LIGHT WEIGHT ROTARY-WING UAV FOR LARGE … Aniqah Mohd Azhar.pdf2.0 Application UAV in mapping – An Experimental The simulation was developed with three models which comprises of

Asia Geospatial Forum, 24-26 September 2013, Kuala Lumpur, Malaysia

LIGHT WEIGHT ROTARY-WING UAV FOR LARGE SCALE MAPPING

APPLICATIONS

1Norhadija Darwin,

1Nurul Farhah Abdul Hamid,

2Wani Sofia Udin

1Noor Aniqah Binti Mohd

Azhar & 3Anuar Ahmad

1Department of Geoinformation,

Faculty of Geoinformation & Real Estate

Universiti Teknologi Malaysia,

81310 Skudai, Johor, MALAYSIA

2Institute for Science and Technology Geospatial (INSTEG),




3Institute for Science and Technology Geospatial (INSTEG),




ABSTRACT

This paper aims to demonstrate the potential use of unmanned aerial vehicle (UAV) system attached with

calibrated high resolution digital based on a simulation model. In this study, a strip of aerial images was

captured using a calibrated high resolution compact digital camera known as Canon Power Shot SX230

HS and it has 12 megapixel image resolutions. The digital camera was calibrated in the laboratory and

field. For laboratory calibration, a 3D test field in form of calibration plate was used. The dimension of

the calibration plate is 0.4m x 0.4m and consists of 36 grid targets at different heights. For field

calibration, a 3D test field has been constructed which comprise of 81 target points at different heights

and located on a flat ground with dimension of 9m x 9m. The light weight UAV can be used in various

applications such as coastal, archeological and meandering. The UAV equipped with an autopilot system

and automatic method known as autonomous flying, can be utilized for rapid and low cost data

acquisition. In this study, the UAV system has been employed to acquire aerial images of a simulation

model at low altitude. From the aerial images, photogrammetric image processing method is completed to

produce mapping outputs such a digital terrain model (DTM), contour line and orthophoto. In term of the

accuracy, of measurement, a milimeter-level is reached by ground control point (GCP) and check point

(CP) using conventional ground surveying method (i.e total station). It will anticipate that the UAV will

be used for surveying and guideline with good accuracy. Finally, the UAV has shown great potential and

produce accurate results or products using high resolution camera calibration.

Keywords: UAV; high resolution; environmental survey


1.0 Introduction

Basically, there are several methods in geoinformation that can be used to map the environmental sites

such as aerial photogrammetry, remote sensing, LIDAR (Light Detection and Ranging), GPS (Global

Positioning system), TLS (Terrestrial Laser Scanning) and total station. The geoinformation technology

could also be used in environmental survey and could assist the developers for societal impact in the

developing country. The remote sensing and aerial photogrammetry is widely used for mapping

environmental sites. For remote sensing, with the existing of high resolution satellite imagery such as

Ikonos, QuickBird and WorldView 2, it can be used for environmental survey. On the other hand, the

development of remote sensing technology where the satellites can capture high-resolution imagery with

the capability of producing stereo imagery using IKONOS satellite images (Li et al., 2004).

However, there are some limitations or draw back for these methods. The problem related to this

technology is the difficulties of possessing clear image of the study area. According to Biesemans et al.,

(2005) and Everaercts (2008), the limitation of satellites and manned aircraft are flight costs, slow and

weather-dependent data collection, limited availability, limited flying time, low ground resolution. In

aerial photogrammetry, the aircraft can be flown under the cloud and imagery can be obtained much

easier than satellite imagery.

There are few ways to produce map by photogrammetric technique which are by Analog Photogrammetry

(from about 1900 to 1960) and Analytical Photogrammetry (1960 until present). However, the presence of

Digital photogrammetry in the photogrammetric industry has revolutionized the industry. Nowadays,

most countries in the world have produced their topographic map using aerial photogrammetry. Recently,

digital photogrammetry has embraced UAV technology known as UAV photogrammetry. According to

Eisenbeiss (2009), UAV photogrammetry can be understood as a new photogrammetric measurement

tool. UAV photogrammetry opens various new applications in the close range domain, combining aerial

and terrestrial photogrammetry, and also introduces low-cost alternatives to the classical manned aerial

photogrammetry. UAVs have been under development since the beginning of flight. For the first

development of UAV, it was introduced for military purpose only. According to Pardesi (2005), during

World War I, the US decided to make a contribution in the novel area of the flying bomb. The most

important development for unmanned aviation during the interwar years was radio control.

UAV system has been used to produce digital map and orthophoto of UTM Johor Bahru (Anuar Ahmad,

2011; Anuar Ahmad & Wan Aziz Wan Mohd Akib, 2010; Anuar Ahmad, 2009a, 2009b). In the study

carried out, fixed wing UAV was used to acquire the digital aerial photograph at low altitude of

approximately 300m. The output of the study showed that the digital map was produced at large scale and

accurate. Therefore, UAV system has expanded data capture opportunities for photogrammetry

techniques. Usually, the UAV system uses the concepts of close range photogrammetry (CRP). In CRP,

the photography is acquired where the object-to-camera distance is less than 300m (Cooper and Robson,

1996; Wolf and Dewitt, 2000). Moreover, Baoping et al., (2008) stated that numerous UAV had been

developed by organization or individual worldwide including a complete set of UAV which used high

quality fibers as material for plane model. The development of this technology is very beneficial for

monitoring purpose for limited time and budget. It is supported that UAV has been practiced in many

applications such as farming, surveillance, road maintenance, recording and documentation of cultural

heritage (Bryson and Sukkarieh, 2009).

In this study, two main hardwares are used which comprise of light weight rotary-wing UAV and high

resolution digital camera. Low altitude UAV is preferable in this study because it focuses on simulation

model which covered small area only. The compact digital camera provides small format images. Figure

1 show examples of UAV known as Hexacopter and compact digital camera used in this study.


In this study Canon Power Shot SX3 digital camera has been used in acquiring simulation model images.

This digital camera has 14x optical zoom lens and 2.0” LCD screen. Table 1 depicts the compact digital

camera specification.

Table 1: Canon PowerShot XS230 HS digital camera specifications

Specification

Maximum Resolution 4000 x 3000 pixels

Effective pixels 12.10 megapixels

Lens 14.00x zoom, f3.1-5.9, 28-392mm (35mm equivalent)

LCD size 3”

Sensor size 1/2..3”, 460K dots/None

Sensor type CCD

Dimensions 4.2 x 2.4 x 1.3 in. (106 x 62 x 33 mm)

Weight (Body) 218g includes batteries

Shutter 15-1/3200

ISO 100-3200

Memory type SD/SDHC

File formats JPEG (conforms to Exif 2.2), conforms to DCF2.0, DPOF,

PRINT Image Matching III, AVI (Motion JPEG), with

WAV (PCM), mono

In this study, micro UAV known as Hexacopter (Figure 1) has been used in acquiring images of the

simulation model. The specification of the rotary wing used in this study is shown in Table 2.

Table 2: Hexacopter Specification

Specification

Weight 1.2kg

Rotor 6 rotor

Endurance Up to 36 minutes

Payload 1kg

GPS on board Yes

Special function Automatically return to home location (1st point)

Stabilizer Inbuilt stabilizer to deal with wind correction

Capture data Using software to reached waypoints

Flight control Manual and autonomous

Camera stand Flexible camera holder

a b

Figure 1: (a) Hexacopter; (b) Digital Camera


2.0 Application UAV in mapping – An Experimental

The simulation was developed with three models which comprises of simulation of coastal area,

archaeological site and meandering flume using high calibrated digital camera. In this study, a

rotary wing UAV was employed to acquire aerial images of a simulation model of environmental

area. The dimension of this simulated model is 2.4m x 7.2m for coastal area and 2.4m x 3.5m for

archaeological site. The dimension of meandering flume is 12.0 × 3.0m and the channel width is

0.5m. This laboratory flow channel was attempted to replicate physical structures such as

meandering streams found in the real world. The following section discusses the experimental

conducted in this study.

2.1 Model of Coastal Area

Digital images were acquired using the Canon PowerShot SX3 HS digital camera. It has wide angle lens

for acquiring of aerial photography at the height of 3 meter. The scale of simulation model is 1:500 for

dimensions 2.4m x 7.2m. There are 37 ground control points (GCPs) and 7 check points (CPs). Figure 1

depicts the simulation model of coastal area.

Figure 2: Simulation Model of Coastal Area

2.2 Model of Archaeological Site

The dimension of the archaeological site simulation model is 2.4m x 3.5m. It consists of sand, some

broken porcelains, and some artificial bones and 35 GCPs and 6 CPs for ground control point.


Figure 3: Simulation Model of Archeological Site

2.3 Model of Riverbed Topography

A laboratory flow channel or meandering flume is located at the Universiti Teknologi Malaysia.

The dimension of this flume is 12.0 × 3.0m and the channel width is 0.5m. Photogrammetric

control targets were established on the flood plain and inside the channel bed. Ninety (90) GCPs

were registered as a full control (XYZ) and 31 check points (CPs) were established evenly in the

channel bed. The targets must be placed on the meandering flume and maintained until image

acquisition is completed. The distribution of the GCPs is flexible and they need to be seen on a

pair of photograph (i.e stereopair) at known location. Figure 3 represents where the control

points were placed throughout the area of interest. These targets were 10 mm in diameter and of

red and black design

Figure 4: Simulation Model of Riverbed Topography


3.0 Methodology

Figure 5: Flowchart of Research Methodology


3.1 Flight Planning

Since the development UAV is utilizing autonomous flight including two main stations known as ground

control station (GCS) and pilot station (PS). Generally, flight planning shows a flight map which consists

of waypoints on a topographic map showing the starting and ending points of each line. Flight planning

encompasses calculation of boundary of the study area, number of strips required, pixel size, photo scale,

flying height and percentage of end lap and side lap. The aerial photographs should overlap at least 60

percent and side lap at least 30 percent. Before any observation starts, first need is to properly install the

instruments. There are few important components of the system in autonomous fly that should be

installed as shown in Figure 6.

Figure 6: Instrument Installation Diagram

3.2 Data Acquisition

The aerial images acquisition is carried out using the hexacopter. In this experiment, for the

hexacopter was equipped with a Canon PowerShot SX 230 HS digital camera in acquiring the simulation

model images. The digital camera was used to acquire photographs of the simulation model at a constant

distance of 3 meter from the model. The hexacopter uses the autonomous flight control system and

controlled by two operators where one act as pilot on ground and the other in charge of monitoring flight

mission at ground station. The establishment of ground control point (GCP) and check point (CP) was

performed after the acquisition of aerial image. The 3D coordinates of these GCPs and CPs were

determined using total station. Figure 7 represents how the light weight rotary wing UAV and Ground

Control Station were carried out.


3.3 Digital Camera Calibration

Digital camera calibration becomes essential to achieve the precision of the measurement task.

However, when talk about non-metric digital camera, there are consideration must be aware the internal

especially geometry camera instability. Camera parameters usually could be recovered through camera

calibration process which comprised of focal length (c), principal point offset (xp, yp) which represent the

coordinates of the center of the image, radial lens distortion (k1, k2, k3), tangential lens distortion (p1, p2)

and others. Nowadays, many camera calibration approaches are presented. However, in this study the

automatic self-calibration bundle adjustment are adopted.

In this study, two camera calibration methods are used. The first method is a lab calibration which

comprise of 3D calibration plate with a dimension of 0.4 x 0.4 meter. A bar scale of length 553 mm is

used too. A second camera calibration is performed on the field where the 3D test field was used with a

dimension of 9 x 9 meter. This test field comprised of 81 wooden pegs located into the ground as

illustrated as Figure 8. The size of each wooden peg is 2 x 3 inches. All the wooden pegs are at different

height and the 3D coordinates of these wooden pegs were determined using close traverse. Three scale

Aircraft accordance

to waypoint &

within 5 seconds

captured the images

Ground Control

Station (GCS)

Aircraft is flying

Control by

Connect both

Radio Controller

used by Pilot Landing

Set Coming

Home (CH)

Communication

Set by Flight

Planning

Net

Figure 7: Cropcam UAV and Ground Control Station


bars were used where the length of 332 mm, 582 mm, and 1178 mm. Calibration site was built near the

Faculty Geoinformation and Real Estate (FGRE), Universiti Teknologi Malaysia.

Figure 8: Three dimensional test field 9 x 9 meter.

3.3.1 Laboratory Calibration

Three digital cameras was setup at different configurations (convergent, generic network and

stereo) and different heights at 80cm, 100cm and 120cm. All camera configurations, images of calibration

plate were taken at each position in landscape position of 0 degree and in portrait position of 90 degrees.

The light rays from the camera station are pointing towards center of calibration plate. For convergent and

stereo configuration, eight images were taken from four stations around the calibration plate while for

generic network configuration sixteen images were taken from eight stations around the calibration plate

at different height per dataset as illustrated as Figure 9.

After images of the calibration plate were acquired, these images were downloaded into a

computer for data processing and analyzed using Australis software. As standard procedure of camera

calibration, the results comprised of eight camera calibration parameters which include focal length (c),

principal point (xp, yp), radial distortions (k1, k2, k3) and tangential distortions (p1, p2).


Figure 9: Image acquisition from eight camera station.

3.3.2 Field Calibration

Field camera calibration site is located near the Faculty of Geoinformation and Real Estate

(FGRE), Universiti Teknologi Malaysia (UTM) as shown as Figure 10. For the field calibration, a test

field area with dimension of 9 x 9 m and 81 target points was established. The 3D coordinates of each

target of the test field was determined based on close traverse around the test field using total station.

Image acquisition is divided into two parts based on convergent and stereo configuration. For the

convergent case, the UAV was flown at the height of approximately 5 m while for the flying height is

stereo 20 m.

Figure 10: Field calibration site (red box) located near FGRE building.

9 m

9 m


The UAV was flown manually, due to the test field is near to the building. A total of 32 images

for camera configuration in convergent and 28 images of the stereo camera configuration were acquired.

Eight images per camera configuration were chosen for image processing. The field calibration process

was performed similar to laboratory calibration using Australis software.

3.4 Preliminary Results and Analysis for Digital Camera Calibration

In this section, the results of measurement for different camera configuration setup, camera

elevation and different calibration methods which are laboratory calibration and field calibration using

Canon PowerShot SX230 HS are briefly discussed. After the image processing, camera calibration

parameters were obtained from the camera calibration software which utilizes self-calibration bundle

adjustment. The results for the different camera setup, different camera elevation and different methods

are tabulated in the following sections.

3.4.1 Camera Configuration Setup versus Camera Elevation

Table 3, 4 and 5 show the mean and standard deviation for the camera calibration parameters for

the three camera configurations and three camera elevations respectively. The results of camera

calibration which utilized camera configuration setup at the position of 80 cm height (Table 3) showed

that the lowest standard deviation for focal length is ±0.00567mm achieved by the generic network

configuration. While the lowest and best standard deviation of xp and yp, is ±0.005612mm and

±0.005497mm respectively for generic network configuration.

For the rest of camera calibration parameters, the standard deviations are very small and close to

one another. For the case of stereo camera configuration for every camera elevation, the photogrammetric

calibration software failed to process the image due to weak geometry which means the results depend on

the configuration position of the camera and the angle between the cameras. The smaller the angle, the

less will be the accuracy of the result. On the other hand, for aerial photogrammetry normally height-base

ratio A/B, is employed. Based on this configuration, the higher accuracy could be achieved when the

intersection angle is near 90˚ and also other constraints must be considered.

Table 3. Camera calibration parameters for camera configuration setup at 80 cm height.

Camera

Calibration

Parameters

Camera Configuration Setup at 80cm Elevation

Convergent

(Mean) Std. Dev.

Generic

Network

(Mean)

Std. Dev. Stereo

(Mean) Std. Dev.

c (mm) 5.105 ± 0.015185 5.099660 0.005668 Failed Failed

xp (mm) -0.040 ± 0.008704 -0.040620 0.005612 Failed Failed

yp (mm) -0.019 ± 0.011944 -0.023320 0.005497 Failed Failed

k1 0.002 ± 0.000310 0.001795 0.000264 Failed Failed

k2 0.000 ± 0.000224 -0.000179 0.000390 Failed Failed


k3 0.000 ± 0.000031 0.000030 0.000248 Failed Failed

p1 0.001 ± 0.000115 0.000500 0.000041 Failed Failed

p2 0.001 ± 0.000151 0.000615 0.000065 Failed Failed


Camera

Calibration

Parameters

Camera Configuration Setup at 100cm Elevation

Convergent

(Mean) Std. Dev.

Generic

Network

(Mean)

Std. Dev. Stereo

(Mean) Std. Dev.

c (mm) 5.096 ± 0.006751 5.099 ± 0.006838 Failed Failed

xp (mm) -0.055 ± 0.005864 -0.044 ± 0.003905 Failed Failed

yp (mm) -0.026 ± 0.006116 -0.022 ± 0.004072 Failed Failed

k1 0.002 ± 0.001103 0.002 ± 0.000264 Failed Failed

k2 0.000 ± 0.001157 0.000 ± 0.000175 Failed Failed

k3 0.000 ± 0.000350 0.000 ± 0.000029 Failed Failed

p1 0.000 ± 0.000065 0.001 ± 0.000052 Failed Failed

p2 0.001 ± 0.000095 0.001 ± 0.000041 Failed Failed

The results of camera calibration which utilizes camera configuration setup at the position of

100cm height are shown in Table 4. In this table, the lowest standard deviation for focal length is

±0.006751mm achieved by the convergent configuration. While for the principal point offset coordinates

xp and yp, is ±0.003905mm and ±0.004072mm respectively for generic network configuration. For the

remaining results of camera calibration, the differences in standard deviation are small.

In Table 5 shows the results of camera calibration which utilizes camera configuration setup at

the position of 120cm height. The performance of generic network configuration is still better than other

camera configuration where the standard deviation for focal length is ±0.006694mm. While, for the

principal point offset coordinates xp and yp is ±0.011368mm and ±0.004021mm respectively which are

better than convergent configuration. Once again the camera calibration results indicate that generic

network configuration is the most efficient camera configuration for camera calibration.


Camera

Calibration

Parameters

Camera Configuration Setup at 120 cm Elevation

Convergent

(Mean) Std. Dev.

Generic

Network

(Mean)

Std. Dev. Stereo

(Mean)

Std.

Dev.

c (mm) 5.107 ± 0.011803 5.105 ± 0.006694 Failed Failed

xp (mm) -0.046 ± 0.016438 -0.046 ± 0.011368 Failed Failed

yp (mm) -0.008 ± 0.007599 -0.009 ± 0.004021 Failed Failed

k1 0.002 ± 0.000430 0.002 ± 0.000180 Failed Failed

k2 0.000 ± 0.000280 0.000 ± 0.000182 Failed Failed


k3 0.000 ± 0.000113 0.000 ± 0.000051 Failed Failed

p1 0.001 ± 0.000141 0.001 ± 0.000111 Failed Failed

p2 0.000 ± 0.000112 0.000 ± 0.000051 Failed Failed

3.5.2 Laboratory Calibration versus Field Calibration

Table 6 shows the camera calibration parameters and standard deviation for laboratory calibration

and field calibration. The results of the laboratory calibration and field calibration showed slight

difference of standard deviation for both methods. Similarly, for radial lens distortion and tangential lens

distortion the standard deviation for both methods showed slight difference. Based on the results of both

methods, the field camera calibration method is reliable and significant in calibrating non-metric digital

camera.

Table 6. Camera calibration parameters for laboratory calibration and field calibration

Camera

Calibration

Parameter

Two Camera Calibration Methods

Lab Calibration Standard

Deviation Field Calibration

Standard

Deviation

c (mm) 5.095 ± 6.894e-003 5.116 ± 1.066e-002

xp (mm) -0.035 ± 5.997e-003 -0.028 ± 9.176e-003

yp (mm) -0.020 ± 5.470e-003 -0.026 ± 1.317e-002

k1 0.002 ± 4.666e-004 0.001 ± 9.189e-005

k2 -0.001 ± 4.164e-004 0.000 ± 1.850e-005

k3 0.000 ± 1.040e-004 0.000 ± 1.145e-006

p1 0.000 ± 9.747e-005 0.001 ± 9.641e-005

p2 0.001 ± 1.295e-004 0.001 ± 8.528e-005

In photogrammetric application especially for close range photogrammetry, both convergent and

generic network configurations are widely used. In general, it is found that the standard deviation of focal

length improve well as the height increases. For the other camera calibration parameters, the standard

deviations are very small, minimum and close to zero value. For generic network configuration, it

produces better result compared to convergent configuration with reference to the standard deviation of

focal length as shown in Table 5. The results also showed that as the height of the camera increases the

standard deviation decreases as shown in Table 3, 4 and 5. For stereo configuration, the result showed that

this configuration are not suitable for camera calibration.

In this study, it is clearly shown that the field calibration has the advantage that the images were

taken under similar conditions to the images taken using UAV. That has proved be very efficient and

provides accurate results for the purpose of camera calibration. Finally, the field calibration can be

employed for obtaining good measurement and results.


4.0 Image Processing and Results

All images were processed using digital photogrammetric software. The process comprises of interior

orientation, which requires the camera calibration parameter (Table 5) and exterior orientation which

require the registration of GCPs and auto generation of tie points. The flow chart provides clear picture on

how the processing was performed (Figure 11).

Figure 11: Process of performing Aerial Triangulation

Each pair of photographs has 60 percent overlapped and eight (8) photographs for coastal area

and two (2) photographs for archeological site. Based on the image processing, there are 267

points, which consists of 37 GCPs and 230 tie points for simulation of coastal area. While for

simulation model of archeological site, there are 57 points which consists of 35 GCPs and 22 tie

points. After performing Aerial Triangulation (AT), the footprint of the AT can be displayed. The

foot prints of the digital photographs showing the location and names of all points (i.e control

points, check points and tie points) that participated in the adjustment. The distribution of GCP

and CP for the digital camera in riverbed topography can be viewed in Figure 12, Figure 13 and

Figure 14 respectively.

Figure 12: Footprint for simulation of coastal

area

Figure 13: Footprint for simulation of

archaeological site


Figure 14: Footprint for Simulation of Riverbed topography

There were two main results were produced i.e. DTM and orthophoto. The generated digital

orthophoto for the simulated model for coastal area, archaeological site and meandering flume

are shown in Figure 15, 16 and 17 respectively. The quality of orthophoto depends on the high

resolution camera calibration and quality of GCPs produced.

Figure 15: Orthophoto for Simulation of Coastal Area

Figure 16: Orthphoto for Simulation of Archeological Site


Figure 17: Orthophoto for Simulation of Riverbed topography

5.0 Analysis and Discussion

Nowadays with the development of digital camera, analysis can be carried out for the small format digital

camera. A small format photograph from digital camera has the potential to be used in aerial

photogrammetry and analysis can be carried out for the product of aerial photogrammetry such as

orthophoto, DTM, contour line and digital map.

For point analysis, the RMSE was below one (1) meter that indicates the orthophoto has sub-meter

accuracy. The smaller the RMSE, the better orthophoto could be produced. It can be concluded that the

higher the GCPs was, the better the RMSE. Table 7 shows the comparison of check points between

coordinates from ground survey (i.e. total station) and coordinates obtained from ERDAS Imagine for

coastal area, where the calculated RMSE is ± 0.004, ± 0.006 and ± 0.002 meter (<1 meter) for coordinate

x, y and z respectively.

Table 7: Comparison check points for coastal area

Check

Points

Total Station Erdas Imagine Software Differences

X Y Z X Y Z ΔX ΔY ΔZ

1039 10013.262 9993.894 20.155 10013.284 9993.909 20.130 0.022 0.015 -0.025

1048 10013.017 9995.136 20.108 10012.995 9995.121 20.133 -0.023 -0.015 0.025

1050 10011.993 9994.318 20.183 10012.016 9994.338 20.169 0.023 0.020 -0.014

1051 10011.835 9994.314 20.157 10011.847 9994.329 20.137 0.012 0.015 -0.020

1055 10011.188 9995.444 19.972 10011.174 9995.453 20.001 -0.014 0.009 0.029

1026 10010.346 9994.063 20.086 10010.330 9994.044 20.061 -0.016 -0.019 -0.025

1061 10009.942 9996.762 19.847 10009.965 9996.777 19.862 0.023 0.015 0.015

RMSE ±0.004 ±0.006 ±0.002


From Table 8 it shows that the RMSE of differences in coordinates from the image processing software

and total station were less than 1.0 for archeological site which indicate the good results were achieved in

this simulation model.

Table 8: Comparison of check points for Archeological Site

Check

Points

Erdas Imagine Total station Diff. in Coordinates

X(m)

Y(m)

Z(m)

X(m)

Y(m)

Z(m)

∆X(m)

∆Y(m)

∆Z(m)

C01 10010.687 9998.507 20.275 10010.783 9998.575 20.332 -0.096 -0.068 -0.057

C02 10009.475 9999.649 20.190 10009.559 9999.707 20.278 -0.084 -0.058 -0.088

C03 10009.719 9997.949 20.197 10009.782 9997.970 20.262 -0.063 -0.021 -0.065

C04 10010.130 9998.232 20.246 10010.188 9998.319 20.319 -0.058 -0.087 -0.073

C05 10008.896 9999.169 20.154 10008.953 9999.266 20.213 -0.057 -0.097 -0.059

C06 10009.937 9999.477 20.199 10009.962 9999.533 20.314 -0.025 -0.056 -0.115

RMSE 0.171 0.173 0.204

The accuracy of orthophoto planimetry and vertical of meandering flume is shown in Table 9. In

planimetry accuracy, a sub-meter ±0.049m and ±0.025m were obtained for X and Y coordinates

respectively. Meanwhile the RMSE for Z coordinates is ±0.091. For average RMSE, ±0.055m

was obtained by averaging the planimetry and vertical RMSE of small format digital imagery

orthophoto of riverbed.

Table 9: Comparison check points for riverbed topography

Aerial

Triangulation

RMSE (m)

X(m) Y(m) Z(m)

31 Check Points ±0.049 ±0.025 ±0.091

Mean ±0.055m

6. Conclusion

In conclusion, this study found that using the high resolution digital camera for environmental survey

application can be used where it showed that the digital camera must be calibrated for obtaining accurate

measurement or results. The best method of calibration depends on the type of applications. Practically,

for most applications the digital camera is calibrated on site, hence, laboratory and field calibration is the

reliable and useful method of calibration and could be employed for obtaining accurate measurement. It is

also proven that the light weight rotary-wing UAV was successfully used for capturing the images of the

simulated model for mapping applications. From these experiment it have shown that this UAV-system is

easy to handle, can cover small areas, able to fly within the predicted flight path, can fly closer to the

object and thus the resolution can be increased dramatically. Moreover, it offers great advantages in

inaccessible and dangerous areas which involved archaeological site recording for documentation

purposed and also for the monitoring of coastal erosion. Recently, this UAV-system have shown the

capability in photogrammetry especially for close range applications with the low-cost budget for rapid

data acquisition.


Acknowledgement

Faculty of Geoinformation & Real Estate, Universiti Teknologi Malaysia (UTM) is greatly

acknowledged. The authors also would like to thank the Sustainability Research Alliance, Universiti

Teknologi Malaysia for providing the fund to enable this study is carried out.

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