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http://dx.doi.org/10.7780/kjrs.2020.36.2.1.2

Evaluation of Depth Measurement Method Based on Spectral Characteristics Using Hyperspectrometer  

You, Hojun (Department of Civil and Environmental Engineering, Dankook University)
Kim, Dongsu (Department of Civil and Environmental Engineering, Dankook University)
Shin, Hyoungsub (Environment Remotesensing Institute Inc.)
Publication Information
Korean Journal of Remote Sensing / v.36, no.2_1, 2020 , pp. 103-119 More about this Journal
Abstract
Recently, the rapid redeposition and erosion of rivers artificially created by climate change and the Four Rivers Restoration Project is questionable. According to the revised law in Korea, the river management agency will periodically carry out bed changes surveys. However, there are technical limitations in contrast to the trend of increasing spatial coverage, density and narrowing of intervals. National organizations are interest in developing innovative bed changessurvey techniquesfor efficiency. Core of bathymetry survey is to measure the depth of rivers under a variety of river conditions, but that is relatively more risky, time-consuming and expensive compared to conventional ground surveys. To overcome the limitations of traditional technology, echo sounder, which has been mainly used for ocean depth surveying, has been applied to rivers. However, due to various technical limitations, it is still difficult to periodically investigate a wide range of areas. Therefore, technique using the remote sensing has been spotlighted as an alternative, especially showing the possibility of depth measurement using spectral characteristics. In this study, we develop and examine a technique that can measure depth of water using reflectance from spectral characteristics. As a result of applying the technique proposed in thisstudy, it was confirmed that the measured depth and the correlation and error corresponding to 0.986 and 0.053 m were measured in the depth range within 0.95 m. In the future, this study could be applied to the measurement of spatial depth if it is applied to the hyperspectral sensor mounted on the drone.
Keywords
Hyperspectral; Fluvial remote sensing; Hydraulic measurement;
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1 Na, S.I., C.W. Park, K.H. So, H.Y. Ahn, and K.D. Lee, 2019b. Photochemical Reflectance Index (PRI) Mapping using Drone-based Hyperspectral Image for Evaluation of Crop Stress and its Application to Multispectral Imagery, Korean Journal of Remote Sensing, 35(5): 637-647 (in Korean with English abstract).   DOI
2 Osowski, S., 1996. A Neural networks in algorithmic use, WNT, Warsaw, Polish.
3 Pan, Z., C. Glennie, C. Legleiter, and B. Overstreet, 2015. Estimation of water depths and turbidity from hyperspectral imagery using support vector regression, Geoscience and Remote Sensing, 12(10): 2165-2169.   DOI
4 Park, J.I., S.Y. Choi, and M.H. Park, 2017. A study on green algae monitoring in watershed using fixed wing UAV, Journal of The Korean Institute of Intelligent Systems, 27(2): 164-169.   DOI
5 Suh, Y.S., 2006. Study on Abnormal Distribution of High Concentration Chlorophyll a in the East Sea of Korea in Spring Season using Ocean Color Satellite Remote Sensing, Journal of Environmental Science International, 15(1): 59-66 (in Korean with English abstract).   DOI
6 Stumpf, R.P., K. Holderied, and M. Sinclair, 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types, Limnology and Oceanography, 48(2): 547-556.   DOI
7 Winterbottom, S.J. and D.J. Gilvear, 1997. Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography, Regulated Rivers: Research and Management: An International Journal Devoted to River Research and Management, 13(6): 489-499.   DOI
8 Yeo, H.K., S.J. Kim, and J.G. Kang, 2015. Management of Small River Using Real Scale Experiments, The Korean Society of Civil Engineers, 63(2):43-47 (in Korean with English abstract).
9 Yu, Y.H., Y.S. Kim, and S.G. Lee, 2008. A study on estimation of water depth using hyperspectral satellite imagery, Aerospace Engineering and Technology, 7(1): 216-222.
10 Chuvieco, E., 2009. Fundamentals of satellite remote sensing, CRC Press, Boca Raton, FL, USA.
11 Fausch, K.D., C.E. Torgersen, C.V. Baxter, and H.W. Li, 2002. Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes: A continuous view of the river is needed to understand how processes interacting among scales set the context for stream fishes and their habitat, BioScience, 52(6): 483-498.   DOI
12 Marcus, W.A. and M.A. Fonstad, 2010. Remote sensing of rivers: the emergence of a subdiscipline in the river sciences, Earth Surface Processes and Landforms, 35(15): 1867-1872.   DOI
13 Hagan, M.T., H. B. Demuth, M. H. Beale, and O.D. Jesus, 1996. Neural network design (vol. 20), PWS Publishing Company, Gretna, LA, USA.
14 Jeon, E.I., K.W. Kim, S.B. Cho, and S.H. Kim, 2019. A Comparative Study of Absolute Radiometric Correction Methods for Drone-borne Hyperspectral Imagery, Korean Journal of Remote Sensing, 35(2): 203-215 (in Korean with English abstract).   DOI
15 Kim, H.G. and T.G. Kim, 1996. The water Quality Management of Daecheong Reservoir using remote sensing, Journal of the Korean Society of Environmental Engineering, 18(10): 1383-1396 (in Korean with English abstract).
16 Kim, J.G. and H.S. Jeon, 2002. The extract of red tide patch on the coast of the Southern Sea using RS, Journal of the Korean Society of Civil Engineers, 22(1): 791-799 (in Korean with English abstract).
17 Lee, J.C., Y.H. Jeong, S.R. Cha, H.G. Kim, W.Y. Yang, and T.H. No, 2008. Estimation of Estuary Area Water Depth with Morphology Image Processing, Proc. of 2008 the Korean Society of Civil Engineers, Daejeon, Korea, Oct. 29-31, vol. 1, pp. 3996-3999 (in Korean with English abstract).
18 Kim, D.H. and J.H. Yom, 2018. Machine Learning Based Estimation of Chlorophyll-a Concentrations in the Nakdong River Using Satellite Imagery, Proc. of 2018 the Korean Society of Survey, Geodesy, Photogrammetry, and Cartography, Yongin, Korea, Apr. 19-20, vol. 1, pp. 231-236 (in Korean with English abstract).
19 Lee, Z., K.L. Carder, C.D. Mobley, R. G. Steward, and J.S. Patch, 1998. Hyperspectral remote sensing for shallow waters, I. A semianalytical model, Applied Optics, 37(27): 6329-6338.   DOI
20 Lee, Z., K.L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, 1999. Hyperspectral remote sensing for shallow waters: 2, Deriving bottom depths and water properties by optimization, Applied Optics, 38(18): 3831-3843.   DOI
21 Lee, H., T. Kang, G. Nam, R. Ha, and K. Cho, 2015. Remote Estimation Models for Deriving Chlorophyll-a Concentration using Optical Properties in Turbid Inland Waters: Application and Valuation, Journal of the Korean Society on Water Environment, 31(3): 272-285 (in Korean with English abstract).   DOI
22 Legleiter, C.J., D.A. Roberts, W.A. Marcus, and M.A. Fonstad, 2004. Passive optical remote sensing of river channel morphology and in-stream habitat:Physical basis and feasibility, Remote Sensing of Environment, 93(4): 493-510.   DOI
23 Legleiter, C.J. and M.F. Goodchild, 2005. Alternative representations of in-stream habitat: classification using remote sensing, hydraulic modeling, and fuzzy logic, International Journal of Geographical Information Science, 19(1): 29-50.   DOI
24 Mikrut, Z. and R. Tadeusiewicz, 2000. Neural networks in image processing and recognition, Biocybernetics and Biomedical Engineering, 6(1): 459-493.
25 Legleiter, C.J. and D.A. Roberts, 2009a. A forward image model for passive optical remote sensing of river bathymetry, Remote Sensing of Environment, 113(5): 1025-1045.   DOI
26 Lyzenga, D.R., N.P. Malinas, and F.J. Tanis, 2006. Multispectral bathymetry using a simple physically based algorithm, IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2251-2259.   DOI
27 Lyzenga, D.R., 1978. Passive remote sensing techniques for mapping water depth and bottom features, Applied Optics, 17(3): 379-383.   DOI
28 Lyzenga, D.R., 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data, International Journal of Remote Sensing, 6(1): 115-125.   DOI
29 Mertes, L.A., 2002. Remote sensing of riverine landscapes, Freshwater Biology, 47(4): 799-816.   DOI
30 Mobley, D.C. and L.K. Sundman, 2001. Hydrolight 4.2 technical documentation, Sequoia Scientific, Incorporated, Redmond, WA, USA.
31 Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements, Applied Optics, 38(36): 7442-7455.   DOI
32 Nguyen, G.H., A. Bouzerdoum, and S.L. Phung, 2008. A supervised learning approach for imbalanced data sets, Proc. of 2008 19th International Conference on Pattern Recognition, Tampa, FL, Dec. 8-11, vol. 1, pp. 1-4.
33 Na, S.I., C.W. Park, K.H. So, H.Y. Ahn, and K.D. Lee, 2019a. Selection on Optimal Bands to Estimate Yield of the Chinese Cabbage Using Dronebased Hyperspectral Image, Korean Journal of Remote Sensing, 35(3): 375-387 (in Korean with English abstract).   DOI
34 Legleiter, C.J., D.A. Roberts, and R.L. Lawrence, 2009b. Spectrally based remote sensing of river bathymetry, Earth Surface Processes and Landforms, 34(8): 1039-1059.   DOI