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Article

Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia

by
Muhammad Yudhi Rezaldi
1,*,
Ambar Yoganingrum
1,
Nuraini Rahma Hanifa
2,
Yoshiyuki Kaneda
3,
Siti Kania Kushadiani
1,
Abdurrakhman Prasetyadi
1,
Budi Nugroho
1 and
Agus Men Riyanto
2
1
Research Center for Informatics, National Research and Innovation Agency, Bandung 40135, West Java, Indonesia
2
Research Center for Geotechnology, National Research and Innovation Agency, Bandung 40135, West Java, Indonesia
3
Institute of Education, Research and Regional Corporation for Crisis Management Shikoku (IECMS), Kagawa University, Takamatsu 760-0016, Kagawa, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(23), 11310; https://doi.org/10.3390/app112311310
Submission received: 29 October 2021 / Revised: 22 November 2021 / Accepted: 25 November 2021 / Published: 29 November 2021
(This article belongs to the Special Issue Advanced Measures for Earthquake and Tsunami Disaster Mitigation)
Browse Figures
Review Reports Versions Notes

Abstract

:
Three-dimensional (3D) modeling of tsunami events is intended to promote tsunami safety. However, the developed 3D modeling methods based on Computational Fluid Dynamics and photorealistic particle visualization have some weaknesses, such as not being similar to the original environment, not measuring the wave’s end point, and low image accuracy. The method for 3D modeling of tsunamis that results from this research can fulfil those weaknesses because it has advantages, such as being able to predict the end point of waves, similar to the original environment, and the height and area of inundation. In addition, the method produces more detailed and sharper spatial data. Modeling in this research is conducted using Agisoft Metashape Professional software to a produce 3D orthomosaic from pictures taken with Unmanned Aerial Vehicle (UAV) technique or drone (photogrammetry), and 3ds max software is used for wave simulation. We take a sample of an area in Cilacap, Indonesia that was impacted by the 2006 southwest coast tsunamis and may be vulnerable to future big megathrust earthquakes and tsunamis. The results could be used to provide several benefits, such as the creation of evacuation routes and the determination of appropriate locations for building shelters.

1. Introduction

Inculcating knowledge and building awareness to prepare people to understand the dangers of tsunamis and deal with such disasters in the future are still issues today. Lessons learned from the 2011 tsunamis in Tohoku, Japan are that past understanding of tsunamis and the provision of continuous tsunami education may be effective ways to promote safe and reliable disaster management (e.g., [1]). Therefore, it is essential to inform the public about the possibility of large tsunami waves beyond those that occurred before 2011 [2]. It has been reported that people who live in earthquake and tsunami-prone areas, such as Lampung Bay, Indonesia [3] and the port city of Valparaíso, Chile [4], have insufficient knowledge and awareness about earthquakes and tsunamis. In fact, the Lampung Bay area, Indonesia is prone to tectonic and volcanic activity from Mount Anak Krakatau. The region experienced a tectonic earthquake that caused a tsunami in 2018 [5], and the mighty eruption of Krakatoa caused a tsunami in 1883 [6]. Meanwhile, the port city of Valparaíso, Chile, experienced four major earthquakes in the 18th century and two major earthquakes that triggered tsunamis in 2010 and 2015. Likewise, it has been reported that people on the southern coast of Makran, Iran have low levels of awareness and are reluctant to move out of the area. In 1945, a tsunami struck the area where they live [7]. Furthermore, individuals who live in regions where a tsunami has not occurred, such as the beaches of Kamakura, Japan, and different coastal cities in Florida, USA, may have low levels of awareness. The city of Kamakura experienced tsunamis in the years 1241, 1495, 1703, and 1923. Florida has several potential seismic and volcanic resources in the Caribbean, a sea that has created tsunamis in the past. Although residents and visitors in those areas have a relatively high level of awareness, their knowledge about evacuation is still lacking [8].
Three-dimensional modeling is considered to have several advantages. Sugawara (2018) showed that 3D modeling is able to simulate the details, such as the vertical movement of water and soil scouring behind it [9]. Likewise, Marras & Mandli (2021) showed the three-dimensional nature of flow approaching the coast and moving inland and demonstrated that it is important to simulate its turbulent nature. Moreover, the 3D model with the Reynolds-averaged Navier-Stokes (RANS) equation simulates tsunami inundation better than the two-dimensional model with depth-averaged flow [10,11]. In order to convey information to the general public, 3D modeling developed based on photorealistic visualization and animations is more easily understood by society [12,13]. A photorealistic visualization and 3D animation method were developed to allow the public to understand the tsunami phenomenon intuitively. However, the spatial resolution of the particles and flooded area method used in this model are not sufficient due to cost and time limitations as well as the available photos being out-of-date [12]. The weaknesses of the photorealistic visualization method were solved by developing the Unmanned Aerial Vehicle (UAV) approach and photogrammetry technique [14].
Unmanned Aerial Vehicle (UAV) platforms and photogrammetry techniques have been utilized for disaster management. These approaches have been used for investigation and information gathering in post-disaster areas for rescue and the development of emergency management [15,16,17,18,19]. Regarding tsunami disasters, UAV photogrammetric methods have been applied to analyze dislocations and boulder deposits as a sign of high-energy waves on the coast, which may indicate the presence of tsunamis [20,21,22]. Moreover, [23] argued that the tsunami inundation model made by the “Digital Elevation Model’’ (DEM)-UAV is much more reliable than the DEM-National Topographic Map (RBI) because it includes geometric error correction steps using Ground Control Point (GCP) and DEM data in the photogrammetric process. Unfortunately, the authors did not discuss how to determine safe areas and evacuation routes in detail. In addition, they did not create 3D buildings that make the inundation area more real to allow the wider public to understand them more easily.
The use of UAV photogrammetry for 3D tsunami mapping and modeling will likely provide several benefits for conveying tsunami hazard information to the community. This method produces a model that “shows the current area situation”, is flexible, has a high image resolution, is accurate, and is low cost [15,24,25]. Moreover, the approach is most useful in situations where the speed of information is important [19]. In addition, for geodata acquisition, UAV is more suitable if applied to a small area [20]. Furthermore, the model resembles the original object; thus, the community can easily understand the information conveyed [14].
The research purpose is to carry out 3D modeling of tsunami hazards using UAV photogrammetry. This paper develops a series of procedures for building a 3D model to provide information during normal periods and during the occurrence of a tsunami, including infomration about the distribution, distance, and depth of inundation. We measure the height and shape of the exterior of the building by referring to orthomosaic and DEM data obtained from the UAV platform and processed by the Agisoft metashape professional application. This technique can indicate safe areas so that evacuation routes can be planned. Thus, this study fills a gap in previous research. The study focused on the area of Cilacap, Indonesia as the sample location. Cilacap is an oil and gas industrial city that has the largest oil refinery in Indonesia and several supporting facilities for oil and gas activities [26].

2. Material and Methods

This research was carried out using an experimental method [27] to experiment with the technique of using drones in photogrammetry to produce good quality aerial photos and also to experiment with Agisoft software settings during the processing of aerial photos to accurately produce a 3D orthomosaic and formulate a visualization technique to estimate the extent of tsunami wave distribution. This research used drones to take aerial photos in the field and a computer laboratory to process aerial photos into 3D models. The stages of experiments carried out in this research can be seen in the Figure 1.

2.1. Literature Review

Before starting the research, a review of the literature from various previous studies was carried out to find out how tsunami modeling has been conducted previously, what the prospects of photogrammetry for 3D animation modeling are, and why Cilacap was chosen as a sample location for tsunami modeling.

2.1.1. Previous Tsunami Modeling

Tsunami modeling is incredibly beneficial for visualizing tsunami events that have already happened and will happen in the future [28]. Current modeling is in the forms of numerical simulations [29,30,31], 2D tsunami simulation modeling [24,32,33], and 3D tsunami simulation modeling [12,34,35].
A deeper review of 3D tsunami simulation modeling identified three types of 3D tsunami simulation modeling that are widely used today. They are high-resolution Modeling based on Computational Fluid Dynamics (CFD), modeling based on photorealistic particle visualization methods, and photogrammetry/unmanned aerial vehicles (UAV) methods [14]. Of these three models, the photogrammetry/unmanned aerial vehicle (UAV) typeis the most visually realistic, has a high level of accuracy, and is most easily understood by ordinary people [12,34,35,36,37].
Several types of software are commonly used to create virtual cities for tsunami modeling, such as Lumion and City Engine. However, the results obtained with virtual cities do not represent the original environment, because they are made based on the properties provided in the software. Therefore, the photogrammetry/UAV technique is better, because it can produce a virtual city that follows the original environment [12,35,36]. However, this technique also has weaknesses because it takes a long time to carry out the production process and requires a computer with very high specifications. Moreover, the production costs are expensive compared with those required to create a virtual city with the software [14].

2.1.2. Photogrammetry Prospects for 3D Animation Modeling

Unmanned Aerial Vehicle Photogrammetry (UAV-Ph) has recently acquired popularity as a cost-effective and efficient method of generating high-quality maps and geometric data [38]. which is essential for 3D modeling [39]. Two of the most interesting features of UAV-Ph methods are their ability to flexibly adjust the scale required for high-precision surveying and mapping products by changing the flying height and their ability to vertically orient the image to the target surface [40]. Sashi and Jain (2007) said that the precision obtained in the photogrammetry of 3D modeling corresponds perfectly with the original object, which is required for any further work, like reconstruction [41]. UAV-Ph is also used to document archaeological sites to detect debris [42]. The geometric information obtained is applied to 3D modeling and reconstruction, in some cases of cave geometry or ancient Turkish theatre. Photogrammetry is considered the best type of technology for processing image data because it can provide accurate, metric, and detailed 3D information in any application range and can estimate the accuracy and reliability of unknown parameters based on the measured image correspondence [43,44]. The main application areas of photogrammetry are cartography and mapping, accurate 3D documentation of cultural heritage [45,46], reverse engineering, building monitoring and deformation analysis [47]; human movement analysis [48], industrial surveying [49], Urban Planning, forensics [50], navigation, heating, telecommunications, and others.

2.1.3. Cilacap as a Study Area

The district is located in an area that is vulnerable to severe earthquakes that could potentially trigger a tsunami. Based on data from paleo-tsunami deposits, tsunamis hit Cilacap district in 1883, 1982, and 2006 [51,52]. The last tsunami generated waves as high as 5–8 m in the southwest to the center of the island of Java. The waves were triggered by a 7.8-Richter-Scale earthquake off the coast of Pangandaran in the south. Waves moved from Pangandaran beach to Widarapayung beach in Cilacap and reached a height of 21 m on Nusakambangan Island, located in the northern city of Cilacap [53]. In the future, Cilacap district has the potential to be hit by a tsunami with a wave height of 20 m due to a 8.7–9.2 SR megathrust earthquake in southern Java [51,54,55].
The Indian Ocean borders Cilacap district in the south and west, as well as rivers in the south and east. The geographical location gives this district the potential to be hit by tsunami waves from three directions, especially in the southern part of the district [54,56,57].
Cilacap is the largest district in Central Java province, Indonesia, having an area of around 6.2% of the total area of Central Java province. Several areas in Cilacap have become marine industries and port centers and are connected to the rail network. This district has a special economic role in the oil industry and agriculture sector. The district of Cilacap is one of the most densely populated urban-coastal areas in Central Java Province, Indonesia [58]. In addition, the district is developing dynamically, with several buildings being located just a few meters from the shoreline [59]. The Indonesia National Disaster Management Authority (BNPB) estimates that tsunami inundation in Cilacap will impact 10 sub-districts with a total of 86,149 people exposed.

2.2. Tsunami Data from Cilacap

The data used for tsunami modeling in this research were obtained from previous research conducted by [51,60]. The data included tsunami height model along the southern coast of Java, which was made based on the worst-case scenario where the source of the tsunami being modeled on the southern coast of Java broke at the same time (Cilacap is one of the areas included in the area of the south coast of Java) (see Figure 2). Based on the obtained data, the lowest tsunamis produce waves that are 0.83 m in height, and the highest produce waves that reach 18.22 m in height. This is shown in Figure 3.

2.3. Location Determination Coordinates and GCP

The model required the determination of location coordinates, the installation and measurement of Ground Control Points (GCP), aerial photos, aerial photo processing, tsunami distribution point formulation, tsunami distribution point visualization, and tsunami distribution point distance measurement (start point distance and endpoint distance). Table 1 below is the location of the coordinates for determining the location to fly the drone.
To fly the drone at the research location, used the DroneDeploy software to establish the autopilot setting, as illustrated in Figure 4.
Before flying the drone, ten Ground Control Points (GCPs) were installed on the ground for the global positioning of object points (Premark). The coordinate point parameters used as control points in the coordinate transformation are listed in Table 2.

2.4. UAV Process Drone Photogrammetry

After determining the location for the mapping area, a process of taking aerial photos with Unmanned Aerial Vehicles (UAV) using the drone was carried out. Table 3 presents the specifications of the equipment and the parameters of the autopilot track mode used.
A total of 4853 aerial photos were produced using aerial image recording with drones, with a resolution of 4000 × 3000 pixels. Figure 5 shows some of the successful aerial photos taken.
The thirteen drone flights were planned when designing an application for autopilot settings. However, 16 drone flights were used during the actual implementation on the field due to the adjustment of wind speed conditions. Each drone flight took 18 to 23 min to complete. Gimbal orientation was 90’, the front lap and side lap values were 85 per cent using the 3D crosshatch method, the GSD was 3.3 cm/px, and the flight speed was 5 m/s.
There were no issues with the region’s reconstruction because no structures had high density or complexity. The drones were flown high enough above the buildings (100 m from ground level) to prevent the overlapping value being reduced. Some of the issues experienced while flying drones on the field, such as changes in wind speed in the morning, afternoon, and evening, resulted in reductions in the drone flying speed. Changes in the amount of light, which can be more significant during the day, as well as reflections of light from the sea surface, might impact camera calibration quality. To overcome this problem, before we could fly the drone, we had to wait until the amount of sunlight reflected during the day had lowered.

2.5. Orthomosaic Process

The next stage was to process the aerial photos using photogrammetry techniques to produce a 3D orthomosaic, using Agisoft metashape professional software. It is critical to calibrate the camera before undergoing model reconstruction so that the model produced has a high level of accuracy. If an image is captured with a different camera, this step is essential. Images were only taken with one camera in this research, but the camera calibration process was carried out nevertheless. The stages and settings are shown in Table 4, and the visual results of the 3D orthomosaic modeling process are shown in Figure 6 and Figure 7.

2.6. Formulation of the Distribution Area

The process of determining the distribution area and the distance of tsunami waves involved measurements through distribution area formulations. These were generated through a comparison between the DEM of the first building, which was closest to the shoreline; the maximum height of the building closest to the shoreline; the maximum height of the tsunami wave; and the degree of the tsunami wave slopes after arriving on land and hitting buildings, trees, or stones.
The method used to measure the tsunami area distribution was the Simple Additive Weighting Method (SAW), which has the following equation:
WD = P1:P2:P3:P4
where
WD is the tsunamis wave distribution area (tsunamis endpoint);
P1 is the DEM of the building closest to the beach;
P2 is the height of the building closest to the beach;
P3 is the tsunami wave height prediction;
P4 is the slope degree of the tsunami waves.

2.7. Three-Dimensional Modeling Process

This stage was carried out to create a model of tsunami height and the distribution of tsunami waves from the starting point (coastal line) to the endpoint. This process was based on the calculation of the distribution area formulation (see Figure 8) with the following data: P1 = 0, P2 = 15 m, P3= 18.22 m, P4= 100. This 3D modeling process was conducted using 3ds max software with a plane and polygon mesh tool, which was then molded into a tsunami wave with a height and wave distribution that was changed to fit the data from the starting point to the endpoint of the tsunami wave. After that, elements that matched the color of ocean water were applied to the model to make it look more genuine. This tsunami model was merged with the 3D orthomosaic model created previously with Agisoft metashape professional software by importing it into the 3ds max program and combining it with ocean wave modeling, resulting in single modeling. The process is shown in Figure 9.

2.8. Three-Dimensional Modeling Visualization Results

The results of the 3D Modeling Process below show the conditions before the tsunami and the conditions when the tsunami happened. The differences can be clearly viewed in Figure 10.

2.9. Measurement Coordinates of Tsunami Distribution

After determining the distribution of sea waves created in the 3D modeling visualization process, the next step was to measure the distance between the starting point and the tsunami wave end using Agisoft metashape professional software, as shown in Figure 11.

3. Results and Discussion

The modeling methods that are used widely today are difficult for ordinary people to understand. Therefore, this research tried to design a method that ordinary people can easily understand. The method used here for tsunami modeling involves the application of photogrammetry techniques from drones/Unmanned Aerial Vehicles (UAV). The resulting 3D orthomosaic pictures are further processed to simulate tsunami waves using 3D modeling software (3ds max). The simulation involves the implementation of distribution area formulations.
The results of mathematical calculations used to measure tsunami characteristics, such as wave distribution, height, and distance from the starting and ending points, are plotted into an orthomosaic 3D image resulting in visualization or animation that is similar to the actual tsunami event. Therefore, the visualization is easily understood by ordinary people.
This research developed a formula to calculate the distribution area of tsunami waves and the distance of tsunami waves to the endpoint. The formulation was obtained through a comparison between the DEM of the first building closest to the shoreline, the maximum height of the building closest to the shoreline, the maximum height of the tsunami wave, and the degree of inclination of the tsunami wave after hitting land, a building, a tree, or stones. The formula can be used as a guide to conduct 3D modeling of tsunami waves, the height of the tsunami, the distribution of the tsunami waves, and the furthest distance (endpoint) from the tsunami waves.
When a tsunami wave reaches the beach and collides with the ground, buildings, trees, or rocks, the slope of the tsunami wave decreases, about a 100 drop in altitude, allowing a straight line to be drawn and the tsunami’s endpoint location to be determined.
The 3D modeling of tsunami waves was conducted using 3ds max software, while the 3D orthomosaic results from the sample area in the research location (Cilacap) were produced using Agisoft metashape professional software. For this orthomosaic process, photogrammetry data obtained from the UAV/Drone process were required from the sample research area (Cilacap).
Although the 3D tsunami model developed in this research can be carried out for any area, a location must be selected to be used as a sample. In this research, Cilacap was chosen as the sample location for tsunami simulation, because it has a historical record of past tsunami events (paleotsunamis), and there is a prediction that an 8.7–9.2 magnitude megathrust earthquake will occur in southern Java with the potential to cause a tsunami up to 20 m in height’. Therefore, the 3D tsunami model generated in this research is expected to convey information about the coordinates of the tsunami wave distribution and the furthest distance (endpoint) from the tsunami wave (refer to Table 5). The model can be used as a recommendation to determine safe locations for evacuation routes and locations to build a shelter or to determine which existing buildings can be used as alternative shelters when tsunamis happen.
From the visualization of 3D tsunami modeling, the wave direction, how the water has infiltrated land and entered the river, the height of the wave, and the distribution area can be understood. Therefore, this tsunami model can also be used as a disaster education media to deliver accurate disaster information.
Tsunami hazard modeling through three-dimensional animations is predicted to mitigate wider negative consequences from tsunami disasters. A visually appealing and realistic model could help to boost public acceptance of the tsunami preparedness education program. According to our research, three-dimensional modeling can more correctly depict the power of tsunami waves by integrating three-dimensional models of nearby buildings. Three-dimensional modeling at a high resolution allows for a more accurate description of complicated tsunami events. Not only practical to society, the tsunami hazard map produced by three-dimensional modeling is also critical and influential for guiding the government and the Disaster Management Agency in their decision-making about evacuation structures. This is because it will allow them to assess the impact of a tsunami’s magnitude on a structure. This modeling technique can also be used to assess tsunami risk reduction. Additionally, three-dimensional modeling enables the identification of risk factors, the assessment of the economic impact of disaster damage, and can assist with spatial planning and disaster management.

4. Conclusions

Tsunami inundation scenarios and mitigation efforts can be conveyed through tsunami modeling. However, ordinary people have difficulty understanding currently used tsunami models. The contribution of this research is the development of a method that can be used to determine the distribution point of tsunami waves through the 3D orthomosaic technique that compares the heights of the buildings closest to the shoreline and a Digital Elevation Model (DEM) of the buildings closest to the shoreline and predicts the height of tsunamis waves and their slopes as they reach land. The 3D visualization results from this modeling provide a better understanding of the behaviors of tsunami inundation on land. Moreover, the 3D visualization is easier to understand by ordinary people and the policy-making community. Hence, it can be used for developing strategic policies for tsunami risk reduction, for example, for preparing evacuation routes and locations for building shelters and contingency planning. The model can also be used as a medium for disaster education and the delivery of accurate disaster information.
This research is limited to describing the process of conducting 3D modeling, the prediction of distribution, and determining the endpoint distance of the tsunami wave. Therefore, t further improvement in this area of research is needed in order to determine the strength of tsunami waves, the time of wave landing, the time of inundation from the shoreline to the endpoint of the waves on land, and the passage of time to the safe zone or assembly points. Future research should consider using these factors. Additionally, this modeling technique could be used to assess tsunami risk reduction. Three-dimensional modeling enables the identification of risk factors, the assessment of the economic impact of disaster damage, and assistance with spatial planning and disaster management’’.

Author Contributions

Data curation, A.Y., N.R.H., A.P., B.N. and A.M.R.; Formal analysis, A.P., B.N. and A.M.R.; Funding acquisition, Y.K.; Investigation, N.R.H.; Methodology, M.Y.R.; Project administration, S.K.K.; Supervision, M.Y.R.; Validation, N.R.H. and Y.K.; Visualization, M.Y.R.; Writing—original draft, M.Y.R.; Writing—review & editing, A.Y., N.R.H., Y.K. and S.K.K. All authors have read and agreed to the published version of the manuscript.

Funding

Kagawa University, Japan.

Acknowledgments

The writing team would like to express their deepest gratitude to the National Research and Innovation Agency (BRIN), InaTews Program, Research Center for Geotechnology, and Research Center for Informatics. In particular, we would like to extend many thanks and our appreciation to Kagawa University in Japan, which helped fund the publication of this paper.

Conflicts of Interest

In authoring and publishing this paper, the authors state that they have no conflict of interest.

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Applsci 11 11310 g001 550
Figure 1. Research workflow.
Figure 1. Research workflow.
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Figure 2. Map of Cilacap, Source: www.google.com/maps (accessed on 15 September 2021).
Figure 2. Map of Cilacap, Source: www.google.com/maps (accessed on 15 September 2021).
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Figure 3. Maximum tsunami height along the south coast of Java, the x-axis presents longitude information and the y-axis is models tsunami height. Taken from Widiyantoro et al., 2020.
Figure 3. Maximum tsunami height along the south coast of Java, the x-axis presents longitude information and the y-axis is models tsunami height. Taken from Widiyantoro et al., 2020.
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Figure 4. The setting of the autopilot flight area developed using DroneDeploy software.
Figure 4. The setting of the autopilot flight area developed using DroneDeploy software.
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Figure 5. The sample of aerial photographs taken using drones in Cilacap on July 2021.
Figure 5. The sample of aerial photographs taken using drones in Cilacap on July 2021.
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Figure 6. Orthomosaic modeling process conducted using Agisoft metashape professional software.
Figure 6. Orthomosaic modeling process conducted using Agisoft metashape professional software.
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Figure 7. The final results of the Orthomosaic model produced using Agisoft metashape professional software.
Figure 7. The final results of the Orthomosaic model produced using Agisoft metashape professional software.
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Figure 8. Illustration of Distribution Area Formulations.
Figure 8. Illustration of Distribution Area Formulations.
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Figure 9. Three-dimensional modeling process using 3ds max software.
Figure 9. Three-dimensional modeling process using 3ds max software.
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Figure 10. Tsunami wave modeling results using 3ds max software.
Figure 10. Tsunami wave modeling results using 3ds max software.
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Figure 11. Measuring the distance spread of tsunami waves using Agisoft metashape professional software.
Figure 11. Measuring the distance spread of tsunami waves using Agisoft metashape professional software.
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Table
Table 1. Determination of Location Coordinates.
Table 1. Determination of Location Coordinates.
Object TypeLatitudeLongitude
Point 17°43′34.81″ S109°1′2.67″ E
Point 27°43′50.25″ S109°1′1.77″ E
Point 37°43′50.39″ S109°1′26.60″ E
Point 47°43′35.17″ S109°1′32.46″ E
Point 57°43′29.21″ S109°1′23.71″ E
Point 67°43′28.87″ S109°1′18.27″ E
Point 77°43′34.73″ S109°1′14.32″ E
Table
Table 2. Position of object point (Premark) GCP.
Table 2. Position of object point (Premark) GCP.
Object TypeLatitudeLongtitudeDescription
Bridge 17°43′43.10″ S109°1′13.29″ EBudi Utomo street
Bridge 27°43′36.14″ S109°1′16.81″ EStep street
School7°43′38.54″ S109°1′6.51″ EBudi Utomo street
House 17°43′42.58″ S109°1′14.29″ EBudi Utomo street riverbanks
House 27°43′43.29″ S109°1′12.63″ EBudi Utomo street riverbanks
Building 17°43′40.88″ S109°1′7.96″ EBudi Utomo street
Building 27°43′49.18″ S109°1′16.36″ EPenyu street
Building 37°43′34.51″ S109°1′19.45″ EBudi Utomo street
Building 47°43′36.84″ S109°1′19.34″ EBudi Utomo street
Seafront building7°43′39.45″ S109°1′27.18″ Ecomparison parameter
Table
Table 3. Equipment specifications and parameters.
Table 3. Equipment specifications and parameters.
ParametersDescription
Ground control points (GCP)Premark, at 10 points
Drone typeDji Phatom 4 and Dji Mavic Pro
CameraSensor 1/2.3 (CMOS)
Adjustable Aperturef/2.8 and f/2.2
Software track autopilotDJI GO 4 and DroneDeploy
Area to be mapped46 Hectares
Flight duration204:24 min
Flight altitude100 m above takeoff position
Resolution3.3 cm/px
Photo resolution4000 pixels (Width) × 3000 pixels (Height)
Totals of photos4853
Time27 June 2021
Table
Table 4. Stages and settings used for the 3D orthomosaic process conducted with Agisoft metashape professional software.
Table 4. Stages and settings used for the 3D orthomosaic process conducted with Agisoft metashape professional software.
StagesDescription
Add Photos4853 Photos with TIF and BMP format
“Reference Settings”WGS 84 (EPSG::4326)
Camera accuracy (m): 10
Camera accuracy (deg): 2
Marker accuracy (m): 0.005
Scale bar accuracy (m): 0.001
Marker accuracy (pix): 0.1
Tie point accuracy (pix): 4
“Camera Calibration”FC220 (4.73 mm) 4853 image, 1600 × 1200 pix
Camera type: Frame
Pixel size (mm): 0.0039356 × 0.0039356
Focal length (mm): 4.73
Type: Auto
F: 1201.85
cx: 3000
Cy: 2000
Align PhotosAccuracy: High
Generic preselection (check list)
Reference preselection (check list)
Key point limit: 150.000
Tie point limit: 70.000
Adaptive camera model fitting (check list)
Build Dence CloudQuality: High
Input GCPGCP point data in TXT format (tab delimited)
Depth filtering: Mild
Build MeshSurface Type: Height field,
Source data: Sparse cloud
Face count: Medium (51,257)
Interpolation: Enabled (default)
Calculate vertex colors (check list)
Build TextureMapping mode: Orthophoto
Blending mode: Mosaic (default)
Texture size/count: 160000 × 1
Enable color correction (check list)
Enable hole filing (check list)
Build DEMProjection: Geographic: WGS 84 (EPSG::4326)
Source data: Sprse cloud
Interpolation: enabled (default)
Build OrthomosaicProjection: Geographic: WGS 84 (EPSG::4326)
Surface: DEM
Blending mode: Mosaic (default)
Enable hole filing (check list)
Export ModelSave as type: Wavefront OBJ (*.obj)
Table
Table 5. Distribution points and distances from tsunamis waves.
Table 5. Distribution points and distances from tsunamis waves.
Object TypeLatitude and Longitude Start PointsLatitude and Longitude End PointsDistance
Scatter point 17°43′39.52″ S 109°01′28.46″ E 8.388 m7°43′35.28″ S 109°01′16.88″ E 7.766 m381 M
Scatter point 27°43′45.98″ S 109°01′24.64″ E 7.530 m7°43′42.81″ S 109°01′13.14″ E 6.443 m367 M
Scatter point 37°43′49.06″ S 109°01′25.11″ E 7.206 m7°43′46.55″ S 109°01′03.24″ E 5.639 m674 M
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Rezaldi, M.Y.; Yoganingrum, A.; Hanifa, N.R.; Kaneda, Y.; Kushadiani, S.K.; Prasetyadi, A.; Nugroho, B.; Riyanto, A.M. Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia. Appl. Sci. 2021, 11, 11310. https://doi.org/10.3390/app112311310

AMA Style

Rezaldi MY, Yoganingrum A, Hanifa NR, Kaneda Y, Kushadiani SK, Prasetyadi A, Nugroho B, Riyanto AM. Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia. Applied Sciences. 2021; 11(23):11310. https://doi.org/10.3390/app112311310

Chicago/Turabian Style

Rezaldi, Muhammad Yudhi, Ambar Yoganingrum, Nuraini Rahma Hanifa, Yoshiyuki Kaneda, Siti Kania Kushadiani, Abdurrakhman Prasetyadi, Budi Nugroho, and Agus Men Riyanto. 2021. "Unmanned Aerial Vehicle (UAV) and Photogrammetric Technic for 3D Tsunamis Safety Modeling in Cilacap, Indonesia" Applied Sciences 11, no. 23: 11310. https://doi.org/10.3390/app112311310

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