Technical Paper PSM

8/7/2019 Technical Paper PSM

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HEIGHT PREDICTION USING KRIGING INTERPOLATIONMETHOD AT COASTAL AREA

Syed Shahrul Nizam b Syed ZakariaFaculty of Geoinformation and Real Estate

Universiti Teknologi [email protected]

ABSTRACT

The focus on this study is to conduct the height prediction by using Kriginginterpolation method at coastal area which is lacking of position and height information todevelop this area. Kriging is the optimum geostatic technique in order to conduct heightprediction. The purpose of this study to predict the height by using this interpolation method.Surfer 8.0 software was used in the height interpolation which is involving a bundle sampledata obtained from observation on the field. A Semivariogram graph was generatedautomatically by the Surfer 8.0 software. This is a Linear Semivariogram which computedbase on the distance between data point value and semivarians was already computed bythis software. The results obtained from the processing are the points of the East (x), North(y) and height (z) which has been interpolated. The grid contains the attribute of x, y and zvalue at any location on the coastal area surface base on the grid cell selected. The

accuracy of prediction result was analysing by conducted two tests of sample data which arecomparing between existing heights on the field. Two tests which are different patternsdistribution of data were introduced during the tests which are grid points heights and spotpoints heights. The standard deviation obtain from that test 1 is and test 2is . Test 2 shows the better result than test 1, so it is more suitable for use indeveloping coastal area.

1.0 INTRODUCTION

The rapidly development of coastal areas such as construction of ports, jetties andtourist resorts affecting the nature of the area.This development must be planned carefully in

order not to lose our natural resources. The coastal area is an area of low elevation, the areais more vulnerable to flood disaster or sink with sea water. Therefore, the height informationof land surface near the coastal area is needed to plan the development of this area. If thisarea does not follow the careful planning, it will cause the flow of drainage not smoothly andthe construction will below the sea water level. Various methods have been introduced forthe development of coastal areas. The Department of Survey and Mapping is one of thegovernment department have been develop an infrastructure that involves establishment ofhorizontal and vertical control throughout our country for development purposes. Theexamples are the establishment of GPS reference station for horizontal control and benchmark for vertical control. Other than that, the GPS Real Time Kinematic (RTK) service whichis known as MyRTKnet also has been introduced to make sure that the data collection wouldbe more quickly and easily. However, the new technology provided has a few constraints

that related of the coastal area location and physical surface. The constraints are thelocation of the coastal areas are far from the RTK reference GPS station that provided the
mailto:[email protected]:[email protected]:[email protected]


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correction of the positioning and the physical surface of coastal area that are different typesof surface such as sand, rock, mud and mangroves. All the constraints prevent the surveywork cannot be done to the entire surface at the coastal areas. These studies are combinestwo survey method to obtain the topographical information which is GPS MyRTKnet andconventional measurement by using total station equipment to cover the area of Tg. Setajamto Tg. Lompat at Desaru, Johor Bahru. At the early stages, the topographic data is needed todetermine the height distribution of this coastal area. The data can be used to map thesurface of coastal area by using Kriging Interpolation method. By using Kriging interpolationmethod, the points are cannot be surveyed could be determined and increase the density ofthe data. It can predict the height of other point by using the known value from topographicdata. In addition, this study also could find the capability of Kriging interpolation method afteranalyse the prediction height by comparing the predict height with known height on the field.

2.0 METHODOLOGY

The successful study base on systematic method and procedure are used. In this

chapter would discuss the methods used to conduct the Kriging Interpolation and finallyproduce the contour map. The whole method of this study is shown in figure 1. This figureshows the flow chart to conduct on this study.

Figure 2.1: Flow chart of the study

Figure 1: Flow Chart of the Study


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2.1 PRE-ANALYSIS OF THE STUDY

At this phase, the important step to understand clearly is the requirement of thestudy. That is understanding of the study can be found by searching information fromreading material at library, internet browsing, news paper and the related thesis. Base on the

knowledge and information obtained, the problem statement of the study can be determined.The objective and scope of the study can be used as a guide in the result to be showed. Inaddition, it also helps to focus on the important things about the requirement of the study toanswer the following questions.

i. How to get the data?ii. What equipment and software involved?iii. The data used in each process?iv. What the analysis involved?v. How to present the result analysis?

2.2 DATA COLLECTION

In this study, there are three main categories of data and all the data obtained by conductthe observation and obtained from license surveying company that Geometra Surveys Sdn.Bhd.

a) The first data is horizontal control survey was conduct by using traverse method fromexisting boundary stone and GPS MyRTKnet observation at established monumentpoint. The misclosure for traverse survey is more than 1:10 000 which is the first classsurvey. The coordinate system used in this study is Johor Cassini Geocentric. Thehorizontal control points were have after the survey work are 6 new GPS point and 3pillar GPS point that already exist at this area. The survey method used for new GPSpoint is RTK GPS method.

b) The second data is vertical control survey was conduct by second order levelling surveymethod. Two existing acceptable bench mark (BM) near the survey area was used as

vertical datum. The allowable misclosure for this survey is not more than 0.020 k wherek is the distance close loop in kilometer. The level height value was transfered to thehorizontal control point base on close loop leveling survey from BM J4302 to BM J4303.

c) The third data s height distribution of the study area was collect by using total stationequipment. The traverse work was begin from GPS station DG1 to DG3A and collect theheights detail point during the traverse work. The heights details were arranged bychainage which are from chainage 1 until chainage 75 to cover along the coastal area.

There are abouts 15 heights details every each chainage station. The distance betweenheights details about 10m and distance between chainage stations is about 50m.

2.3 DATA PROCESSING

At this stage, the data from data collected are presented in AutoCAD software. The data arewant to use were selected from AutoCad plan. The data are consists of Johor CassiniGeocentric coordinate and height of NGVD related to the coordinates points were used tomap the contour of the study area and Kriging Interpolation. A few points were selected forthe test by comparing heights prediction and known height from observation at respectivepoints. After that, the Kriging Interpolation and contour map were conducted by using Surfer

8.0 software.


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2.4 SELECTION OF DATA

For this study, not all of the data were used to processing. The AutoCad plan that consists ofall the data would be filtered to be used in Surfer 8.0 software. There are 75 chainagestations that consist of heights and coordinates detail were selected from AutoCad plan.

Each chainage station, there are about 15 points were selected as XYZ data for the Surfer8.0 software. These data are entered to the Microsoft Excel to facilitate the preparationof the data. Then, 9 GPS station were used as horizontal control of the study area.

a) ID command was used to the coordinates of the point selected in the AutoCad software.b) The selected data in AutoCad software were transferred to the Microsoft Excel manually

by copy and paste the data.c) 9 GPS stations were chosen as the horizontal control reference point.

2.5 DATA ENTRY IN SURFER 8.0 SOFTWARE

All the data were entered into the Surfer software to produce a contour map by using GIS

mapping as seen in figure 2.

Figure 2: Flow Chart Create a Contour Map

2.6 DATA FILE

Data entry in Surfer software consists of three different columns that contain different typesof data. The first and second columns are for coordinates of each point which are eastingand northing. The third column is height information respective with the point coordinate. Thesteps later were shown the data entry in Surfer software.


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a) Click the Surfer 8 icon and the main display will appear

b) Then, click icon New Worksheet to open the data entry menu as shown in figure 3.

Figure 3: The Main Display of Data Entry

c) Because of the data to be used were stored in Microsoft Excel, so it is easily to transfer

into the Surfer software directly. The steps were highlight and copy the data as shown in

figure 2.5 and paste the data into Surfer data entry display as shown in figure 4.

Figure 4: Data Entry Display in Surfer


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d) After all the data entered into the Surfer data entry, save the data and change intoformat Golden Software Data (*DAT) as shown in figure 5.

Figure 5: Steps to Save the Data Entry

2.7 CREATE GRID FILE

Grid file was needed to create the grid-base map. The grid-base map contains datainformation to create a contour maps, image maps, shaded relief maps and so on. Thegrid file is generated from the data entry done before. The steps to generate a grid file are:

a) Navigate the File| New| Plot Document| Ok to open back the main Surfer display to beginthe Grid File process.

b) Navigate the Grid| Data as shown in figure 6. The new open window was shown the dataentry and select the file. Then, click open.


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Figure 6: Grid Data Display

c) Then, the new dialog windows as shown in figure 7 appear. There are severalparameters that have the following functions:

Figure 7: Grid Data Parameter


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i. Data Columns to determine the X, Y and Z data.ii. Gridding Method to determine the interpolation used. This study the Kriging

Interpolation method used.iii. Output Grid File to determine the location of Gridding result to be saved.iv. Grid Line geometry to determine the maximum and minimum value of X and Y

axis. While Spacing is size of each grid cell and # of lines is number of grid linesrespected to the X and Y axis.

v. This study used different grid size. So, to create the grid file of 10m x 10m and20m x 20m, the spacing column Spacing can be changed to number 10 or 20in both X and Y Direction.

vi. Firstly, the Gridding process was done for Grid Cell 10m x 10m and the outputwas saved as test10.grd. Then, the similar process was done to Grid Cell 20m x20m.

vii. The grid report appears and saves as reference later.

2.8 CREATE CONTOUR MAP

To create a contour map is based on Grid Files that have been made. So, thefollowing step was done:

Navigate to the Map| Contour Map| New Contour or click at button located onthe right plot window. Then, select the Grid File have been made and a contour mapwould show on the plot window.

3.0 DATA COLLECTION, RESULT AND ANALYSIS

This section focused on results of data collection, results of Kriging Interpolation and

analysis of the results. The results and analysis would answer the objective of this study.This section divided into five categories which are:

Data Collection results.

Investigation of heights distribution.Prediction and adding heights values.

Comparison of height prediction and height obtained from observation on the field.

Contour map result.

3.1 DATA COLLECTION RESULTS

First data is horizontal control at study area. There are 6 new GPS point (DG1-DG3A) afterdone the RTK GPS observation and 3 existing GPS pillar. The traverse method was used toconnect all the GPS point to the chainage station along the coastal area. There are 75chainage station have been made by traverse work. Figure 8 show the horizontal controlpoints and traverse line.


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Figure 8: Horizontal Control Stations and Travese Lines

Table 1 below show the list horizontal control points coordinates in Johor Cassini Geocentriccoordinate system and only 5 chainage stations coordinates have showed as example.

Table 1: List of Horizontal Control Coordinates

Station North (m) East (m)

DG1 -65746.37 81477.68DG1A -65789.97 81522.50

DG2 -64166.63 80232.00

DG2A -64153.87 80304.79

DG3 -62380.23 79404.23

DG3A -62299.40 79336.15

Pillar PC2B -64989.84 81002.85

Pillar PC11B -62556.82 79412.61

GPS station

Traverse

lines

Chainage Stations

eg:CH 1-75


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Pillar PC 12B -62609.18 79452.83

Ch1 -65992.470 81656.791

Ch2 -65920.140 81650.004

Ch3 -65889.093 81652.638

Ch4 -65842.469 81561.317

Ch5 -65790.032 81522.571

Second data is vertical control at study area obtained after levelling survey work. Thelevelling survey was initially by doing the verification of vertical control datum which is fromBM J4302 to BM J4303. Then, the heights values were transferred to the horizontal controlpoints have been established by using close loop levelling survey. The vertical controlnetwork could be seen in figure 9. The data from the survey results as shown in table 2 whilethe verification results as shown in table 3 below.

Figure 9: Vertical Control Network


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Table 2: The NGVD Heights at each GPS Station and Bench Mark

GPS station / Bench Mark (BM) NGVD Height (m)

DG1 4.69

DG1A 4.29

DG2 5.26

DG2A 4.77

DG3 7.75

DG3A 8.09

BM 4302 8.214

BM 4303 6.394

Table 3: Verification Result

JUPEM Bnch Mark (NGVD)Result Close Loop Levelling

survey toDifferent

BM J4303 = 6.394m BM J4303 = 6.401m - 0.007m

Third data is height details from total station collection data. The detailing work has donebetween GPS stations and chainage stations. There were consist of 75 chainage stationwhich is each station consist of 15 points detail (N, E and Height). The distance betweenchainage stations is 50m while the distance between points detail is 10m. The data

presented as shown in figure 10.

Figure 10: The heights points Details


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3.2 INVESTIGATION OF HEIGHTS DISTRIBUTION

The investigation has been conduct at study area to determine the information can be usedto develop this area. The information required are horizontal control and vertical controlinfrastructure to be used in mapping and development planning. The result obtained after

the investigation that only two bench marks and three GPS pillars can be used. Besidesthat, a few bench marks along the road near the coastal area have been lost and displacefrom its original position. While, the GPS pillars were obtained from previous projectobservation but the height of two pillars which are PC 11B and PC 12B unknown. Theinformation of existing GPS pillar and two bench marks as seen in table 4 and 5.

Table 4: Three GPS Pillar stations

Stesen GDM 2000 JOHOR CASSINI

GEOCENTRIC

NGVD Ht

(m)

Latitud (dms) Longitud (dms) Utara (m) Timur (m)

Pillar

PC2B 1 27 16.903 104 17 20.256 -64989.84 81002.85 4.56

Pillar

PC 11B 1 28 36.125 104 16 28.837 -62556.82 79412.61 n/a

Pillar

PC 12B 1 28 34.420 104 16 30.137 -62609.18 79452.83 n/a

Table 5: The Two Bench Marks

Batu aras (BM)Nilai ketinggian ( m ),

(NGVD)Catatan

J4302 8.214Di sebelah kiri jalan Bandar Penawar

Desaru. Berhampiran tiang lampu JD 230

J4303 6.394Di sebelah kiri jalan Bandar Penawar

Desaru. Berhampiran tiang lampu JD 252

After the surveying work has been done as describe in collection data section, so theinformation required mapping the contour and development planning were obtained. Thereis an example of comparison between the original condition and after the survey work hasbeen done as shown in figure 11. The infrastructure information at this coastal area becomedenser than before that has horizontal and vertical information.


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Figure 11: Comparison between the Original Condition and After Surveying Work

3.3 PREDICTION AND ADDING HEIGHTS VALUES

This study was using Kriging Interpolation method to predict the unknown values base onthe known value of X, Y and Z data. The prediction also used the weights around the knownpoints were obtained from Semivariogram model. The results of this interpolation enables

every location on the surface point of the study area has its own height value. Therefore, thismethod could dense or adding the height data on the surface. This study produces the gridsurfaces which are the grid cell size 10m x 10m and 20m x 20m as shown in figure 12 and13.

Figure 12: Grid Surface Result of 10 x 10 (m)


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Figure 13: Grid Surface Result of 20 x 20 (m)

Base on the results show the position and height value of each point (East, North andHeight) that represented as X, Y and Z on the top window. The prediction result on two gridcell sizes which are 10m x 10m and 20m x 20m show that, the same point giving the resultsimilar height prediction.

3.4 COMPARISON OF HEIGHT PREDICTION AND HEIGHT OBSERVATION

The prediction results enables the information on the point that cannot be observed becauseof physical constraints would be obtained. The question is whether the prediction heights willbe acceptable and represented the actual value of points on the earth. Therefore two tests

were done to prove the accuracy of the predicted height results can be accepted and baseon the comparison between the prediction value and known value. Two tests were differentpattern positions of data as manipulated variable would affect the accuracy of predictionheight. Two tests were conducted at different location of study area where also differentpattern of data collection which are grid points and spot points as shown in figure 14 below.

Point data symbol

Test 1: Grid points

Test 2: Spot points

Figure 14: Pattern of Data


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TEST 1

Pattern of data in test 1 is grid points data. There are 16 points data would be used tocalculate the prediction points represent as red triangle. The distance between two gridsdata points are 200m and distance between each data points are about 30m. The prediction

points also have their known value that obtained from field observation data base on theircoordinate. The results after done the processing as seen in figure 15.

Figure 15: Test 1 Data Pattern

TEST 2

Pattern of data in test 2 is spot points data. There are 20 points data would be used tocalculate the prediction points represent as red triangle. The distance between each datapoints are about 30m. The prediction points also have their known value that obtained fromfield observation data base on their coordinate. The results after done the processing asseen in figure 16.


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Figure 16: Test 2 Data Pattern

COMPARISONS RESULTS

The results obtain from two tests by comparison of value as shown in table 6 and 7.

Table 6: Test 1 Results

No

Prediction Point Known Point Height Diff.

H, (m)Easting Northing Height Easting Northing Height

1 79566.179 -62442.591 3.968393 79566.413 -62442.169 4.140 0.172

2 79650.120 -62442.591 1.844716 79650.796 -62442.462 1.610 0.235

3 79533.201 -62341.342 4.029537 79533.598 -62341.715 3.910 0.120

4 79564.180 -62341.342 2.863346 79564.384 -62341.934 2.650 0.210

Standard Deviation


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Table 7: Test 2 Results

Based on the results of both tests, the test 1 give in higher standard deviation which is. For the test 2, the results give in value better standard deviation which is. Therefore, finding that the accuracy of prediction results were depending to the

composition and pattern of data distribution to predict the unknown value. So, enable gettingthe best result, the data distribution are enough or very dense to do the prediction.

3.5 COUNTOR MAP RESULTS

Figure 17 below is the result of a contour map of an area of 2.5 km x 2 km that covering thearea of the study. Contour line interval is every 1 m. Contour map was generated using agrid cell size of 10 m x 10 m to produce smooth contour lines. The smaller grid cell size,

smoother the contour lines.

Test 1

Test 2

Figure 17: Contour Map

No

Prediction Point Known Point Height Diff.

H, (m)Easting Northing Height Easting Northing Height

1 81595.422 -65950.747 4.555302599 81595.665 -65950.836 4.570 0.015

2 81639.466 -65930.6962 3.366566587 81639.156 -65930.657 3.380 0.013

3 81575.402 -65891.597 9.34986058 81575.989 -65891.769 9.331 0.019

4 81625.452 -65888.5897 3.714158806 81625.110 -65888.441 3.730 0.016

Standard Deviation


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4.0 CONCLUSION

Height interpolation is the prediction of unknown value base on the known available data.The method used in this study is Kriging Interpolation method. Surfer 8.0 software was usedas medium to conduct the Kriging Interpolation analysis. The ability of Surfer 8.0 software to

produce the Semivariogram model automatically helps the analysis quickly. The results ofKriging analysis enables to produce Grid surface that contain position and height valueanywhere on the surface. The analysis of prediction results were done by comparisonsbetween predictions heights points and heights of points obtained from observation on thefield. Finding that the accuracy of prediction results are depending to the composition andpattern of data distribution to predict the value. In addition, the prediction data and contourmap can be used to develop the coastal area because the accuracy can be acceptable afterdone the tests which are and .

ACKNOWLEDGEMENT

I would like to gratitude to Assoc Prof Dr. Md. Nor Kamarudin as supervisor to complete this

project by giving advice and guidance along this study.

REFERENCES

Carr,J.R. (1995). Order relation correction experiments for probability kriging. MathematicalGeology

David Kidner (2000).What's the point? Interpolation and extrapolation with a regular gridDEM.

Dr.Samad b Hj Ab (2005). Geocentric Datum of Malaysia 2000 (GDM2000) and CCS

Infrastructure.

Dr Tajul Ariffin Musa. (2009). Satellite Surveying 2 Lecture Note. Fakulti Kejuruteraan DanGeoinformasi, UTM

Galen Scott. (2009). National Geodetic Survey and Office of Ocean and Coastal ResourceManagement.

Geodesy (2001), A Country Report on the Geodetic and Tidal Activities in Malaysia

Geoff Bohling (2005), Assistant Scientist Kansas Geological Survey, Kriging

Author

Syed Shahrul Nizam b Syed Zakariawas born in 1988. He is a B. Eng. (Geomatic) student

in Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate.

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