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International Journal of Remote Sensing
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Evaluating the Potentiality of Using Control- free Images from a Mini Unmanned Aerial
Vehicle (UAV) and Structure-from-Motion (SfM) Photogrammetry to Measure Paleoseismic Offsets
Xue Li, Baosong Xiong, Zhaode Yuan, Kefeng He, Xiaoli Liu, Zhumei Liu &
Zhaoqing Shen
To cite this article: Xue Li, Baosong Xiong, Zhaode Yuan, Kefeng He, Xiaoli Liu, Zhumei Liu
& Zhaoqing Shen (2021) Evaluating the Potentiality of Using Control-free Images from a Mini Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) Photogrammetry to Measure Paleoseismic Offsets, International Journal of Remote Sensing, 42:7, 2417-2439, DOI:
10.1080/01431161.2020.1862434
To link to this article: https://doi.org/10.1080/01431161.2020.1862434
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Published online: 04 Jan 2021.
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Evaluating the Potentiality of Using Control-free Images from a Mini Unmanned Aerial Vehicle (UAV) and Structure-from- Motion (SfM) Photogrammetry to Measure Paleoseismic Offsets
Xue Lia,b, Baosong Xionga, Zhaode Yuanc, Kefeng Hed, Xiaoli Liua,b, Zhumei Liua and Zhaoqing Shene
aKey Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan, China; bInstitute of Disaster Prevention Science and Technology, Sanhe, Hebei, China; cState Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China; dSchool of Geodesy and Geomatics, Wuhan University, Wuhan, China; eSchool of Highway, Chang’an University, Xi’an, China
ABSTRACT
Paleoseismic offsets are important parameters for evaluating fault activity. With the development and popularization of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) photogram- metry, more and more low-cost mini-UAVs have been used for geoscience studies like active faults and paleoearthquakes. In this study, we take the Gebi ridge in the middle of the Altyn Tagh fault (ATF) in western China as an example of using the control-free images acquired by a mini-UAV and SfM to measure paleoseismic offsets. The measurement accuracies of the control-free images acquired by a mini-UAV were evaluated, and then the horizontal offsets of the land surface caused by paleoearthquakes were mea- sured. After comparing the total number and anomalies of paleoearthquake events identified by UAV-based photos with those revealed by geological trenches nearby, the following con- clusions can be drawn. (1) Although the absolute positioning accu- racy of the control-free image from the mini-UAV and SfM is poor, the accuracy of relative horizontal measurement is acceptable. (2) Without the help of ground control points (GCPs), the accuracy of relative vertical measurement for aerial images is not sufficient to measure vertical offset. (3) Oblique photography can improve not only the accuracy of paleoseismic landform mapping but also flight safety. (4) Up to 11 paleoearthquakes have been identified through paleoseismic offset measurements using control-free images in the study area. And both the total number and the anomalies of paleoearthquakes are consistent with the geological evidence in nearby geological trenches. By taking into account the above fac- tors, it can be concluded that it is feasible to measure the horizontal offset of the land surface caused by paleoearthquakes using con- trol-free images from mini-UAVs and SfM.
ARTICLE HISTORY Received 23 February 2020 Accepted 3 November 2020 KEYWORDS
Unmanned Aerial Vehicle;
Structure from Motion;
Oblique Photography;
Relative Distance;
Paleoseismic Offset
CONTACT Xue Li [email protected] Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China
2021, VOL. 42, NO. 7, 2417–2439
https://doi.org/10.1080/01431161.2020.1862434
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any med- ium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
1. Introduction
Earthquakes are among the most dangerous natural disasters that threaten human survival and safety. Throughout the 20th Century, the total number of deaths caused by earthquakes around the world has accounted for more than half of all deaths caused by all types of natural disasters (Engdahl 2002). It is well acquainted that destructive earth- quakes are highly correlated with fault activities. Most of the epicentres with magnitude greater than 6 are distributed along active fault zones. Evidence of paleoearthquake activities found on the active faults can help to identify the recurrence cycle of the earthquakes and assess the risk of earthquakes. Paleoearthquake refers to the earthquake event that has been recorded geologically while unrecorded in human history, whose recurrence interval ranges from hundreds to tens of thousands of years. Through studying paleoearthquakes, researchers can understand more about the mechanism of earth- quakes, and make effective predictions to reduce the loss of life and property (Zhang et al. 2013; Peng et al. 2017).
Remote sensing technology has played an important role in the study of active faults and paleoearthquakes. Over the past 20 years, the quantity and quality of tectonic movements measurements made by using satellites have increased dramatically.
Remote sensing technology has improved our ability to observe geometric features of active faults (Elliott, Walters, and Wright 2016). High-resolution optical remote sensing images can help to identify the ground ruptures caused by recent destructive earthquakes (Xu et al. 2015). Moreover, they can also help to identify paleoearthquakes by quantitative analysis of tectonic geomorphologies such as terraces, alluvial fans, and stream channels.
For example, Klinger et al. (2011) has found evidence of ground surface slip caused by five paleoearthquakes along the Fuyun fault in China using Quickbird satellite imagery.
Compared with satellite imagery, airborne LiDAR (Light Detection and Ranging) data can provide more detailed 3D land-surface models. Chen et al. (2014) quantitatively extracted tectonic geomorphology feature parameters along active faults using airborne LiDAR data. Wei, Arrowsmith, and He (2015a) used high-precision terrain data from airborne LiDAR to study the descent and uplift of river terraces caused by fault activities.
Zielke et al. (2010), and Zielke and Arrowsmith (2012, 2015) proposed a method to measure the tectonic geomorphic offset for strike-slip active faults, based on high- resolution DEM (Digital Elevation Model) data acquired by airborne LiDAR, and then applied the method to the measurement of the ground surface offset caused by earth- quakes occurring in 1857 and earlier on the San Andreas Fault. Ferrater, Arrowsmith, and Masana (2015) obtained the ground surface offsets caused by the latest earthquake on the Alhama de Murcia Fault by using airborne LiDAR.
In recent years, with the development of unmanned aerial vehicle (UAV) and Structure- from-Motion (SfM) photogrammetric technology, the ability to obtain fine 3D (three- dimensional) models of the ground surface has been enhanced significantly (Fonstad et al. 2013; Hugenholtz et al. 2013; Tonkin et al. 2014; Clapuyt, Vanacker, and Van Oost 2015; Qu, Huang, and Zhang 2018). Some studies have confirmed that it is possible for UAV-based SfM to generate the ground surface 3D model with the same density as airborne LiDAR, especially in the sparsely vegetated areas (Steve and Arko 2012;
Johnson et al. 2014; Steve, Arko, and Jon 2015; Wei et al. 2015b; Nouwakpo, Weltz, and Mcgwire 2016; Sanz-Ablanedo et al. 2018). As UAVs have the advantage of low cost, easy
operation and portability, UAV-based SfM method is widely used in various industries (Colomina and Molina 2014; Gomez, Hayakawa, and Obanawa 2015; Smith, Carrivick, and Quincey 2015; Ding et al. 2019; Xiang, Xia, and Zhang 2019), including agricultural survey (Christiansen et al. 2017), the coastal environment (Mancini et al. 2013), damaged build- ings (Nex et al. 2019), dam modelling (Ridolfi et al. 2017), and field path recording (Ćwiąkała et al. 2018).
UAV-based SfM has also played an important role in geosciences research (James and Robson 2012; Westoby et al. 2012). It has been widely used in dynamic glacier monitoring (Ryan et al. 2015; Ewertowski et al. 2019), landslide monitoring (Lucieer, Jong, and Turner 2014), and river terrace surveys (Javernick, Brasington, and Caruso 2014; Li et al. 2019;
Woodget et al. 2015). As for the study of active faults and paleoearthquakes, SfM is often used to obtain the mosaic photo of trench wall (Reitman et al. 2015; Bemis et al. 2014).
Moreover, to analyse the tectonic geomorphology features, high-resolution orthophotos and digital surface models (DSMs) are often built by UAV-based SfM (Johnson et al. 2014;
Bemis et al. 2014; Bi et al. 2016). Based on UAVs and SfM, some studies have been carried out to measure the land surface offset caused by active strike-slip faults to estimate their slip rate (Angster et al. 2016; Gao et al. 2017).
Due to limitations such as time-effectiveness and terrain environment, many studies have focused on improving the measurement accuracy of UAVs without ground control points (GCPs) (Zhao et al. 2008; Chiang, Tsai, and Chu 2012; Chudley et al. 2018). Moreover, the emergence of consumer-grade UAVs equipped with real – time kinematic global positioning system (RTK-GPS) ensured the accuracy of surveying without GCPs (Fazeli, Samadzadegan, and Dadrasjavan 2016). Still, a large number of UAVs without RTK-GPS are used for the study of active faults and paleoearthquakes (Wei et al. 2015b; Johnson et al. 2014; Bemis et al. 2014; Bi et al. 2016; Angster et al. 2016; Gao et al. 2017). Considering that it is very difficult to lay out GCPs in certain study area, it is necessary to evaluate the accuracy of control-free images from low-cost mini-UAVs without RTK-GPS.
This study intends to evaluate the potential of measuring paleoseismic offsets based on a low-cost mini-UAV and SfM photogrammetry without GCPs. First, the effects of absolute positioning accuracy with or without GCPs will be discussed. Then, according to the characteristics of the paleoseismic offsets measurements, the relative distance accuracies of control-free aerial images will be evaluated.
2. Study area
The study area of this research is located on the Gebi ridge with about 3500 m long and 500 m wide, which lies in the western part of the Xorkoli section of the central Altyn Tagh fault (ATF). The ATF is located at the boundary between the Qinghai-Tibet Plateau and the Tarim Basin in western China (Figure 1(a)). It is considered to be one of the longest active strike-slip faults in the world, with a total length of about 2000 km (Molnar and Tapponnier 1975). The ATF is a sinistral strike-slip fault, and the slip rates in the western part of the ATF are higher and gradually decreases towards the east direction. The Xorkoli section is located in the centre of the ATF striking ENE (69°) and is bounded by the Pingding Mountain bend to the west and the Aksay bend to the east (Figure 1(b)).
According to GPS observations, the slip rate of the central ATF is about 10 mm year−1 (Elliott et al. 2015, 2018).
The latest paleoseismic study of the Xorkoli section comes from Yuan et al. (2018a), who dug two trenches T1 and T2, namely copper mine trenches (Figure 1(c)). T1 was dug in 2013 and is 17 m long, 3 m wide, and 4.5 m deep. T2 was dug in 2016 and is 105 m long (main body 50 m), 20 m wide, and 13 m deep. The trench walls of T1 and T2 reveal at least 11 paleoearthquakes in the last 6000 years. Moreover, the geological evidence shows that the existence of the sixth of 11 paleoearthquakes is uncertain (Yuan 2018b).
The copper mine trenches are located in a narrow fault valley trending NE with an average altitude of about 3000 m. To the southeast of the fault valley lays the Qingxinjie Mountain, and to the northwest of the fault valley lays the Jinyan Mountain. The altitudes of the mountain peaks on the north and south sides of the fault valley are about 5000 m.
The geomorphology in the fault valley is dominated by alluvial fans, salt lakes, and plains in arid climates. The geomorphology near the trenches is not suitable for paleoseismic offset measurement, because the landmarks, such as stream channels, are not well developed. Therefore, the Gebi ridge, which is about 60 km west of the copper mine trench along the ATF, was selected as the study area for paleoseismic offset measurement in this research.
The Gebi ridge is about 30 km away from the Pingding Mountain bend. The ATF cuts through the ridge to form a narrow and steep V-shaped canyon. The length of the canyon is about 6 km, the widest part is about 200 m long, and the narrowest part is less than 10 m long. The average altitude of the canyon is about 3800 m, and the altitudes of the ridges on both sides of the canyon are more than 4000 m. Because of the steep slopes on both sides of the canyon, the stream channels are well developed here. The landmarks of these channels cutting through the ATF are prominent, and this is one of the most important reasons why the Gebi ridge was chosen as the study area.
Figure 1. Location of the study area and its spatial relationship with the Copper Mine trenches: (a) location of the Xorkoli section of the ATF in China; (b) distribution of the study area and the Copper Mine trenches in the Xorkoli section; (c) aerial image of the Copper Mine trenches T1 and T2; (d) partial mosaic photo of T2 trench wall.
3. Materials and methods 3.1. Mini-UAV and SfM software
The low-cost mini-UAV chosen in this study is the Phantom 4 by DJI (DJI Technologies, Shenzhen, China), which is a small quadcopter with a wingspan of 350 mm, a propeller diameter of 24 mm, and a propeller length of 127 mm. The weight of the quadcopter is about 1.4 kg, and the maximum horizontal flight speed is 15 m s−1. The Phantom 4 has a dual-mode satellite positioning module, which receives signals from GPS and Global Navigation Satellite System (GLONASS). After integrating the inertial measurement unit (IMU), altimeter, and compass, the aircraft can fly along a route with a vertical accuracy of ±0.5 m and a horizontal accuracy of
±1.5 m. The Phantom 4 integrates a consumer-grade camera with a CMOS (Complementary Metal Oxide Semiconductor) sensor, which is 8.8 mm in length and 6.6 mm in width, and it has 12.4 million effective pixels. The equivalent focal length of the lens is 20 mm (Field of View, FOV 94°), the physical focal length is 3.61 mm, the lens aperture is fixed at f2.8, and the maximum resolution of the obtained photo is 4000 × 3000 pixels. The camera is installed on three-degree-of- freedom gimbals to maintain stability during image acquisition. The Phantom 4 is controlled by an external tablet PC and a remote controller, and its working fre- quency ranges from 2.4 GHz to 2.483 GHz with a maximum effective control distance of about 5000 m in Federal Communications Commission (FCC) mode and 3500 m in Conformite Europeenne (CE) mode (DJI Technologies 2016).
DJI Technologies company has released its own route planning software for the Phantom 4, DJI GS Pro. Since DJI has opened its Software Development Kit (SDK) and Application Programming Interface (API) to all users to develop application software, many third-party route planning software packages have become available for the DJI series of drones. The route planning software used in this study is Altizure, which is distributed by Altizure.com (Shenzhen, China) and is suitable for oblique photography. As the Phantom 4 has only one camera, to achieve oblique photography, Altizure automa- tically generates five flights for the designated work area, one flight for ortho photo- graphy and the other four flights for oblique photography from the east, south, west, and north directions, respectively. After a comprehensive consideration of factors such as terrain, control distance, and battery power, we planned to conduct five aerial photo- graphy missions (APMs) to survey the study area, each of which contained five flights (Figure 2). According to the altitude of different takeoff points in each APM, the flight height was set from 120 m to 150 m. The flight speed was set as 15 m s−1. The camera angle for oblique photography was fixed at 45°, and both the across- and along-track overlap rates were fixed to 70%. Then, the photo interval was automatically set to 3 seconds.
ContextCapture (CC) 4.1.0 (from Smart3D, Bentley Systems, Inc., Pennsylvania, United States) was used to produce the orthophoto and DSM from the aerial photos acquired by Phantom 4. The workflow was as follows: first, we created a new project and added all original aerial photos. As each photo has a location information including latitude, longitude, and altitude, recorded by means of the satellite posi- tioning module on the drone (the coordinate system is WGS84), the approximate
spatial distribution of the camera location can therefore be obtained before aero- triangulation. Second, aerotriangulation was performed to obtain the precise coordi- nates of the camera location, the elements of interior and exterior camera orientation and the spare point clouds. Third, the spatial scale of the reconstruction and the reasonable size of tiles were set up by users, and then dense point clouds could be obtained by performing aerotriangulation. Finally, a reference 3D model and ortho- photo/DSM were generated from the dense point clouds.
3.2. UAV image accuracy assessment 3.2.1. Ground control points layout
Arrangement of GCPs is usually needed in aerial survey to constrain the aerotriangulation calculations and improve the spatial accuracy of the orthophoto/DSM and point clouds. If the number of GCPs is sufficient and their layout is reasonable, the mapping accuracy of orthophoto/DSM produced from the images acquired by the non-metric camera of the mini-UAV can reach tens of centimetres or even several centimetres (Su and Wang 2012;
Lin, Xie, and Su 2014; Zhang et al. 2018).
In order to evaluate the accuracies of the control-free images acquired by the mini- UAV, 28 ground test points (GTPs) were arranged along the north and south sides of the ATF (Figure 2). Every GTP was a black/white target printed on a piece of paper with 29.7 cm long and 21.0 cm wide, and the paper was fixed on the ground by nails and rocks.
The coordinate of each GTP was measured by a Trimble R8 Global Navigation Satellite System (GNSS) RTK composed of a base station and a rover with a dynamic horizontal accuracy specification of ±1 cm +1 ppm RMS (Root Mean Square) and a dynamic vertical accuracy specification of ±2 cm +1 ppm RMS.
3.2.2. Accuracy evaluation of absolute positioning
The absolute positioning accuracy can be evaluated by comparing errors between the coordinates of the GTPs on orthophoto/DSM and their actual coordinates measured by RTK. The errors along the East direction, North direction and elevation are represented as dx, dy and dz respectively. These errors can be evaluated by comparing measured and extracted coordinated values:
Figure 2. Spatial distribution of designed APMs and GTPs. Every APM contains five flights, one flight for ortho photography and the other four flights for oblique photography.
dx¼Ximage XRTK (1)
dy¼Yimage YRTK (2)
dz¼Zimage ZRTK (3)
where Ximage, Yimage and Zimage represent 3-dimensional coordinates of GTP measured on orthophoto and DSM, respectively, while XRTK, YRTK and ZRTK represent 3-dimen- sional coordinates of GTP measured on ground by RTK-GPS. Moreover, the error vectors lying on the horizontal plane (i.e., dH) and 3-dimension (i.e., d3D) are eval- uated for each GTP:
dH¼
ffiffiffiffiffiffiffiffiffiffiffiffiffi d2xþd2y q
(4) d3D¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi d2xþd2yþd2z q
(5) To estimate the overall quality of absolute positioning accuracy, the root-mean-square error (RMSE) is evaluated as follows:
RMSE¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n
Xn
i¼1
d2i s
(6) where di is the deviation of the ith measured value from the ith true value, n represents the total number of GTPs. Based on the above method, the absolute positioning accura- cies of drone images with and without control point correction can be evaluated.
3.2.3. Accuracy evaluation of relative distance
It is noted that the requirements for paleoseismic offset measurement are different from those of traditional topographic mapping. First, paleoseismic offset measurement is a kind of linear measurement along a strike-slip fault, and it focuses on the distance between two points. Secondly, paleoseismic offset measurement pays more attention to the measurement of horizontal displacement, as this is an important parameter for the study of strike-slip faults. Therefore, it can be concluded that paleoseismic offset measure- ment requires higher accuracy of relative distance than for absolute positioning.
To assess relative distance accuracy, the horizontal (i.e., εHi;j) and 3-dimensional (i.e., ε3Di;j) distance errors of every two GTPs are calculated as follows:
εHi;j ¼ LHi;j L0Hi;j
��
�
��
� (7)
ε3Di;j ¼ L3Di;j L03D
i;j
��
�
��
� (8)
where LHi;j and L0H
i;j represent the horizontal distance between the ith GTP and the jth GTP measured on orthophoto/DSM and by RTK, respectively, while L3Di;j and L03Di;j represent the 3-dimensional distance between the ith GTP and the jth GTP measured on orthophoto/
DSM and by RTK. The RMSE, Mean and Standard Deviation (Std Dev) are used to evaluate the relative distance accuracy.
To reveal the correlation between the length and accuracy of relative distance mea- surements, all the εHi;j calculated from GTP pairs are divided into different groups. The Mk and σk indicate the mean value and the standard deviation of group k, respectively. Thus, the upper and lower limits of the confidence interval of relative distance measurements can be expressed as Mk+σk and Mk-σk, respectively. Finally, a linear fitting method is used to fit the peak of Mk+σk, the trough of Mk-σk, and Mk, respectively.
3.3. Quantification and documentation of paleoseismic offset
It is well known that the horizontal movements on both sides of a strike-slip fault caused by paleoearthquakes often produce tectonic geomorphological markers (TGMs), such as triangular facets, staggered terraces and stream channels. High-resolution optical remote sensing images can help to identify these typical tectonic geomorphological features, which can be used to measure the offset of the land surface displacement caused by paleoearthquakes (Klinger et al. 2011; Zielke, Klinger, and Arrowsmith 2015). The elements that usually need to be interpreted include the geometry and spatial distribution of the fault line and the stream or gully channels crossing the fault line.
According to the geometry and spatial distribution of the fault line and the gully channels, reasonable TGMs should be identified first. Then, the offsets of land surface caused by paleoearthquakes can be measured. The direction of the gully channel often changes at the intersection with the fault. Therefore, trendlines of channel head and tail and their projection distance onto the fault line can help measure the offsets. When there is a unique match of one channel head and one channel tail across the fault, the projection distance A-B can be measured as the offset (Figure 3(a)). When there are one
Figure 3. Schematic diagram of the measurement of paleoearthquakes offsets from a dextral strike- slip fault: (a) one channel head and one channel tail; (b) one channel head and two-channel tails; (c) two-channel heads and one channel tail; (d) two-channel heads and two-channel tails; (e) one main and one secondary head, two main tails; (f) two-channel heads and one tail along the fault trace, the cross ‘×’ represents that A-Aʹ is not the offset caused by paleoearthquakes.
channel head and two-channel tails and B was formed after the abandonment of Bʹ, both A-B and A-Bʹ are offsets (Figure 3(b)). When there are two-channel heads and one channel tail, extra information about the head and tail, for example relative size, should be considered to determine which one of A-B and Aʹ-B is the offset, or whether both A-B and Aʹ-B are offsets (Figure 3(c)). When there are two-channel heads and two- channel tails, A-B and Aʹ-Bʹ are the offsets. However, extra information about A and Bʹ is needed to judge whether A-Bʹ is an offset (Figure 3(d)). When there are one main head, one secondary head and two main tails, Aʹ is likely due to headward erosion of B’ into fault scarp, and therefore A-B and A-Bʹ are the offsets and Aʹ-Bʹrequires extra information to be determined (Figure 3(e)). When there are two-channel heads and one tail along the fault trace, A has no relation with Aʹ. Therefore, A-Aʹ is not the offset (Figure 3(f)).
The paleoseismic offsets can be measured on the interpreted orthophoto (Klinger et al.
2011). However, geological background knowledge and errors from interpretation and manual measurement can affect the accuracy of offset measurement. Based on MATLAB scripts, Zielke developed the LaDiCaoz software, which can automatically measure the precise offsets from terrain data using a terrain profile fitting algorithm (Zielke and Arrowsmith 2012). With the help of the DSM, which is generated from the aerial photos acquired by the UAV, the optimal displacement amount can be calculated (Figure 4).
As LaDiCaoz only uses high-resolution DSM for measurement, when the depths of the channel head and tail are shallow, it is not always possible to find the optimal offset. The offsets of shallower stream channels are suitable for measurement on the orthophoto, because their spectrum and texture are significantly different from background, especially in sparsely populated arid areas. Therefore, in this study, both of orthophoto and DSM generated by UAV-based SfM are used to measure the paleoseismic offsets.
4. Results
4.1. Measurement accuracy of Mini-UAV 4.1.1. Orthophoto and DSM without GCPs
The total number of aerial photos acquired by the Phantom 4 in the study area is 2615, i.e.
485 orthophotos and 2130 oblique photos. After aerotriangulation without GCP correc- tion, the camera location of every photo was adjusted and 385,620 tie points were extracted automatically. The position uncertainties (PU) of camera locations and tie points
Figure 4. Back slipping and visual assessment of channel reconstruction: (a) position of fault trace, channel trace, and profile location; (b) top shows red profile with overlay of optimal back-slipped blue profile. Middle shows initial position and shape of blue profile. Bottom shows the goodness of fit and the optimal displacement; (c) hillshade plot, back slipped by optimal displacement amount (here 7.1 m) for visual assessment of the reconstruction.
(Figure 5(a,c)), the position distance error (PDE) of camera locations (Figure 5(b)) and reprojection error (RE) of tie points (Figure 5(d)) are shown in Table 1. Then, a dense point cloud with about 1.5 billion points was created by 3D reconstruction, and the average density of the point clouds is about 660 pixels/m2. The resolution of aerial photos ranges from 2.4 cm to 15 cm, and the final resolution of the generated orthophoto and DSM (Figure 5(e,f)) is about 7 cm.
4.1.2. Absolute positioning accuracy
The coordinates of the GTPs measured from the orthophoto were compared with those measured from the RTK. The maximum, minimum, average, and RMSE of East direction error (dx), North direction error (dy), elevation error (dz), horizontal distance error (dH) and 3-dimensional distance error (d3D) are shown in Table 2. It can be found that dz is much
Figure 5. Quality report of orthophoto and DSM generated from CC: (a) position uncertainties of corrected camera locations; (b) distance error of camera locations after aerotriangulation; (c) tie point position uncertainties; (d) reprojection error of tie points; (e) orthophoto; (f) hillshade map generated from the DSM.
Table 1. Quality report of aerotriangulation from CC.
Camera Locations Tie Points
PU (m)
PDE (m) PU (m) RE (pixels) x-direction y-direction z-direction
Minimum 0.0016 0.0018 0.0015 0.79 0.0079 0.01
Mean/Median* 0.0068 0.0078 0.0069 12.07* 0.0957* 0.71
Maximum 0.0858 0.2172 0.1208 33.42 8.4920 1.86
‘*’ indicates the value is median.
larger than dx and dy, which results in d3D much larger than dH. As seen from Figure 6(a), the horizontal direction errors of the GTPs (dx and dy) are concentrated in the fourth quadrant. And the histogram of horizontal distance error shows an obvious normal distribution (Figure 6(d)). It indicates that there are significant systematic and accidental errors in horizontal positioning on the control-free images. Moreover, the histograms of dz
and d3D are very similar, which shows that dz dominates d3D. 4.1.3. Relative distance accuracy
The number of GTPs in this study is 28. It means that there are 378 GTP pairs. The horizontal distance of these GTP pairs measured by RTK ranges from 73.66 m to 3106.35 m. The correlation between the actual distance and the measured distance on the orthophoto is shown in Figure 7(a), and the residual error after fitting these two kinds of distance measurement values is shown in Figure 7(b). It indicates that the relative horizontal distance on the orthophoto without GCPs is very close to the actual distance.
The Minimum, Maximum, Mean, Std Dev and RMSE of ƐH and Ɛ3D are shown in Table 3.
The data illustrates that the measurement accuracy of the relative horizontal distance is much higher than that of the relative 3-dimensional distance. Figure 7(c) shows the relationship between the measured distance and the measurement error. It can be found that the measurement error of relative horizontal distance increases linearly with the increase of the measured distance.
Table 2. Absolute position accuracy.
dx (m) dy (m) dz (m) dH (m) d3D (m)
Minimum 0.70 −1.86 119.86 2.56 119.97
Mean 2.27 −3.50 140.53 4.25 140.59
Maximum 3.20 −5.29 157.45 5.72 157.51
RMSE 2.57 2.71 132.27 3.73 132.32
Figure 6. Evaluation of horizontal absolute positioning accuracy: (a) distribution of dx and dy; (b) histogram of dx; (c) histogram of dy; (d) histogram of horizontal distance error dH. The red line is a fitted normal curve; (e) histogram of dz;(f) histogram of 3-dimension distance error d3D.
In order to quantify the relationship between the horizontal measured dis- tance and the measurement error, the distances of all GTP pairs were grouped at 50 m intervals, and all GTP paris were divided into 32 groups. The mean value, peak and trough values of confidence intervals for relative horizontal measurement errors are shown in Figure 8(a). The coefficient of determination (R2) of linear fitting for the mean, peak and trough values are 0.8618, 0.9090 and 0.9596, respectively. According to the fitting formulas, when the measured horizontal relative distance is less than 100 m, the mean error is less than 0.257 m, and the confidence interval of the horizontal error is less than 0.683 m. These errors are usually smaller than random error of paleoseismic offset measurement. Moreover, as almost all the measurements of paleoseismic offset in this study are less than 100 m, it can be concluded that the accuracies of the horizontal measurements of paleoseismic offset using control-free images from a mini-UAV are acceptable and the measured results are reliable.
Figure 7. Evaluation of horizontal relative distance accuracy: (a) correlation between actual distance of GTP pairs and distance measured on orthophoto; (b) residual error after linear fitting; (c) relationship between measured distance and measurement error.
Table 3. Relative distance accuracy.
Minimum (m) Maximum (m) Mean (m) Std Dev (m) RMSE (m)
ƐH 0.0014 2.7977 0.7917 0.5642 0.9717
Ɛ3D 0.0157 37.5911 12.4432 9.2517 15.4984
Figure 8. Relation between relative distance error and measured distance: (a) horizontal error; (b) vertical error. The confidence interval of the error is labelled in grey.
However, for relative vertical error, there is no significant linear trend between the measured distance and measurement error (Figure 8(b)). It indicates that the measure- ment error in the vertical direction does not increase linearly with the increase of the measured distance. Moreover, the measurement error in the vertical direction is much greater than that in the horizontal direction. Considering that the vertical displacement caused by the movement of a strike-slip fault is often far less than the horizontal displacement, it can be concluded that the control-free image from a low-cost mini- UAV can only be used to measure the horizontal offset but not the vertical offset of paleoearthquakes.
4.2. Paleoseismic offset
Based on the orthophoto and DSM generated from the control-free aerial photos, the geometric distributions of ATF and gully channels traces are interpreted as shown in Figure 9. In the eastern part of the study area, the ATF cuts through the Quaternary alluvial deposits and alluvial fans, whereas the gully channels are developed mainly in the western part of the study area, which runs along with the ATF for about 2 km. Taking the western end of the study area as the starting point of the survey, paleoearthquake offsets distributed along the ATF within a range of about 2 km east from the starting point were measured by the method described in section 3.3.
The recurring offset data usually indicate that they are caused by the same paleoearth- quake, while isolated offset data are usually not considered to be the evidence of paleoearthquake. Taking site A in Figure 9 as an example, the gully channels on both sides of the ATF are labelled numerically and alphabetically, respectively, where the channel head identified by a number and the channel tail by a letter. As the ATF is a sinistral strike-slip fault, the direction of back slip is dextral during reconstruction. With 7.1 ± 0.6 m back slip, four channels in Site A are connected (1-A, 2-B, 4-G, and 6-L). It indicates that the horizontal offset in the area caused by the last earthquake is about 7.1 ± 0.6 m. By continuing to use dextral back slip, another four earlier paleoearthquakes were identified, with cumulative offsets of 13.7 ± 1.1 m, 22.4 ± 1.1 m, 33.4 ± 1.1 m, and 42.8 ± 1.3 m, respectively (Figure 10).
Figure 9. Distribution of ATF and gully channel traces. The background image is the hillshade map of the study area generated from the DSM.
In this study, a total of 202 gully channel offsets were measured to analyse the probable number of paleoearthquakes (PNP). According to the probability distribution of offset data, up to 11 paleoearthquakes were identified in the study area (Figure 11).
After assigning all measurements to the corresponding PNP, the average offset (AO) and standard deviation from the same paleoearthquake measurements are regarded as the paleoseismic offset. Finally, the cumulative offsets of the 11 paleoearthquakes are 7.08 ± 0.58 m, 13.72 ± 1.17 m, 22.39 ± 1.09 m, 33.39 ± 1.07 m, 42.78 ± 1.36 m, 48.48 ± 0.79 m, 57.47 ± 1.52 m, 70.23 ± 1.76 m, 83.82 ± 1.86 m, 90.27 ± 1.15 m, and 99.64 ± 1.15 m, respectively.
5. Discussion
5.1. Effectiveness and reliability of paleoearthquake identification
From the above test results of measurement accuracy, it can be seen that although the absolute positioning accuracy of the orthophoto and DSM generated by UAV-based SfM without GCPs is poor, the horizontal relative distance accuracy of these images is accep- table. Especially when the horizontal relative distance is less than 100 m, the horizontal error is usually less than 0.257 ± 0.341 m. Due to the ambiguity of channel shape and undercut depth, the horizontal distance measurement error of paleoearthquake offset is usually about 1 m (Klinger et al. 2011; Zielke, Klinger, and Arrowsmith 2015), which is higher than the horizontal relative error on imaging products mentioned above.
Therefore, it is effective to measure the paleoearthquake offsets by using SfM and aerial photos acquired by a mini-UAV without GCPs.
After comparing with the paleoearthquake events revealed by the copper mine trenches, it can be concluded that the results of paleoearthquake offset measurement from UAV photos without GCPs are reliable for the following two reasons.
Figure 10. Successive reconstructions of offset channels at site A. The channels on the two sides of the ATF are labelled numerically (channel head) and alphabetically (channel tail), respectively.
First, the paleoearthquake events in Gebi ridge are consistent with those in the copper mine trenches. Although the Gebi ridge is 60 km away from the copper mine trenches, both of them belong to the Xorkoli section of the ATF. It means that these two places have the same seismic activity background, because the ground displace- ment caused by a large earthquake may be as long as several tens to hundreds of kilometres. Therefore, the earthquake events revealed at the copper mine trenches can be used as a reference at the Gebi ridge.
Second, the number of paleoearthquakes and the sequence of abnormal paleoearthquakes identified by the offset measurement are the same as the paleoearthquake events revealed by the copper mine trenches. The probability distribution of offset in Figure 11 shown that there are 11 paleoearthquake events, which is the same with the evidence found in the copper mine trenches (Yuan et al. 2018a; Yuan 2018b). In addition, the frequency of geomorphic offset observations of paleoearthquake events are inversely proportional to their occurrence. It means that the earlier a paleoearthquake occur, the less evidence of geomorphic offset can be retained due to erosion or weathering. It has been verified that the number of paleoseismic offset measurements decreases expo- nentially with the increasing offset size (Klinger et al. 2011). Figure 12 shows the relation between the number of measurements (NoMs) and the offset size.
Clearly, the NoMs of the 11 paleoearthquakes decreases exponentially as the offset size increases. Moreover, the NoMs of the sixth offset was obviously less, which indicates that there is doubt whether it was caused by a paleoearthquake.
This result is highly consistent with that found in the copper mine trenches. It further proves that it is feasible and reliable to study the paleoseismic offsets using a control-free mini-UAV.
Figure 11. Probability distribution of paleoseismic offsets. The comparison between measurements and synthetic density probabilities computed from the offset data. Offset values are labelled with symbols representing the probable number of paleoearthquakes (PNP). The average offset (AO) was calculated from the offset data that were identified as coming from the same paleoearthquake, and the number of measurements (NoMs) indicates the credibility of the paleoearthquake.
5.2. Necessity for ground control points
The feasibility of paleoearthquake offset measurement by using UAV-based SfM without GCPs has been verified, but this does not mean that the GCPs are useless. To verify the influence of GCPs on measurement accuracy, 13 out of 28 GTPs were selected as GCPs to be used in aerotriangulation to correct the orthophoto and DSM. It should be ensured that the number of GCPs distributed on both sides of the fault line should be equal, and the spacing of the GCPs should be approximately the same. In addition, GCPs should be selected at both the edge and the middle of the study area. It can be found from Table 4 that after GCPs correction, both absolute positioning accuracy and relative distance accuracy have been greatly improved. Moreover, the huge improvement of the accuracy in the vertical direction greatly improve the measurement accuracy of the 3-dimensional distance. It indicates that if the high-precision images are intended to obtain from UAV- based SfM, GCPs are undoubtedly necessary, especially when vertical measurement accuracy is required. The corrected aerial images may even meet the need to measure the vertical offset caused by paleoearthquakes.
After the GCPs correction, the horizontal and vertical error of relative distance mea- surement are shown in Figure 13. Compared with Figure 8, it can be found that horizontal Figure 12. Relation between number of measurements (NoMs) and offset size. The sixth offset is an anomaly because of its smaller NoMs.
Table 4. Absolute positioning accuracy and relative distance accuracy with or without GCPs.
Absolute Position Accuracy
RMSE dx (m)
RMSE dy
(m)
RMSE dz
(m) RMSE dH (m) RMSE d3D (m)
no GCPs 2.57 2.71 132.27 3.73 132.32
with GCPs 0.29 0.18 0.12 0.34 0.36
Relative Distance Accuracy Mean ƐH (m) Std Dev ƐH
(m)
RMSE ƐH
(m)
Mean Ɛ3D
(m)
Std Dev Ɛ3D
(m)
RMSE Ɛ3D (m)
no GCPs 0.79 0.56 0.97 12.44 9.25 15.50
with GCPs 0.26 0.26 0.36 0.14 0.10 0.17
error no longer increases with measured distance, and the distribution of the horizontal and vertical errors from corrected images is more random. It indicates that the trend of measurement error increasing with measured distance is caused by mini-UAV imaging system, and GCP correction can eliminate or reduce these system errors.
5.3. Advantages of oblique photography
Oblique photography is good at capturing the side textures of buildings, so it is often used for 3D buildings reconstruction, and the ortho photos are more than capable for generating a DSM at most of time. However, oblique photography is not dispensable in the mapping of paleoseismic geomorphology. Although paleoearthquake offset mea- surement is mainly based on orthophoto and DSM acquired by UAV, and is more concerned with horizontal relative distance measurement, oblique photography can help to increase the overlap of aerial images and improve mapping accuracy, especially in steep terrain area. Taking the study area of this paper as an example, the overlap of aerial photos from oblique photography (including ortho photography) and the ortho- only photography are shown in the Figure 14. It can be found that oblique photography can not only obtain a larger mapping area but also maintain a high overlap of the entire area. It is well known that low overlap will reduce the mapping accuracy (Wei et al. 2015b;
Zhang et al. 2018). In the absence of GCPs, the overlap of aerial photos has great impact on mapping accuracy.
Terrain is one of the most important factors affecting the overlap of mini-UAV images. If the terrain altitudes of the survey area change, the overlap of the aerial photos will also change. The typical landforms of active faults are often distributed in front of steep
Figure 13. Relation of horizontal and vertical error with measurement distance on the corrected images. The dashed lines indicate the average values of the horizontal and vertical error.
mountains. Considering that most mini-UAVs have a limited flying height (usually less than 500 m), when the mini-UAV approaches a steep mountain slope, the overlap of aerial orthophotos will drop sharply as shown in Figure 15. Assuming that the designated flight height is h0 and the ortho-photography interval is 2 seconds, when the actual flight height is h1, the required photography interval should be adjusted to 1.5 seconds, and when the actual altitude is h2, the required photography interval should be adjusted to 1 second.
Existing route planning software does not adjust the flight height or the photo interval according to the terrain, and therefore the actual overlap of the aerial images will decrease. When the terrain is quite steep, this will even lead to hollows in mosaic images.
Figure 14. Camera location and aerial photos overlap of the study area. The number of overlapping aerial photos of each pixel is calculated by aerotriangulation: (a) ortho and oblique photography; (b) ortho-only photography.
Figure 15. Impact of terrain on the overlap of aerial photos acquired by mini-UAVs.
However, this problem can be solved by oblique photography. Moreover, oblique photo- graphy can also save the mini-UAV from collisions due to being too close to the moun- tains, and thereby helping to improve flight safety.
6. Conclusions
In this study, paleoseismic offset measurements are performed using low-cost mini-UAVs and SfM photogrammetry after accuracy evaluation of aerial photos without GCPs on the Gebi ridge in the Xorkoli section of the ATF. The results show that the absolute positioning accuracies of control-free images from the mini-UAV are poor. The RMSEs of 28 GTPs in the x-, y-, and z-directions are 2.57 m, 2.71 m, and 132.27 m, respectively. However, the measurement accuracy of the relative horizontal distance as calculated from 378 GTP pairs is acceptable, and the error of relative horizontal measurements increased linearly with the measured distance. If the measurement distance is less than 100 m, the horizontal error is less than 0.257 ± 0.341 m. After the measurement and consistency analysis of the paleoseismic offset data, 11 paleoearthquake events are identified; however, there are some doubts about the sixth earthquake because of the smaller NoMs. This result is completely consistent with the findings in a nearby geological trench.
In summary, it is feasible to measure the horizontal offset of the land surface caused by paleoearthquakes using control-free images from a mini-UAV. However, GCPs are needed to measure the vertical offset. Moreover, oblique photography can help improve the overlap of aerial photos and ensure flight safety, especially in areas with steep terrain.
Acknowledgements
The authors wish to thank Longfei Han and Jingtang Tan from the Institute of Geology, China Earthquake Administration for their assistance in the field survey and software teaching.
Disclosure statement
The authors declare no conflict of interest.
Funding
This study was supported by the foundation of director of Institute of Seismology, CEA (Grant No.
IS201616248 and IS201716155), the National Natural Science Foundation of China (Grant No.
41401428) and Civil Engineering Design Academy of Chang’an University CO.LTD. (Grant No.
220221180379).
Data availability statement
The data that support the findings of this study are available from the corresponding author, X. Li, upon reasonable request.
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