imagery in Jiangsu Coastal Zone. Journal of Remote Sensing, 11(3).
23. Usha, K. and S. Bhupinder. 2013. Scientia Horticulturae Potential Applications of Remote Sensing in Horticulture A Review. Scientia Horticulturae, 153: 71-83. [DOI:10.1016/j.scienta.2013.01.008]
24. Yang, C, et al. 2017. Remote Sensing of Hydrological Changes in Tian-e-Zhou Oxbow Lake, an Ungauged Area of the Yangtze River Basin. Remote Sensing, 10(1): 27 pp. [DOI:10.3390/rs10010027]
25. Zhou J., D.L. Civco and J.A. Silander. 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal Of Remote Sensing, 19: 743-757. [DOI:10.1080/014311698215973]

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