Undergraduate Program in Geodesy and Geomatics EngineeringGD4202 Quality Management System

Quality Management of Bathymetric Surface Modelling

Ririn Indahyani*

Faculty of Earth Sciences and Technology, Institute of Technology, Bandung*ririn.indahyani@students.itb.ac.id

Abstract. Aim of this writing is making of Bathymetric Surface Modelling from spot depth data and how to improve the quality management from the spot depth data. Output of bathymetric surface model as Digital Surface Model (DSM). Making process of DSM needs the good quality of spot depth data. So, the quality control is needed before to make DSM. The quality of spot depth include quality in horizontal and vertical position. In vertical position, TVU is used and in horizontal position THU is used. Both of them are compared with standar deviation, that value of standar deviation smaller than THU or TVU is good quality. The interpolation contour is needed to make DSM after quality of spot depth data. There are many methods of interpolation, kriging and natural neighbour example. The output in this writing is the good of bathymetric surface model by using natural neighbour interpolation.

Keywords: Digital Surface Model, TVU, THU, interpolation methods.

1 IntroductionSpot depth is depth from surface water to topography of seabed. The spot depth get from sounding activity. The spot depth lies on line of sounding that called sounding line. The sounding line is divided into main line and cross line [1]. The cross line cross the main line, the function is to check quality of spot depth in main line as horizontal and vertical position, also depth itself. Shortly, the cross line as function as Quality Assesment (QA).

From spot depth data can be make DSM. DSM represents top faces of all objects on the terrain (both vegetation and man-made features) or terrain itself in open areas [2]. In this work, DSM represents contour of surface topography of seabed from spot depth. Making DSM is needed contour’s interpolation. Some interpolation usually is used known as Kriging, Spline, Triangular Irregular Network (TIN), Inverse Distance Weight (IDW), Natural Neigborhood, etc.

Received ________, Revised _________, Accepted for evaluation __________( Diterima tanggal _______, Perbaikan diterima tanggal ________, Diterima untuk dinilai tanggal _______ )

2 Ririn Indahyani

2 Material and Method

2.1 Spot depth DataThe data used for bathymetric surface model is spot depth. Spot depth content of horizontal (x) dan vertical (y) position and h (depth). The depth is distance from surface water to seabed. The depth is result of sounding that include factor of distance transducer, tide, etc.

The x and y positions represent in projected coordinate system cartesian UTM WGS 1984, zone 48 S. The spot depth that used is spot depth 1 and select the one thousands data of spot depth minimally. The data can be seen in figure 1.

Figure 1 Selection of spot depth 1

The red outline is the spot depth data that select to be processed. Its content of 1038 data. The all depth data lie in below one hunderd meter of depth. The orde 1b is used based on IHO S-44 [3].

2.2 Quality Control and Quality Assesment of spotdepthTo check of Quality spot depth selection, there are many steps:

1. Check data (x and y) that redundant. It means in x and y have same in position have same of depth. Also check data (x and y) in same position have different depth then average. To check redundant data is outlier or not, used equations:

TVU > stdev*1.96 (1)

TVU = √a2+(b*d)2 (2)

Equation (1) and (2) get from IHO S-44 [3].

Making of Digital Surface Model of Spotdepth Data 3

Where, a=0.5; b=0.013; d=average depth. Stdev is standar deviation and 1.96 is scale factor in confidence level 95 %.

2. Check the cross and fix point. To check use equations:

THU>stdev*2.45 (3)

THU=5+5% of depth metre (4)

Where THU is total horizontal uncertainty, 2.45 is scale factor in confidence level 95 %. Equation (3) and (4) based on IHO-S44 [3]. From the check quality, the data become 823 data.

2.3 Select the interpolation methodThere are two interpolation methods to make contour, kriging and natural neighbour. Kriging use linear combine from weight to estimate value in data sample. Kriging gives error and confidence. This method use semivariogram to represent spatial differences and value of data sample. Semivariogram represent weight in interpolation [4]. The natural neighbour interpolation is a geometric estimation technique that uses natural neighbourhood regions generated around each point in the data set. Like IDW, this interpolation method is a weighted-average interpolation method. However, instead of finding an interpolated point's value using all of the input points weighted by their distance, natural neighbors interpolation creates a Delauney Triangulation of the input points and selects the closest nodes that form a convex hull around the interpolation point, then weights their values by proportionate area [5].

2.4 Created Contour

After interpolation methods, created contour from select interpolation methods. To make contour use arcscene or surfer software.

3 Result and DiscussionTable 2 shows the quality assessment of sample of fix and cross point. There are two samples. The Table 2a shows the point has bad quality because standar deviation is bigger than THU. Analytically, the difference x and y position in Table 2a is bigger about 5.9 m in x and 5.1 m in y. although, in Table 2b is good quality because standar deviation is smaller than THU. The differences x and y in Table 2b is smaller than Table 2a.

4 Ririn Indahyani

Table 2 Sample of Quality Assesment (2a and 2b)

Figure 3 Representation of kriging interpolation Table 3 Value of kriging interpolation

Figure 4 Representation of natural neighbour Table 4 Value of natural neighbour

interpolation interpolation

Figure 5 DSM by using natural neighbour (5a) and kriging interpolation (5b)

From Figure 3 and Table 3, Figure 4 and Table 4 show that natural neighbour interpolation has smaller standar deviation than kriging. It means natural neighbour interpolation good from data to make DSM and also the contour is smoother than kriging method. This methods give different result from other people, just because its depend on the data. From that data is varied in depth, but

Point 2 x (m) y (m) h (m) stdev*2.45

Main 678014.5 9367389.3 -1.3 2.165515

Cross 678012.1 9367388.6 -1.56

depth rata2 -1.43

THU 4.9285

Classification statisticsCount 73453Minimum -47.08341599Maximum -1.317789197Sum -745718.5656Mean -10.13989864Standar Deviation 13.92407178

Classification statisticsCount 65580Minimum -47.03961563Maximum -1.33706552Sum -513168.1322Mean -7.82507065Standar Deviation 11.70080632

2a 2b

(5a) (5b)

Making of Digital Surface Model of Spotdepth Data 5

almost content of -11 m until -1m of depth. In Figure 5, natural neigbbour represents spikes in clearly than kriging interpolation.

4 ConclusionTo make good bathymetric surface model is needed quality management of spot depth data. So, to improve high quality management must be based on IHO S-44. IHO-S44 as international standar quality of bathymentric survey. In this data set, natural neighbour interpolation gives good quality of bathymetric surface model. The natural neighbour interpolation is recommended to use for futher.

AcknowledgementThis writing is one of task the Quality Management System in 2016.

References

[1] Poerbandono & Djunarsjah, E. (2005). Survei Hidrografi. PT. Refika Aditama, Bandung.

[2] Kufmann, Mario. (2011). Recommended accuracy and update requirement for depth data. Journal of IRIS Research.

[3] Interntional Hydrograhic Bureau. (2008). IHO Standards for Hydrographic Surveys.5th Edition, Monaco.

[4] Bohling, Geoff. (2005). KRIGING. Kansas Geoling Survey.[5] Ledoux, Hugo & Gold, Christoper. An efficient Natural Neighbour

Interpolation Algorithm for Geoscientific Modelling. Hongkong. Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University.