APPLICATION OF INNOVATIVE AIRBORNE LiDAR SURVEY SYSTEM FOR A HIGHWAY PROJECT IN MALAYSIA

Transcription

1 APPLICATION OF INNOVATIVE AIRBORNE LiDAR SURVEY SYSTEM FOR A HIGHWAY PROJECT IN MALAYSIA TRUDY RANJINI GANENDRA & ZULHAIMI ZAKARIA (Board of Engineers Malaysia, Institute of Engineers Malaysia) MALAYSIA ABSTRACT This Paper describes the application of an innovative airborne laser terrain survey technique, known as the DVG-Helix Survey System, in the survey for a highway project on the East coast of Malaysia. The DVG-Helix System, a total Remote Sensing system, utilizes LiDAR technology as one of its main components. The DVG-Helix System and its predecessors are custom built survey systems that have been used in Malaysia since year The level of accuracy that has been achieved by the DVG-Helix LiDAR survey system is described and compared with conventional land survey methods employed for highway projects. 1. INTRODUCTION 1.1 Preamble There are several methods of gathering ground elevations, including conventional land survey, satellite imagery, aerial photogrammetry, radar based technology and LiDAR based technology. Each has their own benefits, accuracy and costs and it is important to assess the projects requirements to ensure that the relevant quality of data is acquired at the lowest cost. For highway projects, satellite imagery and aerial photogrammetry are effective for macro and less detailed studies such as corridor identification and asset management purposes but generally not accurate enough for design. They also face problems of cloud or vegetation cover and geometric distortions obscuring the actual ground elevations. For detailed design, the most commonly used survey method is conventional land survey utilizing total stations (10). However, for medium to large scale highway projects, ground survey accuracy by conventional land survey is problematic to attain since accuracy decreases for increasing lengths of traverse line. Also, larger projects would be subject to longer survey times, higher accumulated error and higher probabilities of human error. For the project in this study, LiDAR technology was used to provide a unified and high accuracy in digital ground elevation data for a large area in a short time. The

2 LiDAR system used was the DVG-Helix system. Conventional land survey was done for a small portion of the area for a check survey. 1.2 Study Area The study area is part of the East Coast Expressway Phase2 (ECE 2), which is on the East coast of Peninsular Malaysia and links Kuantan to Kuala Terengganu. DVG- Helix surveyed the corridor of approximately 200km length and 1km width. The area consists of various terrain conditions including oil palm plantation, rubber plantation, dense forest, open areas, urban areas, flat and undulating. There are also some major urban and industry areas located within the corridor such as Jabur, Ceneh, AMBS, Paka, Bukit Besi, Dungun, Kuantan, Ajil, Kerteh, Kuala Berang and Kuala Terengganu. The land survey area and thus the area of interest for this paper is approximately 19.1km by 300m, and from Kampung Telemung to Bukit Payung, including several different terrain conditions. 1.3 Data The two types of data used in this study are: the airborne LiDAR data which was collected by Ground Data Solutions using the DVG-Helix in August 2004 and the land survey data which was collected using conventional land survey techniques by Jurukur Konsultant in March LiDAR data collected for ECE2 Project in 2004 LiDAR or Light Detection and Ranging is a laser mapping technique in which laser pulses are emitted towards surfaces and the time for their return is measured. With a Global Positioning System (GPS), an Inertial Measurement Unit (IMU) and other components, a LiDAR system is able to accurately place in 3 dimensions and time, the ground point from which the laser pulse returned. The lasers in LiDAR systems send out thousands of pulses per second so a very dense point cloud of the survey area can be produced. Then these points are processed and classified into various feature categories such as: ground, vegetation, building, etc, permitting production of a Bare Earth model of the area, feature identification and other uses. For this project, the LiDAR survey system used was DVG-Helix, a unique proprietary hardware and software system specially designed by LiDAR Services International Inc Canada and optimized for the demanding terrain and weather conditions prevalent in Malaysia. In particular, the system was helicopter mounted and had a dual laser configuration, both scanning and profiling, to ensure that actual ground levels were measured, even in areas of undulating terrain and dense vegetation. The main components of the system were: a) Scanning Laser 60 o : Riegl LMS-Q140 b) Inertial Measurement Unit (IMU) : from Germany (INAV-FMS) c) Video Camera : Sony TRV-900 d) GPS on board helicopter : Novatel DL Propak receiver e) GPS on the ground : Leica System 500 f) Profiling Laser : Infrared of wavelength (904nm)

3 The LiDAR data was acquired between 30 th August 2004 and 5 th October 2004, by the DVG-Helix system mounted on a Bell 206B helicopter, flying between km/hr at 250m above ground based on prevalent conditions. Calibration flights were conducted pre and post mission for every flight to ensure the accuracy of the data. The data was collected in terms of WGS84 (11) latitude, longitude and ellipsoidal height (12) and then transformed to Terengganu Cassini projection (14) with the vertical coordinates based on the adjusted Mean Sea Level (MSL) height as described below. The WGS84 datum was used in conjunction with the EGM96 (11) geoid model (the best available undulation model for the area at the time of the survey) to convert from ellipsoidal height to MSL height. Terengganu Cassini with Jabatan Ukur dan Pemetaan Malaysia (JUPEM) published MSL was specified by the client. The EGM96 model accounted for the long wavelength geoid undulations (13), but there were still local differences which had to be accounted for to obtain the final adjusted MSL values. To derive the adjusted MSL, 23 suitable Bench Marks (BM) along the project area were observed by Static GPS survey. Then the relationship between the JUPEM published BM values and static GPS vertical values were analyzed. The differences were caused by the differences between the EGM96 model and the JUPEM published MSL. It was assumed that applying the mean value of the differences between JUPEM published MSL values and the EGM96 values to the EGM96 values would produce a geoid surface that would closely approximate the published BM heights Land survey carried out by Jurukur Konsultant in March 2005 The land survey data used in this study was collected and processed in March 2005 by Syarikat Jurukur Konsultant. The data was collected in Terengganu Cassini projection and the vertical values were based on MSL height. Data collection was performed using 3 types of Total Stations: Geodimeter 510N, Nikon 352 & Leica 307. The linear misclosure (12) achieved for this project satisfied JUPEM second class survey requirements which are 1:4000. A total of 7 BMs were used along the route in order to get the height level for the whole route. The list of BMs used for this survey are as listed below. JUPEM BM Reference Number Table 1: Height Values for BMs used by Surveyor Height Values As Used By LiDAR in 2004 Height Values As Used By Surveyor in 2005 Height Values Purchased From JUPEM Headquarters in 1 T0095 Not Used m m 2 T1316 Not Used m m 3 T0080 Not Used m NA 4 T m m m 5 T1411 Not Used m NA 6 T0910 Not Used m NA 7 T1314 Not Used m m

4 * NA - Not available from JUPEM Headquarters when enquired between March and April It was noted that the ground survey data had problems due to the JUPEM published BMs in the project area having two different reference datums. Information as to which BM belonged to which datum was not available and additionally, some BM values have changed during this project. Please refer to Table METHODOLOGY 2.1 Direct Point Comparison There are several methods of comparing the accuracy and quality of acquired data including direct point comparison, surface comparison and grid comparison. For this study, the most common method, direct point comparison, was chosen. Surface and grid comparison was not applicable to this set of data as the land survey data was much less dense than the LiDAR data, producing surfaces of different resolution, which would be difficult to compare. For the direct point comparison, a triangulated surface model is created from the three closest laser points around each known (land surveyed) point to compute a corresponding laser elevation for each known xy location. This effectively interpolates a laser elevation from the three laser points that are closest to the examined point. 2.2 Statistical Test The land survey points and LiDAR derived points were subjected to various forms of statistical analysis, using the formulas as shown below: a) Average Where: Average = ( Z LiDAR (i) Z Land (i) ) / n ZLiDAR(i) = vertical value of the i th point in LiDAR dataset. ZLand(i) = vertical value of the i th point in land survey dataset. i is an integer from 1 to n where n = total number of points b) Root mean square, rms Where: rms = ( ( Z LiDAR (i) Z Land (i) )² / n) (ZLiDAR(i) ZLand(i)) 2 = sum of squared differences between vertical values of the 2 datasets.

5 c) Standard Deviation, σ Where: σ = ( ( Z LiDAR (i) Average )² / n) (ZLiDAR Average) 2 = sum of squared differences between LiDAR value and Average value. The American Society of Photogrammetry and Remote Sensing (ASPRS) have published a guideline called Vertical Accuracy Reporting for LiDAR Data. ASPRS specifies that the accuracy of the dataset is determined by comparing the coordinates of 20 checkpoints (30 is preferred) in a test dataset with an independent dataset of significantly greater accuracy. ASPRS suggests the fundamental for vertical test accuracy is:- Vertical Accuracy at 95 percent confidence level = * rms For this project, LiDAR accuracy was specified to be ± 1m in jungle and steep slope areas and better than ±0.5m in clear areas in the horizontal coordinates. Height values were to be accurate to within ±0.3m in ellipsoidal height (RMSE) (10) at 85% confidence level. Therefore the following was also investigated: Vertical Accuracy at 85 percent confidence level = * rms Unfortunately, due to the variance of the datum, the land survey data could not be classified as being of greater accuracy and as such, the ASPRS guideline could not be applied directly, but the concepts have been considered. 2.3 Surface comparison In order to explore the difference between the LiDAR points and the known points, surface comparison method was applied where the land survey elevations were subtracted from the derived LiDAR elevations and the difference was plotted as a surface. However, varying datum in the dataset did not allow for quantitative analysis. 3. RESULTS The map of the elevation difference between the LiDAR and land survey data showed large continuous areas where the difference stayed near constant. This continuous area constant varied between 0.875m and 0.360m but in most areas was approximately 0.6m, as can been seen from Figure 1. There were also solitary points and multiple points in miniscule areas which showed large elevation differences of the order of meters. Further analysis using the digital imagery of the area proved that these points were not ground points but features such as invert levels, culverts, sumps, buildings, head walls and road signages. However, the ground survey did not identify them as such, producing large elevation differences that were actually the height or depth of the features.

6 Figure 1: Map of the elevation difference between land survey data and LiDAR data for a portion of the study area which includes Block C. Table 2: Statistical Analysis for whole Study area and Blocks A to M Name Area (km²) Points Number Average dz (m) Root Mean Square Standard Deviation Accuracy at 85% confidence level (m) Accuracy at 95% confidence level (m) Whole , Area Block A Block B Block C Block D Block E Block F Block G Block H Block I Block J Block K Block L Block M The statistical analysis for the study area of 5.73km² and 10,928 ground points showed an average elevation difference of 0.43m between the LiDAR and land survey data, with a standard deviation (SD) of However, 13 blocks of area were found where the elevation difference stayed quite constant and these were analyzed. The blocks totaled an area of km², representing 9.23% of the land survey area, and exhibited average elevation differences between 0.634m to 0.690m with SD between to The areas cover differing terrain from flat to undulating and open to densely vegetated.

7 4. ANALYSIS The pattern of large areas at near constant elevation difference over the study area seems to indicate that the LiDAR and land survey data have similar ground surfaces but with different datums applied over different areas. The variation of the datum also can be deduced from Table 1 which elucidates how more than one datum has been used by the LiDAR and land survey data and from the method of land survey which would assure that the datum for a traverse line or area using the same benchmarks would stay the same but the next traverse line or area which used different benchmarks would have a different datum. Based on this conclusion, blocks of areas where the elevation difference did not vary greatly were chosen for the statistical analysis as it was assumed that these were areas where the land survey datum was constant. These blocks would allow for analysis of the data while removing the effect of the changing datum. Thus, while the standard deviation for the whole area was quite large, this is due in part to the variation of the datum. However, in analyzing Blocks A to M, where it was attempted to remove the varying datum error, very close agreement in the data sets is found where the average elevation difference was m with deviation of the data of between 0.054m to 0.196m at 85% confidence levels and 0.060m to 0.233m at 95% confidence levels. 5. CONCLUSION While the analysis seems to demonstrate high agreement between LiDAR and land survey data in spite the datum shifting, the evaluations were not ideal and preferably any future study should apply the ASPRS guidelines directly. Furthermore, it is essential that the LiDAR and check land survey follow the same datum and transformation model, thus it is recommended that GPS observation as well as conventional land survey is used to produce the check survey data. While past highway surveys have suffered from being required to apply the geoid models of different states, sometimes with more than one datum each, over the same road, JUPEM have now produced MyGeoid for the whole of Malaysia which should minimize these sorts of errors. Also of concern are the solitary points or groups of points which land surveyors may not indicate as features, leaving the engineer to assume them to be ground points, which is incorrect and may cause errors in analysis or design. For LiDAR data, there is a distinct demarcation between ground points and other types of points avoiding this error. However, a very significant advantage of LiDAR is how all points are measured in a consistent reference frame so that even in areas of unreliable geoid model and datum, it is possible to produce a continuous, realistic and understandable set of data of the area. Furthermore, if the geoid model and/or datum were corrected later, it is easily applied to the same LiDAR data, such that no new data acquisition is needed whereas conventional survey technique most likely requires a new survey.

8 6. REFERENCE 1. Evaluation of LIDAR Derived Surface Coordinates for Alachua County 2. Airborne Laser Swath Mapping: Accuracy Assessment for Surveying and Mapping Applications 3. Coastal & Highway Mapping by Airborne Laser Swath Mapping Technology 4. Evaluation of Airborne LiDAR Digital Terrain Mapping for Highway Corridor Planning and Design 5. Evaluation of LiDAR for Highway Planning, Location and Design 6. Terms of Reference for Survey Works and Digital Ground Modeling-Unit RekaBentuk Cawangan Jalan, Ibu Pejabat JKR 7. Wehr, A., Lohr, U., Airborne laser scanning an introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 54 (2-3), pp Baltsavias, E., 1999b. A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 54 (2-3), pp Baltsavias, E., 1999a. Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 54 (2-3), pp Comparison of LiDAR and Conventional Mapping Methods for Highway Corridor Studies Digital Elevation Model Technologies and Applications: The DEM User Manuals -David F. Maune 12. Geomatics - Barry F. Kavanagh 13. Optimizing gravimetric geoid solution A Technical Manual on the Geocentric Datum of Malaysia (GDM2000) ACKNOWLEDGEMENT We would like to take this opportunity to thank Lembaga Lebuhraya Malaysia (LLM), MTD Capital Bhd and Jurukur Konsultant for providing data and support for this study.