Adv Search
Volume 29 Issue 2
Apr.  2019
Turn off MathJax
Article Contents
Article Navigation >  Chinese Geographical Science  > 2019  >  20(2): 192-201

LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
Citation: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
shu

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

doi: 10.1007/s11769-018-1013-z
  • 1.

    College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;

  • 2.

    College of Earth Sciences, Guilin University of Technology, Guilin 541006, China

Funds:  Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), Plan for Changbai Mountain Scholars of Jilin Province, China
More Information
  • Corresponding author: CHEN Shengbo
  • Received Date: 2017-11-16
  • Publish Date: 2019-04-01
  • Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 (Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0-5 m because of the influence of human activities. The inversion accuracy of 5-10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5-20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.
  • [1] Abileah R, 2013. Mapping near shore bathymetry using wave kinematics in a time series of WorldView-2 satellite images. Proceesings of 2013 IEEE International Geoscience and Re-mote Sensing Symposium (IGARSS). Melbourne, VIC:IEEE, 2274-2277. doi: 10.1109/IGARSS.2013.6723271
    [2] Benny A H, Dawson G J, 1983. Satellite imagery as an aid to bathymetric charting in the Red Sea. The Cartographic Journal, 20(1):5-16. doi: 10.1179/caj.1983.20.1.5
    [3] 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.
    [4] Clark R K, Fay T H, Walker C L, 1987. A comparison of models for remotely sensed bathymetry. MS, USA:Naval Ocean Re-search and Development Activity Stennis Space Center, AD-A197973.
    [5] Clarke G L, James H R, 1939. Laboratory analysis of the selective absorption of light by sea water. Journal of the Optical Society of America, 29(2):43-55. doi:10.1364/JOSA.29. 000043
    [6] Curcio J A, Petty C C, 1951. The near infrared absorption spec-trum of liquid water. Journal of the Optical Society of America, 41(5):302-304. doi: 10.1364/JOSA.41.000302
    [7] Di Kaichang, Ding Qian, Chen Wei et al., 1999. Shallow water depth extraction and chart production from TM images in Nansha Islands and nearby sea area. Remote Sensing for Land and Resources, 3:59-64. (in Chinese)
    [8] 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. doi: 10.1109/TGRS.2014.2377300
    [9] Figueiredo I N, Pinto L, Gonçalves G, 2016. A modified Lyz-enga's model for multispectral bathymetry using Tikhonov regularization. IEEE Geoscience and Remote Sensing Letters, 13(1):53-57. doi: 10.1109/LGRS.2015.2496401
    [10] Flener C, Lotsari E, Alho P et al., 2012. Comparison of empirical and theoretical remote sensing based bathymetry models in river environments. River Research and Applications, 28(1):118-133. doi: 10.1002/rra.1441
    [11] Gitelson A, 1992. The peak near 700 nm on radiance spectra of algae and water:relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17):3367-3373. doi:10.1080/014311692 08904125
    [12] Gordon H R, 1979. Diffuse reflectance of the ocean:the theory of its augmentation by chlorophyll a fluorescence at 685 nm. Ap-plied Optics, 18(8):1161-1166. doi: 10.1364/AO.18.001161
    [13] Huang R Y, Yu K F, Wang Y H et al., 2017. Bathymetry of the coral reefs of Weizhou Island based on multispectral satellite images. Remote Sensing, 9(7):750. doi: 10.3390/rs9070750
    [14] Jawak S D, Vadlamani S S, Luis A J, 2015. A synoptic review on deriving bathymetry information using remote sensing tech-nologies:models, methods and comparisons. Advances in Re-mote Sensing, 4(2):57480. doi: 10.4236/ars.2015.42013
    [15] Jay S, Guillaume M, Minghelli A et al., 2017. Hyperspectral re-mote sensing of shallow waters:considering environmental noise and bottom intra-class variability for modeling and in-version of water reflectance. Remote Sensing of Environment, 200:352-367. doi: 10.1016/j.rse.2017.08.020
    [16] Johnson S Y, Cochrane G R, Golden N E et al., 2017. The Cali-fornia seafloor and coastal mapping program:providing science and geospatial data for California's State waters. Ocean and Coastal Management, 140:88-104. doi:10.1016/j. oce-coaman.2017.02.004
    [17] Lee Z, Hu C, Arnone R et al., 2012. Impact of sub-pixel variations on ocean color remote sensing products. Optics Express, 20(19):20844-20854. doi: 10.1364/OE.20.020844
    [18] Li Jiabiao, 1999. Principles, Technology and Methods of Multi-beam Survey. Beijing:China Ocean Press. (in Chinese)
    [19] Li J R, Zhang H G, Hou P F et al., 2016. Mapping the bathymetry of shallow coastal water using single-frame fine-resolution op-tical remote sensing imagery. Acta Oceanologica Sinica, 35(1):60-66. doi: 10.1007/s13131-016-0797-x
    [20] Li Qingquan, Lu Yi, Hu Shuibo et al., 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5):1216-1229. (in Chinese)
    [21] Li Xian, Chen Shengbo, Wang Xuhui et al., 2008. Study based on radioactive transfer model of the quantitative remote sensing of water bottom reflectance. Journal of Jilin University (Earth Science Edition), 38(S1):235-237. (in Chinese)
    [22] Lu Tianqi, Chen Shengbo, Guo Tiantian et al., 2016. Offshore bathymetry retrieval from SPOT-6 image. Journal of Marine Sciences, 34(3):51-56. (in Chinese)
    [23] Lyzenga D R, 1978. Passive remote sensing techniques for map-ping water depth and bottom features. Applied Optics, 17(3):379-383. doi: 10.1364/AO.17.000379
    [24] Lyzenga D R, 1979. Shallow-water reflectance modeling with applications to remote sensing of the ocean floor. Proceedings of the 13th International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan:Environmental Research Institute of Michigan, 583-602.
    [25] Lyzenga D R, 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 2(1):71-82. doi: 10.1080/01431168108948342
    [26] Manessa M D M, Kanno A, Sagawa T et al., 2018. Simulation-based investigation of the generality of Lyzenga's multispectral bathymetry formula in Case-1 coral reef water. Estuarine, Coastal and Shelf Science, 200:81-90. doi:10.1016/j.ecss. 2017.10.014
    [27] Mgengel V, Spitzer R J, 1991. Application of remote sensing data to mapping of shallow sea-floor near by Netherlands. Interna-tional Journal of Remote Sensing, 57(5):473-479.
    [28] Odermatt D, Gitelson A, Brando V E et al., 2012. Review of con-stituent retrieval in optically deep and complex waters from satellite imagery. Remote Sensing of Environment, 118:116-126. doi: 10.1016/j.rse.2011.11.013
    [29] Paredes J M, Spero R E, 1983. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Applied Optics, 22(8):1134-1135. doi:10.1364/AO.22. 001134
    [30] Poupardin A, Idier D, de Michele M D et al., 2016. Water depth inversion from a single SPOT-5 dataset. IEEE Transactions on Geoscience and Remote Sensing, 54(4):2329-2342, doi: 10.1109/TGRS.2015.2499379
    [31] Salama M S, Verhoef W, 2015. Two-stream remote sensing model for water quality mapping:2SeaColor. Remote Sensing of Environment, 157:111-122. doi: 10.1016/j.rse.2014.07.022
    [32] Sandidge J C, Holyer R J, 1998. Coastal bathymetry from hyper-spectral observations of water radiance. Remote Sensing of Environment, 65(3):341-352. doi:10.1016/S0034-4257(98) 00043-1
    [33] Shu Xiaozhou, Yin Qiu, Kuang Dingbo, 2000. Relationship be-tween algal chlorophyll concentration and spectral reflectance of inland water. Journal of Remote Sensing, 4(1):41-45. (in Chinese)
    [34] Su H B, Liu H X, Heyman W D, 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31(4):281-298. doi: 10.1080/01490410802466652
    [35] Su H B, Liu H X, Wang L et al., 2014. Geographically adaptive inversion model for improving bathymetric retrieval from sat-ellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1):465-476. doi:10.1109/TGRS. 2013.2241772
    [36] Su H B, Liu H X, Wu Q S, 2015. Prediction of water depth from multispectral satellite imagery-the regression Kriging alter-native. IEEE Geoscience and Remote Sensing Letters, 12(12):2511-2515. doi: 10.1109/LGRS.2015.2489678
    [37] Zhao Jianhu, Liu Jingnan, 2008. Multi-beam Sounding Technology and Image Data Processing. Wuhan:Wuhan University Press. (in Chinese)es G, 2016. A modified Lyz-enga's model for multispectral bathymetry using Tikhonov regularization. IEEE Geoscience and Remote Sensing Letters, 13(1):53-57. doi: 10.1109/LGRS.2015.2496401
    [38] Flener C, Lotsari E, Alho P et al., 2012. Comparison of empirical and theoretical remote sensing based bathymetry models in river environments. River Research and Applications, 28(1):118-133. doi: 10.1002/rra.1441
    [39] Gitelson A, 1992. The peak near 700 nm on radiance spectra of algae and water:relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17):3367-3373. doi:10.1080/014311692 08904125
    [40] Gordon H R, 1979. Diffuse reflectance of the ocean:the theory of its augmentation by chlorophyll a fluorescence at 685 nm. Ap-plied Optics, 18(8):1161-1166. doi: 10.1364/AO.18.001161
    [41] Huang R Y, Yu K F, Wang Y H et al., 2017. Bathymetry of the coral reefs of Weizhou Island based on multispectral satellite images. Remote Sensing, 9(7):750. doi: 10.3390/rs9070750
    [42] Jawak S D, Vadlamani S S, Luis A J, 2015. A synoptic review on deriving bathymetry information using remote sensing tech-nologies:models, methods and comparisons. Advances in Re-mote Sensing, 4(2):57480. doi: 10.4236/ars.2015.42013
    [43] Jay S, Guillaume M, Minghelli A et al., 2017. Hyperspectral re-mote sensing of shallow waters:considering environmental noise and bottom intra-class variability for modeling and in-version of water reflectance. Remote Sensing of Environment, 200:352-367. doi: 10.1016/j.rse.2017.08.020
    [44] Johnson S Y, Cochrane G R, Golden N E et al., 2017. The Cali-fornia seafloor and coastal mapping program:providing science and geospatial data for California's State waters. Ocean and Coastal Management, 140:88-104. doi:10.1016/j. oce-coaman.2017.02.004
    [45] Lee Z, Hu C, Arnone R et al., 2012. Impact of sub-pixel variations on ocean color remote sensing products. Optics Express, 20(19):20844-20854. doi: 10.1364/OE.20.020844
    [46] Li Jiabiao, 1999. Principles, Technology and Methods of Multi-beam Survey. Beijing:China Ocean Press. (in Chinese)
    [47] Li J R, Zhang H G, Hou P F et al., 2016. Mapping the bathymetry of shallow coastal water using single-frame fine-resolution op-tical remote sensing imagery. Acta Oceanologica Sinica, 35(1):60-66. doi: 10.1007/s13131-016-0797-x
    [48] Li Qingquan, Lu Yi, Hu Shuibo et al., 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5):1216-1229. (in Chinese)
    [49] Li Xian, Chen Shengbo, Wang Xuhui et al., 2008. Study based on radioactive transfer model of the quantitative remote sensing of water bottom reflectance. Journal of Jilin University (Earth Science Edition), 38(S1):235-237. (in Chinese)
    [50] Lu Tianqi, Chen Shengbo, Guo Tiantian et al., 2016. Offshore bathymetry retrieval from SPOT-6 image. Journal of Marine Sciences, 34(3):51-56. (in Chinese)
    [51] Lyzenga D R, 1978. Passive remote sensing techniques for map-ping water depth and bottom features. Applied Optics, 17(3):379-383. doi: 10.1364/AO.17.000379
    [52] Lyzenga D R, 1979. Shallow-water reflectance modeling with applications to remote sensing of the ocean floor. Proceedings of the 13th International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan:Environmental Research Institute of Michigan, 583-602.
    [53] Lyzenga D R, 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 2(1):71-82. doi: 10.1080/01431168108948342
    [54] Manessa M D M, Kanno A, Sagawa T et al., 2018. Simulation-based investigation of the generality of Lyzenga's multispectral bathymetry formula in Case-1 coral reef water. Estuarine, Coastal and Shelf Science, 200:81-90. doi:10.1016/j.ecss. 2017.10.014
    [55] Mgengel V, Spitzer R J, 1991. Application of remote sensing data to mapping of shallow sea-floor near by Netherlands. Interna-tional Journal of Remote Sensing, 57(5):473-479.
    [56] Odermatt D, Gitelson A, Brando V E et al., 2012. Review of con-stituent retrieval in optically deep and complex waters from satellite imagery. Remote Sensing of Environment, 118:116-126. doi: 10.1016/j.rse.2011.11.013
    [57] Paredes J M, Spero R E, 1983. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Applied Optics, 22(8):1134-1135. doi:10.1364/AO.22. 001134
    [58] Poupardin A, Idier D, de Michele M D et al., 2016. Water depth inversion from a single SPOT-5 dataset. IEEE Transactions on Geoscience and Remote Sensing, 54(4):2329-2342, doi: 10.1109/TGRS.2015.2499379
    [59] Salama M S, Verhoef W, 2015. Two-stream remote sensing model for water quality mapping:2SeaColor. Remote Sensing of Environment, 157:111-122. doi: 10.1016/j.rse.2014.07.022
    [60] Sandidge J C, Holyer R J, 1998. Coastal bathymetry from hyper-spectral observations of water radiance. Remote Sensing of Environment, 65(3):341-352. doi:10.1016/S0034-4257(98) 00043-1
    [61] Shu Xiaozhou, Yin Qiu, Kuang Dingbo, 2000. Relationship be-tween algal chlorophyll concentration and spectral reflectance of inland water. Journal of Remote Sensing, 4(1):41-45. (in Chinese)
    [62] Su H B, Liu H X, Heyman W D, 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31(4):281-298. doi: 10.1080/01490410802466652
    [63] Su H B, Liu H X, Wang L et al., 2014. Geographically adaptive inversion model for improving bathymetric retrieval from sat-ellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1):465-476. doi:10.1109/TGRS. 2013.2241772
    [64] Su H B, Liu H X, Wu Q S, 2015. Prediction of water depth from multispectral satellite imagery-the regression Kriging alter-native. IEEE Geoscience and Remote Sensing Letters, 12(12):2511-2515. doi: 10.1109/LGRS.2015.2489678
    [65] Zhao Jianhu, Liu Jingnan, 2008. Multi-beam Sounding Technology and Image Data Processing. Wuhan:Wuhan University Press. (in Chinese)
    [66]  
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(522) PDF downloads(480) Cited by()

Proportional views
Related

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

doi: 10.1007/s11769-018-1013-z
  • 1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
  • 2. College of Earth Sciences, Guilin University of Technology, Guilin 541006, China
Funds:  Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), Plan for Changbai Mountain Scholars of Jilin Province, China
    Corresponding author: CHEN Shengbo
  • Received Date: 2017-11-16
  • Publish Date: 2019-04-01

Abstract: Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 (Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0-5 m because of the influence of human activities. The inversion accuracy of 5-10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5-20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.

LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
Citation: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
shu

    HTML

Reference (66)

Catalog

    Copyright © Editorial office of Chinese Geographical Science

    Tel: +86-0431-85542219   Email: egeoscien@neigae.ac.cn  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return