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Article

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

by
Xiangli He
1,2,
Chong Xu
1,2,*,
Shengquan Tang
3,
Yuandong Huang
1,4,
Wenwen Qi
1,2 and
Zikang Xiao
1,2
1
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
2
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
3
Beijing Aerospace Institue for Metrology and Measurement Technology, Beijing 100076, China
4
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(15), 2866; https://doi.org/10.3390/rs16152866
Submission received: 3 July 2024 / Revised: 2 August 2024 / Accepted: 3 August 2024 / Published: 5 August 2024
(This article belongs to the Topic Geotechnics for Hazard Mitigation)
Browse Figures
Versions Notes

Abstract

:
The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

1. Introduction

Natural disasters such as earthquakes, landslides, floods, and forest and grassland fires have caused serious damage to human society and the ecological environment [1,2,3]. When natural disasters occur, the ground infrastructure and communication equipment are often destructed and damaged, which seriously restricts the timeliness of transmitting disaster area information to distant command centers and makes it difficult to achieve real-time information transmission [4,5]. Therefore, in the event of sudden major natural disasters, providing fast, accurate, and effective means of emergency information acquisition and transmission is crucial for real-time emergency monitoring and rapid response to emergency rescue [6,7,8].
So far, the methods of obtaining disaster area information mainly include ground survey (monitoring), satellite remote sensing, and aerial remote sensing [9,10,11]. When or after a disaster occurs, if relevant investigators are arranged to conduct a detailed survey in the disaster area, it will not only pose a great threat to their life safety but also be extremely costly and inefficient [12]. Although ground monitoring (such as ground remote sensing vehicles, ground monitoring stations, etc.) can obtain information chronically and quickly, it is difficult to achieve long-distance and large-area monitoring due to the limitations of ground conditions and environment [13]. Especially when roads in disaster areas are damaged, ground monitoring methods are basically ineffective and cannot complete the task. During the last two decades, there has been an increase in the use of remote sensing technology for large-scale and high-resolution mapping and monitoring of natural hazards [14]. Remote sensing has become an important tool for hazards risk assessment and rescue operations [15,16]. For example, satellite remote sensing uses optical sensors carried on satellites to observe the ground and transmit ground information to ground receiving stations in real time [9,17]. In terms of resolution, the Worldview-4 satellite launched by the United States on 11 November 2016 has a resolution of 0.31 m, which can obtain the highest resolution civilian optical remote sensing data currently available [18]. The successful launch of first sub-meter-level stereoscopic satellite, Gaofen-7, marks that the resolution of low-orbit remote sensing satellite can reach 0.65 m in China [19]. However, these satellites are not in geosynchronous orbit and have a long re-entry period, which makes the observation of the same location periodic and delayed. Such timeliness is not enough to meet the needs of post-disaster emergency response. In general, although satellite remote sensing can obtain information related to emergencies over a large area and with high resolution, it has the disadvantages of time limit, fixed orbit, long revisit period, and other inflexibility [20]. Even geosynchronous orbit satellites, such as Gaofen-5, can continuously observe the same location, but the information resolution is low, about 50 m [19]. If there is heavy fog or cloud cover, satellite remote sensing cannot meet the requirements of monitoring the details of the disaster area [13,21].
There has been a significant improvement in the spatial resolution of remote sensing technology in particular with the introduction of aerial remote sensing [22]. Low-cost aerial remote sensing is now indispensable for onsite rapid data collection to aid disaster management [23,24,25]. Aerial remote sensing is a method of collecting data on the ground topography and landforms by means of photography, video recording, and topographic mapping through photoelectric equipment on a flying platform [26]. Aerial remote sensing is very effective in obtaining relevant disaster information accurately truthfully and comprehensively [27]. In particular, through telephoto high-resolution photography, higher-resolution images can be obtained compared to satellite remote sensing, providing high-precision data support for the emergency response [21,28,29]. In addition, compared to floating platforms (near-space vehicles) and short-range unmanned aerial vehicles (UAV), long range manned (unmanned) aircraft have extremely fast flight speeds and can reach the disaster site within a few hundred kilometers around the airport, basically within two hours, which can meet the effect of rapid response. These indicate that aerial remote sensing technology is an effective supplementary means for ground observation and satellite remote sensing, having advantages such as high spatial resolution, high temporal resolution, high degree of automation, flexibility, and low cost [14,30,31]. Aerial remote sensing technology has also been successfully applied to multiple earthquake and geological disaster cases [27,31]. For example, after the Ms 8.0 Wenchuan earthquake in 2008 [32], the Ms 7.0 Jiuzhaigou earthquake in 2017 [33,34], and other disasters [35,36,37,38], aerial remote sensing was used to immediately investigate and obtain data such as high-resolution topography and real 3D models of the disaster area, which provided strong support for rapid and accurate assessment of the disaster situation and played an important role in emergency response and post-earthquake recovery and reconstruction [31].
However, although the image resolution obtained by aerial remote sensing is relatively high, due to the fact that airborne cameras are generally ordinary digital cameras with small image frames, a large number of images are obtained with significant image distortion [39]. Moreover, due to the large tilt angle, irregular tilt direction, and large amplitude of turbulence during shooting, there are differences in scale and rotation between adjacent images [40]. Chen et al. (2017) adopt a combination of oblique and vertical shooting methods to improve this problem [39], while others use image correction techniques on the images obtained by the aviation platform for detailed monitoring of natural hazards [14,41]. Additionally, the inertial stabilization platform is used to isolate the influence of aircraft carrier angular motion, vibration, etc. on the load visual axis, which is the basis and guarantee of high-precision aerial remote sensing imaging [42]. Regarding the aerial remote sensing platform, in addition to the continuous improvement of imaging technology, its data transmission stability and speed are also key performance factors affecting disaster rescue. The use of aerial remote sensing to obtain these observations is attractive, but often challenged by lack of suitable solutions to get live images back to decision makers [43]. In previous cases, the images were mostly read and interpreted after landing [31,34,35,44]. The aerial remote sensing platform may be beyond radio line of sight during flight, so its communication cannot rely solely on ground communication. Satellite communication is essential to ensure real-time acquisition of observation results from aerial remote sensing platforms [43]. At the beginning of aerial remote sensing applications, the bandwidth of satellite communication was relatively low and could not meet the real-time transmission requirements of high-definition remote sensing images. With the development of technology and the growth of application demand, the proportion of constellations used for satellite communication and earth observation is increasing, currently even reaching 68% [45]. In recent years, microsatellites are mainly used for earth observation and remote sensing, and large satellite constellations make many simultaneous and distributed measurements or observations possible, such as for disaster monitoring, and increase temporal resolution of collected data [45,46]. Moreover, the use of higher frequencies than the S-band (mainly for telemetry) and X-band (for data transmission), such as the Ku-, K-, and Ka-bands, has become more widely available, which allows higher data rates [45,47]. These are the latest technologies for large spacecraft and enable real-time transmission and acquisition of high-definition photos and videos required for disaster emergency response, but they are still emerging technologies in the field of small aerial remote sensing platform.
In this paper, we present a novel technical solution for an aerial remote sensing platform based on real-time satellite communication to achieve rapid arrival at disaster sites, high-quality acquisition of the latest disaster site information, and real-time transmission of on-site information. The innovation of this technical solution lies in the application of civilian heavy-duty unmanned aerial vehicles, self-developed orthographic image stabilization platform, and particularly airborne mobile communication devices that can achieve a transmission rate of no less than 2 Mbps in the Ku band. Additionally, as the actual performance of the aerial remote sensing platform potentially is influenced by multiple factors [14,42,47], we innovatively conducted double verification for the feasibility of this technical solution through both ground and flight tests. That is, we evaluate the performance of the proposed aerial remote sensing platform and its applicability for real-time emergency monitoring in disaster events by verifying the image stabilization capability of the image stabilization platform and the communication capability of the airborne satellite communication system in ground and flight tests.

2. Design Scheme of Aerial Remote Sensing Platform Based on Real-Time Satellite Communication

In terms of natural disaster monitoring, aerial remote sensing technology has the advantages of high imaging quality and flexibility. Combined with real-time satellite communication technology, it has the characteristics of real-time monitoring. This aerial remote sensing platform with real-time satellite communication capability mainly consists of aircraft platform, ground observation system, wireless communication system, ground satellite information receiving station, data processing and application analysis system, etc. (Figure 1). This design is based on aircraft as a platform, using remote sensing equipment installed in it to obtain raw data from natural disaster areas. Within the effective range, ground information can be effectively collected by adjusting the flight altitude to form images or other format data materials. The information is transmitted in real time to the geostationary satellite through the onboard satellite base station, and then sent to the designated ground fixed base station, so that global synchronous and uninterrupted data transmission can be achieved. Accordingly, the obtained on-site data of the disaster area can be transmitted in real time to the command center to formulate disaster response plans in the shortest possible time. Due to the real-time uplink and downlink communication capability of satellite transmission, the command center can also command aerial platforms to adjust their routes in a timely manner, conduct mobile investigations of disaster sites, and achieve real-time monitoring and emergency response in the event of natural disasters.
In the aerial remote sensing platform, the aircraft is the heavy-duty twin-rotor unmanned aerial vehicle, Camel 500, produced by Chongqing Tuohang Technology Co., Ltd., Chongqing, China This aircraft has a maximum takeoff weight of 500 kg and a load capacity of 50% (with the highest load ratio of unmanned aerial vehicles of the same class). It has a transportation capacity of flying with a load of 200 kg for 1 h, about 100 km, or a load of 150 kg for 3 h, about 300 km. This aircraft has a large loading space, multiple hanging points, rich delivery methods, and good adaptability to the center of gravity, which is much better than conventional helicopters. The ground satellite information receiving station and data processing and application analysis system are mature equipment, which will not be detailed in this paper.
The ground observation system is a combination of an orthographic image stabilization platform and a visible light camera loaded on it. The image stabilization platform is a mechanical image stabilization device. Its basic principle is to detect the shaking of the camera platform through inertial devices such as gyroscope sensors, and then adjust the servo system to isolate the external shaking of the photoelectric equipment, achieving the goal of stabilizing the image. Due to the equipment base fixed on the aircraft shaking with the turbulence of the aircraft, if there is no stabilization platform, the camera equipment will shake accordingly. In cases of significant shaking, this can seriously affect the image stitching effect and the shooting effect of photoelectric devices with longer exposure times. The airborne orthographic stabilization platform is generally installed at the belly position of the aircraft. Drill holes in the lower part of the camera and install a visible transparent cover. By controlling the self-stability of the frame in both pitch and roll directions, it ensures that the optical device lens loaded on the frame is always perpendicular to the ground plane during flight and perceives and collects data (mainly images) from ground targets. Optical systems have high resolution performance, so high-precision ground information can be obtained. Based on the spatial stability of the shooting of camera, multiple frames of images can be effectively concatenated to obtain ground remote sensing information for larger areas. The designed technical indicators of the image stabilization platform (Table 1) include external dimensions, working temperatures, total weight, effective load (camera and lens), operation range in both pitch and roll states, maximum angular velocity and acceleration in the search state, and stability accuracy within the swing range.
The wireless communication system is composed of airborne mobile communication equipment. Through the mobile communication equipment, mobile carriers such as vehicles, ships, and airplanes can track satellites in real time during when they are moving, continuously transmitting multimedia information such as voice, data, and images, which can meet the needs of emergency communication for military and civilian under various mobile conditions. During the transmission process, the mobile communication equipment autonomously tracks satellites, fully utilizing the characteristics of large coverage area, strong anti-interference ability, and stable line of satellite communication. It can achieve communication functions of point-to-point, point-to- multipoint, and point-to-the main station of mobile satellites. The technical indicators of the mobile communication equipment in this design scheme are shown in Table 2.

3. Results and Analysis of the Ground Test

Due to the non-standard design of earth observation systems and wireless communication systems, the performance of products from different manufacturers varies greatly. Therefore, it is necessary to verify whether their performance meets the needs of real-time monitoring of natural disasters. The performance verification work is divided into two steps, namely the ground test and the flight test. The ground test involves simulating the flight environment through ground carriers to assess the performance of the orthographic stabilization platform and the transmission capability of the airborne mobile communication equipment.

3.1. Performance Test of the Orthographic Stabilization Platform

3.1.1. Analysis of Key Indicators

The purpose of the orthographic stabilization platform is to ensure that the optical axis of the visible light camera is always perpendicular to the earth’s surface, ensuring clear imaging quality and accurate positioning of the target object. Therefore, its key indicator is stability accuracy.
In a stable state, the image stabilization platform should compensate for the swaying and undulating motion of the carrier body and maintain the spatial stability of the optical axis. Considering the typical indicators of the aircraft (flight altitude of 3000 m, speed less than 300 km/h, fuselage roll of ±10 °, cycle of 7 s), in order to meet the task of detecting ground targets and effectively concatenate the photos taken by the camera, the optical axis fluctuation must be limited to a certain range to ensure that the overlapping area of adjacent two frames of images is not less than 1/4. Considering the aircraft’s speed and photography frequency (one frame of image per second), the aircraft’s travel per second is 83.3 m. If the overlap area of the two frames of images is not less than 1/4, the coverage distance of each frame of image in the flight direction is 111 m. The corresponding field of view (FOV) angle and stability accuracy threshold can be expressed using the following formulas:
α t a r g = t g − 1 a / L
β = 1 4 α t a r g
Among them, a is the coverage distance, L is the target distance, α t a r g is the FOV angle, and β is the stable accuracy threshold. The stable accuracy calculation principle is shown in Figure 2. When the coverage distance is 111 m and the target distance is 3000 m, the FOV angle is 2.12°. Therefore, if the stable accuracy is less than 0.530°, the image stitching is effective.

3.1.2. Test Method and Results of the Stability Accuracy

The test of stability accuracy is achieved through optical path bending and an inclinometer scheme. The optical path turning test is designed with a two-axis manual swing table and a photoelectric bending device—that is, a 45° flat mirror is placed below the lens, the monitored target directly below it is converted into the target directly in front by bending the light path (Figure 3). The swing table is shaken with a swinging range and a swinging period, and the trajectory data of the target in front is recorded and imaged in the camera. Its peak is then taken as the stable accuracy.
On 8 September 2020, after setting up the optical path bending test conditions, different swing conditions were applied to the manual swing device. The test results are shown in Table 3. When the swinging period is 20 s and the amplitude is ±15 °, the stability accuracy is the best, only ±0.04°. When the swinging period is 15 s and the amplitude is ±15°, the stability accuracy is ±0.045°. When the swinging period is 7 s and the amplitude is ±10°, the stability accuracy is ±0.05°. All results are better than the calculated stability accuracy threshold of 0.535° mentioned above, which indicates that the stability accuracy test on ground under simulated airborne conditions meets the usage requirements.
The inclinometer scheme involves installing an inclinometer on the inner frame. When the swinging table shakes, the inclinometer data is collected and analyzed. This scheme is easy to operate. However, due to the poor performance of the inclinometer in dynamic environments, the inclinometer can only maintain accuracy in a stationary environment. When the equipment has high stability, the inclinometer on the stable surface is approximately in a stationary environment. At this time, the credibility of the test data is high, otherwise the data error is large. In general, the inclinometer scheme is a mutually influencing scheme. In the case of poor test results, the actual indicators of the equipment may not necessarily be worse than the test results. However, if the test results meet the requirements of the indicators, the actual indicators of the equipment must exceed the required indicators. On 8 September 2020, the load was equipped with an inclinometer and the manual swing device was subjected to the same swing conditions as the optical path bending test. The test results are shown in Table 3. When the swinging period is 20 s and the amplitude is ±15°, the stability accuracy is the best, only ±0.035°. When the swinging period is 15 s and the amplitude is ±15°, the stability accuracy is ±0.042°. When the swinging period is 7 s and the amplitude is ±10°, the stability accuracy is ±0.045°. All results are better than the results from optical path bending test, also indicating that its stability accuracy met the requirements of the usage conditions.
In the ground test, the swing environment of the orthographic stabilization platform fully compares the swing environment during the flight process, and a margin of over 50% is reserved for the motor capacity, which means that the stabilization platform can maintain stability accuracy in flight maneuvers that are more severe than the ground test environment. In addition, the components used in the orthographic stabilization platform are all wide temperature devices, and the machined parts have undergone surface anodizing or chrome plating treatment, which can adapt to external environments ranging from −30 °C to 65 °C. Moreover, the entire equipment was placed in a high and low temperature box for high and low temperature operation testing and assessment, and it was able to maintain normal working conditions, indicating that the equipment has the adaptability to the onboard environment.

3.2. Transmission Capability Test of the Airborne Communication System

3.2.1. Analysis of Key Indicators

The airborne communication system is used to achieve real-time communication between the flight carrier and the ground data processing center in the flight environment. The working link of the airborne communication system is shown in Figure 4, which operates in a bidirectional transmission mode of “aircraft–satellite–ground” and “ground–satellite–aircraft”. In order to meet the data volume and speed requirements of real-time monitoring of natural disasters, airborne communication systems should have the following functions. First, the real-time transmission system has a bidirectional real-time transmission function. Second, the reliability of the servo system connecting to the satellite reaches over 98%, and the success rate of image transmission reaches over 80%. Third, the data transmission rate reaches 2 Mbps. Fourth, the data compression rate of the real-time transmission system can be selected between 2 and 128 times; fifth, the opening time of the real-time transmission system equipment shall not exceed 5 min (excluding aircraft takeoff time), and the data transmission delay time shall not exceed 5 s. Based on the above functions, the key indicators are transmission speed and transmission success rate. Only with high speed and success rate can real-time and data integrity requirements be met.

3.2.2. Test Method and Results

In order to achieve test in the ground environment, a ground motion carrier is chosen for the communication system in the motion environment, approximating a bumpy flight state, and a ground simulation communication link is built. In this test, the manual pushing method is used in a suitable terrain, loading the communication antenna (Figure 5a). As shown in Figure 5a, the transmitting end is a flat antenna (with an equivalent diameter of 0.45 m) in the airborne mobile communication equipment. A parabolic antenna of 4.5 m is used to receive data in the ground station. The testing satellite is AsiaSat-5 communication satellite with a working frequency of 14,470 MHz. Static and dynamic tests are conducted at 256 Kbps, 512 Kbps, 1024 Kbps, and 2048 Kbps transmission bandwidths. The static test refers to the process where the onboard antenna is aligned with the satellite and no longer moves, while the dynamic test refers to the process of aligning the airborne antenna with the satellite and then moving the antenna base position along an 8-shaped trajectory on a site with a slight slope on the ground. During the testing process, the communication system will send the pre-stored image data to the processing center near the ground station via satellite. The processing center will then transmit the received images back to the communication system. Finally, the success rate of the first transmission is calculated.
On 20 September 2020, the communication capability test on ground was conducted in an open parking lot in Beijing with a slope of 20°. The road conditions were sandy and gravel, providing a certain shaking and bumpy environment for the test. The test results are shown in Table 4. As shown in the table, the transmission rate of the mobile communication equipment meets the requirement of 2 Mbps bandwidth. Under this bandwidth condition, the data packet loss rate is only 0.2% in the static environment, and data packet loss rate is 0.5% in the dynamic environment, both of which meet the requirements. Based on the use of image compression function (no less than 16 times), this indicates that 1080P video and high-resolution images of no less than 32 Mbps can be transmitted in real-time in the later stage.
In summary, the dynamic environment provided by the ground test conditions of the communication equipment is relatively harsh compared to the flight environment, with a pitch slope close to 20°, exceeding the typical aircraft sway amplitude of about 10°. At the same time, simulate the vibration of the aircraft fuselage through testing on sand and gravel road surfaces. Therefore, the experimental results in this environment have high reference value. In addition, the airborne communication system is driven using servo motors and leaves a margin of over 30% for motor capability, which means that the communication system can maintain tracking accuracy in flight maneuvers that are more severe than ground test environments. The components used in the communication system are also wide temperature devices, and the machined parts have undergone surface anodizing or chrome plating treatment, which can adapt to external environments ranging from −30 °C to 65 °C. The entire equipment was placed in a high and low temperature box for high and low temperature operation testing and assessment, and it can also maintain normal working conditions, indicating that the equipment can adapt to the airborne environment.

4. Results and Analysis of the Flight Test

Flight test is an evaluation of the task completion ability of equipment with environmental adaptability working on a flight carrier after passing ground performance testing. The flight test was carried out on the premise of good performance of satellite communication equipment and orthographic stabilization platform by a heavy-duty twin rotor unmanned aerial vehicle-Camel 500, at the flight test site of unmanned aerial vehicle manufacturer Chongqing Camel Navigation Technology Co., Ltd. and on Nanpingba Island in Chongqing, China. The flight test site is relatively small, with a length of ~300 m and a width of ~300 m, so the test mainly focuses on hovering and small-scale flight for debugging of aircraft, platform equipment, and communication link. Nanpingba Island is located in the middle of the Yangtze River, with dense gullies on both sides. The narrow river channel—where the river flows rapidly and flood disasters, landslides, and mudslides are prone to occur—makes it a typical site for natural disasters. Therefore, it is used as the observation site for this flight test. Due to airspace control restrictions, this flight test site is limited to an island area of ~2000 m length and 1000 m width for turnaround flights to assess the reliability, stability, and real-time performance of the system.

4.1. Test Objects and Technical Indicators

The flight test also verifies whether the performance of ground observation systems and wireless communication systems meets the needs of real-time monitoring of natural disasters. In flight tests, the ground observation system suitable for natural disaster monitoring is a combination of orthographic stabilization platform and visible light cameras loaded on it, while the wireless communication system is composed of airborne communication equipment. The orthographic stabilization platform and airborne communication equipment are non-standard-design equipment, and their performance during the flight test to a certain extent reflects their ability to complete tasks. Therefore, this test mainly analyzes the performance of the orthographic stabilization platform and its optical load as well as the transmission capacity of the airborne communication equipment in the flight environment.
The flight system includes aircraft, communication equipment, and orthographic stabilization platform (Figure 5b). The technical indicators of the communication equipment and the orthographic stabilization platform in flight tests are the same as those of the ground test mentioned above. Due to the differences between the aircraft used in flight tests and the typical aircraft expected during ground testing, the key indicators of the stabilization platform vary due to changes in flight altitude, speed, and other parameters. Considering the index condition of Camel 500 (flight altitude of 200–500 m, speed less than 100 km/h, fuselage roll of ±10°, and cycle of 7 s) in flight tests, in order to meet the task of detecting ground targets and effectively splicing the photos taken by the camera, the optical axis must be limited to a certain range of fluctuations to ensure that the overlapping area of adjacent two frames of images is not less than 1/4. Considering the aircraft’s speed and photography frequency of 1 s, the aircraft’s travel per second is ~28 m. If the overlap area of the two frames of images is not less than 1/4, the coverage distance of each frame of images in the flight direction is ~37 m. When the target distance is 500 m, the corresponding FOV angle is calculated by Formula (1) to be 4.23°. According to Formula (2), if the stable accuracy is less than 1.05°, the effectiveness of image stitching can be ensured.

4.2. Flight Test Process

The working link of the airborne communication equipment is also shown in Figure 4, which is a bidirectional working mode. The communication link includes a mobile communication system, satellites, and satellite ground stations. The transmitting end is an airborne mobile communication antenna (equivalent diameter of 0.45 m) manufactured by China National Science and Technology Corporation and installed on the Camel 500 aircraft (Figure 5b). It can achieve a transmission rate of no less than 2 Mbps in the Ku band under propeller obstruction. The ground station uses a 4.5 m parabolic antenna for reception, located within the China Academy of Water Resources and Hydropower Sciences. The testing satellite is AsiaSat-5 communication satellite with a working frequency of 1419 MHz, for static and dynamic testing at a transmission bandwidth of 2048 Kbps.
The test work includes communication link debugging, aircraft installation matching, and quasi-static/small-scale hanging flight testing. On 16 January 2022, an uplink and downlink test was conducted on the ground station at the office area of the Chinese Academy of Water Resources and Hydropower Sciences, with a test bandwidth of 2 Mbps. The results show that its function is good. On 17 January 2022, a transmission and reception performance test was conducted at the site of Chongqing Tuohang Technology Co., Ltd. The test bandwidth was confirmed to be 2 Mbps and the transmission and reception were normal through self-transmit and self-receive. The installation and matching process of the aircraft took about 15 days. The Camel 500 aircraft was used as the main body and loaded under the fuselage. The total loaded weight is less than 150 kg. The installation-matching effect of the aircraft is shown in Figure 5b, with the communication equipment located on the right side of the aircraft and the stabilization platform located on the lower side of the aircraft.
The quasi-static hanging flight is conducted at the flight test site of Chongqing Tuohang Technology Co., Ltd. Through static hovering, slow turning, and small-scale flight, the imaging quality of the stabilization platform and the data transmission speed during the flight process are analyzed and the related parameters are adjusted in real time, providing technical preparation for subsequent typical field flight tests. Due to the lateral installation of the mobile communication system, when the flight direction is different, the space between the antenna and the satellite may be obstructed by metal propellers, resulting in a loss of some bandwidth. To test the impact of obstruction on communication bandwidth, the aircraft rotates 90° in the static process every 1 min to test the transmission effect. Small-scale flights are bounded by the airspace of the flight test site. During the flight, the aircraft tilts in both pitch and roll directions, with a tilt angle of no more than 10°, which tests the ability of the stabilization platform to maintain vertical shooting of the ground during maneuvering. During the testing process, the airborne communication system located in Chongqing sent the ground data of the flight test site captured by the industrial camera loaded on the image stabilization platform to the processing center near the Beijing ground station via satellite. The processing center stored the received images and performed image stitching and other tasks.
After completing flight tests at the test site and optimizing equipment performance, link connectivity, and bandwidth, a formal flight test was conducted on 18 January 2022 at a typical disaster site, Nanpingba Island. The communication bandwidth of the flight equipment was configured to be 2 Mbps, and the two flight heights were 250 m and 450 m, respectively. The flight is conducted within the airspace of Nanpingba Island, with a maneuvering speed of no more than 100 km/h. During the flight, the aircraft tilts to a certain extent in the pitch and roll directions, with a tilt angle of no more than 10°, to test the ability of the stabilization platform to maintain vertical shooting of the ground during maneuvering. During the testing process, the airborne communication system located in Chongqing sent the ground data of Nanpingba Island captured by the industrial camera loaded on the image stabilization platform to the processing center near the Beijing ground station via satellite. The processing center stored the received images and performed image stitching and other tasks.

4.3. Flight Test Results

The purpose of the flight test is to connect the real-time communication link of image information, assess the communication anti-interference ability and shooting vibration resistance of equipment in aircraft maneuvering state, and verify the engineering applicability of the system. Therefore, the main assessment includes imaging quality, image concatenability, and real-time data transmission.
In the flight test, the aircraft carried mobile communication equipment and image stabilization platform with a reliable working time of more than 2 h and conducted effective maneuvers at the flight test site. The results show that the image stabilization platform has good image stabilization ability and captured clear images in high-frequency fuselage vibration environments greater than 13 Hz. The communication link has a bandwidth of no less than 2 Mbps and a packet loss rate of no more than 1% in a 13 Hz propeller interference environment. In addition, the aircraft performed small-scale maneuvering flights, with periodic tilting vibrations applied in pitch and roll directions, and then the imaging quality was evaluated. The shooting object of the flight test site is weeds on the ground. Based on the actual size estimation of weeds, with clear leaf contours, the resolution of the image obtained in the flight altitude of 325.3 m is less than 1 cm (Figure 6), which meets the emergency observation requirements. Table 5 shows that the stability accuracy of the equipment under airborne conditions meets the usage requirements.
During the flight test, satellite data transmission was carried out on the captured images of the image stabilization platform using the mobile Communication system. The aircraft hovered in four directions, with each direction separated by 90°, to analyze the impact of the relative positions of the antenna, fuselage, and satellite on communication quality. The ground receiving station located in Beijing received data and analyzed them. The results show that mobile communication equipment has a transmission rate of the bandwidth of 2 Mbps in 114 received packets, without data loss. Considering the use of image compression function (not less than 16 times), it can transmit 1080P real-time video and high-resolution images of not less than 32 Mbps in real-time in the later stage.
The images collected during the Nanpingba Island flight test at altitudes of 250 m and 450 m were concatenated, and the results are shown in Figure 7a,d. Figure 7b,c are enlarged partial views of Figure 7a, from which the situation of farmland and small boats by the river can be clearly captured. Figure 7e is a frame from Figure 7d, from which the conditions of the houses and roads can be clearly seen. From the image quality and continuity of splicing, it can be seen that under the condition of 2 Mbps bandwidth, the packet loss rate is 0%, the transmission is very stable, the imaging and splicing effects are good, the ground buildings are clearly visible, and the landscapes on the ground can be effectively identified. These pieces of information are all of concern in disaster relief. After receiving image information, the Beijing ground station compared the timestamp of the image with the receiving time, with a time delay of no more than 2 s. The above data indicate that in the event of natural disasters, the aerial remote sensing combined with real-time satellite transmission can be used to emergency observation with mature and reliable engineering technology and application conditions.

5. Discussion

In the event of a major natural disaster, providing fast, accurate, and effective means of emergency information collection and transmission is crucial for real-time emergency monitoring and rapid response for emergency rescue [6,7,8]. Aerial remote sensing has gradually become an irreplaceable technical means in modern spatial data acquisition due to its advantages of high spatial and temporal resolution, large scale, and fast mapping over a large area and has been widely applied in many fields [31]. Aerial remote sensing platforms can be divided into two categories based on whether they are piloted: manned and unmanned. In the Wenchuan earthquake in 2008 and the Ya’an earthquake in 2013, manned aircraft were involved in disaster assessment and post-disaster dynamic remote sensing monitoring [31]. However, the cost of this method is high, and it is not flexible enough. With the continuous advancement of science and technology and the further expansion of market demand, unmanned aerial vehicle remote sensing systems dominate in both quantity and application fields [49]. Unmanned aerial vehicles can be further classified into military and civilian, with many different sizes and models. The original aerial remote sensing platforms relied heavily on military aircraft or used small unmanned aerial vehicles. The purchase, maintenance, and operation costs of military aircraft are very high, and flight usually requires strict airspace management and approval, resulting in poor operational flexibility [50]. Small unmanned aerial vehicles have limited battery life and are suitable for short-term tasks [51]. They cannot conduct long-term monitoring and have low payload capacity. They can only carry lighter sensors and camera equipment, which limits the types and accuracy of data collection. Their flight altitude and range are limited, making it difficult to cover large areas. In the solution presented in this paper, we use the Camel 500 produced by Chongqing Tuohang Technology Co., Ltd., which is a civilian heavy-duty twin rotor unmanned aerial vehicle. The cost of using civilian heavy-duty unmanned aerial vehicles for aerial remote sensing platforms is lower than that of military aircraft. They can fly at lower altitudes, are suitable for a wider range of mission requirements, and can operate in areas that are not strictly restricted airspace. Moreover, their operational risks are relatively low, especially when used in densely populated and hazardous areas, making them safer. They also have a long battery life, suitable for long-term monitoring and data acquisition tasks, equipped with high-precision sensors and camera equipment that can obtain high-resolution and high-precision remote sensing data. In summary, civilian heavy-duty drones have significant advantages in terms of cost, flexibility, safety, and data quality [50,52,53].
In the operation of aerial remote sensing systems, although high-quality sensors are used, the image quality obtained is not ideal. The main factor limiting high-resolution imaging is often not caused by electronic or optical systems [54]. The aircraft carrier is difficult to maintain stability due to the influence of gusts, turbulence, etc., which in turn makes it difficult to maintain the stability of the imaging payload visual axis, resulting in degradation of imaging quality [42]. The inertial stabilization platform is used to isolate the influence of aircraft carrier angular motion, vibration, etc., on the load visual axis and is the foundation and guarantee of high-precision aerial remote sensing imaging [55,56]. The orthographic image stabilization platform in our solution is independently developed and designed. Based on the calculation principle (Figure 2), stability accuracy, that is, angular position error less than 0.53°, can meet the requirements of image overlay and stitching. In the ground test, the stability accuracy of the stabilization platform is less than 0.05° (Table 3), far better than the principle’s threshold. Zhou et al. (2019) adopt parameter optimized adaptive composite control combining single neuron with proportional-integral-derivative (PID) to significantly improve the stability accuracy of a stabilization platform, with an angular position error of 0.2904° under dynamic base conditions, which was 39.9% higher than traditional PID control [42]. Nevertheless, the angular position error of the stable platform in our solution is smaller. Additionally, in flight testing, the stabilization platform demonstrated excellent image stabilization capability in high-frequency fuselage vibration environments greater than 13 Hz. The images captured during the test are clear, with a resolution of less than 1 cm and an overlap rate of over 70%. This resolution is much higher than that of satellite remote sensing platforms. For example, the resolution of Worldview-4 satellite can only reach 0.31 m, which is the highest resolution of civilian optical remote sensing data currently available [18]. Generally, the ultra-high-resolution data acquired using aerial remote sensing platforms can reach up to centimeter levels [26]. Bagheri (2017) developed a high-resolution aerial remote-sensing system for precision agriculture and the spatial resolution of the imagery also just was 3.6–95 mm [57]. It can be seen that the image resolution obtained in our testing is comparable to or even better than the current level of aerial remote sensing platforms.
With the continuous improvement of emergency management systems, the requirements for post disaster emergency response to natural disasters such as major earthquakes and geological hazards are becoming increasingly high. After a major disaster occurs, it is urgent not only to quickly obtain information about the disaster area but also to transmit it in real time to decision-making departments to determine emergency rescue plans [8]. Therefore, the speed and accuracy of data transmission are crucial for aerial remote sensing platforms to perform emergency response tasks. These emerging applications and the associate need for transmitting at higher data rate or performing more complex tasks, have raised the need for larger bandwidths and higher-frequency bands [45]. In recent years, the use of higher frequencies than the common VHF/UHF bands such as S-band and X-band has become more widely. Downlinks on S-band would be expected to be able to implement data rates from 100 Kbps to 1 Mbps [45]. In our solution, higher frequency bands such as Ku are used. The testing results show the transmission rate of the mobile communication system meets the requirement of 2 Mbps bandwidth. Under these bandwidth conditions, the packet loss rate in static environments is only 0.2%, while in dynamic environments it is 0.5% (Table 4), with a latency of no more than 2 s. Zulkifley et al. (2021) proposed that for payload communication, drones need to send back real-time telemetry data, images, or videos under remote monitoring, which results in drones requiring approximately 4 Mbps of data throughput under certain settings to achieve 1080P quality streaming video resolution [58]. In our solution, we consider the use of image compression function (not less than 16×), which can transmit 1080P real-time video and high-resolution images of not less than 32 M in real time in the later stage. From this, it can be seen that our solution can fully meet emergency response needs.
So far, disaster management based on aerial remote sensing has been commonly used for building damage assessment after earthquakes. In this assessment, due to its ability to obtain on-site data and process it as information on the area, quantity, rate, and type of damage, rescue teams will have a better understanding of the safe path and potential damage caused by secondary impacts [26]. In addition, it is also used for landslide dynamic monitoring [14,35] and change detection of coastal facilities to help assess the vulnerability caused by natural disasters such as tornadoes [59,60]. However, these applications do not require high real-time performance, but for natural disaster emergency response, fast information acquisition and transmission are required. With the rise of low-cost and high-payload commercial drones in recent years, as well as the maturity of airborne mobile communication equipment technology in the Ku- and KA-bands [45], aerial remote sensing platforms are more maturely applied to natural disaster emergency rescue. For example, during the rainstorm disaster in Henan Province, China on 20–21 July 2021, the “pterosaur-2H” emergency relief UAV produced by Aviation Industry Corporation of China, Ltd., Beijing, China operated for about 8 h in the disaster area. The aerial camera, optoelectronic equipment, and synthetic aperture radar were used to take pictures and monitor the disaster area, and relevant information was sent back to the command center in real time to realize efficient and accurate command of emergency rescue operations (https://mp.weixin.qq.com/s/pk0JGhBuyyrR9a-y1hr65w, accessed on 25 July 2021). According to the test results, it is believed that the aerial remote sensing platform based on real-time satellite communication proposed in this paper will be successfully applied to natural disaster rescue in the future.

6. Conclusions and Prospect

In order to respond to precise and rapid disaster emergency needs, we have proposed a technical solution for emergency monitoring based on an aerial remote sensing platform and verified its feasibility through testing. The conclusions are as follows:
(1) In this study, we designed an aerial remote sensing platform based on real-time satellite communication, which mainly includes aircraft, ground observation system, wireless communication system, ground satellite information receiving station, and data processing and application analysis system. Among them, the aircraft platform is the heavy-duty twin rotor unmanned aerial vehicle, Camel 500, produced by Chongqing Tuohang Technology Co., Ltd. The ground observation system is a combination of an orthographic stabilization platform and a visible light camera loaded on it. The wireless communication system is composed of a mobile communication system using the Ku-band.
(2) In the ground test, under different swinging conditions, the stability accuracies of the orthographic stabilization platform are less than 0.05°, which is better than the theoretical threshold of stability accuracy. The transmission rate of the mobile communication system meets the requirement of 2 Mbps bandwidth. Under these bandwidth conditions, the data packet loss rates are only 0.2% in the static environment and 0.5% in the dynamic environment, both of which meet the requirements.
(3) In the flight test, the orthographic stabilization platform showed good image stabilization ability in high-frequency fuselage vibration environments greater than 13 Hz. The images captured in the test are clear, with a resolution of less than 1 cm, an overlap rate of more than 70%, a transmission rate bandwidth of no less than 2 Mbps, a packet loss rate of no more than 1%, and a time delay of no more than 2 s, all of which meet the emergency observation requirements.
(4) The research results indicate that aerial remote sensing platforms based on real-time satellite communication have enormous potential in natural disaster emergency response. They can not only provide high-quality remote sensing data support but also achieve fast and stable communication with ground command centers. However, further research is needed on the performance of image stabilization platforms in complex flight environments. In the future, as the stability and reliability of satellite communication technology continue to improve and the equipment and technology of aerial remote sensing platforms continue to innovate and improve, the platform will adapt to the needs of different types of natural disaster prevention, reduction, and relief.

Author Contributions

Conceptualization, X.H. and C.X.; methodology, S.T.; validation, X.H., Y.H. and W.Q.; formal analysis, Z.X.; investigation, X.H. and Y.H.; data curation, X.H. and C.X.; writing—original draft preparation, X.H.; writing—review and editing, X.H., C.X., S.T., Y.H., W.Q. and Z.X.; visualization, X.H. and S.T.; funding acquisition, X.H. and C.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant number 42077259) and the Foundation of National Institute of Natural Hazards, Ministry of Emergency Management of China (grant number 2023-JBKY-57; ZDJ2020-10).

Data Availability Statement

The data and materials are available and transparent. The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We appreciate editors and reviewers for their helpful and professional comments on the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ali, R.; Kuriqi, A.; Kisi, O. Human–environment natural disasters interconnection in China: A review. Climate 2020, 8, 48. [Google Scholar] [CrossRef]
  2. Mata-Lima, H.; Alvino-Borba, A.; Pinheiro, A.; Mata-Lima, A.; Almeida, J.A. Impacts of natural disasters on environmental and socio-economic systems: What makes the difference? Ambiente Soc. 2013, 16, 45–64. [Google Scholar] [CrossRef]
  3. Xu, C.; Xu, X.; Yao, X.; Dai, F. Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 2014, 11, 441–461. [Google Scholar] [CrossRef]
  4. Ma, X.; Chen, W.; He, S.; Gao, A.; Zhu, R.; Liu, A. Assumption of application research of field communication system for seismic emergency based on BeiDou Navigation Satellite System. Prog. Earthq. Sci. 2020, 50, 1–7. [Google Scholar]
  5. Ming, H. Discussion on Emergency Communication Support Plan for Disaster Site Rescue. Smart City Appl. 2023, 6, 28–30. [Google Scholar] [CrossRef]
  6. Damaševičius, R.; Bacanin, N.; Misra, S. From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management. J. Sens. Actuator Netw. 2023, 12, 41. [Google Scholar] [CrossRef]
  7. Zhou, L.; Wu, X.; Xu, Z.; Fujita, H. Emergency decision making for natural disasters: An overview. Int. J. Disaster Risk Reduct. 2018, 27, 567–576. [Google Scholar] [CrossRef]
  8. Chen, K.; Fang, C.; Li, R. Research on airborne remote sensing emergency flight organization strategy in natural disaster. Remote Sens. Land Resour. 2018, 30, 55–59. [Google Scholar] [CrossRef]
  9. Van Westen, C. Remote sensing for natural disaster management. Int. Arch. Photogramm. Remote Sens. 2000, 33, 1609–1617. [Google Scholar]
  10. Bello, O.M.; Aina, Y.A. Satellite remote sensing as a tool in disaster management and sustainable development: Towards a synergistic approach. Procedia-Soc. Behav. Sci. 2014, 120, 365–373. [Google Scholar] [CrossRef]
  11. Dong, X.; Deng, B.; Yuan, F.; Fu, X.; Zhang, W.; Ju, Y.; Ren, X. Application of Aerial Remote Sensing in Geological Hazards: Current Situation and Prospects. Geomat. Inf. Sci. Wuhan Univ. 2023, 48, 1897–1913. [Google Scholar] [CrossRef]
  12. Wang, S.; Tang, C.; He, J.; Zhang, W.; Fang, Q.; Chen, X. Use of unmanned aerial vehicle for precise investigation of geological hazard in strong seismic zone. J. Eng. Geol. 2016, 24, 713–719. [Google Scholar] [CrossRef]
  13. Li, Y.; Xu, L.; Chen, Y.; Deng, Z. Intergrated Space-Air-Train-Ground muti-source techniques for early detection of subgrade disasters and service status of railway subgrade. Geomat. Inf. Sci. Wuhan Univ. 2024, 49, 1392–1406. [Google Scholar] [CrossRef]
  14. Lucieer, A.; de Jong, S.M.; Turner, D. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog. Phys. Geogr. 2014, 38, 97–116. [Google Scholar] [CrossRef]
  15. Dong, L.; Shan, J. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J. Photogramm. Remote Sens. 2013, 84, 85–99. [Google Scholar] [CrossRef]
  16. Joyce, K.E.; Belliss, S.E.; Samsonov, S.V.; McNeill, S.J.; Glassey, P.J. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog. Phys. Geogr. 2009, 33, 183–207. [Google Scholar] [CrossRef]
  17. Kaku, K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. Int. J. Disaster Risk Reduct. 2019, 33, 417–432. [Google Scholar] [CrossRef]
  18. Sefercik, U.G.; Alkan, M.; Jacobsen, K.; Atalay, C.; Buyuksalih, G. Quality analysis of Worldview-4 DSMs generated by least squares matching and semiglobal matching. J. Appl. Remote Sens. 2021, 15, 034515. [Google Scholar] [CrossRef]
  19. Chen, L.; Letu, H.; Fan, M.; Shang, H.; Tao, J.; Wu, L.; Zhang, Y.; Yu, C.; Gu, J.; Zhang, N. An introduction to the Chinese high-resolution Earth observation system: Gaofen-1~7 civilian satellites. J. Remote Sens. 2022, 9769536. [Google Scholar] [CrossRef]
  20. Lu, M.; Liao, X.; Yue, H.; Huang, S.; Xu, C.; Lu, H.; Bai, Y. Determining the distribution of unmanned aerial vehicles airports for the emergency monitoring of floods in China. J. Geo-Inf. Sci. 2019, 21, 854–864. [Google Scholar] [CrossRef]
  21. He, J.; Tang, C.; Wang, S.; Zhang, K. Application of UAV images to geological disaster investigation. Eng. Surv. Mapp. 2017, 26, 40–45. [Google Scholar] [CrossRef]
  22. Niethammer, U.; James, M.; Rothmund, S.; Travelletti, J.; Joswig, M. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng. Geol. 2012, 128, 2–11. [Google Scholar] [CrossRef]
  23. Bendea, H.; Boccardo, P.; Dequal, S.; Giulio Tonolo, F.; Marenchino, D.; Piras, M. Low cost UAV for post-disaster assessment. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, 1373–1379. [Google Scholar]
  24. Boccardo, P.; Chiabrando, F.; Dutto, F.; Giulio Tonolo, F.; Lingua, A. UAV deployment exercise for mapping purposes: Evaluation of emergency response applications. Sensors 2015, 15, 15717–15737. [Google Scholar] [CrossRef] [PubMed]
  25. Xu, Z.; Yang, J.; Peng, C.; Wu, Y.; Jiang, X.; Li, R.; Zheng, Y.; Gao, Y.; Liu, S.; Tian, B. Development of an UAS for post-earthquake disaster surveying and its application in Ms7. 0 Lushan Earthquake, Sichuan, China. Comput. Geosci. 2014, 68, 22–30. [Google Scholar] [CrossRef]
  26. Yao, H.; Qin, R.; Chen, X. Unmanned aerial vehicle for remote sensing applications—A review. Remote Sens. 2019, 11, 1443. [Google Scholar] [CrossRef]
  27. Fan, X.; Xu, Q.; Alonso-Rodriguez, A.; Subramanian, S.S.; Li, W.; Zheng, G.; Dong, X.; Huang, R. Successive landsliding and damming of the Jinsha River in eastern Tibet, China: Prime investigation, early warning, and emergency response. Landslides 2019, 16, 1003–1020. [Google Scholar] [CrossRef]
  28. Chen, G.; Qiang, X.; Peng, S.; Sun, T.; Bai, J.; Ye, Z. Application research of UAV photogrammetry technology in the emergency rescue of Baige landslide. J. Catastrophology 2020, 35, 203–210. [Google Scholar] [CrossRef]
  29. Zheng, S.; Li, Z. Geohazard Monitoring Based on Tilt Photography. Bull. Surv. Mapp. 2018, 88–92. [Google Scholar] [CrossRef]
  30. Lei, T.; Zhang, Y.; Wang, X.; Li, L.; Pang, Z.; Zhang, X.; Kan, G. The application of unmanned aerial vehicle remote sensing for monitoring secondary geological disasters after earthquakes. In Proceedings of the Ninth International Conference on Digital Image Processing (ICDIP 2017), Hong Kong, China, 19–22 May 2017. [Google Scholar] [CrossRef]
  31. Xu, Q.; Guo, C.; Dong, X. Application status and prospect of aerial remote sensing technology for geohazards. Acta Geod. Et Cartogr. Sin. 2022, 51, 2020–2033. [Google Scholar] [CrossRef]
  32. Zhang, Z.; Guo, D.; Ke, T.; Wang, J. The use of aerial photogrammetry in the fast response for China earthquake rescue. J. Remote Sens. 2008, 12, 852–857. [Google Scholar]
  33. Li, Q.; Zhang, W.; Yi, Y. An information extraction method of in 2017 Jiuzhaigou earthquake-induced landslide: A case study of the Jiuzhaigou earthquake in 2017. J. Univ. Chin. Acad. Sci. 2020, 37, 93–102. [Google Scholar] [CrossRef]
  34. Liang, R.; Dai, K.; Shi, X.; Guo, B.; Dong, X.; Liang, F.; Tomás, R.; Wen, N.; Fan, X. Automated mapping of ms 7.0 jiuzhaigou earthquake (China) post-disaster landslides based on high-resolution UAV imagery. Remote Sens. 2021, 13, 1330. [Google Scholar] [CrossRef]
  35. Turner, D.; Lucieer, A.; De Jong, S. Time series analysis of landslide dynamics using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736–1757. [Google Scholar] [CrossRef]
  36. Fan, X.; Xu, Q.; Scaringi, G.; Dai, L.; Li, W.; Dong, X.; Zhu, X.; Pei, X.; Dai, K.; Havenith, H.-B. Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 2017, 14, 2129–2146. [Google Scholar] [CrossRef]
  37. Fan, X.; Zhan, W.; Dong, X.; van Westen, C.; Xu, Q.; Dai, L.; Yang, Q.; Huang, R.; Havenith, H.-B. Analyzing successive landslide dam formation by different triggering mechanisms: The case of the Tangjiawan landslide, Sichuan, China. Eng. Geol. 2018, 243, 128–144. [Google Scholar] [CrossRef]
  38. Ma, S.; Wei, J.; Xu, C.; Shao, X.; Xu, S.; Chai, S.; Cui, Y. UAV survey and numerical modeling of loess landslides: An example from Zaoling, southern Shanxi Province, China. Nat. Hazards 2020, 104, 1125–1140. [Google Scholar] [CrossRef]
  39. Chen, T.; Hu, Z.; Wei, L.; Hu, S. Data processing and landslide information extraction based on UAV remote sensing. J. Geo-Inf. Sci. 2017, 19, 692–701. [Google Scholar]
  40. Jin, W.; Ge, H.; Du, H.; Xu, X. A review on unmanned aerial vehicle remote sensing and its application. Remote Sens. Inf. 2009, 1, 88–92. [Google Scholar] [CrossRef]
  41. Travelletti, J.; Delacourt, C.; Allemand, P.; Malet, J.-P.; Schmittbuhl, J.; Toussaint, R.; Bastard, M. Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations. ISPRS J. Photogramm. Remote Sens. 2012, 70, 39–55. [Google Scholar] [CrossRef]
  42. Zhou, X.; Shi, Y. Single neuron/PID adaptive compound control and parameter optimization for the inertially stabilized platform. Chin. J. Sci. Instrum. 2019, 40, 189–196. [Google Scholar] [CrossRef]
  43. Skinnemoen, H. UAV & satellite communications live mission-critical visual data. In Proceedings of the 2014 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, Yogyakarta, Indonesia, 13–14 November 2014. [Google Scholar] [CrossRef]
  44. Deng, F.; Dou, A.; Wu, W.; Chen, Z.; Yuan, X. Rapid Investigation of Disaster Situation in Extreme Disaster Area ofJiu-zhaigou Earthquake in Sichuan Based on UAV Remote Sensing. J. Catastrophology 2018, 33, 210–215. [Google Scholar] [CrossRef]
  45. Burleigh, S.C.; De Cola, T.; Morosi, S.; Jayousi, S.; Cianca, E.; Fuchs, C. From Connectivity to advanced internet services: A comprehensive review of small satellites communications and networks. Wirel. Commun. Mob. Comput. 2019, 2019, 6243505. [Google Scholar] [CrossRef]
  46. Sandau, R.; Roeser, H.-P.; Valenzuela, A. Small Satellite Missions for Earth Observation: New Developments and Trends; Springer: Berlin/Heidelberg, Germany, 2010; Available online: https://link.springer.com/book/10.1007/978-3-642-03501-2 (accessed on 2 July 2024).
  47. Cui, X.; Xi, X.; Zhang, Y. Design of high dynamic Ku/Ka dual-antenna civil aviation system. Comput. Meas. Control 2019, 27, 240–244. [Google Scholar] [CrossRef]
  48. GJB 367A-2001; General Specifications for Military Communication Equipment. General Equipment Department of the Chinese People’s Liberation Army, Chinese National Standards: Beijing, China, 2001.
  49. Liao, X.; Xiao, Q.; Zhang, H. UAV remote sensing: Popularization and expand application development trend. J. Remote Sens. 2019, 23, 1046–1052. [Google Scholar] [CrossRef]
  50. Watts, A.C.; Ambrosia, V.G.; Hinkley, E.A. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sens. 2012, 4, 1671–1692. [Google Scholar] [CrossRef]
  51. Zhang, J.; Liu, F.; Wang, J. Review of the light-weighted and small UAV system for aerial photography and remote sensing. Natl. Remote Sens. Bull. 2021, 25, 708–724. [Google Scholar] [CrossRef]
  52. Yin, W.; Wang, Y. Research on autonomous capability assessment technology for large civilian UAVs. In Proceedings of the 2021 7th International Conference on Mechanical Engineering and Automation Science (ICMEAS), Seoul, Republic of Korea, 28–30 October 2021. [Google Scholar] [CrossRef]
  53. Li, D.; Li, M. Research Advance and Application Prospect of Unmanned Aerial Vehicle Remote Sensing System. Geomat. Inf. Sci. Wuhan Univ. 2014, 39, 505–513. [Google Scholar] [CrossRef]
  54. Qian, Y.; Liang, W.; Gao, X. Design of Optoelectronic Platform for stable image on Airborne Reconnaissance. Acta Photonica Sin. 2009, 38, 2108–2111. [Google Scholar]
  55. Zhou, X.; Gao, H.; Zhao, B.; Zhao, L. A GA-based parameters tuning method for an ADRC controller of ISP for aerial remote sensing applications. ISA Trans. 2018, 81, 318–328. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, H.G.; Williams, T.C. Strategic inertial navigation systems-high-accuracy inertially stabilized platforms for hostile environments. IEEE Control Syst. Mag. 2008, 28, 65–85. [Google Scholar] [CrossRef]
  57. Bagheri, N. Development of a high-resolution aerial remote-sensing system for precision agriculture. Int. J. Remote Sens. 2017, 38, 2053–2065. [Google Scholar] [CrossRef]
  58. Zulkifley, M.A.; Behjati, M.; Nordin, R.; Zakaria, M.S. Mobile network performance and technical feasibility of LTE-powered unmanned aerial vehicle. Sensors 2021, 21, 2848. [Google Scholar] [CrossRef] [PubMed]
  59. Sturdivant, E.J.; Lentz, E.E.; Thieler, E.R.; Farris, A.S.; Weber, K.M.; Remsen, D.P.; Miner, S.; Henderson, R.E. UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery. Remote Sens. 2017, 9, 1020. [Google Scholar] [CrossRef]
  60. Barlow, J.; Gilham, J.; Ibarra Cofrã, I. Kinematic analysis of sea cliff stability using UAV photogrammetry. Int. J. Remote Sens. 2017, 38, 2464–2479. [Google Scholar] [CrossRef]
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Figure 1. Composition diagram of the aerial remote sensing platform based on real-time satellite communication.
Figure 1. Composition diagram of the aerial remote sensing platform based on real-time satellite communication.
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Figure 2. Schematic diagram of stable accuracy calculation principle. a is the coverage distance, L is the target distance, α t a r g is the FOV angle, and β is the stable accuracy threshold.
Figure 2. Schematic diagram of stable accuracy calculation principle. a is the coverage distance, L is the target distance, α t a r g is the FOV angle, and β is the stable accuracy threshold.
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Figure 3. Layout diagram of the testing environment for the orthographic stabilization platform.
Figure 3. Layout diagram of the testing environment for the orthographic stabilization platform.
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Figure 4. Schematic diagram of the composition of the “aircraft–satellite–ground” real-time transmission system.
Figure 4. Schematic diagram of the composition of the “aircraft–satellite–ground” real-time transmission system.
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Figure 5. Photos of the testing systems. (a) The carrier of the mobile communication system in the ground test. (b) The system composition in the flight test.
Figure 5. Photos of the testing systems. (a) The carrier of the mobile communication system in the ground test. (b) The system composition in the flight test.
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Figure 6. Ground weeds captured in the flight test site of Chongqing Tuohang Technology Co., Ltd.
Figure 6. Ground weeds captured in the flight test site of Chongqing Tuohang Technology Co., Ltd.
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Figure 7. Image acquisition and stitching at flight altitudes of 250 m (a–c) and 450 m (d,e) during the Nanpingba Island flight test. (b,c) are enlarged partial views of (a), and (e) is an enlarged partial view of (d).
Figure 7. Image acquisition and stitching at flight altitudes of 250 m (a–c) and 450 m (d,e) during the Nanpingba Island flight test. (b,c) are enlarged partial views of (a), and (e) is an enlarged partial view of (d).
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Table 1. Technical indicators of the orthographic stabilization platform.
Table 1. Technical indicators of the orthographic stabilization platform.
Technical IndicatorsStatesIndicator Values
Operation rangePitch−10°–+10°
Roll−10°–+10°
Maximum angular velocityPitch20°/s
Roll20°/s
Maximum angular accelerationPitch20°/s2
Roll20°/s2
Stability accuracy≤0.05°
Effective load (camera and lens)≤8 kg
Maximum external dimensions600 mm × 300 mm × 800 mm
Total platform weight (including load)≤20 kg ± 20%
Working temperatures−20 °C–+55 °C
Table 2. Technical indicators of the mobile communication equipment.
Table 2. Technical indicators of the mobile communication equipment.
Technical IndicatorsStatesIndicator Values
Operating FrequencyTransmission14 GHz–14.5 GHz
Reception12.250 GHz–12.750 GHz
Antenna GainTransmission≥27 dBi
Reception≥27 dBi
Antenna main lobe beam widthAzimuth≤2.5°
Pitch≤16.8°
First sidelobe≤−11 dB
Polarization TypeLinear polarization
Polarization isolation (duplexer outlet)Transmission≥19 dB
Reception≥19 dB
Insertion loss of transmission branch
(excluding radome)		≤1.5 dB
Port power capacity≥200 W
Transmission and reception isolation≥95 dB
Antenna motion rangeAzimuth±270° (continuous)
Pitch20°–80°
Polarization surface adjustment range±90°
Maximum rotation speed of antennaAzimuth≥60°/s
Pitch≥60°/s
Polarization≥30°/s
Tracking methodAutomatic tracking
Tracking Accuracy≤0.3° (R.M.S)
Star capture time≤10 s
Recapture time≤6 s
Weight≤27 kg
Reference source frequency stability2 × 10−8
Working temperature−40 °C~60 °C
Storage temperature−55 °C~70 °C
Working humidity≤95% (+30 °C)
Standards for impact vibrationGJB367A [48]
Table 3. Stability accuracy test results of the orthographic stabilization platform.
Table 3. Stability accuracy test results of the orthographic stabilization platform.
Swing ConditionsStability Accuracy
Swinging PeriodAmplitudeOptical Path Bending TestInclinometer Scheme
20 s ±15°±0.04°±0.035°
15 s ±15°±0.045°±0.042°
7 s ±10°±0.05°±0.047°
Table 4. Static and dynamic test data of the airborne communication system.
Table 4. Static and dynamic test data of the airborne communication system.
Transmission BandwidthsNumber of Sent PacketsEb/NoNumber of Received PacketsNumber of Lost PacketsPacket Lost Rate (%)
StaticDynamicStaticDynamicStaticDynamicStaticDynamic
256 Kbps100011.011.1999997130.10.3
512 Kbps100010.19.810009980200.2
1024 Kbps10007.27.110009960400.4
2048 Kbps10004.34.29989952100.20.5
Table 5. Performance of image stabilization platform in low-speed small-scale maneuvering flights.
Table 5. Performance of image stabilization platform in low-speed small-scale maneuvering flights.
Swing ConditionsImage Quality
Swinging PeriodAmplitudeFlight HeightImage ResolutionOverlap Rate
20 s ±15°300 m<1 cm>70%
15 s ±10°300 m<1 cm>70%
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He, X.; Xu, C.; Tang, S.; Huang, Y.; Qi, W.; Xiao, Z. Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response. Remote Sens. 2024, 16, 2866. https://doi.org/10.3390/rs16152866

AMA Style

He X, Xu C, Tang S, Huang Y, Qi W, Xiao Z. Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response. Remote Sensing. 2024; 16(15):2866. https://doi.org/10.3390/rs16152866

Chicago/Turabian Style

He, Xiangli, Chong Xu, Shengquan Tang, Yuandong Huang, Wenwen Qi, and Zikang Xiao. 2024. "Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response" Remote Sensing 16, no. 15: 2866. https://doi.org/10.3390/rs16152866

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