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Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China
Computers & Geosciences 68 (2014) 22–30
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Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo
Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China Zhiqiang Xu a, Jiansi Yang a, Chaoyong Peng a,n, Ying Wu b, Xudong Jiang a, Rui Li b, Yu Zheng a, Yu Gao a, Sha Liu a, Baofeng Tian a a b
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China Beijing Digital LandView Technology Company Limited, Beijing 100085, China
art ic l e i nf o
a b s t r a c t
Article history: Received 7 February 2014 Received in revised form 1 April 2014 Accepted 2 April 2014 Available online 12 April 2014
The main objective of early impact analysis after a disaster is to produce georeferenced data about the affected areas, in support of humanitarian action. Crucial information is the identification of the disaster areas and the estimation of the number of people involved. Satellite imageries are mainly used as input data for early impact analysis in medium and large scale map. Analyses aimed at defining the damages of infrastructure and/or to facilities require suitable data, such as high resolution satellite images. Unfortunately, satellite images are not always available in a few days after the event. Therefore in situ surveys are preferred. Innovations in Unmanned Aircraft System (UAS) have allowed them to become valuable tools in capturing and assessing the extents and amount of damages. Flexibility, safety, ease of operation, and relatively low-cost of ownership and operation facilitate UAS implementation in disaster situations. In this paper, an example of UAS was developed for rapidly obtaining disaster information. Data acquisition at specified scales was successfully performed with the chosen fixed-wing UAS. For the image analysis, a photogrammetric workflow was applied to cope with the very high resolution of the images acquired without ground control points. Tests showed that the system plays an important role in the work of investigating and gathering information about disaster in epicentral areas of the earthquake, such as road detection, secondary disaster investigation, and rapid disaster evaluation. It can effectively provide earthquake information to salvation headquarters for swiftly developing the relief measures and improving the efficiency of emergency rescue. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Unmanned aircraft system Emergency rescue Disaster assessment Regional panorama Photogrammetry
1. Introduction Of all earthquakes happened in China since the founding of PRC, the Wenchuan Earthquake which occurred in Sichuan province of China on May 12th, 2008 which quickly spread its impact to more than 10 provinces and 400 cities, may be the most serious catastrophe because it has the largest affected areas, the most difficult relief work, and the most powerful destruction force (Peng et al., 2014). In order to effectively minimize the losses of the disaster (Peng et al., 2013), accurately develop relief measures, and practically improve efficiency of emergency rescue, the salvation headquarters must rapidly obtain the earthquake information and swiftly assess the disaster damage. In the case of communication interruption and ground transport disruption, Remote Sensing (RS) becomes an important means for obtaining earthquake information, emergency response, and post-earthquake
n
Corresponding author. Tel.: þ 86 1060212219; fax: þ 86 1060212337. E-mail addresses: [email protected], [email protected] (C. Peng). http://dx.doi.org/10.1016/j.cageo.2014.04.001 0098-3004/& 2014 Elsevier Ltd. All rights reserved.
disaster assessment. However, Satellite Remote Sensing (SRS) is subject to certain restrictions such as spatial resolution and run cycle when it is used to get the disaster information. Meantime, the limitations also happened to the traditional manned aerial photography (Boccardo, 2013). Airport and weather conditions will seriously affect the use of manned aerial photography, and there may exist some potential dangers to pilots when flying. As a supplementary resort to SRS and manned aerial photography, the Unmanned Aircraft System (UAS) should have some distinct advantages as follows: real-time, flexible, high image resolution, low cost, etc. It refers to a class of aircrafts which can fly without the onboard presence of a pilot (Bendea et al., 2008; Gaszczak et al., 2011; Al-Tahir et al., 2011; Al-Tahir and Arthur, 2012; Meo et al., 2012) and can offer numerous opportunities in disaster related situations when equipped with remote sensing instrumentations (Lu et al., 2011; Watts et al., 2012; Baiocchi et al., 2013a, 2013b). High-resolution images can be analyzed and used to produce hazard maps, dense surface models, detailed building renderings, comprehensive elevation models, and other characteristics in the disaster area. These data can then be analyzed with remote sensing methods or visual interpretation to
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coordinate rescue efforts, record building responses to the shaking, detect building failures, investigate access issues, and verify experimental disaster modeling. The data can also be gathered before a disaster in order to document immediate pre-event conditions of critical facilities and infrastructure, monitor susceptible environmental concerns, and document historical conditions and sites (Adams and Friedland, 2011; Noguera et al., 2011). In short, remote sensing based on UAS yields the best possible spatial and temporal resolutions for the respective research question or application (d'OleireOltmanns et al., 2012). Nowadays, many agencies have found public uses for unmanned aircraft. In response to this growing demand for public use of unmanned aircraft operations, Civil Aviation Administration of China (CAAC) have developed guidance in two policies titled “Air Traffic Management Approaches for Civilian UAS” (MD-TM-2009-002) and “Interim Provisions for Management of Civilian Remotely Piloted Aircraft Systems Drivers” (AC-61-FS-2013-20), respectively. In these documents, CAAC set out guidance for public use of unmanned aircraft by defining a process for evaluating applications for Certificate(s) of Waiver or Authorization for unmanned aircraft to operate in the National Airspace System. For the means by which authority to operate in the vicinity of a natural disaster was granted, there is an alternative; because of the emergency rescue requirements, we only need to hand over the designed flight plans to the field headquarter for censoring. If the plan is approved, we will have the authority to carry out our flight plans. The aim of this paper is to describe the application of a fixedwing aircraft type HW18 (HoverWings, China) equipped with an optical digital camera (Sigma DP2) system. Furthermore, the paper presents the results of field tests in the Yuxi County, Yunnan, China and application in the Ms7.0 Lushan Earthquake. The main objectives of the UAS surveys are very high resolution monitoring of the investigated post-earthquake areas with optical remote sensing data and the creation of detailed digital maps. The very high image resolution as well as the photogrammetric potential of small-format aerial photography data enables a broad range of applications with a much higher degree of detail than satellite data and higher efficiency and spatial completeness than traditional field work. The main prerequisite for the design of the UAS survey approach is to meet varying requirements of scale, resolution and accuracy.
2. System design The whole system consists of three main components, including an UAS, a ground station, and an image processing system (Fig. 1).
Fig. 1. The whole system.
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2.1. UAS specifications The choice of potential unmanned platforms for low-altitude Earth observation is large and continues to grow. It is difficult to generalize advantages or disadvantages of particular platforms, because possible applications and working conditions vary greatly around the world. Tethered systems navigated manually such as kites and blimps are ideal for the precise coverage of small sites that requires only a few images. However, these systems are hardly used for systematic surveys of larger areas, where regular overlaps along evenly spaced flight lines are preferable for an efficient processing workflow. Global positioning system (GPS) and inertial navigation system (INS) technologies are two examples of recent development in UAS technology. These developments have led to the availability of a range of auto piloted systems such as planes, drones and multicopters that can autonomously follow prescribed flight lines. Despite the opportunities provided by UAS, both hardware and software limitations result in some compromises. As a remote sensing platform, the UAS is relatively limited in both its payload capacity and flight duration (Pastor et al., 2007). It is necessary to balance platform accessibility with the technological limitations inherent of small-scale platforms and the data quality of low-cost sensors. Such cost and weight limitations necessitate a reduction in manufacturing quality of the sensor. Reductions are readily achieved through the use of cheaper construction materials and methods, limited data storage capacity, or the absence of on-board processing features. For this study, a fixed-wing aircraft type HW18 (HoverWings, China) is employed (Fig. 1). It is equipped with a digital camera (Sigma DP2) system. The aviation fuel powered system is based on a model airplane. Its weight is approximately 2.3 kg without payload with a wingspan of 120 cm and a length of 110 cm. At a ground speed of 70–110 km/h, the flight time with full payload (maximum take-off weight is 5.2 kg) is more than 100 min. The plane is hand-launched without catapult requirements (Fig. 2). The working principle of the UAS is shown in Fig. 3. During take-off and flight, the UAS is autonomously controlled by the HoverWings Autopilot and its GPS/IMU components. The UAS follows predefined flight paths computed by the flight-planning software Pix4UAV (Pix4D, 2010) which can automatically generate an accuracy report to assess the quality of the results. After completing the flight plan, the UAS returns to the starting point and remains rotary in a predefined altitude. The starting point sets also the center for the so-called bounding box. This is a circular unit with a diameter of 150 m. The bounding box limits the maximum distance between the UAS and the starting point while the pilot is using the assisted flying mode (see below). Whenever the UAS hits
Fig. 2. Hand-launching the UAV.
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Fig. 3. Working principle of the UAV.
the limit of the bounding box it automatically turns to remain within its given extent. Therefore, it is an essential safety aspect. The system also features a half-autonomous mode, the so-called assisted flying mode. In this mode, the pilot is permanently supported by the autopilot software during UAS control. Height loss while flying turns or destabilization owing to wind is corrected automatically. Turn radii are limited within the software to avoid material stress which might cause UAS damage. Furthermore, steering is simplified. Software-controlled interaction of yaw rudder and roll-aileron enables the pilot to turn the UAS left or right directly. Limits to maximum values of pitch and roll angles are set in the software. This flying mode enables the pilot to safely steer and land the plane while confining the navigation area to a predefined range. This is especially useful for difficult terrain where fully-autonomous landing may not be possible. It also increases the flexibility during the survey, due to that the pilot may cover extra targets not previously included in flight planning. However, the assisted flying mode does not relieve the pilot from his or her responsibility. The pilot must be able to steer and land the plane safely. Therefore, passing the training courses offered by HoverWings prior to independent surveys is indispensable. The UAS installed a 14 MP digital fixed lens camera called Sigma DP2 (2009) as the optical onboard sensor. It is a high-end compact digital camera, with a 14-megapixel Foveon X3 sensor (2652 1768 3 layers), a fixed 24.2 mm f/2.8 lens (41 mm equivalent), a 2.5″ LCD, and a pop-up flash. It is also one of the few ‘compact’ cameras featuring a sensor with a size equivalent to APS-C, which would produce DSLR quality images. This camera with DSLRcomparable quality is much smaller (113.3 59.5 56.1 mm3 incl. lens) and lighter (260 g excluding battery and card) than traditional single-lens reflex cameras. These properties make it highly suitable for unmanned platforms. This camera features several characteristics important for photogrammetric analysis of the images: single focal length, large image sensor, multiple recording modes (lossless compression RAW data, JPEG, Movie, etc.), and lack of image stabilizer. The camera is entirely concealed in the body of the plane. It points vertically to the ground through an opening in the plane bottom. The camera exposure is triggered by the on-board computer in the regular intervals computed by the flight-planning software. Thus, a continuous image series is taken during the survey.
Generally, the flight-plans are designed as parallel flight lines in order to achieve regular stereoscopic overlap along the line. At the end of each line, the roll of the UAS may reach large angles up to 601, where the image axis is off-nadir. Due to this, a rather large overshoot must be taken into account – this always results in a considerably larger area covered by the flight plan than the actual image acquisition area. Large amounts of oblique images at the flight-line ends are avoided due to restricted triggering control. Maximum pitch angles (e.g., 101 off-nadir) are set within the flight-planning software. 2.2. Ground station The ground station is used to remotely control the UAS, receive telemetry data, and display real-time transferring images. Its processing flow is shown in Fig. 4. As can be seen from Fig. 4, the ground station is composed of three parts: a ground monitoring station, a remote control transmitter, and a telemetry receiving antenna. The hardware of the ground station is based on the embedded PC104 architecture, and contains a 17″ LCD, a digital video recorder, a membrane keyboard and a spherical mouse, while the software system consists of Windows-based software modules for navigation, video processing, map generation and information processing. Navigation interface, video interface and the task management and status bar interface can be displayed on the LCD and switched in real time. Graphical interface can realize the real-time display of reconnaissance images and some related position information, and store them into the digital video recorder. The task management module is used for flight planning before or on flying. In addition, the flight path and related parameters can be stored in real-time and then be used for playback. 2.3. Image processing system For all applications that involve measuring and mapping, georeferencing and geometric correction of the images is imperative. However, a highly exact geometric correction requires time, effort, a digital elevation model, and excellent ground control. Our UAS application may not really require such efforts, and depending
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Fig. 4. Processing flow of the ground station.
Fig. 5. Working flow for collecting and processing of disaster images.
on the image and relief characteristics, simpler solutions might be quite sufficient. There are two possibilities for reconstructing the location of an image within a given ground coordinate system. The first possibility is to use ground control points (GCPs). These are features that appear on the photographs and their locations in a reference system are known. The second possibility determines the exterior orientation-positions (X, Y, Z) and rotations (yaw, pitch and roll of the platform or κ, φ and ω of the image) of the images during the flight. Although the recorded data of the latter are not precise enough to enable direct georeferencing compared to image scales and resolution, the system uses these data as initial values for approximate orientation. The reason is that GCPs are impossible to be deployed for emergency rescue immediately after the earthquake and data timeliness requirement is very high.
The whole procedure for collecting and processing of disaster images is shown in Fig. 5 and is photogrammetry-based. The exterior orientation values are taken from the log-files recorded during the survey flight for initial direct georeferencing of the images. The log files contain values for different angles (κ, φ and ω) and a GPS value for the center of each image. During image processing, the values for image orientation are refined in iterative triangulation calculations. In addition, camera calibration parameters are used for image orientation. These parameters were derived from self-calibration using a well-suited study site. The preparation in advance to small-format aerial photographs (SFAPs) processing using the workflow is applied. Firstly, all SFAPs acquired during survey flying are screened and well-suited SFAPs with stereoscopic overlap are selected for further processing.
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Taking the information saved in the log files enables visualization of image distribution and location using GIS software. Selecting well-suited SFAPs for further processing can then be done in a much faster manner using an attribute-based selection of points, e.g., by excluding images exceeding a certain threshold for maximum deviance from a nadir position during image acquisition or images with deviating flying height (taken during the starting and landing phase).
3. Field test After the system development completion, we carried out field tests in Yuxi County, Yunnan, China for obtaining the system optimal parameters, such as flight heights and camera parameters. The required image scales and resolutions vary depending on the processes observed. In order to take into account the different site extents and observation scales, different flight plan designs are employed for image acquisition. 3.1. Test of UAS flight parameters With different flight parameters and flight heights, the resolution of images taken by UAS will be different. In order to take aerial photographs in higher resolution, flight tests of the UAS parameters were carried out to obtain the optimal flight parameters suitable for the system. First, different flight heights were selected for the system, including 200 m, 300 m, 500 m, and 800 m. The tests were carried out in the same environment: (a) wind speed of 5 m/s, (b) infinite focal length, (c) ISO 200, (d) exposure time of 1/1000 s, (e) interval timer shooting 4 s (minimum shooting interval of Sigma DP2), (f) length of the flight line of 8 km, (g) flying speed of 30 m/s, (h) horizontal and vertical viewing angles of 601 and 451. The results showed that the resolution of images taken at these flight heights all meets our requirements. However, for the 200 m flying height, only oblique images were acquired during the UAS turns in Table 1 The image overlaps taken at flight heights of 300 m, 500 m, and 800 m. Flight heights (m)
Shooting interval (s)
Overlap (%)
300
4 5
63–36 54–20
500
4 5
73–64 67–55
800
4 5
86–75 82–69
the overshoot zone and some shots were missed. Taking into account the minimum turn radius of the UAS and its average speed, this case represents almost the lower survey limit and should not be set when on flying. The overlap rates of images collected at other three flight heights are shown in Table 1. From Table 1, we can see that the image overlap rate is increasing with the flight height and the shooting interval, and all of them met our splicing requirements. After acquiring the image overlap rates at different flight heights, we tested the camera parameters such as ISO sensitivity and exposure time in four types of weather: cloudless, dark cloud, fog, and cloudy. As to ISO sensitivity, the fixed scale mode is better than the auto control mode in the first two types of weather, while the auto control mode is better in the latter two types of weather. That is because the light in the cloudy, foggy weather is not constant and will change according to states of clouds or fog. In order to obtain the optimal exposure time of the camera, the UAS was tested at different flight heights (300 m, 500 m and 800 m) with the same ISO sensitivity and infinite focal length. Six types of exposure time were set when flying, including 1/250 s, 1/320 s, 1/500 s, 1/640 s, 1/700 s, and 1/1000 s. The results showed that the clarity and resolution of images taken at flight heights of 300 m and 500 m were lower than those taken at flight height of 800 m (Fig. 6). In addition, due to high-speed movement of the camera during flight, long exposure time would blur images, especially for 1/250 s, 1/320 s, and 1/500 s. Therefore, in the real field test, only 800 m flight height and exposure time with 1/1000 s are set for images taken. 3.2. Data analysis The image resolution formula can be obtained referring to the 1:500, 1:1000, 1:2000 aerial photography standard (GB/T 69622005, 2005), according to the relation of focus length, flight height and resolution. The formula is as follows: f ¼ HC=A
ð1Þ
where f is the focus length, H is the flight height, C is the CCD size, A is the ground covering area which can be calculated by A ¼pixel number resolution. For images taken at flight height of 800 m, fixed focus model was proposed during shooting. The focus length is 41 mm, with image sensor size of 20.7 13.8 mm2, and image size of 2640 1760 pixel. Substituting data into formula (1), the calculated resolution is 0.06 m, and the test result is between 0.1 and 0.12 m. 3.3. Regional panoramic image of Yuxi County After the UAS flight parameters were determined, we selected Yuxi County for flying test in order to obtain the panoramic image with high resolution and verify our UAS performance. Yuxi County
Fig. 6. Images taken from three different flight heights. (a) 300 m, (b) 500 m, and (c) 800 m.
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Fig. 7. The flight plan automatically calculated by Pix4UAV.
has a northeast–southwest distance of 8 km and a northwest– southeast distance of 6 km. The total area for mapping is about 42 km2. According to the overlap requirement, at least 25 lines are needed at 800 m flight height. In order to obtain more auxiliary data, the final number of lines is 30, and the total distance is 200 km. 1711 Images (the size of each image is 3 MB) are obtained in three flights, and the amount of data is 20 GB. After excluding images exceeding a certain threshold for maximum deviance from a nadir position during image acquisition or images with deviating flying height, 1610 images are left and software Pix4UAV is used to rapidly splice these images. The automatically calculated flight plan is shown in Fig. 7. In the triangulation process, the total keypoint observation and 3D points for bundle block adjustment is 1,268,824 and 387,784, respectively, while the mean reprojection error is 0.711432 pixels. The geotag localization variances calculated by Pix4UAV are 0.251682 m for X, 0.258808 m for Y, and 0.374265 for Z. Finally, the panoramic image of Yuxi County with high resolution is obtained by using point cloud densification in Pix4UAV. The violin strings of Nie Er Square, which is the landmark of Yuxi County, can be clearly distinguished by magnifying the Square step by step, as shown in Fig. 8.
referred to as Lushan Earthquake) (Fang et al., 2013). The location of the mainshock determined by China Earthquake Network Center is 30.31N, 103.01E and the focal depth is 13 km. This earthquake caused 193 fatalities, 25 missing and more than ten thousand injuries. Direct economic losses were estimated more than ten billion Yuan (RMB). In order to verify our system performance in this earthquake, we started at 15:00 (Beijing Time) from Beijing on the same day and reached the Lushan field headquarter at 12:00 on April 22. After preliminary understanding of the disaster, we designed the flight plan and committed it to China Earthquake Administration for censoring. When the plan was approved, we carried out the aerial flight on April 29 and 30. It totally took us nine hours to complete this flight. The flight plan was from Longmen Township to Lushan County, and the total round-trip distance was about 40 km (Fig. 9(a)). Table 2 is the longitude and latitude of the flight line waypoints. Similar flight parameters acquired from the field test in Yuxi County were used in this flight and the flight height was set to 600 m. 1101 Images were obtained from this flight for 25.51 km2 disaster area mapping and the total amount of data was about 9.16 GB. We spent two hours on splicing these images with Pix4UAV and the result is shown in Fig. 9(b).
4. Application in Ms7.0 Lushan Earthquake
5. Discussion
At 08:02 am on April 20, 2013 (Beijing Time), a strong earthquake (Ms7.0) occurred in Lushan County, Sichuan Province (hereafter
The results derived from the processing workflow gain high potential for post-earthquake epicentral area mapping, although
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Fig. 8. Panorama and landmark of Yuxi county (Nie Er Square).
Fig. 9. (a) The flight plan and (b) the splicing results for Lushan Earthquake.
this workflow is less accurate than those with installed fieldmeasured GCPs. Compared to traditional remote sensing data, these data products are still much more detailed and accurate. The image resolution of ca. 10–15 cm is approximately one order of magnitude better than recent high-resolution images taken by WorldView or Quickbird. Larger areas may be analyzed in considerable detail with such commercially available imagery. Anyhow UAS data from flying height of around 800 m account for the coverage of post-earthquake epicentral areas with even higher precision at user-specified times, repeat rates and lighting conditions. In our experience and for our UAS, the flying height must not be below 300 m, otherwise the accuracy of the initial exterior orientation values taken from the in-flight GPS logs would be too
low compared to the area size covered by the image. If the initial position of the image during the triangulation process is too inaccurate, no overlap with the neighboring images may be computed. Thus, no tie points and therefore no relative orientation between the images can be established. Larger image extents acquired at higher flying heights decrease the relative mispositioning of the images. Accordingly, this allows the necessary initial triangulation and subsequent refinement by Pix4UAV software. Image processing that aims for results with a high level of details is rather work-intensive and still requires future improvement. However, processing required manual input may also be seen as an advantage in that the user remains in a position with more control over the process (Hendrickx et al., 2011). Recent techniques
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Table 2 Longitude and latitude of the flight line waypoints. No.
Long.
Lat.
No.
Long.
Lat.
No.
Long.
Lat.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
102.877 102.927 102.875 102.920 102.886 102.893 102.933 102.868 102.942 102.897 102.915 102.894 102.870 102.948 102.868 102.900 102.955 102.863 102.863 102.956 102.953
30.153 30.158 30.161 30.162 30.162 30.164 30.164 30.167 30.165 30.168 30.170 30.173 30.174 30.177 30.178 30.178 30.181 30.182 30.188 30.190 30.193
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
102.866 102.863 102.865 102.958 102.852 102.857 102.850 102.846 102.955 102.959 102.961 102.965 102.970 102.968 102.974 102.986 102.999 103.014 102.827 103.023 103.033
30.193 30.199 30.202 30.204 30.207 30.208 30.211 30.212 30.215 30.215 30.219 30.224 30.224 30.229 30.232 30.233 30.243 30.249 30.252 30.264 30.266
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
102.819 103.040 102.799 103.044 102.791 103.037 103.038 102.790 102.786 103.035 102.783 103.045 102.772 102.778 102.786 102.795 102.803 102.807 102.812
30.270 30.281 30.290 30.291 30.292 30.297 30.300 30.304 30.306 30.310 30.316 30.321 30.330 30.332 30.340 30.342 30.349 30.363 30.371
may lead to further facilitation, such as the workflows based on Structure from Motion (SfM) techniques (Turner et al., 2012).
6. Conclusions Earth observation and GIS techniques can significantly contribute to improving efforts in developing proper disaster mitigation strategies, and providing relevant agencies with very important information for alleviating impacts of a disaster and relief management. However, the traditional use of satellite and aerial images for this task has been challenged by technical and financial issues. UAS is becoming increasingly popular as photogrammetric platforms for civilian use due to their relatively low cost, ease of operation and the emergence of low cost navigation and imaging sensors with performances comparable to higher priced sensors. The operational nature and cost factors make this technology in applicable level to build a low cost mapping system. This paper has presented the application of an UAS for rapid image collection of post-earthquake disaster. Data acquisition at specified scales was successfully performed with the chosen fixedwing UAS. Tests showed that the system plays an important role in the work of investigating and gathering information about disaster in earthquake epicentral areas, such as road detection, secondary disaster investigation and rapid disaster evaluation. It can effectively provide earthquake information to the salvation headquarters for swiftly developing the relief measures and improving the efficiency of emergency rescue. For the image analysis, a photogrammetric workflow was applied to cope with the very high resolution of the acquired SFAPs taken from large flying heights (i.e., 800 m above ground). The image resolution still remains below 15 cm at these flying heights. Since the aerial survey was carried out in post-earthquake epicentral area and covered a very large area, no GCPs were distributed in the field. Instead, direct georeferencing using the UAS GPS log was applied for creating the image block. The log files contain – among other information – the measured X/Y/Z GPS position for each image acquired as well as the information on tilt of the image axes (κ, φ and ω). This information was used as initial values of exterior orientation for the images in the photogrammetric block file. The accuracy of these values is directly out-
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putted by the software Pix4UAV. It depends on the measurement accuracy and precision of the GPS and IMU units, and the alignment of the camera with respect to the IMU coordinate system. Moreover, there may possibly occur a time-lag between triggering command and actual triggering of the camera, which may influence the accuracy. These values can therefore be expected to deviate up to several meters and degrees from the actual values. We will investigate these problems in future work.
Acknowledgments The authors highly appreciate the Editor Jef Caers and two anonymous reviewers for their very constructive suggestions and critics, which helped in improving the manuscript. We also thank Prof. Zhong Zheng for proof reading the manuscript. This research was supported by Seismological Research Project number 201108002. Research was also partially funded by Seismic Monitoring System Operation & Maintenance, Project number 043207.
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Contents lists available at ScienceDirect
Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo
Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China Zhiqiang Xu a, Jiansi Yang a, Chaoyong Peng a,n, Ying Wu b, Xudong Jiang a, Rui Li b, Yu Zheng a, Yu Gao a, Sha Liu a, Baofeng Tian a a b
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China Beijing Digital LandView Technology Company Limited, Beijing 100085, China
art ic l e i nf o
a b s t r a c t
Article history: Received 7 February 2014 Received in revised form 1 April 2014 Accepted 2 April 2014 Available online 12 April 2014
The main objective of early impact analysis after a disaster is to produce georeferenced data about the affected areas, in support of humanitarian action. Crucial information is the identification of the disaster areas and the estimation of the number of people involved. Satellite imageries are mainly used as input data for early impact analysis in medium and large scale map. Analyses aimed at defining the damages of infrastructure and/or to facilities require suitable data, such as high resolution satellite images. Unfortunately, satellite images are not always available in a few days after the event. Therefore in situ surveys are preferred. Innovations in Unmanned Aircraft System (UAS) have allowed them to become valuable tools in capturing and assessing the extents and amount of damages. Flexibility, safety, ease of operation, and relatively low-cost of ownership and operation facilitate UAS implementation in disaster situations. In this paper, an example of UAS was developed for rapidly obtaining disaster information. Data acquisition at specified scales was successfully performed with the chosen fixed-wing UAS. For the image analysis, a photogrammetric workflow was applied to cope with the very high resolution of the images acquired without ground control points. Tests showed that the system plays an important role in the work of investigating and gathering information about disaster in epicentral areas of the earthquake, such as road detection, secondary disaster investigation, and rapid disaster evaluation. It can effectively provide earthquake information to salvation headquarters for swiftly developing the relief measures and improving the efficiency of emergency rescue. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Unmanned aircraft system Emergency rescue Disaster assessment Regional panorama Photogrammetry
1. Introduction Of all earthquakes happened in China since the founding of PRC, the Wenchuan Earthquake which occurred in Sichuan province of China on May 12th, 2008 which quickly spread its impact to more than 10 provinces and 400 cities, may be the most serious catastrophe because it has the largest affected areas, the most difficult relief work, and the most powerful destruction force (Peng et al., 2014). In order to effectively minimize the losses of the disaster (Peng et al., 2013), accurately develop relief measures, and practically improve efficiency of emergency rescue, the salvation headquarters must rapidly obtain the earthquake information and swiftly assess the disaster damage. In the case of communication interruption and ground transport disruption, Remote Sensing (RS) becomes an important means for obtaining earthquake information, emergency response, and post-earthquake
n
Corresponding author. Tel.: þ 86 1060212219; fax: þ 86 1060212337. E-mail addresses: [email protected], [email protected] (C. Peng). http://dx.doi.org/10.1016/j.cageo.2014.04.001 0098-3004/& 2014 Elsevier Ltd. All rights reserved.
disaster assessment. However, Satellite Remote Sensing (SRS) is subject to certain restrictions such as spatial resolution and run cycle when it is used to get the disaster information. Meantime, the limitations also happened to the traditional manned aerial photography (Boccardo, 2013). Airport and weather conditions will seriously affect the use of manned aerial photography, and there may exist some potential dangers to pilots when flying. As a supplementary resort to SRS and manned aerial photography, the Unmanned Aircraft System (UAS) should have some distinct advantages as follows: real-time, flexible, high image resolution, low cost, etc. It refers to a class of aircrafts which can fly without the onboard presence of a pilot (Bendea et al., 2008; Gaszczak et al., 2011; Al-Tahir et al., 2011; Al-Tahir and Arthur, 2012; Meo et al., 2012) and can offer numerous opportunities in disaster related situations when equipped with remote sensing instrumentations (Lu et al., 2011; Watts et al., 2012; Baiocchi et al., 2013a, 2013b). High-resolution images can be analyzed and used to produce hazard maps, dense surface models, detailed building renderings, comprehensive elevation models, and other characteristics in the disaster area. These data can then be analyzed with remote sensing methods or visual interpretation to
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coordinate rescue efforts, record building responses to the shaking, detect building failures, investigate access issues, and verify experimental disaster modeling. The data can also be gathered before a disaster in order to document immediate pre-event conditions of critical facilities and infrastructure, monitor susceptible environmental concerns, and document historical conditions and sites (Adams and Friedland, 2011; Noguera et al., 2011). In short, remote sensing based on UAS yields the best possible spatial and temporal resolutions for the respective research question or application (d'OleireOltmanns et al., 2012). Nowadays, many agencies have found public uses for unmanned aircraft. In response to this growing demand for public use of unmanned aircraft operations, Civil Aviation Administration of China (CAAC) have developed guidance in two policies titled “Air Traffic Management Approaches for Civilian UAS” (MD-TM-2009-002) and “Interim Provisions for Management of Civilian Remotely Piloted Aircraft Systems Drivers” (AC-61-FS-2013-20), respectively. In these documents, CAAC set out guidance for public use of unmanned aircraft by defining a process for evaluating applications for Certificate(s) of Waiver or Authorization for unmanned aircraft to operate in the National Airspace System. For the means by which authority to operate in the vicinity of a natural disaster was granted, there is an alternative; because of the emergency rescue requirements, we only need to hand over the designed flight plans to the field headquarter for censoring. If the plan is approved, we will have the authority to carry out our flight plans. The aim of this paper is to describe the application of a fixedwing aircraft type HW18 (HoverWings, China) equipped with an optical digital camera (Sigma DP2) system. Furthermore, the paper presents the results of field tests in the Yuxi County, Yunnan, China and application in the Ms7.0 Lushan Earthquake. The main objectives of the UAS surveys are very high resolution monitoring of the investigated post-earthquake areas with optical remote sensing data and the creation of detailed digital maps. The very high image resolution as well as the photogrammetric potential of small-format aerial photography data enables a broad range of applications with a much higher degree of detail than satellite data and higher efficiency and spatial completeness than traditional field work. The main prerequisite for the design of the UAS survey approach is to meet varying requirements of scale, resolution and accuracy.
2. System design The whole system consists of three main components, including an UAS, a ground station, and an image processing system (Fig. 1).
Fig. 1. The whole system.
23
2.1. UAS specifications The choice of potential unmanned platforms for low-altitude Earth observation is large and continues to grow. It is difficult to generalize advantages or disadvantages of particular platforms, because possible applications and working conditions vary greatly around the world. Tethered systems navigated manually such as kites and blimps are ideal for the precise coverage of small sites that requires only a few images. However, these systems are hardly used for systematic surveys of larger areas, where regular overlaps along evenly spaced flight lines are preferable for an efficient processing workflow. Global positioning system (GPS) and inertial navigation system (INS) technologies are two examples of recent development in UAS technology. These developments have led to the availability of a range of auto piloted systems such as planes, drones and multicopters that can autonomously follow prescribed flight lines. Despite the opportunities provided by UAS, both hardware and software limitations result in some compromises. As a remote sensing platform, the UAS is relatively limited in both its payload capacity and flight duration (Pastor et al., 2007). It is necessary to balance platform accessibility with the technological limitations inherent of small-scale platforms and the data quality of low-cost sensors. Such cost and weight limitations necessitate a reduction in manufacturing quality of the sensor. Reductions are readily achieved through the use of cheaper construction materials and methods, limited data storage capacity, or the absence of on-board processing features. For this study, a fixed-wing aircraft type HW18 (HoverWings, China) is employed (Fig. 1). It is equipped with a digital camera (Sigma DP2) system. The aviation fuel powered system is based on a model airplane. Its weight is approximately 2.3 kg without payload with a wingspan of 120 cm and a length of 110 cm. At a ground speed of 70–110 km/h, the flight time with full payload (maximum take-off weight is 5.2 kg) is more than 100 min. The plane is hand-launched without catapult requirements (Fig. 2). The working principle of the UAS is shown in Fig. 3. During take-off and flight, the UAS is autonomously controlled by the HoverWings Autopilot and its GPS/IMU components. The UAS follows predefined flight paths computed by the flight-planning software Pix4UAV (Pix4D, 2010) which can automatically generate an accuracy report to assess the quality of the results. After completing the flight plan, the UAS returns to the starting point and remains rotary in a predefined altitude. The starting point sets also the center for the so-called bounding box. This is a circular unit with a diameter of 150 m. The bounding box limits the maximum distance between the UAS and the starting point while the pilot is using the assisted flying mode (see below). Whenever the UAS hits
Fig. 2. Hand-launching the UAV.
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Fig. 3. Working principle of the UAV.
the limit of the bounding box it automatically turns to remain within its given extent. Therefore, it is an essential safety aspect. The system also features a half-autonomous mode, the so-called assisted flying mode. In this mode, the pilot is permanently supported by the autopilot software during UAS control. Height loss while flying turns or destabilization owing to wind is corrected automatically. Turn radii are limited within the software to avoid material stress which might cause UAS damage. Furthermore, steering is simplified. Software-controlled interaction of yaw rudder and roll-aileron enables the pilot to turn the UAS left or right directly. Limits to maximum values of pitch and roll angles are set in the software. This flying mode enables the pilot to safely steer and land the plane while confining the navigation area to a predefined range. This is especially useful for difficult terrain where fully-autonomous landing may not be possible. It also increases the flexibility during the survey, due to that the pilot may cover extra targets not previously included in flight planning. However, the assisted flying mode does not relieve the pilot from his or her responsibility. The pilot must be able to steer and land the plane safely. Therefore, passing the training courses offered by HoverWings prior to independent surveys is indispensable. The UAS installed a 14 MP digital fixed lens camera called Sigma DP2 (2009) as the optical onboard sensor. It is a high-end compact digital camera, with a 14-megapixel Foveon X3 sensor (2652 1768 3 layers), a fixed 24.2 mm f/2.8 lens (41 mm equivalent), a 2.5″ LCD, and a pop-up flash. It is also one of the few ‘compact’ cameras featuring a sensor with a size equivalent to APS-C, which would produce DSLR quality images. This camera with DSLRcomparable quality is much smaller (113.3 59.5 56.1 mm3 incl. lens) and lighter (260 g excluding battery and card) than traditional single-lens reflex cameras. These properties make it highly suitable for unmanned platforms. This camera features several characteristics important for photogrammetric analysis of the images: single focal length, large image sensor, multiple recording modes (lossless compression RAW data, JPEG, Movie, etc.), and lack of image stabilizer. The camera is entirely concealed in the body of the plane. It points vertically to the ground through an opening in the plane bottom. The camera exposure is triggered by the on-board computer in the regular intervals computed by the flight-planning software. Thus, a continuous image series is taken during the survey.
Generally, the flight-plans are designed as parallel flight lines in order to achieve regular stereoscopic overlap along the line. At the end of each line, the roll of the UAS may reach large angles up to 601, where the image axis is off-nadir. Due to this, a rather large overshoot must be taken into account – this always results in a considerably larger area covered by the flight plan than the actual image acquisition area. Large amounts of oblique images at the flight-line ends are avoided due to restricted triggering control. Maximum pitch angles (e.g., 101 off-nadir) are set within the flight-planning software. 2.2. Ground station The ground station is used to remotely control the UAS, receive telemetry data, and display real-time transferring images. Its processing flow is shown in Fig. 4. As can be seen from Fig. 4, the ground station is composed of three parts: a ground monitoring station, a remote control transmitter, and a telemetry receiving antenna. The hardware of the ground station is based on the embedded PC104 architecture, and contains a 17″ LCD, a digital video recorder, a membrane keyboard and a spherical mouse, while the software system consists of Windows-based software modules for navigation, video processing, map generation and information processing. Navigation interface, video interface and the task management and status bar interface can be displayed on the LCD and switched in real time. Graphical interface can realize the real-time display of reconnaissance images and some related position information, and store them into the digital video recorder. The task management module is used for flight planning before or on flying. In addition, the flight path and related parameters can be stored in real-time and then be used for playback. 2.3. Image processing system For all applications that involve measuring and mapping, georeferencing and geometric correction of the images is imperative. However, a highly exact geometric correction requires time, effort, a digital elevation model, and excellent ground control. Our UAS application may not really require such efforts, and depending
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Fig. 4. Processing flow of the ground station.
Fig. 5. Working flow for collecting and processing of disaster images.
on the image and relief characteristics, simpler solutions might be quite sufficient. There are two possibilities for reconstructing the location of an image within a given ground coordinate system. The first possibility is to use ground control points (GCPs). These are features that appear on the photographs and their locations in a reference system are known. The second possibility determines the exterior orientation-positions (X, Y, Z) and rotations (yaw, pitch and roll of the platform or κ, φ and ω of the image) of the images during the flight. Although the recorded data of the latter are not precise enough to enable direct georeferencing compared to image scales and resolution, the system uses these data as initial values for approximate orientation. The reason is that GCPs are impossible to be deployed for emergency rescue immediately after the earthquake and data timeliness requirement is very high.
The whole procedure for collecting and processing of disaster images is shown in Fig. 5 and is photogrammetry-based. The exterior orientation values are taken from the log-files recorded during the survey flight for initial direct georeferencing of the images. The log files contain values for different angles (κ, φ and ω) and a GPS value for the center of each image. During image processing, the values for image orientation are refined in iterative triangulation calculations. In addition, camera calibration parameters are used for image orientation. These parameters were derived from self-calibration using a well-suited study site. The preparation in advance to small-format aerial photographs (SFAPs) processing using the workflow is applied. Firstly, all SFAPs acquired during survey flying are screened and well-suited SFAPs with stereoscopic overlap are selected for further processing.
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Taking the information saved in the log files enables visualization of image distribution and location using GIS software. Selecting well-suited SFAPs for further processing can then be done in a much faster manner using an attribute-based selection of points, e.g., by excluding images exceeding a certain threshold for maximum deviance from a nadir position during image acquisition or images with deviating flying height (taken during the starting and landing phase).
3. Field test After the system development completion, we carried out field tests in Yuxi County, Yunnan, China for obtaining the system optimal parameters, such as flight heights and camera parameters. The required image scales and resolutions vary depending on the processes observed. In order to take into account the different site extents and observation scales, different flight plan designs are employed for image acquisition. 3.1. Test of UAS flight parameters With different flight parameters and flight heights, the resolution of images taken by UAS will be different. In order to take aerial photographs in higher resolution, flight tests of the UAS parameters were carried out to obtain the optimal flight parameters suitable for the system. First, different flight heights were selected for the system, including 200 m, 300 m, 500 m, and 800 m. The tests were carried out in the same environment: (a) wind speed of 5 m/s, (b) infinite focal length, (c) ISO 200, (d) exposure time of 1/1000 s, (e) interval timer shooting 4 s (minimum shooting interval of Sigma DP2), (f) length of the flight line of 8 km, (g) flying speed of 30 m/s, (h) horizontal and vertical viewing angles of 601 and 451. The results showed that the resolution of images taken at these flight heights all meets our requirements. However, for the 200 m flying height, only oblique images were acquired during the UAS turns in Table 1 The image overlaps taken at flight heights of 300 m, 500 m, and 800 m. Flight heights (m)
Shooting interval (s)
Overlap (%)
300
4 5
63–36 54–20
500
4 5
73–64 67–55
800
4 5
86–75 82–69
the overshoot zone and some shots were missed. Taking into account the minimum turn radius of the UAS and its average speed, this case represents almost the lower survey limit and should not be set when on flying. The overlap rates of images collected at other three flight heights are shown in Table 1. From Table 1, we can see that the image overlap rate is increasing with the flight height and the shooting interval, and all of them met our splicing requirements. After acquiring the image overlap rates at different flight heights, we tested the camera parameters such as ISO sensitivity and exposure time in four types of weather: cloudless, dark cloud, fog, and cloudy. As to ISO sensitivity, the fixed scale mode is better than the auto control mode in the first two types of weather, while the auto control mode is better in the latter two types of weather. That is because the light in the cloudy, foggy weather is not constant and will change according to states of clouds or fog. In order to obtain the optimal exposure time of the camera, the UAS was tested at different flight heights (300 m, 500 m and 800 m) with the same ISO sensitivity and infinite focal length. Six types of exposure time were set when flying, including 1/250 s, 1/320 s, 1/500 s, 1/640 s, 1/700 s, and 1/1000 s. The results showed that the clarity and resolution of images taken at flight heights of 300 m and 500 m were lower than those taken at flight height of 800 m (Fig. 6). In addition, due to high-speed movement of the camera during flight, long exposure time would blur images, especially for 1/250 s, 1/320 s, and 1/500 s. Therefore, in the real field test, only 800 m flight height and exposure time with 1/1000 s are set for images taken. 3.2. Data analysis The image resolution formula can be obtained referring to the 1:500, 1:1000, 1:2000 aerial photography standard (GB/T 69622005, 2005), according to the relation of focus length, flight height and resolution. The formula is as follows: f ¼ HC=A
ð1Þ
where f is the focus length, H is the flight height, C is the CCD size, A is the ground covering area which can be calculated by A ¼pixel number resolution. For images taken at flight height of 800 m, fixed focus model was proposed during shooting. The focus length is 41 mm, with image sensor size of 20.7 13.8 mm2, and image size of 2640 1760 pixel. Substituting data into formula (1), the calculated resolution is 0.06 m, and the test result is between 0.1 and 0.12 m. 3.3. Regional panoramic image of Yuxi County After the UAS flight parameters were determined, we selected Yuxi County for flying test in order to obtain the panoramic image with high resolution and verify our UAS performance. Yuxi County
Fig. 6. Images taken from three different flight heights. (a) 300 m, (b) 500 m, and (c) 800 m.
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Fig. 7. The flight plan automatically calculated by Pix4UAV.
has a northeast–southwest distance of 8 km and a northwest– southeast distance of 6 km. The total area for mapping is about 42 km2. According to the overlap requirement, at least 25 lines are needed at 800 m flight height. In order to obtain more auxiliary data, the final number of lines is 30, and the total distance is 200 km. 1711 Images (the size of each image is 3 MB) are obtained in three flights, and the amount of data is 20 GB. After excluding images exceeding a certain threshold for maximum deviance from a nadir position during image acquisition or images with deviating flying height, 1610 images are left and software Pix4UAV is used to rapidly splice these images. The automatically calculated flight plan is shown in Fig. 7. In the triangulation process, the total keypoint observation and 3D points for bundle block adjustment is 1,268,824 and 387,784, respectively, while the mean reprojection error is 0.711432 pixels. The geotag localization variances calculated by Pix4UAV are 0.251682 m for X, 0.258808 m for Y, and 0.374265 for Z. Finally, the panoramic image of Yuxi County with high resolution is obtained by using point cloud densification in Pix4UAV. The violin strings of Nie Er Square, which is the landmark of Yuxi County, can be clearly distinguished by magnifying the Square step by step, as shown in Fig. 8.
referred to as Lushan Earthquake) (Fang et al., 2013). The location of the mainshock determined by China Earthquake Network Center is 30.31N, 103.01E and the focal depth is 13 km. This earthquake caused 193 fatalities, 25 missing and more than ten thousand injuries. Direct economic losses were estimated more than ten billion Yuan (RMB). In order to verify our system performance in this earthquake, we started at 15:00 (Beijing Time) from Beijing on the same day and reached the Lushan field headquarter at 12:00 on April 22. After preliminary understanding of the disaster, we designed the flight plan and committed it to China Earthquake Administration for censoring. When the plan was approved, we carried out the aerial flight on April 29 and 30. It totally took us nine hours to complete this flight. The flight plan was from Longmen Township to Lushan County, and the total round-trip distance was about 40 km (Fig. 9(a)). Table 2 is the longitude and latitude of the flight line waypoints. Similar flight parameters acquired from the field test in Yuxi County were used in this flight and the flight height was set to 600 m. 1101 Images were obtained from this flight for 25.51 km2 disaster area mapping and the total amount of data was about 9.16 GB. We spent two hours on splicing these images with Pix4UAV and the result is shown in Fig. 9(b).
4. Application in Ms7.0 Lushan Earthquake
5. Discussion
At 08:02 am on April 20, 2013 (Beijing Time), a strong earthquake (Ms7.0) occurred in Lushan County, Sichuan Province (hereafter
The results derived from the processing workflow gain high potential for post-earthquake epicentral area mapping, although
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Fig. 8. Panorama and landmark of Yuxi county (Nie Er Square).
Fig. 9. (a) The flight plan and (b) the splicing results for Lushan Earthquake.
this workflow is less accurate than those with installed fieldmeasured GCPs. Compared to traditional remote sensing data, these data products are still much more detailed and accurate. The image resolution of ca. 10–15 cm is approximately one order of magnitude better than recent high-resolution images taken by WorldView or Quickbird. Larger areas may be analyzed in considerable detail with such commercially available imagery. Anyhow UAS data from flying height of around 800 m account for the coverage of post-earthquake epicentral areas with even higher precision at user-specified times, repeat rates and lighting conditions. In our experience and for our UAS, the flying height must not be below 300 m, otherwise the accuracy of the initial exterior orientation values taken from the in-flight GPS logs would be too
low compared to the area size covered by the image. If the initial position of the image during the triangulation process is too inaccurate, no overlap with the neighboring images may be computed. Thus, no tie points and therefore no relative orientation between the images can be established. Larger image extents acquired at higher flying heights decrease the relative mispositioning of the images. Accordingly, this allows the necessary initial triangulation and subsequent refinement by Pix4UAV software. Image processing that aims for results with a high level of details is rather work-intensive and still requires future improvement. However, processing required manual input may also be seen as an advantage in that the user remains in a position with more control over the process (Hendrickx et al., 2011). Recent techniques
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Table 2 Longitude and latitude of the flight line waypoints. No.
Long.
Lat.
No.
Long.
Lat.
No.
Long.
Lat.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
102.877 102.927 102.875 102.920 102.886 102.893 102.933 102.868 102.942 102.897 102.915 102.894 102.870 102.948 102.868 102.900 102.955 102.863 102.863 102.956 102.953
30.153 30.158 30.161 30.162 30.162 30.164 30.164 30.167 30.165 30.168 30.170 30.173 30.174 30.177 30.178 30.178 30.181 30.182 30.188 30.190 30.193
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
102.866 102.863 102.865 102.958 102.852 102.857 102.850 102.846 102.955 102.959 102.961 102.965 102.970 102.968 102.974 102.986 102.999 103.014 102.827 103.023 103.033
30.193 30.199 30.202 30.204 30.207 30.208 30.211 30.212 30.215 30.215 30.219 30.224 30.224 30.229 30.232 30.233 30.243 30.249 30.252 30.264 30.266
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
102.819 103.040 102.799 103.044 102.791 103.037 103.038 102.790 102.786 103.035 102.783 103.045 102.772 102.778 102.786 102.795 102.803 102.807 102.812
30.270 30.281 30.290 30.291 30.292 30.297 30.300 30.304 30.306 30.310 30.316 30.321 30.330 30.332 30.340 30.342 30.349 30.363 30.371
may lead to further facilitation, such as the workflows based on Structure from Motion (SfM) techniques (Turner et al., 2012).
6. Conclusions Earth observation and GIS techniques can significantly contribute to improving efforts in developing proper disaster mitigation strategies, and providing relevant agencies with very important information for alleviating impacts of a disaster and relief management. However, the traditional use of satellite and aerial images for this task has been challenged by technical and financial issues. UAS is becoming increasingly popular as photogrammetric platforms for civilian use due to their relatively low cost, ease of operation and the emergence of low cost navigation and imaging sensors with performances comparable to higher priced sensors. The operational nature and cost factors make this technology in applicable level to build a low cost mapping system. This paper has presented the application of an UAS for rapid image collection of post-earthquake disaster. Data acquisition at specified scales was successfully performed with the chosen fixedwing UAS. Tests showed that the system plays an important role in the work of investigating and gathering information about disaster in earthquake epicentral areas, such as road detection, secondary disaster investigation and rapid disaster evaluation. It can effectively provide earthquake information to the salvation headquarters for swiftly developing the relief measures and improving the efficiency of emergency rescue. For the image analysis, a photogrammetric workflow was applied to cope with the very high resolution of the acquired SFAPs taken from large flying heights (i.e., 800 m above ground). The image resolution still remains below 15 cm at these flying heights. Since the aerial survey was carried out in post-earthquake epicentral area and covered a very large area, no GCPs were distributed in the field. Instead, direct georeferencing using the UAS GPS log was applied for creating the image block. The log files contain – among other information – the measured X/Y/Z GPS position for each image acquired as well as the information on tilt of the image axes (κ, φ and ω). This information was used as initial values of exterior orientation for the images in the photogrammetric block file. The accuracy of these values is directly out-
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putted by the software Pix4UAV. It depends on the measurement accuracy and precision of the GPS and IMU units, and the alignment of the camera with respect to the IMU coordinate system. Moreover, there may possibly occur a time-lag between triggering command and actual triggering of the camera, which may influence the accuracy. These values can therefore be expected to deviate up to several meters and degrees from the actual values. We will investigate these problems in future work.
Acknowledgments The authors highly appreciate the Editor Jef Caers and two anonymous reviewers for their very constructive suggestions and critics, which helped in improving the manuscript. We also thank Prof. Zhong Zheng for proof reading the manuscript. This research was supported by Seismological Research Project number 201108002. Research was also partially funded by Seismic Monitoring System Operation & Maintenance, Project number 043207.
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