On the surface roughness characteristics of the land fast sea-ice in the Bohai Sea

The surface roughness characteristics (e.g., height and slope) of sea ice are critical for determining the parameters of an electromagnetic scattering, a surface emission and a surface drag coefficients. It is also important in identifying various ice types, retrieval ice thickness, surface temperature and drag coefficients from remote sensing data. The point clouds (a set of points which are usually defined by X, Y, and Z coordinates that represents the external surface of an object on earth) of land fast ice in five in situ sites in the eastern coast Bohai Sea were measured using a laser scanner-Trimble GX during 2011–2012 winter season. Two hundred and fifty profiles selected from the point clouds of different samples have been used to calculate the height root mean square, height skewness, height kurtosis, slope root mean square, slope skewness and slope kurtosis of them. The root mean square of the height, the root mean square of the slope and the correlation length are about 0.090, 0.075 and 11.74 m, respectively. The heights of 150 profiles in three sites manifest the Gaussian distribution and the slopes of total 250 profiles distributed exponentially. In addition, the fractal dimension and power spectral density profiles were calculated. The results show that the fractal dimension of land fast ice in the Bohai Sea is about 1.132. The power spectral densities of 250 profiles can be expressed through an exponential autocorrelation function.

[1]  Donald B. Percival,et al.  Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data , 2011, 1101.1444.

[2]  杨路,et al.  A Note on Wu's Non-Degenerate Condition , 1993 .

[3]  J. Freud.  Theory Of Reflectance And Emittance Spectroscopy , 2016 .

[4]  C. Kottmeier,et al.  Geometric and aerodynamic roughness of sea ice , 1996 .

[5]  T. Gneiting,et al.  Estimators of Fractal Dimension : Assessing the Roughness of Time Series and Spatial Data , 2010 .

[6]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[7]  Giorgio Franceschetti,et al.  An electromagnetic fractal‐based model for the study of fading , 1996 .

[8]  Dwight L. Jaggard,et al.  Scattering from fractally corrugated surfaces , 1990 .

[9]  Jong-Sen Lee,et al.  Surface roughness and slope measurements using polarimetric SAR data , 2002, IEEE Trans. Geosci. Remote. Sens..

[10]  D. Barber,et al.  C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness , 2013 .

[11]  Penina Axelrad,et al.  Sea Ice Roughness From Airborne LIDAR Profiles , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[13]  Peter Hall,et al.  Fractal analysis of surface roughness by using spatial data , 1999 .

[14]  Lipi Mohanty,et al.  Optical scatter measurement and analysis of innovative daylight scattering materials , 2012 .

[15]  Peter Lemke,et al.  Sea-ice roughness and drag coefficients in a dynamic–thermodynamic sea-ice model for the Arctic , 1999 .

[16]  Peter Wadhams,et al.  Ice in the ocean , 2000 .

[17]  Thuy Le Toan,et al.  The effect of surface roughness on multifrequency polarimetric SAR data , 1997, IEEE Trans. Geosci. Remote. Sens..

[18]  L. LeSchack,et al.  Power Spectrum Analysis of Undersea and Surface Sea-Ice Profiles , 1972, Journal of Glaciology.

[19]  S. D. Smith,et al.  Wind stress on Arctic sea ice , 1973 .

[20]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[21]  Wolfgang Dierking,et al.  Laser profiling of the ice surface topography during the Winter Weddell Gyre Study 1992 , 1995 .

[22]  Tilmann Gneiting,et al.  Stochastic Models That Separate Fractal Dimension and the Hurst Effect , 2001, SIAM Rev..