IJERD – International Journal of Environmental and Rural Development (2023) 14-1

Research article
erd

Research article
Verifying Optimum Flying Conditions for UAV
Photogrammetry in Assessing Hydro-structural Facilities

SARVESH MASKEY*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: sm207888@nodai.ac.jp

HIYORI ISEKI
Department of Bioproduction and Environment Engineering, Tokyo University of Agriculture,
Tokyo, JapanKISEKI KURASHINA
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, JapanKE ZHANG
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan

HIROMU OKAZAWA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Received 9 February 2023

Accepted 25 May 2023(*Corresponding Author)

Abstract Monitoring hydro-structural infrastructure is becoming an important and challenging
issue. In Japan, there are an increasing number of aging hydro-structural facilities, which need
to be inspected to guarantee their strength and durability function. Unmanned Aerial Vehicle(UAV) photogrammetric images have been used in the detection of structural deterioration ofvarious infrastructure. However, there is a challenge in determining the optimum flying
conditions (altitude, angle of camera, and flying patterns) of UAVs for obtaining images. The
designs, shapes, and sizes of hydro-structural facilities also pose a challenge for obtaining ideal
images. In this study, verification of various flying conditions for producing images and 3Dmodels was proposed for water gates and open surface water channels. Additionally, anevaluation method for the images and 3D models was suggested. In this study, UAV was usedfor visual inspection and obtaining the image of the water gate and open surface water channel
at the Tone diversion gate in Saitama, Japan. Various geometric patterns were pasted on thesurface and walls of the structures to evaluate the detection of the patterns with the images
taken from UAVs at different flying conditions. The obtained ortho-mosaic images were
processed by a PIX4D mapper to obtain a point cloud-based 3D model and were comparedwith the actual measurements of the patterns. The results of the study suggested that altitudeand camera angles affected the quality of images significantly. Cameras at lower angles
provided better images, although cameras at high altitudes with wider angles also
demonstrated a high degree of efficiency. The circular flying pattern generated satisfactory
results, where flyovers were conducted multiple times while changing altitudes and angles of
the camera. Additionally, this study proposes a method named “leveling” for the evaluation ofimages and 3D models.
Keywords UAV photogrammetry, hydro-structural assessment, 3D models, level

INTRODUCTION
In Japan, many hydro-structural facilities were constructed during the post-war period and are now
in deteriorating conditions (MAFF, 2022). The Ministry of Agriculture, Forestry and Fisheries has
begun full-scale efforts for 'stock management' to extend the service life of facilities and reduce life
cycle costs to reduce such damage and ensure safe living conditions in the future. Stock managementⒸ ISERD

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consists of continuous facility monitoring, periodic functional diagnosis, deterioration forecasting
based on this, investigation of efficient deterioration countermeasure methods, formulation offunctional maintenance plans, and implementation of appropriate countermeasure works promptly.
Among these, facility monitoring, and functional diagnosis are often time-consuming and laborintensive, such as visual inspection, percussion inspection, and surveying. However, the workforce
and inspection technicians are decreasing due to a decrease in population and aging, resulting in aserious shortage of manpower, (Tabata et al., 2018). Machines and robots are expected to play anactive role in solving these problems. Unmanned Aerial Vehicles (UAVs) can take aerial
photographs by flying at lower altitudes than manned aircraft and other vehicles that have beencommonly used with high-resolution aerial photographs (Miyazaki et al. 2018). Therefore, researchis being conducted to construct 3D models and use them for inspections with the aim of recording
information on deterioration and damage at a higher resolution from the aerial photographs taken(Saito et al., 2022). Previous studies have shown that a 3D model can be produced in detail fromaerial images by flying a UAV at low altitude and taking photographs, however, the sides of abuilding cannot be accurately modeled when the camera angle is directly below the angle of abuilding (Miyazaki et al., 2018). It has also been found that cracks and exposed rebar can be identified
from images taken by UAVs (Tabata et al., 2018). However, there lacks of optimal imaging techniquefor hydro-structural facilities by varying the imaging conditions, such as flight altitude and camera
angle.
OBJECTIVE
The objectives of this study were to verify optimum flying patterns for UAV imaging and 3Dmodeling focusing on camera altitude and angles in hydro-structural facilities assessment.
Additionally, this study tries to develop a method for quantitatively evaluating 3D models.
METHODOLOGY
Description of the Study Site
In this study, a field survey was conducted in two hydro-structural facilities of Tone Water Supply
Works of Japan Water Agency in Gyoda City, Saitama Prefecture, Japan. The water gate and open
surface water channel were the target structures for this study (Fig.1). The width and height of thewater gate were approximately 17.4 m and 7.3 m respectively. Likewise, the width and height of thechannel were 14.4 m, and 2.5 m, respectively.

Fig. 1 Water gate (left) and open surface water channel (right)
Installation of Ground Control Points (GCPs) and Real Time Kinetic Survey
Ground control points (GCPs) were set up around the facility and their positional information wasmeasured. The GCPs were surveyed using the RTK satellite positioning system (Real Time
Kinematic Global Navigation Satellite System). The RTK survey was done to increase the location
accuracy.

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Flight Conditions of UAV Survey
Small rotary-wing UAVs (Inspire 2/DJI and ANAFI/Parrot) were used for aerial photography, with
the Zenmuse X5S (RGB) camera mounted on the Inspire 2 and RGB camera on ANAFI (Fig. 2).
Inspire 2/DJI was used for the water gate, and ANAFI/ Parrot was used for the water channel due toits easy operation for a smaller area. Aerial photography was performed using a flight planningapplication (Pix4D capture/Pix4D, Pix4D scan/Pix4D) installed in UAVs. The UAV was flown indouble grid and circular flight for the water gate and grid (oblique and horizontal) for the water
channel with an overlap of 80% for both the top and side. The flight altitude was set to the lowest
altitude at which it was possible to fly, with the grid/double-grid flight pattern flown at 20 m, and thecircular flight pattern at 20 m, 30 m, and 40 m (Table 1). The camera angle was 45 , 60 and 80 for
all flight paths (Table 2).
Table 1 Flight plan parameters for the Water gate survey
Flight path
Double grid
Circular flight

Flight altitude (m)
20
20
30
40

Flight angle (°)
45, 60, 80
45, 60, 80
45, 60, 80
45, 60, 80

Table 2 Flight plan parameters for open water channel survey
Flight path
Oblique grid
Horizontal grid

Flight altitude (m)2020Flight angle (°)
60
45, 60, 80

Fig. 2 Drones used in this study
Inspire 2 (left) and ANAFI (right)

Dimensional Measurements of the Hydro-structural Facilities
Measurements of facilities were conducted for the target structures. Seven measurements were taken
for sections of the water gate and two measurements were carried for the channels, respectively. A
measuring tape was used for the measurements, and measurements were taken while pulling the tapeat each end to prevent the center from sagging.
Conversion of Aerial Image Data into 3D Models
Pix4D mapper (Pix4D) application was used to create 3D models (point clouds) from aerial images(USDA, 2022). First, aerial images were added to the Pix4D mapper software for initial processing.
In the initial processing, characteristics of geographical features were extracted as key points fromthe images added to the Pix4D mapper. Images with the same key points are then searched for andⒸ ISERD

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matched. The camera's internal and external parameters were corrected. To improve the positional
accuracy of the 3D model and the ortho mosaic image, GCP latitude, longitude, and elevation data
were added and optimized for re-estimation with respect to camera positioning information andimage distortion. Finally, point clouds were densified based on automatic tie points and comparedand evaluated from the resulting 3D models.
Evaluation of the Model
Reproducibility of 3d Models with the Actual Image
The reproducibility of the target structures in the 3D models (point clouds) created from different
flying conditions was checked. The point cloud data were compared with the aerial images to assessthe differences. The dimensions of the 3D model were measured by creating polylines in Pix4D
mapper. The polylines were created in such a way that they were close to the positions measured
during the survey; three polylines were created at one measurement site, the mean error and standard
deviation from the actual measurement were calculated, and the values were compared for eachflying condition.
Leveling of 3d Models and Actual Images
An evaluation method unique to this study was developed where three types of patterns (triangle,
circle, and square) were classified into levels from 1 to 5 (Fig. 3). The points and levels were set sothat the smaller the side length of the pattern (shown in fig. 3 by yellow line) higher the point andlevel. For the triangular pattern, point 1 was given for level 1 where the side length was 20 cm,
likewise, points and levels were assigned for each length and pattern. Additionally, the level was set
to 0 if the figure was not visible in the 3D data. These patterns were pasted on the surface of thetarget structures and aerial images were taken. Verification for actual images and 3D models was
performed with points allotted for each level. The aerial imaging conditions were evaluated bycomparing the average of the points. Furthermore, multiple regression analysis was performed toassess the influence of altitude and camera angle on the levels.
Level 1 (1 point ) : 20 cm

Level 1 (1 point ) : 10 cm

Level 1 (1 point ) : 10 cm

Level 2 (2 point) : 10 cm

Level 2 (2 point) : 6.0 cm

Level 2 (2 point) : 4.5 cm

Level 3 (3 point) : 5.0 cm

Level 3 (3 point) : 4.5 cm

Level 3 (3 point) : 3.0 cm

Level 4 (4 point) : 2.5 cm

Level 4 (4 point) : 2.5 cm

Level 4 (4 point) : 1.5 cm

Level 5 (5 point) : 1.3 cm

Level 5 (5 point) : 1.5 cm

Level 5 (5 point) : 0.7 cm

Fig. 3 Geometric patterns with dimensions and allocated points for the level
RESULTS AND DISCUSSION
Reproducibility Evaluation of 3D Models (Point Clouds)
A 3D model was created by combining aerial images obtained under various flight conditions toverify the differences and the dimensional error compared to the measured facility. The results
showed that for the double grid flight pattern, the camera angle affected the results (Fig. 3, left image).
The reproducibility was high for 45° followed by 60° angles for the pillar sections of the gate. Similar
results were obtained for circular flight patterns, where reproducibility was high for 45° followed by
60° angles (Fig. 4, right image). On the other hand, when the camera angle was at an angle of 80°,
the reproducibility increased with altitude for the upper part of the water gate indicating thatmodeling depended on the flight altitude (Fig. 5, left image). Furthermore, there was no difference
in recognizing the pattern pasted insides of the open channel for all flight conditions, however, at a
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camera angle of 80°, reproducibility was lower (Fig. 5, right image). In addition, a comparison of thedimensions of the facility measured and the dimensions of the 3D model showed that the errors were
within 10 cm for all flight methods (Table 3, Table 4, Table 5 and Table 6).

Fig. 4 3D point cloud model created from aerial images of water gate
Grid flight pattern (left) and circular flight pattern (right) at varying camera angles
at an altitude of 20 m

Fig. 5 3D point cloud model created from aerial images of a circular flight patternover a flow control
Weir at different altitudes at a camera angle of 80 ° (left image), 3D points cloud model created from aerial
images of a grid flight over an open channel at an altitude of 20 m with varying camera angles (right image)Evaluation of Level
The result of the level is shown in Tables 3 to 6. In the case of the double grid flying pattern, when
the UAV flying altitude was fixed and the camera angle varied, the level became lower as the camera
angle was moved closer to the nadir, indicating that the camera angle had a significant effect onleveling. In addition, it was understood that too many photographs are not good for the leveling, since
the level of the result of the analysis of the three patterns together was not the highest. In circularflight, the higher the flight altitude, the lower the level, indicating that the altitude of the flight also
has a significant effect.
Table 3 Level and dimensional error assessment during double grid flight over water gate
Level evaluation
Aerial image
3D modelMeasurement error (cm)Avg. error
Std. deviation

Altitude(m)Angle ( )No. of
pics

20

45

440

3.12

1.28

2.41

±3.51

20

60

314

2.81

1.20

1.67

±2.57

20

80

747

2.79

0.93

1.77

±2.38

1501

2.97

0.84

1.35

±1.85

Total

Furthermore, from the result of levels at each altitude when the camera angle was 80 °, it was
found that it was necessary to take photographs while keeping the camera angle close to the nadir.
The results of the multiple regression analysis of altitude and angle effect on the image and modellevels for the double grid and circular flight are shown in Tables 7 and 8. According to the analysis,Ⓒ ISERD

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the influence of altitude is high at both the image levels and model levels. Furthermore, at the modellevel, the angle was also found to influence the level, although not to a large scale.
Table 4 Level and dimensional error assessment of circular flights over water gate
Altitude (m)Angle ( )No. of
pics

20
20
20
30
30
30
40
40
40

45
60
80
45
60
80
45
60
80

75
78
80
78
81
80
76
78
78

Level evaluation
3D
Aerial image
model
2.65
0.90
2.60
0.98
1.66
0.56
1.88
0.90
1.99
0.79
1.87
0.67
1.39
0.68
1.36
0.67
1.28
0.74

Measurement error (cm)Avg. error

Std. deviation

2.05
1.82
1.60
3.51
2.48
2.22
2.77
3.50
1.98

±3.01
±2.24
±3.63
±4.71
±3.69
±3.14
±3.54
±4.53
±2.77

Table 5 Level and dimensional error assessment for each combined circularly flown aerial
image of water gate
Combination flight pattern
Circular
Circularflightflight
20 m 45
30 m 60
20 m 45
30 m 80
20 m 45
40 m 60
20 m 45
40 m 80
20 m 60
30 m 80
20 m 60
40 m 80
30 m 45
40 m 60
30 m 45
40 m 80
30 m 60
40 m 80
20 m 45 + 30 m 60 +40 m 80
Total

No. of
pics
156
155
153
153
158
156
156
156
159

Level evaluation
3D
Aerial image
Model
2.70
1.28
2.77
1.30
2.83
1.24
2.77
1.18
2.79
1.26
2.74
1.24
2.07
0.90
2.04
0.93
2.29
0.89

Measurement error (cm)Avg. error

Std. deviation

1.94
1.44
1.73
1.20
1.63
1.65
1.37
2.44
1.53

±2.59
±2.18
±2.25
±2.07
±2.21
±2.61
±1.76
±2.85
±1.88

234

2.79

1.21

1.87

±2.86

704

2.64

0.98

1.70

±2.10

Table 6 Level and dimensional error assessment during grid flying overan open water channel
Direction
Oblique 1
Oblique 2
Oblique
1and 2
Horizontal
Horizontal
Horizontal

Level evaluation
Aerial image
3D model
3.08
2.00
3.03
1.96

Measurement error (cm)Avg. error
Std. deviation
2.33
±2.94
3.17
±1.17

Altitude
(m）

Angle
(°)

No. of
image

20
20

60
60

258
240

20

60

498

3.05

2.06

2.00

±1.26

20
20
20

45
60
80

158
330
126

2.68
3.18
2.37

1.23
1.82
1.18

4.33
4.83
3.00

±3.50
±5.32
±5.40

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Table 7 Multiple regression analysis of level (Images)
Altitude
Camera angle

Coefficient-0.0636-0.0105

Std. deviation
0.01150.0067t-5.5186-1.5765

p-value***t-2.4277-1.8443p-value***p < 0.01 *** p < 0.05 ** p < 0.1 *Table 8 Multiple regression analysis of level (3D model)
Altitude
Camera angle

Coefficient-0.0144-0.0063

Std. deviation
0.00590.0034p < 0.01 *** p < 0.05 ** p < 0.1 *CONCLUSION
In this study, the optimum UAV flying conditions for UAV imaging were investigated for water gate
and open surface channels. The aerial imaging conditions were compared using three methods:
reproducibility of the 3D model, comparison of the measured dimensions of the facility and thedimensions of the 3D model, and an original evaluation method called 'leveling', to investigate themost suitable flight pattern. According to the results of this study, the following conclusions were
made: (i) Images obtained by circular flight patterns with various combinations were effective forthe water gate. (ii) For open surface water channels, the camera angle should not be close to the nadir.
(iii) For quantitively assessing aerial imaging, figure leveling is useful. (iv) For planning UAV
imaging assessment of hydro-structural facilities, the altitude of the UAV should be set followed by
the angle of the camera. However, the results of this study showed deficiencies in modeling the upper
part of the columns and shadowy areas, and the upper part of the columns from aerial images. In the
future, it is desirable to analyze images taken from the ground to compensate for areas such as thetops of columns, which are difficult to take images by automatic UAV flight. In addition, as hydrostructural facilities are in different shapes and sizes, it is necessary to conduct more similar studies.
ACKNOWLEDGEMENT
The authors like to acknowledge Tone Water Supply Works of Japan Water Agency for assistingand providing guidance for the survey.
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