A spatial resolution effect analysis of remote sensing bathymetry

LIANG Jian; ZHANG Jie; MA Yi

doi:10.1007/s13131-017-1088-x

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2017 > 36(7): 102-109

LIANG Jian, ZHANG Jie, MA Yi. A spatial resolution effect analysis of remote sensing bathymetry[J]. Acta Oceanologica Sinica, 2017, 36(7): 102-109. doi: 10.1007/s13131-017-1088-x

Citation:

LIANG Jian, ZHANG Jie, MA Yi. A spatial resolution effect analysis of remote sensing bathymetry[J]. Acta Oceanologica Sinica, 2017, 36(7): 102-109. doi: 10.1007/s13131-017-1088-x

LIANG Jian, ZHANG Jie, MA Yi. A spatial resolution effect analysis of remote sensing bathymetry[J]. Acta Oceanologica Sinica, 2017, 36(7): 102-109. doi: 10.1007/s13131-017-1088-x

Citation:

LIANG Jian, ZHANG Jie, MA Yi. A spatial resolution effect analysis of remote sensing bathymetry[J]. Acta Oceanologica Sinica, 2017, 36(7): 102-109. doi: 10.1007/s13131-017-1088-x

PDF( 4463 KB)

A spatial resolution effect analysis of remote sensing bathymetry

doi: 10.1007/s13131-017-1088-x

1.
Dalian Maritime University, Information Science and Technology College, Dalian 116026, China
2.
Dalian Maritime University, Information Science and Technology College, Dalian 116026, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
3.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

Received Date: 2016-12-03
Rev Recd Date: 2017-03-01

Abstract

Abstract

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including QuickBird and WorldView-2 remote sensing images with their original spatial resolution (2.4/2.0 m) and four kinds of reducing spatial resolution (4, 8, 16 and 32 m), and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and 16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors (MRE) of QuickBird and WorldView-2 bathymetry are 21.2% and 13.1%, compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors (MAE) are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.
- remote sensing,
- spatial resolution,
- water depth remote sensing inversion

FullText(HTML)

References(23)

References

Atkinson P M, Kelly R E J. 1997. Scaling-up point snow depth data in the U.K. for comparison with SSM/I imagery. International Journal of Remote Sensing, 18(2): 437-443

Fang Bin, Chen Bo, Zhang Yuan. 2007. Research on scale selection and mapping of inspecting biologic diversity using remote sensing. Geography and Geo-Information Science (in Chinese), 23(6): 78-81

Han Peng, Gong Jianya. 2008. A review on cholce of optimal scale in remote sensing. Remote Sensing Information (in Chinese), 23(1): 96-99

Hu Guobiao, Fu Yangbiao, Hu Xiaobao. 2011. Research on remote sensing image scale issue based on variogram suit. Technology Wind (in Chinese),(4): 34-35

Huang Huiping, Wu Bingfang. 2006. Analysis to the relationship of feature size, objects scales, image resolution. Remote Sensing Technology and Application (in Chinese), 21(3): 243-248

Lam N S N, Quattrochi D A. 1992. On the issues of scale, resolution, and fractal analysis in the mapping sciences. Professional Geographer, 44(1): 88-98

Lausch A, Heurich M, Gordalla D, et al. 2013. Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. Forest Ecology and Management, 308: 76-89

Li Xiaowen, Wang Yiting. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica (in Chinese), 68(9): 1163-1169

Liang Jian, Zhang Jie, Ma Yi. 2015. Analysis of the influence of the amount and proportion of control points and check points on the accuracy of bathymetry remote sensing inversion. Marine Science (in Chinese), 39(2): 15-19

Liu Liangyun. 2014. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing (in Chinese), 18(6): 1158-1168

Luan Haijun, Tian Qingjiu, Yu Tao, et al. 2013. Review of up-scaling of quantitative remote sensing. Advances in Earth Science (in Chinese), 28(6): 657-664

Ma Yi, Zhang Jie, An Ni. 2014. Spectral fidelity analysis of compressed sensing reconstruction hyperspectral remote sensing image based on wavelet transformation. In: Li Shutao, Liu Chenglin, Wang Yaonan, eds. Pattern Recognition, Berlin Heidelberg: Springer, 138–148

Marceau D J, Hay G J. 1999. Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing, 25(4): 357-366

Ming Dongping, Wang Qun, Yang Jianyu. 2008. Spatial scale of remote sensing image and selection of optimal spatial resolution. Journal of Remote Sensing (in Chinese), 12(4): 529-537

National Marine Data and Information Service. 2011. 2012 Tide Tables Vol. 3: From the Taiwan Straits to the Beibu Gulf (in Chinese). Beijing: China Ocean Press

National Marine Information Center. 2004. 2005 Tide Tables Vol. 3: From the Taiwan Straits to the Beibu Gulf (in Chinese). Ji’nan: Shandong Cartographic Publishing House

Peng Xiaojuan, Deng Ruru, Liu Xiaoping. 2004. A review of scale transformation in remote sensing. Geography and Geo-Information Science (in Chinese), 20(5): 6-10, 14

Powers R P, Hay G J, Chen G. 2012. How wetland type and area differ through scale: a GEOBIA case study in Alberta’s Boreal Plains. Remote Sensing of Environment, 117: 135-145

Su Lihong, Li Xiaowen, Huang Yuxia. 2001. An review on scale in remote sensing. Advance in Earth Science (in Chinese), 16(4): 544-548

Wang Dapeng, Wang Zhoulong, Li Deyi, et al. 2007. Scale of quantitative desertification evaluation by remote sensing. Journal of Natural Disasters (in Chinese), 16(6): 140-144

Woodcock C E, Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311-332

Zhang Lun. 2013. The information loss of remote sensing image during scaling up procedure (in Chinese) [dissertation]. Beijing: University of Chinese Academy of Sciences

Zhou Mi, Zhang Jielin. 2011. Review on scale transformation for remote sensing image and selection of optimal spatial resolution. World Nuclear Geoscience (in Chinese), 28(2): 94-98

Relative Articles

Supplements(0)

Cited By

Proportional views