Processing mbes data to detect changes to seafloor topography

Smart Computing Review, vol. 1, no. 2, December 2011

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Smart Computing Review

Processing MBES Data to Detect Changes to Seafloor Topography Dong-Moon Kim and Eui-Myoung Kim Dept. of GIS Engineering, Namseoul University / 21, Maeju-Ri, Seonghwan-Eup, Cheonan 331-707, South Korea / {david, kemyoung}@nsu.ac.kr * Corresponding Author: Dong-Moon Kim Received August 24, 2011; revised November 18, 2011; accepted December 5, 2011; published December 31, 2011

Abstract: Performing precision monitoring for changes to seafloor topography is necessary to accurately examine the state of accidents of all sorts on the coast. Thus, this study proposed a new change detection technique to monitor changes to seafloor topography by systemizing the postprocessing technique for MBES sounding data and 3D modeling technique. This study also applied the proposed technique to a research area, monitored and analyzed changes to seafloor topography, and observed topographical changes at 13 points with the area of changes accounting for about 3.1% of the total area (4484.57 ㎥).

Keywords: MBES, Seafloor Topography, MBES, Monitoring, Change Detect

Introduction

T

Oday, the coast is regarded as a place that help to increase the quality of life through providing rest and leisure as well as making a living for many people. There have been many different types of marine accidents on coasts in an increasing frequency, which calls for a systematic maintenance effort in and at the bottom of the sea by achieving an appropriate understanding of accidents happening there [1]. Especially, in urgent need of precision monitoring, are changes to seafloor topography (remnants and structures at the bottom of the sea) in order to get an accurate picture of all kinds of accidents happening on the coast, reflecting the huge importance of the coast in terms of geography and environment [2].

For such purposes, high-tech equipment is used such as MBES and GPS for 3D precision bathymetry for seafloor topography [3]. In addition, research has been conducted to store sounding data in the form of GIS data of vector or raster models after compensation through equipment examination, and to use them to detect changes to seafloor topography, investigate seafloor obstacles, and analyze the behavior of seafloor structures.

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0005352).

DOI: 10.6029/smartcr.2011.02.003

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Those studies include one that processed error sounding data collected on the coast with the post-processing technique and analyzed error sounding by topography [4], one that quantitatively calculated the behavior and damage of seafloor structures by examining the caisson wall inclinations or breakwater damage with MBES sounding [5-7], one that examined and analyzed changes to seafloor topography according to sand mining with MBES data [8], and one that studied postprocessing techniques for MBES data to improve the modeling of seafloor topography [3, 6]. Those previous studies, however, focused on obtaining sounding data and compensation by considering sounding equipments, analysis of topographical changes according to sounding differences, and post-processing techniques, failing to systematically model seafloor topography and detect changes to it [9].

Thus, this study set out to systemize the post-processing technique from a previous study [6] and the 3D modeling technique with MBES sounding data, and to devise a way to effectively detect changes to seafloor topography.

Detecting Changes to Seafloor Topography â– Technique to Detect Changes to Seafloor Topography In general, MBES observation is carried out in the order of: survey plan, determination of the survey area and bayline interval/linear velocity, coordinate compensation through equipment testing and GPS, field study, compensation of speed of sound underwater and angle of direction of the survey boat, compensation of orientation angle of the MBES sonar head, and saving the final sounding data. Essential to the process of sounding data is the convergence of data collected through different kinds of equipment including the MBES sensor, motion sensor, gyroscope, and GPS. There is a possibility that all kinds of error data can be inserted in the process, which raises a need for thorough planning, arrangement, and compensation. In addition, noise is inserted in the observation process in sounding data, which makes it necessary to remove noise included in the outermost beam and depth of water. The conventional data processing technique usually moves to the stage of seafloor topography DEM. The involved procedures follow the following order (Figure 1).

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However, it is common to find erroneous sounding data of various types derived from data processing and conversion in the sounding data from which noise was removed. Therefore, an error sounding data removal technique proposed in a previous study [6] is needed to solve the problem. Equation 1 is useful for calculating the average distance between points in the sounding data, after which one can devise 3 x 3 Window Kernel, obtain focal statistics, and determine the representative cell value (central value).

(1) Here,

the value allocated to each grid, mean of data within a 3x3 grid, and general formula for standard deviation. Selected through , erroneous sounding data are removed and then go through 3D rotational conversion. In the rotational conversion designed to conduct precision modeling for seafloor topography, the target is rotated by using the rotational conversion matrix for the three-dimensional coordinate axes (X, Y, and Z) at the right-handed system based on the roll angle of the MBES scan sensor. The data obtained through rotational conversion should be interpolated with the Spline Interpolation Technique in order to compensate for the void (NoData) in Equation 1. The data interpolated with the Spline Interpolation Technique are converted into DEM, a grid model, after TIN is built which is widely used as a GIS spatial modeling technique. Equation 1 represent the technique that have not been applied to conventional seafloor topography modeling and can be useful to effectively solve such problems as missing or distorted data in certain areas according to the configuration characteristics of seafloor topography. Thus, this study implemented a similar environment to that of the MBES sounding process on the ground, modeled it, and checked its findings in order to apply the techniques and procedures that had not been used to detect changes to seafloor topography and examine its usability. Needed for the MBES survey observation are multiplebeam data in high density. Sound waves usually form a fan shape that is wide along the sides and narrow towards the stem to measure inclined distance. When observing a sea bed, scanning the width changes according to the depth of water. The horizontal resolution of each beam dynamically changes according to the beam width as well as depth of water. The determination principle of the MBES system is the same as that of Terrestrial LiDAR in Figure 2.

T-LiDAR survey was carried out to properly test the proposed data processing technique as follows.

■Test of the technique with T-LiDAR data Selected for T-LiDAR survey was an area of about 40 ㎥ with a certain inclination on the campus of Nam Seoul University (Figure 3). For unification on the coordinate system, one control point and three horizontal control points were set in the area. T-LiDAR sounding was then carried out for the original point and the points of artificial topographical displacement in order to observe the topographical changes (Table 1).

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Korea Geodetic Datum-based 3D coordinates were obtained for the control and standard control points through a GPS survey. The survey area before and after the artificial displacement changes was surveyed by installing T-LiDAR survey equipment at the control point and setting Horizontal Control Point 1 as a Backsight Point. The survey data, which were 3D vector data, were processed in turn by the proposed technique and the conventional one and converted into DEM, which refers to grid-based data proper in order to accurately examine the topographical displacement (Figure 4).

The Local computation operator, a GIS Spatial Analyst Operator, was applied to the survey area DEM before and after the artificial topographical displacement in order to obtain information about the displacement points (Equation 2).

(2) Here, , refers to the displacement of the grid value, DEM(i) the grid value before displacement, and DEM(j) the grid value after displacement. The conventional and proposed techniques were applied to the model, and the two points of artificial displacement were compared (the polygon in Figure 4) with the original point.

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As seen in Figure 5, the area of topographical displacement (the red polygon) was clearly detected via the proposed technique. However, it was not the case with the conventional one. The findings indicate that the proposed technique can help to clearly examine topographical changes.

Application and Analysis The survey area is shown in Table 2. The MBES data (Figure 6) obtained twice (May, 2008 and July, 2010) from the survey area were put to equipment compensation. The error survey data were also removed from them including the outermost beam and survey noise data.

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In Figure 7, the research findings from which error survey data were removed were modeled through 3D axis conversion, interpolation of data density, TIN and DEM according to the proposed technique. In Equation 7, the points of seafloor topographical displacement were identified by applying the GIS spatial computation operator to the data of two points of time after modeling. After excluding the displacement points whose areas are 0.01 ㎥ or under, a total of 14 points were identified as seen in Figure 8. Table 2 shows the volume, area, and displacement characteristics of each point.

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In Table 3, ID refers to the point number in Figure 9 including its volume, area, and code. Code 1 (above) refers to the sediment after the topographical changes, Code -1 (below) corrosion, and Code 0 (same) no changes.

The statistical analysis results of the displacement points show that corrosion happened at eight points with the area of 89.23 ㎡ and volume of 13.99 ㎥, and that the sediment happened at five points with an area of 50.64 ㎡ and volume of 9.85 ㎥. The percentage of topographical change was about 3.1% of the entire area (4484.57 ㎡). The corrosion area was about 0.8% more than the sediment area. Those findings suggest that the proposed technique is effective for the detecting changes to seafloor topography.

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Conclusions In this study, the investigator systematized the post-processing technique and the 3D modeling technique for MBES sounding data, and thus proposed a new change detection technique to help to effectively detect changes to seafloor topography. The proposed technique was applied to the survey area to detect changes to the seafloor topography. The analysis results led to the following conclusions: 

Considering that the convention technique to process and model MBES data has difficulty in completely removing error sounding data, the investigator proposed a technique to detect changes to seafloor topography by systemizing the results from the previous studies including the technique to remove error sounding data, 3D axis conversion, and interpolation of data density in order to solve the problem. The proposed technique seems to be effective in modeling and displacement detection of the areas of erect inclination, depression, or protrusion by reflecting the characteristics of seafloor topography.



For testing purposes, the proposed technique was put to modeling and comparison with the conventional technique based on T-LiDAR survey data. The results show that displacement that was not detected in modeling via the conventional technique appeared clearly in modeling via the proposed one.



The MBES survey data from two points of time were processed and modeled to apply the proposed technique. As a result, changes were detected at 13 points of seafloor topography, and their area was about 3.1% of the entire area (4484.57 ㎡). The corrosion area was 0.8% more than the sediment area.



This study proposed a systemized technique and detected changes to seafloor topography with MBES data by utilizing the proposed technique in order to compensate for the disadvantages of the conventional one. However, MBES data should be obtained regularly from multiple points of time in order to detect changes based on the diverse characteristics of seafloor topography and environment.

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[9] L. Mitas, H. Mitasova, “General variational approach to the interpolation problem,” Computer and Mathematics with Applications, vol.16, no.12, pp. 983-992, 1988. Article (CrossRef Link) [10] G. Brouns, A. De Wulf, D. Constales, “Multibeam data processing: adding and deleting vertices in a delaunay triangulation,” The Hydrographic Journal, vol 101, 2001. Article (CrossRef Link)

Dong-Moon Kim received the MS and PhD degrees in civil engineering from Kangwon National University, Chuncheon, South Korea, in 1996 and 2002, respectively. Currently, he is a professor at the Department of GIS Engineering, Namseoul University, Cheonan, South Korea. His research interests include MBES/T-LIDAR data processing and network adjustment using GPS data.

Eui-Myoung Kim received the MSc degree in urban engineering from Gyeongsang National University, Jinju, South Korea, in 1996, and the PhD in civil engineering from Yonsei University, Seoul, South Korea, in 2000. From 2000 to 2002, he was the Senior Researcher in Korea Institute of Construction Technology, Goyang, South Korea. He worked as a Post-Doctoral Fellow at the University Calgary, Canada, from 2003 to 2005. He was with Korea geoSpatial Information and Communication (KSIC) in Seoul, South Korea, from 2005 to 2006. Currently, he is an assistant professor at the Department of GIS Engineering, Namseoul University, Cheonan, South Korea.

His research interests include sensor modeling of satellite imagery, true ortho-photo generation, LIDAR data processing and GIS software development using open sources.

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