АНАЛИЗ МЕТОДОВ И ВЫБОР СРЕДСТВ ИЗМЕРЕНИЙ ДЛЯ МОНИТОРИНГА ВРЕДНЫХ ВЕЩЕСТВ С ИСПОЛЬЗОВАНИЕМ БЕСПИЛОТНЫХ ВОЗДУШНЫХ СУДОВ

Unmanned aerial vehicles (UAVs) carry sensing instruments to interesting, often remote areas with difficult access to collect in situ data. Atmospheric sensors can providing critical real-time data for environmental monitoring and other earth science applications. However, there remains a significant technology gap in the development of high performance trace gas sensors that meet the stringent requirements for UAV deployment. Most gas sensors are costly, bulky, and too slow to capture data in a speeding aircraft. In order to integrate with UAVs, airborne sensing systems should meet the UAV payload size, weight and power limitations. In addition, the UAV-carried sensors should be able to perform sensitive measurements with large variations of temperature, pressure and altitude and be compatible with the UAV’s cruise speed. This paper presents the investigation of a miniature, lightweight, ultra-low power (microwatts), ultra-fast, thermal conductivity (TCD) sensor platform to enable high resolution, fast analysis of methane (CH4) in the atmosphere. KWJ’s MEMS TCD sensor consists of a microfabricated polysilicon element that is low mass for rapid response and low power operation. The sensor elements are heated to a specified temperature by constant voltage or constant current. The thermal conductivity of the target gas in which the device resides will be reflected in the rate of cooling and equilibrium temperature of the device. TCD sensors with different geometries and dimensions were exposed to CH4 in air mixtures over a range of concentrations (100ppm - 1000ppm). The results showed that the sensor’s response is linearly dependent on the CH4 concentration. A lower limit of detection (LLD) for CH4 of ~100 ppm in dry air was obtained using the TCD sensors. Fabrication of MEMS TCD sensors. KWJ fabricated sensor elements are comprised of passivated polysilicon and fabricated with multiple sensing structures on one chip. Some of the elements are designed to accommodate a coating for high sensitivity and selectivity but these are not discussed herein. Figure 1 below is an SEM image of a 50 x1 µm TCD element suspended over a cavity. Characterization of TCD sensors. A benchtop measurement system, including testing chamber, environmental control system, gas delivery system, sensor control/measurement electronics and data acquisition system, was used for characterizing the response of the TCD sensors. The sensor element was placed in the test chamber and exposed to CH4/air gas mixtures with different concentrations. A pulse current waveform is applied to excite the sensor. The typical pulse comprised a hot excitation (1.5mA) applied for 40 ms and the cold excitation (0.25mA) for 4960 ms to provide a total pulse and measurement cycle of 5 seconds. Both the sensor control and data acquisition were timed in a coordinated fashion via the PC interface (Labview) such that the TCD resistance was acquired during the correct portion of the repetitive heating and cooling cycles. With the pulse excitation, the power consumption is very low (1.5mA*4V = 6mW continuous and 6*0.04/5=48 µW) per reading duration of 40 ms. The UAV would only move about 2.3 feet at a 40 mph air speed and thus the airborne readings at 40 ms would provide a resolution of a few feet. Of course, the response time of the device is measured in microseconds and so resolution of a few inches or less could be obtained with optimization of this device. The TCD sensor element was exposed to dry CH4 with different concentrations (1000ppm, 750ppm, 500ppm, 250ppm and 100ppm) in air at ambient temperature. The linear sensor response is illustrated below. These results provide a Lower Limit of Detection (LLD) for CH4 of about ~100 ppm CH4. Because of its small size, low mass, rapid response, and low power consumption, the novel sensor platform can be easily adapted to be deployed with different UAV platforms (balloons or different types of aircrafts). The structure of the novel sensor elements can accommodate metal electrodes and, therefore, can be used with selective and high sensitivity coatings for specialized applications. These features will be explored in future work. The MEMS approach used here provides a platform that can bridge the gap between the current large gas sensing equipment and the requirements for tiny payload UAV carrying gas sensor instrumentation for environmental measurements and earth science applications. Figure 1. MEMS TCD bridge (50 x 1 μm) and CH4 calibration curve. Figure 1

A substantial amount of air pollution is emitted from urban sources. Hence, investigating air pollutant dispersion from urban sources is of great importance. The mechanisms influencing air pollutant dispersion also need to be studied thoroughly. Unmanned Aerial Vehicle (UAV)-based systems offer great potential for mobile exploration of air pollutants in the lower atmosphere due to the high maneuverability of multi-rotor UAVs. The aim of this study was to develop an effective UAV system that can perform high-resolution three-dimensional profiling of pollutants, such as particulate matter (PM), ultrafine particles (UFP), black carbon (BC), as well as meteorological parameters, including air temperature, relative humidity, pressure, wind speed, and wind direction. Different experiments were performed to finalize the positioning of the instruments on the UAV platform so as not to destabilize the drone during flight, even when the wind speed is high and during turbulent flight conditions. Another very crucial question is where to place the air inlet of the measurement devices. In addition, field tests were conducted to evaluate the stability of the UAV platform and the in-flight performance of the sensors. This UAV platform was deployed to perform vertical profiles at the University campus in Stuttgart-Vaihingen and in an area near the campus, close to the federal highway B14. The measurement campaign was performed on three days in February 2021, with a maximum flight height of 120 m above ground. The vertical profiles showed that concentrations were higher on the ground due to the proximity to the source and that high wind speeds assisted pollutant dispersion. The horizontal profiles showed that the pollutant concentrations were higher at the roadside and decreased with increasing distance from the road. In conclusion, this UAV platform represented a low-cost, practical, and reliable method for studying the three-dimensional distribution of pollutants near the source.

Development and utilization of hexacopter unmanned aerial vehicle platform to characterize vertical distribution of boundary layer ozone in wintertime

Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach.

Large areas across the United States are potentially contaminated with UXO, with some ranges encompassing tens to hundreds of thousands of acres. Technologies are needed which will allow for cost effective wide area scanning with 1) near 100 % coverage and 2) near 100 % detection of subsurface ordnance or features indicative of subsurface ordnance. The current approach to wide area scanning is a multi-level one, in which medium altitude fixed wing optical imaging is used for an initial site assessment. This assessment is followed with low altitude manned helicopter based magnetometry followed by surface investigations using either towed geophysical sensor arrays or man portable sensors. In order to be effective for small UXO detection, the sensing altitude for magnetic site investigations needs to be on the order of 1 – 3 meters. These altitude requirements means that manned helicopter surveys will generally only be feasible in large, open and relatively flat terrains. While such surveys are effective in mapping large areas relatively fast there are substantial mobilization/demobilization, staffing and equipment costs associated with these surveys (resulting in costs of approximately $100-$150/acre). Surface towed arrays provide high resolution maps but have other limitations, e.g. in their ability to navigate rough terrain effectively. Thus, other systems are needed allowing for effective data collection. An UAV (Unmanned Aerial Vehicle) magnetometer platform is an obvious alternative. The motivation behind such a system is that it would be safer for the operators, cheaper in initial and O&M costs, and more effective in terms of site characterization. However, while UAV data acquisition from fixed wing platforms for large (> 200 feet) stand off distances is relatively straight forward, a host of challenges exist for low stand-off distance (~ 6 feet) UAV geophysical data acquisition. The objective of SERDP SEED 1509:2006 was to identify the primary challenges associated with a low stand off distance autonomous UAV magnetometer platform and to investigate whether these challenges can be resolved successfully such that a successful UAV magnetometer platform can be constructed. The primary challenges which were identified and investigated include: 1. The feasibility of assembling a payload package which integrates magnetometers, accurate positioning systems (DGPS, height above ground measurement), obstacle avoidance systems, power infrastructure, communications and data storage as well as auxiliary flight controls 2. The availability of commercial UAV platforms with autonomous flight capability which can accommodate this payload package 3. The feasibility of integrating obstacle avoidance controls in UAV platform control 4. The feasibility of collecting high quality magnetic data in the vicinity of an UAV.

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.

In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

Abstract: The use of Unmanned Aerial Vehicles (UAVs) is becoming very common for last few years for monitoring agricultural crops efficiently. Low altitude remote sensing (UAV) provides people with high temporal and spatial resolution for non-destructive, accurate and timely estimation of biophysical parameters like Leaf Area Index (LAI), crop growth, plant biomass and final crop yield. Collection of the data by UAV helps to reduce errors and it fills the biasness on an observational scale in precision agriculture. The main objective of this study was to estimate real time LAI and yield of groundnut crop based on Normalized Difference Vegetation Index (NDVI) using low cost multispectral UAV. A field experiment was set up  with three different groundnut cultivars (V1= BARD-479, V2 = Chakwal-2011 and V3 = Chakwal-2016) with three replications. Field data collection regarding LAI was performed in 2019 at two different growth stages (2-3 leaf stage and Peg formation stage) of groundnut on PMAS-Arid Agriculture Research Farm (Knoot), Pakistan. Final yield was calculated at the time of crop maturity. In this study, low cost UAV platform was established with DJI Phantom 4 pro and Parrot Sequoia Sensor to develop a multispectral UAV system used as the survey platform. A Parrot Sequoia camera was mounted on the UAV used as the remote sensor. The sensor provided the information in five narrow bands including Red, Blue, Green, Near infrared (NIR) and Red Edge. The processing of UAV images was performed in the Python environment and NDVI images were created. Then regression model was performed to compare the NDVI data with the LAI and final yield of groundnut crop. The results indicated that the highest value of R 2 = 0.93 was found with NDVI and LAI at peg formation stage while value of R 2 = 0.59 was at 2-3 leaf stage. The strong and positive relationship was found between LAI and yield ( R 2 = 0.97). There was also a strong and positive relationship between NDVI and yield of groundnut with value of R 2 = 0.92. The study showed that low cost multispectral UAV can be effectively used for real time estimation of LAI and groundnut yield nondestructively and accurately. The study results show that this low cost multispectral UAV platform (DJI Phantom 4 Pro with Parrot Squoia) is robust in management decisions of agriculture such as effective fertilizer application, growth monitoring, and yield estimation accurately and timely based on the vegetation indices. This study also proved the low cost multispectral UAV practicability in estimating plant biophysical parameters at a small field experiment scale reliably. Keywords: vegetation indices, UAV, LAI, yield, multispectral camera, NDVI, groundnut, growth stages DOI:  10.33440/j.ijpaa.20200301.70  Citation: Tahir M N, Lan Y B, Zhang Y L, Wang Y K, Faisal N, Shah M A A, et al.  Real time estimation of leaf area index and groundnut yield using multispectral UAV.  Int J Precis Agric Aviat, 2020; 3(1): 1–6.

The development of unmanned aerial vehicles (UAVs) has become an active area of research in recent years, and very interesting devices have been developed. UAVs are important instruments for numerous applications, such as forest surveillance and fire detection, coastal and economic exclusive zone surveillance, detection of watershed pollution and military missions. The work described in this chapter is part of a larger project, named AIVA, which involves the design and development of an aerial platform, as well as the instrumentation, communications, flight control and artificial vision systems, in order to provide autonomous takeoff, flight mission and landing maneuvers. The focus of the chapter is on one of the main innovative aspects of the project: the onboard wireless distributed data acquisition and control system. Traditionally, UAVs present an architecture consisting of one centralized and complex unit, with one or more CPUs, to which the instrumentation devices are connected by wires. At the same time, they have bulky mechanical connections. In the approach presented here, dubbed “fly-by-wireless”, the traditional monolithic processing unit is replaced by several less complex units (wireless nodes), spread out over the aircraft. In that way, the nodes are placed near the sensors and controlled surfaces, creating a network of nodes with the capacity of data acquisition, processing and actuation. This proposed fly-by-wireless platform provides several advantages over conventional systems, such as higher flexibility and modularity, as well as easier installation procedures, due to the elimination of connecting cables. However, it also introduces several challenges. The wireless network that supports the onboard distributed data acquisition and control system needs to satisfy demanding requirements in terms of quality of service (QoS), such as sustainable throughput, bounded delay and reliable packet delivery. At the same time, it is necessary to guarantee that the power consumption of the battery powered wireless nodes is small, in order to increase the autonomy of the system. Currently there are many different wireless network technologies available in the market. Section 2 presents an overview of the most relevant technologies and discusses their suitability to meet the above requirements. Based on this analysis, we chose the Bluetooth wireless network technology as the basis for the design and development of a prototype of the fly-by-wireless system. The system was implemented using commercial off-the-shelf components, in order to provide a good trade-

Abstract. Photogrammetric surveying with the use of Unmanned Aerial Vehicles (UAV) have gained vast popularity in short span. UAV have the potential to provide imagery at an extraordinary spatial and temporal resolution when coupled with remote sensing. Currently, UAV platforms are fastest and easiest source of data for mapping and 3D modelling. It is to be considered as a low-cost substitute to the traditional airborne photogrammetry. In the present study, UAV applications are explored in terms of 3D modelling, visualization and parameter calculations. National Institute of Technology Raipur, Raipur is chosen as study area and high resolution images are acquired from the UAV with 85% overlap. 3D model is processed through the point cloud generated for the UAV images. The results are compared with traditional methods for validation. The average accuracy obtained for elevation points and area is 97.99% and 97.75%. The study proves that UAV based surveying is an economical alternative in terms of money, time and resources, when compared to the classical aerial photogrammetry methods.

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives.

With the emergence and development of unmanned aerial vehicles (UAVs), different sensors have become more miniaturized and intelligent. UAVs equipped with various sensors are now an important approach for acquiring spatial data. Many advantages, such as low cost, short revisiting cycle, flexibility and high precision, have made UAVs powerful tools in geological, agricultural, ecological and forestry growth monitoring, as well as evaluation. Now, UAVs are a hotspot in scientific research. Their application in mining areas (MA), although still in its infancy, is developing rapidly in terms of speed, scale and service scope. This research examines aspects such as UAV platforms, different sensors and their application fields, as well as reviewing the advances of scientific research in MA at the present time. By combining current research and the functions of multiple sensors, an application framework for UAV monitoring in MA is constructed. Finally, the challenge and prospects for the development of UAVs and sensors are also considered. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring in MA, as well as an assessment of applications in mining, reclamation and environment.

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

Unmanned Aerial Vehicles (UAVs) are becoming essential tools in precision agriculture, enabling real-time monitoring, improved land management, and more efficient use of resources. Among them, Vertical Takeoff and Landing (VTOL) UAVs are ideal for operating in confined and uneven agricultural terrains. However, UAVs equipped with inverted-V empennages suffer from aerodynamic drawbacks, including directional instability and adverse yaw under sideslip conditions. This paper advances the state of the art by optimizing the aerodynamic performance of inverted-V tail configurations through the integration of a dorsal fin. Using Computational Fluid Dynamics (CFD), we assess multiple dorsal fin designs on a UAV platform with a maximum takeoff weight of 10 kg, 2 kg payload, 2-hour endurance, 10 m/s stall speed, and a 5 km² operational range. We analyze key aerodynamic metrics—lift and drag coefficients, lift-to-drag ratio, yaw moment coefficient—as well as flow behavior via pressure contours and vorticity plots. Results confirm that sideslip angles degrade aerodynamic efficiency, but a properly designed dorsal fin, particularly variation 2, significantly reduces adverse yaw at higher angles of attack and sideslip. This modification enhances UAV stability and flight performance, marking a meaningful improvement in VTOL UAV design for agricultural applications.

Synthetic Aperture Radar (SAR) systems deployed on Unmanned Aerial Vehicles (UAVs) are emerging as a highly promising solution for a wide range of future remote sensing and monitoring applications. The integration of SAR’s well-established capabilities for high-resolution imaging and change detection with the flexibility, maneuverability, and cost-effectiveness of modern UAV platforms unlocks significant potential for innovative sensing missions. However, the practical implementation of SAR on UAVs presents considerable challenges, primarily due to the inherent limitations in UAV flight stability and precise navigation, which degrade image quality. As a result, robust autofocusing techniques are essential to correct motion-induced phase errors and ensure coherent image reconstruction. While numerous autofocusing methods have been proposed in the literature, most are designed under assumptions that do not hold for low-flying UAV platforms. Specifically, conventional algorithms often overlook the strong dependence of the SAR imaging geometry on both azimuth and range positions of the targets, which becomes pronounced at low altitudes. In this study, we introduce a novel autofocusing approach termed Geometry-Aware Entropy Minimization (GAME). The proposed algorithm reconstructs three-dimensional positioning errors by explicitly modeling the spatially variant acquisition geometry along the UAV trajectory. This geometry-aware formulation enables accurate autofocus correction under the challenging conditions associated with low-altitude UAV SAR acquisitions. The effectiveness of the GAME algorithm is demonstrated using real X-band SAR data collected by an octacopter platform, confirming its applicability and performance in practical scenarios.