Unmanned Aerial Vehicle Technology Research Articles

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

Investigation of the effect of biofuel addition to jet fuel on engine performance and emissions in model jet engine

The use of biofuel reinforced fossil fuels is increasing day by day, thanks to such as reducing the carcinogenic effects of fossil fuels and reducing the global warming effect. The unmanned aerial vehicle breakthrough made in our country, especially in the defense industry, will ensure that the use of smaller unmanned aerial vehicles becomes more widespread in the future, allowing us to reach more effective and economical solutions. In this study, biofuel was obtained from safflower oil, and the effect of adding various amounts of biofuel to kerosene in a model jet engine on engine performance, and emissions was examined. The closest results to kerosene in fuel consumption, thrust force and the use of biofuel to reinforce fossil fuels is increasing day by day. This is due to its ability to reduce the carcinogenic effects of fossil fuels and also to reduce the global warming effect. In Türkiye, especially in the defense industry, there has been a significant breakthrough in unmanned aerial vehicle technology. This breakthrough will ensure that the use of smaller unmanned aerial vehicles becomes more widespread in the future, allowing us to reach more effective and economical solutions. In this study, biofuel was produced from safflower oil. Researchers examined the effect of adding different amounts of biofuel into kerosene fuel in a model jet engine. Engine performance and emissions were analyzed in the study. When the specific fuel consumption of kerosene fuel is compared to the average of all cycles of other fuels, it is 5.6 % more than B10 fuel, 12.4 % more than B20 fuel and 15 % more than B30 fuel. 8.3 % less thrust force was obtained. The highest turbine inlet temperature values were obtained with Kerosene fuel. Similar results were obtained in terms of emissions, but the best results were obtained with Kerosene fuel. When the averages of the entire experiment with biodiesel blend fuels were compared, it was stated that it constituted an acceptable alternative fuel.

Integrated monitoring and prediction of thermal discharge from nuclear power plants using satellite, UAV, and numerical simulation.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

TROG: a fast and robust scene-matching algorithm for geo-referenced images

ABSTRACT In a GNSS-denied/challenged environment, the scene-matching navigation system (SMNS) is a vital autonomous technology for unmanned aerial vehicles (UAV). This paper proposes a novel template-matching framework for multimodal images since UAV navigation requires precision and real-time performance while utilizing onboard computers with low computational power. Specifically, first, the local descriptor is extracted to form a pixel-wise feature representation of an image. Then, the Fast Fourier Transform is applied to measure the similarity based on the feature representation. In the feature extraction part, a gradient-like pixel-level feature descriptor is designed, which is reconstructed by weighting it according to the gradient-like angles, named the three-dimensional reconstruction oriented gradient-like (TROG) descriptor. An optimized similarity measurement template is introduced in the matching part, which improves the traditional feature-based similarity measurement algorithm defined using Fast Fourier Transform (FFT) in the frequency domain. This strategy eliminates redundant computations during the matching process. To verify the effectiveness of the proposed algorithm, satellite imagery data from Google Earth are used as a reference images, and sensed images (including optical, IR, SAR, and Hyperspectral) are captured by UAV and satellites for image matching to testify to the registration accuracy, robustness, and computational efficiency. The experiment demonstrates that TROG is accurate, robust, and attains high real-time performance, making it applicable to UAV navigation and positioning. Additionally, field-flight experiments evaluate scene-matching navigation under satellite-denied conditions and low computational power conditions for UAVs, demonstrating that our scene-matching navigation system can achieve precise positioning with a positioning error of less than 1.637 m, which is comparable to satellite/inertial navigation systems. The experimental results from outdoor flight experiments highlight the value of our proposed algorithm in engineering applications under satellite denial conditions.

Unmanned Aerial Vehicle for Pipeline Surveillance: A Review

Petroleum products being transported through pipelines can be easily targeted by economic and political saboteurs. Unmanned Aerial Vehicle (UAV) heralds a new technology on pipeline safety, surveillance, surveying mapping, assessment and safety for the oil and gas industry. With the advent of this technology, leaks, corrosion, third party interference and other faults leading to the loss of life, environmental pollution can be checked. This review highlights the state of the technology and benefits of UAV to the Nigeria oil and gas industry. The Nigeria oil and gas industry vested with over 2,300,000 barrels of crude oil daily production has over 9000km pipeline route which is prone to rupture from leaks, corrosion and vandalism. The industry will benefit from a flexible compatible and adaptable UAV platform with a multispectral and hyper spectral sensor for extensive safety, real-time imagery within a localized small to medium spatial scale, over larger expanse of space and time. The technology is adjured cheaper for inspection mission over traditional methods. Adoption of these technology would improve the environmental imprints of the oil and gas companies operating in the Niger Delta, and cases of pipeline vandalism and unrest might be lessened.

Investigation on situation awareness model of unmanned aerial vehicle groups communication network based on adversarial network

With the widespread application and development of unmanned aerial vehicle (UAV) technology, ensuring the security and stability of UAV swarm communication networks has become crucial. Given the diverse forms of interference and attacks in current networks, this poses a serious threat to the normal operation of UAV swarm communication. Therefore, how to accurately identify and effectively counter these network threats has become the focus of research. This study comprehensively evaluates the core technology of UAV swarm communication network situational awareness and constructs a situational awareness model based on adversarial networks. The model utilizes adversarial network technology and combines data collection and processing to design four experiments to comprehensively evaluate the performance of the model in different scenarios. The experimental results show that as the amount of data gradually increases, the performance of the model also improves. When processing 100, 1000, and 10,000 data points, the model achieved accuracies of 0.955, 0.962, and 0.982, respectively. Furthermore, the experimental results also indicate that effective noise suppression measures can significantly improve the accuracy and stability of the situational awareness model. Additionally, it is noted that different model structures will affect training duration, accuracy, and stability. Although increasing network scale may lead to increased computational complexity and latency, its accuracy is correspondingly improved. The adversarial network-based situational awareness model proposed in this study can accurately identify and effectively counter interference and attacks in UAV swarm communication networks, thereby providing solid protection for the collaborative combat and information sharing of UAV swarms.

Trends of Unmanned Aerial Vehicles in Logistics Delivery

The integration of unmanned aerial vehicles (UAVs) in logistics operations has attracted significant attention due to their potential to revolutionize delivery processes. This abstract provides an overview of the trends and advancements in utilizing UAVs for logistics. The study explores the current research landscape, identifies key challenges, and offers insights into the future applications of UAVs in logistics. By analyzing a range of scholarly articles, this abstract aims to illuminate the evolving role of UAVs in optimizing delivery efficiency, reducing costs, and addressing logistical complexities. Additionally, the abstract highlights the necessity for further research to address emerging issues and maximize the benefits of UAV technology in the logistics sector.

Air Traffic Controllers' Perspectives on Unmanned Aerial Vehicles Integration into Non-Segregated Airspace

The integration of Unmanned Aerial Vehicles (UAVs) into non-segregated airspace presents both opportunities and challenges for air traffic control (ATC). The aim of the study is to explore the perspectives of air traffic controllers on the current and anticipated challenges, workload, stress factors, performance errors, and mitigation strategies related to UAV integration. The sample consisted of 213 air traffic controllers in Türkiye. UAV operations have been available in Türkiye not only for military purposes but also for purposes such as forest fires, earthquakes, security, and others for a long time, and these UAV operations are provided with air traffic services (ATS) by air traffic controllers. The results show that air traffic controllers are concerned about mid-air collisions due to UAV technology limits and regulatory gaps, along with managing risks and unique flight characteristics. Addressing technology limitations, regulatory ambiguity, and other factors necessitates a comprehensive strategy. Solutions must prioritize collision avoidance systems, clear communication guidelines, and defined no-fly zones. It is recommended that future studies focus on the comprehensive impact of UAVs on air traffic operations and the development of regulations.

Zbieranie danych przestrzennych za pomocą bezzałogowego statku powietrznego

The paper briefly discusses the nature and advantages of unmanned aerial vehicle (UAV) as a spatial data acquisition method. This method and its possibilities are subject of scientific research in the area of geodesy, cadaster, architecture, construction of infrastructure objects and facilities, agricultural activities, cultural and historical heritage and even more over the last decades. This publication discusses in short, the principles and possibilities for creating a three-dimensional data model using the advantages of unmanned aerial vehicle. The building of University of Architecture, civil engineering and geodesy, situated in Semkovo resort, Blagoevgrad district is selected for the purpose of the task. Classical land surveying measurements with a total station and the data collected with an unmanned aerial vehicle are used for the creation of the three-dimensional model. A comparison and evaluation of the obtained model is made. The result of this evaluation indicates that the technology of unmanned aerial vehicle is efficient for representation of high-quality data with a wide scope of advantages such as high range, fast data collecting and processing.

Mountain search and recovery: An unmanned aerial vehicle deployment case study and analysis

AbstractMountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low‐cost, consumer‐grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability‐prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert‐informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state‐of‐the‐art robotics and MSAR.

Improved Identification of Forest Types in the Loess Plateau Using Multi-Source Remote Sensing Data, Transfer Learning, and Neural Residual Networks

This study aims to establish a deep learning-based classification framework to efficiently and rapidly distinguish between coniferous and broadleaf forests across the Loess Plateau. By integrating the deep residual neural network (ResNet) architecture with transfer learning techniques and multispectral data from unmanned aerial vehicles (UAVs) and Landsat remote sensing data, the effectiveness of the framework was validated through well-designed experiments. The study began by selecting optimal spectral band combinations, using the random forest algorithm. Pre-trained models were then constructed, and model performance was optimized with different training strategies, considering factors such as image size, sample quantity, and model depth. The results indicated substantial improvements in the model’s classification accuracy and efficiency for reasonable image dimensions and sample sizes, especially for an image size of 3 × 3 pixels and 2000 samples. In addition, the application of transfer learning and model fine-tuning strategies greatly enhanced the adaptability and universality of the model in different classification scenarios. The fine-tuned model achieved remarkable performance improvements in forest-type classification tasks, increasing classification accuracy from 85% to 93% in Zhengning, from 89% to 96% in Yongshou, and from 86% to 94% in Baishui, as well as exceeding 90% in all counties. These results not only confirm the effectiveness of the proposed framework, but also emphasize the roles of image size, sample quantity, and model depth in improving the generalization ability and classification accuracy of the model. In conclusion, this research has developed a technological framework for effective forest landscape recognition, using a combination of multispectral data from UAVs and Landsat satellites. This combination proved to be more effective in identifying forest types than was using Landsat data alone, demonstrating the enhanced capability and accuracy gained by integrating UAV technology. This research provides valuable scientific guidance and tools for policymakers and practitioners in forest management and sustainable development.

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking from the UAV perspective experiences interference from various complex conditions such as background clutter, occlusion, and being out of view, which can easily lead to tracking drift. Once tracking drift occurs, it will lead to almost complete failure of the subsequent tracking. Currently, few trackers have been designed to solve the tracking drift problem. Thus, this paper proposes a tracking algorithm based on motion prediction and block search to address the tracking drift problem caused by various complex conditions. Specifically, when the tracker experiences tracking drift, we first use a Kalman filter to predict the motion state of the target, and then use a block search module to relocate the target. In addition, to improve the tracker’s ability to adapt to changes in the target’s appearance and the environment, we propose a dynamic template updating network (DTUN) that allows the tracker to make appropriate template decisions based on various tracking conditions. We also introduce three tracking evaluation metrics: namely, average peak correlation energy, size change ratio, and tracking score. They serve as prior information for tracking status identification in the DTUN and the block prediction module. Extensive experiments and comparisons with many competitive algorithms on five aerial benchmarks, UAV20L, UAV123, UAVDT, DTB70, and VisDrone2018-SOT, demonstrate that our method achieves significant performance improvements. Especially in UAV20L long-term tracking, our method outperforms the baseline in terms of success rate and accuracy by 19.1% and 20.8%, respectively. This demonstrates the superior performance of our method in the task of long-term tracking from the UAV perspective, and we achieve a real-time speed of 43 FPS.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Sensing and Control Integration for Thrust Vectoring in Heavy UAVs: Real-World Implementation and Performance Analysis

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.

Research on Obstacle Avoidance Technology for Unmanned Aerial Vehicles Based on Panoramic Visual Perception

Research on Obstacle Avoidance Technology for Unmanned Aerial Vehicles Based on Panoramic Visual Perception

Cooperative networking and information processing system of wireless communication UAV under the background of intelligent service

AbstractIn order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.

Mapping and quantifying medium-term soil loss rates in mountain olive groves using unmanned aerial vehicle technology

Olive groves are one of the main agroecosystems in the Mediterranean region, but water erosion, aggravated by inappropriate soil management, is compromising the environmental sustainability of these crops. National and international public organisations, including the European Union via its Common Agricultural Policy, have acknowledged the problem and recognise the need to quantify the effects of this process. However, the variability of currently available short-term soil erosion measurements, together with limited understanding of the underlying processes, mean there is considerable uncertainty about the long-term effects of soil erosion. This paper presents an innovative procedure called SERHOLIVE4.0 designed to measure and model long-term soil erosion rates in olive groves, by means of structure-from-motion (SfM) techniques by which image information is obtained from unmanned aerial vehicles (UAVs). For the present study, the procedure was evaluated in mountain olive groves, where the erosion rate was calculated from historical surface reconstructions. Overall, this approach was found to be practical and effective. The method includes the following steps: [1] measure the current relief using UAV technology; [2] reconstruct the historical relief from field measurements; [3] calculate soil truncation (h) and obtain a soil erosion rate map; [4] determine the erosive dynamics of the slope and establish the relation between tree truncation, slope and mounds. The method we describe presents the following advantages:•it quantifies soil losses by reference to existing tree mounds;•it is straightforward to apply;•its application enhances the calibration of erosion models.

Integrated high-precision monitoring method for surface subsidence in mining areas using D-InSAR, SBAS, and UAV technologies

The use of unmanned operations to monitor mining induced land subsidence is increasing. This study conducts a detailed comparative analysis of accuracy of measured ground deformation provided by Differential Interferometric Synthetic Aperture Radar (D-InSAR), Small Baseline Subset (SBAS), and Unmanned Aerial Vehicle (UAV) tilt photogrammetry with respect to levelling measurements. Based on such analysis we propose an integrated approach that combines multiple remote sensing methods to achieve a better global accuracy in the land subsidence monitoring in mining areas. Conducted at the Banji Coal Mine, this study collected subsidence data from April 10, 2021, to June 28, 2022, through D-InSAR, SBAS, and UAV techniques. After segmenting the subsidence basin into distinct zones, we qualitatively assessed each area with UAV-derived 3D models and quantitatively evaluated the precision of all applied techniques, benchmarking against leveling data. Our findings indicate that integrating D-InSAR, SBAS, and UAV technologies significantly enhances monitoring accuracy over any single method, demonstrating their combined effectiveness in different subsidence areas. Consequently, the synergistic integration of D-InSAR, SBAS, and UAV technologies, capitalizing on their complementary strengths, enables the achievement of intuitive, comprehensive, and high-precision monitoring of subsidence basins in mining areas.