1. Introduction

2. Research Methodology

3. UAV-Borne Geophysical Survey Methods

3.1. UAV-Borne Geophysical Survey: Passive Methods

3.1.1. Unmanned Aerial Magnetometry

The introduction of UAVs in geophysics, especially in airborne magnetometry, has driven notable global academic and technological progress [35]. UAV-borne magnetometry offers safety, cost-efficiency [22], and prolonged flight endurance, facilitating low-altitude flights and high-resolution magnetic data collection [36,37,38], compared to traditional ground-based or airborne surveys using a low-flying airplane. There is a noticeable trend towards the adoption of UAV-borne magnetometry, with universities and companies actively engaging in pioneering research [39].

Reliable surveying systems on lightweight UAV platforms address the limitations of traditional terrestrial and aerial magnetometry [22,40]. These systems efficiently collect high-quality magnetic data, enhancing spatial resolution at low altitudes [41]. They extend operations to previously inaccessible areas, reducing costs and offering flexibility [42,43]. UAV-based surveys bridge the gap between terrestrial and airborne methods, enhancing detectability [44]. Challenges include ensuring data quality comparable to manned aerial systems and developing lightweight magnetometers for small UAV platforms. Figure 5 depicts a UAV-based magnetometer system, with subsystems detailed in [22].

Different manufacturers offer a variety of magnetic sensors (magnetometers) compatible with UAVs [41]. Table 1 overviews the most widely available cutting-edge options, with confirmed quality and reliability.

Table 2 presents an examination of diverse cutting-edge UAV-borne magnetometry systems currently accessible worldwide, including both commercial options and those developed through scientific research and development (R&D).

In the domain of UAV magnetometry, one of the critical issues is EM interference. Mounting magnetic sensors on aerial vehicles poses challenges due to magnetic interference from propulsion and flight control systems. This interference originates from both the environment and onboard systems, diminishing sensor sensitivity and detection ranges [39,81,87,94]. Environmental factors include anything in the UAV’s surroundings that impacts the magnetic survey, while onboard factors pertain to various UAV components with magnetic characteristics. Due to these magnetic components, UAVs may compromise the accuracy of total magnetic field measurements [60]. Advances in magnetometer technology enable increased use in small to medium UAVs, but miniature UAVs face challenges due to their compact size and shorter distances between interference sources and sensors [22,43,49,81]. Addressing magnetic interference is a paramount challenge in aerial magnetometry development [39,49,94,95,96]. This section explores solutions to counter magnetic interference, considering both active and passive approaches.

• Active Solution: Post-compensation addresses UAV magnetic interference [22,39,96], utilizing calibration flights to gather high-altitude data and calculate compensation coefficients using a model (Equation (1)) [97]

$$\begin{gathered} H_T=C_{1}Cos\alpha +C_{2}Cos\beta +C_{3}Cos\gamma +H_G\left\{C_4{Cos}^2\alpha +C_{5}Cos\alpha Cos\beta +C_{6}Cos\alpha Cos\gamma +C_7{Cos}^2\beta + \\ {C}_{8}Cos\beta Cos\gamma +C_9{Cos}^2\gamma \right\}+H_G\left\{C_{10}Cos\alpha {\left(Cos\alpha \right)}^{\prime }+C_{11}Cos\alpha {\left(Cos\beta \right)}^{\prime }+C_{12}Cos\alpha {\left(Cos\gamma \right)}^{\prime }+ \\ {C}_{13}Cos\beta {\left(Cos\alpha \right)}^{\prime }+C_{14}Cos\beta {\left(Cos\beta \right)}^{\prime }+C_{15}Cos\beta {\left(Cos\gamma \right)}^{\prime }+C_{16}Cos\gamma {\left(Cos\alpha \right)}^{\prime }+C_{17}Cos\gamma {\left(Cos\beta \right)}^{\prime }+ \\ {C}_{18}Cos\gamma {\left(Cos\gamma \right)}^{\prime }\right\}={\sum }_{i=1}^{18}{C}_i{A}_i \end{gathered}$$

(1)

Where ${\mathrm{H}}_{\mathrm{T}}$ is the total interference field intensity, and ${\mathrm{H}}_{\mathrm{G}}$ represents the geomagnetic field. Cosα, cosβ, and cosγ are the directional cosines of the geomagnetic field vector with respect to the UAV’s axes. Cj (1 ≤ j ≤ 18) are compensation coefficients aimed at mitigating magnetic interference effects.

Compensation coefficients (Cj) are estimated using magnetometer data (α, β, γ) through the least squares method according to Equation (2). These coefficients, along with the model, mitigate aircraft interferences during magnetic surveys.

$$C={\left(A^{T}A\right)}^{-1}{A}^T{H}_{int}$$

(2)

Where ${\mathrm{H}}_{\mathrm{i}\mathrm{n}\mathrm{t}}$ and C are column vectors representing ${\mathrm{H}}_{\mathrm{T}}$ and Cj, respectively, and A is the design matrix [22,39].

To evaluate compensation, the “improvement ratio” and the “fourth-difference” metrics are used [22,98]. Calibration flights correlate UAV maneuvers with magnetic field changes for compensation. Flight of maneuver data should mirror UAV behavior, ideally collected at high altitudes. Post-compensation is not suitable for low-altitude flights, especially for multi-rotor UAVs due to instability. Yet, during high-altitude operations, post-compensation can be applied with magnetometers placed away from the UAV’s platform [22,39].

• Passive Solution: Post-compensation may not suffice for UAV magnetometry, necessitating an alternative approach by placing the magnetic sensor away from the aerial platform. Methods include suspending it beneath the UAV with a semi-rigid tether or affixing it to the UAV frame with a rigid bar. Various sensor attachment configurations are illustrated in Figure 6, accommodating different UAV types [41,99,100,101]. Placing the magnetometer away from the aerial platform can lead to sensor errors and fluctuations due to vibrations. Firmly affixing it to the airframe or using an extended boom may compromise flight stability, especially for fixed-wing UAVs [22,43,60,96,98]. Comparative studies suggest optimal sensor-platform distances of 3 to 5 meters to minimize interference [22,43,49,56,66,81,96,102]. For VTOL fixed-wing systems, mounting sensors at the winglets or nose-tip via a fixed-boom configuration is effective [80].

Figure 6. Different arrangements for mounting magnetometers on UAVs: (a-i to a-iv) fixed-boom design for rotary-wings, fixed-wings, helicopters, and airships, respectively; (b-i to b-iv) towed sensor design for the mentioned UAV types; (c-i to c-iv) towed bird design for the mentioned UAV types; and (d) fixed wing-tip design for fixed-wing UAV.

Figure 6. Different arrangements for mounting magnetometers on UAVs: (a-i to a-iv) fixed-boom design for rotary-wings, fixed-wings, helicopters, and airships, respectively; (b-i to b-iv) towed sensor design for the mentioned UAV types; (c-i to c-iv) towed bird design for the mentioned UAV types; and (d) fixed wing-tip design for fixed-wing UAV.

Table 3 presents a comprehensive compilation of studies conducted in the field of UAV-borne magnetometry, with a specific emphasis on applications.

3.1.2. Unmanned Aerial Gravimetry

Integrating UAV-borne gravity surveys with ground-based methods aids subsurface resource exploration, enhancing gravity field determination for diverse applications [124]. The UAV-based gravimetry system, depicted in Figure 7, includes a ground base station and an airborne gravimetry system. The ground base station comprises components like a ground control/command station (GCS), ground data transmission (GDT) station, and ground support equipment (GSE). The GCS facilitates real-time transmission of gravimeter data via satellite links. The airborne gravimetry system consists of an unmanned vehicle, UAV-compatible gravimeter, data-transferring computer, GNSS signal recorder, and uninterruptible power supply (UPS). The UAV, central to any unmanned aerial system (UAS), can take various forms, including airships, helicopters, fixed-wing, and multirotors, with no usage restrictions [23].

Two main operational modes of UAV-based gravimetry exist: continuous-flight and grasshopper (see Figure 8). Continuous-flight mode entails the drone collecting data while traversing an assigned area, ideal for large surveys such as those in the petrochemical industry. Challenges include distinguishing between platform and gravitational accelerations, often addressed using isolation platforms and gimbals. Grasshopper mode involves the UAV landing at specific points for data collection before taking off again. This mode, suitable for rotary-wing drones, collects data during stationary periods. Challenges in this mode include aligning the gravimeter’s axis and sensitivity to angular errors. Grasshopper mode typically requires more time for gravimetry due to flight and landing [125]. Further details and comparisons are available in the cited reference. The successful integration of UAV-compatible gravimeters onto unmanned platforms for continuous-flight and grasshopper mode operations requires several auxiliary systems. These include isolation (self-leveling) platforms, gimbals, and differential GPS capabilities [125,126].

Figure 7. UAV-based gravimetry system (the scheme was depicted based on [23,127,128]).

Figure 7. UAV-based gravimetry system (the scheme was depicted based on [23,127,128]).

Figure 8. Operational modes in UAV-borne gravimetry (R: flight rans, P: sampling points).

Figure 8. Operational modes in UAV-borne gravimetry (R: flight rans, P: sampling points).

Various corrections must be applied during gravity data collection, particularly in airborne gravimetry operations, including UAV-borne methods. These corrections include latitude correction, free-air (elevation) correction, Bouguer correction, topography (terrain) correction, Earth tides, Eötvös correction, and low-frequency translation correction, among others [23,129,130,131]. For brevity, readers seeking more detailed insights into these corrections are referred to the cited references.

This section provides a concise overview of gravity sensors compatible with UAVs. Miniature gravity sensors are designed for UAV integration, considering the payload capacity of UAVs. These sensors fall into two main categories: strapdown systems and Micro-Electro-Mechanical-Systems (MEMS) accelerometers. In strapdown configuration, a triad of accelerometers is directly affixed to the airborne vehicle. MEMS accelerometers offer a lightweight solution for UAV-based gravimetry. Triaxial MEMS devices have been successfully deployed in various UAV-based gravimetry operations. Table 4 lists advanced UAV-compatible gravimeters along with additional details. These are among the most widely available options, and their quality has been confirmed.

This section reviews the state-of-the-art UAV-borne gravimetry systems developed to date. Table 5 provides a comprehensive list of these advanced drone-deployed systems, along with relevant information for each.

Table 6 offers a comprehensive examination of the literature on UAV-borne gravimetry, emphasizing its applications. In contrast to magnetometry, the range of applications is currently restricted due to the emerging status of the field, with most studies focused on system R&D.

3.1.3. Unmanned Aerial Gamma-ray Spectrometry and Radiometry

GRS geophysical technologies are challenging to integrate with drones due to their bulkiness and traditional design [161,162]. New UAV-compatible spectrometer systems, designed within drone weight constraints, and improved data processing methods, e.g., [163,164], show promise for enhanced survey efficiency and accessibility. This shift requires innovative equipment design and data analysis methods to maximize potential.

UAV-borne GRS feasibility relies on precise gamma-ray radiation measurement in low-altitude environments with limited air attenuation [165,166]. UAVs use gamma-ray spectrometers to map soil properties, texture, and contamination [167], advancing from proof-of-concept to common practice [168,169]. Radiation mapper UAVs carry payloads suitable for efficiently mapping radionuclide activities [170]. UAV GRS combines ground-based and traditional airborne methods, using UAVs for gamma radiation investigations at lower altitudes, minimizing risks and costs, especially in smaller survey areas [169,171,172]. Radiometric surveys by UAVs are more common than spectrometric surveys due to lightweight UAV constraints, limiting traditional GRS methods’ effectiveness [171]. Ground-based GRS requires stops, while airborne GRS uses large-volume scintillation detectors for high-quality surveys [173]. Semiconductor detectors are expensive and less adopted compared to classical scintillation detectors, preferred for spectrometric measurements during movement [174,175,176]. Unmanned systems utilize heavy detection units with multiple crystals [171,174,175], impacting UAV flight time and range. Increasing UAV size is not cost-effective. Compact scintillation detectors offer economical and reliable data acquisition for geological mapping, ensuring high-quality outcomes in gamma-ray survey [171,175,177].

Let’s examine how a UAV GRS system operates. In a UAV GRS system (Figure 9), the calibrated gamma-ray spectrometer is mounted beneath the UAV to measure radionuclide concentrations [167]. An onboard rover logs GNSS data for georeferencing, with corrections from a ground-based receiver improving accuracy. The spectrometer captures radiation spectra at each position, synchronized with location data and processed onboard [168,178]. Spectrometer data is transmitted to the GCS via RF systems like TETRA network, UMTS, and others [178,179,180,181]. Post-flight processing includes spectrum analysis for radiation heatmap generation [180]. The GCS software integrates radiation levels, coordinates, and altitude for visualization [181]. Measured radionuclide concentrations obtained from the spectrometer are used in application models, correlating them with soil properties or contaminants. Proper sensor calibration is crucial for accurate results, often achieved through laboratory analyses known as ground truthing [167,180]. During field operations, soil samples are collected. These samples undergo air-drying, milling, and sieving for clay and sand content analysis [169,182]. Lab testing includes radionuclide content, grain-size distribution, and clay fraction analysis, essential for application model development. Models can be developed based on as few as 14-20 soil samples, generating soil maps for various applications [167]. Expert analysis can further enrich the final map.

In this section, spectral data processing is explored, specifically the methods for calculating radionuclide concentrations. Fundamental processing steps involve consolidating data from different detectors and filtering out points during UAV stationarity or survey line transitions [183]. Spectrum analysis techniques, such as the Windows method and Full Spectrum Analysis (FSA) procedure, are used to derive radionuclide concentrations from recorded spectra [161,170,184]. The Windows method extracts ⁴⁰K, ²³⁸U, and ²³²Th concentrations from specific energy windows [161,169]. In contrast, the FSA method, introduced in [185,186], utilizes nearly all spectral data, enhancing precision [161,184]. FSA fits “standard spectra” to acquired spectra, reflecting the detector’s response to pure radionuclide sources (⁴⁰K, ²³⁸U, or ²³²Th) [169,184]. Monte Carlo simulations and modeling generate standard spectra, a standardized practice detailed in references such as [163,169,187]. FSA reduces uncertainties by half compared to the Windows method, improving data quality [184]. It enables the use of smaller detectors while maintaining quality and provides richer count and spectrum structure [161].

In UAV-borne GRS, elevation impacts signal reception and footprint size. Higher altitudes reduce detected signal quantity due to increased air attenuation [163]. Elevating the spectrometer broadens the footprint, known as the “table lamp effect,” expanding the effective measured area [169,182]. Gamma-ray spectra conversion to radionuclide concentrations requires altitude correction for signal attenuation and detector field-of-view variations. Traditional corrections for airborne ranges may not suit UAV altitudes. Previous studies proposed corrections, but wide adoption was lacking. Some surveys (e.g., [175,188,189,190]) used International Atomic Energy Agency (IAEA) corrections even at lower altitudes. In [169] the corrections was refined specifically for UAV GRS operational ranges (0-40 m) using experimental and computational methods, as detailed in the cited study. For more detailed insights of the commonly used footprint and height corrections applied in the domain of UAV-borne spectrometry and radiometery and also some innovative methods in this domain type refer to [169,180,191,192].

To provide an overview of UAV-compatible radiation sensors, it is important to note that several ready-to-use gamma-ray spectrometers have been tested and confirmed to be attachable to drones. These spectrometers are listed in Table 7. In Table 8, a thorough list of state-of-the-art UAV-borne GRS systems is provided, offering detailed technical specifications sourced from published papers.

Table 9 presents an overview of the applications of UAV-borne GRS derived from existing research in the field.

3.1.4. Unmanned Aerial Imaging Geophysics

A UAV photogrammetry system comprises both aerial and ground sections, with the aerial section featuring the UAV and various imaging sensors such as visible-light (RGB), multispectral (MS), hyperspectral (HS), or thermal-infrared (TIR) cameras [209,210]. The system operates through fieldwork, including ground operations and aerial surveys, and office tasks involving survey planning and data processing. Key steps involve acquiring overlapped images, identifying key points, and performing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) [211] to generate dense point clouds, orthomosaics, and different other geospatial products. While this overview doesn’t delve deeply into UAV photogrammetry, interested readers can refer to relevant references (e.g., [212]) for more details. UAV photogrammetry extends beyond civil applications [213], offering valuable geometrical, structural, spatial, and spectral data about the natural Earth. Consequently, it’s a versatile UAV RS method applicable to various geoscientific endeavors.

This section provides an overview of UAV-compatible optical and TIR imaging sensors. In the UAV visible photogrammetry category, two types of visible cameras are commonly used on UAV platforms for imaging and photogrammetry [214]. These include UAV-integrated cameras, exemplified by the 20-MP 4/3-inch image sensor (e.g., used in DJI Mavic 3 drone) and the 20-MP 1-inch image sensor (e.g., used in AUTEL’s EVO II Pro V3 drone), and Digital Single Lens Reflex (DSLR) cameras like the Sony A7RIII. In UAV MS photogrammetry and RS, state-of-the-art sensors include the Parrot Sequoia+, MicaSense Altum-PT, MicaSense RedEdge-MX/P, Sentera 6X, and DJI P4 Multi [214,215]. State-of-the-art TIR sensors include the WIRIS Pro/Pro Sc, Zenmuse XT 2 (dual-light TIR imager), Uncooled FPA 6404, FLIR SC305, and TELEDYNE FLIR VUE Pro. In the realm of UAV HS photogrammetry, cutting-edge sensors include OCI™-UAV-1000/2000, OCI™-F series, and GoldenEye™ (by BaySpec); CHAI S/V-640, S 185 FIREFLEYE SE, S 485 FIREFLEYE XL, and Q 285 FIREFLEYE QE (by Cubert); Nano/Micro-HyperSpec, VNIR-1024, Mjolnir V-1240, and SWIR-384 (by Headwall Photonics HySpex); Rikola, vis-NIR microHSI, Alpha-vis microHSI, SWIR 640 microHSI, and Alpha-SWIR microHSI by MosaicMill NovaSol; MV1-D2048x1088-HS05-96-G2 (by PhotonFocus); Hyperea 660 C1, Pika L, Pika XC2, Pika NIR, and Pika NUV (by Quest Innovations Resonon); VIS-VNIR Snapshot by SENOP; SPECIM FX10/17 (by SPECIM); SOC710-GX (by Surface Optics); and MQ022HG-IM-LS100-NIR/ IM-LS150-VISNIR (by XIMEA) [209]. For the sake of brevity, no further details about the sensors or their specifications are provided here. Readers are referred to the cited references for more information.

In UAV photogrammetry, spatial resolution—denoted by ground sampling distance (GSD)—along with spectral resolution (referring primarily to the number of bands the sensor can capture) and radiometric resolution, are key factors [216,217]. Common corrections in UAV photogrammetry include radiometric and geometric calibration [218,219]. Radiometric calibration aims to derive absolute reflectance measurements from the digital number (DN) [218]. Geometric calibration encompasses band-to-band registration (primarily for multi-lens sensors) and true orthorectification, which allows objects in the orthomosaic to be viewed from a top-down perspective [219]. Additionally, point cloud filtering is another type of correction applied not to images but to point clouds, eliminating non-ground points to generate a bare-land point cloud and digital terrain model (DTM) from the initial point cloud [220].

The geoscientific applications of UAV photogrammetry can be listed as follows:

• Digital Terrain Modeling for Geomorphological Applications: The initial application of UAV photogrammetry in geoscience focuses on ultra-high-resolution digital terrain modeling for geomorphological purposes. A crucial step in this process involves filtering the original point cloud to isolate the natural terrain points [220]. Subsequently, topographical maps are generated by combining the orthomosaic with contour lines. These maps facilitate further geomorphological analysis and applications.
• Landslide Mapping and Monitoring: Table 10 presents a comprehensive review of efforts in landslide mapping and monitoring using UAV photogrammetry.
• Land Subsidence and Ground Failure Mapping:Table 11 provides a thorough list of research conducted in the realm of land subsidence and ground fissure mapping.
• Geothermal Exploration: The application of UAV photogrammetry in geothermal exploration is explored here, with a collection of them provided in Table 12.
• Soil Moisture Mapping: Research conducted in the realm of soil moisture mapping using UAV photogrammetry has been reviewed in Table 13.

• Mineralogy, Mining, and Soil Mapping: Table 14 provides a comprehensive overview of UAV-based imaging and photogrammetry applications in mineralogy, mining, and soil mapping. It is noteworthy that research focusing on mining was gathered and reviewed building on [272].
• Volcanoic Research: Table 15 offeres a collection of researches in the realm of volcanoic mapping using UAV photogrammetry.

3.2. UAV-Borne Geophysical Survey: Active Methods

3.2.1. Unmanned Aerial EM Survey

The EM method, utilizing induced currents to detect conductive underground structures, aids in subsurface geology understanding. It’s divided into time-domain EM (TDEM) and frequency-domain EM (FDEM) survey methods. Aerial EM (AEM) schematics depict induced and measured magnetic fields (see Figure 10). Measurements involve primary EM fields from the transmitter and secondary EM fields correlated with geological features [306,307,308,309]. AEM methods are categorized by excitation modes (AFEM and ATEM), platform types (fixed-wing TEM, fixed-wing FEM, helicopter-borne TDEM or simply HTEM, and helicopter-borne FDEM or simply HFEM), and EM field transmission types (active, passive, and semi-passive/airborne systems) [306,310,311,312,313,314]. Different ATEM and AFEM systems have been developed based on various types of manned aerial platforms [311,313,315,316,317,318,319,320,321,322,323,324,325,326,327]. Despite this variety, UAV-borne EM survey systems are a relatively new topic compared to traditional methods.

AEM has found various geoscientific applications including finding Quick Clay [328,329,330], identifying hazardous substances [331], mapping the fresh-saltwater interface [332], freshwater potential investigation [333], deep groundwater mapping in Antarctica [334,335,336], hydrologic mapping and environmental assessments [337], detection and mapping impermeable aquifer boundaries [310], groundwater and soil investigations [338], exploring the relationship between groundwater and surface water [339], 3D geological modeling of complex buried valleys [340], observations of a collapse-prone volcano [341], mineral exploration [327], gold exploration [342], mapping sub-Phanerozoic basement features [343], UXO detection [63], magnetite ore tonnage estimation [344], and application in the fields of uranium exploration [345,346].

Developing EM-sounding systems for lightweight UAVs faces challenges due to weight and bulkiness issues with conventional techniques, such as helicopter-based systems. Aerial exploration presents difficulties like precise loop positioning and adapting methods to UAVs, requiring bulky generator setups [347]. Integrating EM sensors with UAVs introduces stability, interference, and control challenges, addressed with solutions like sensor suspension, tail fins, noise avoidance, and real-time monitoring [348,349]. These innovations aim to enhance data quality and address engineering challenges in UAV-based EM surveys. The scheme of Figure 10 is also valid for the UAV EM method.

There are two configurations in UAV-borne EM surveys: single-drone and dual-drone configurations. To illustrate these configurations, we focus on the Louhi geophysical EM survey system. Developed by Radai Ltd. under the NEXT project, this system facilitates practical drone-based operations for FDEM methods [309]. It features a single drone equipped with a transmitter, offering flexibility in surveying approaches (see Figure 11). Additionally, a two-drone configuration is utilized to maintain consistent separation distance between the receiver and transmitter drones, allowing for deeper exploration and rapid deployment in challenging terrains [350]. The fixed loop transmitter system enhances source moment and signal-to-noise ratio (SNR), crucial for subsurface exploration. Accurate position and orientation measurements enable conversion to global 3D coordinates, although variations in loop spacing and orientation can introduce noise. Synchronization between the receiver and transmitter units is achieved using GNSS time, allowing for precise data recording and analysis. Payload constraints and synchronization between dual-drone autopilots pose challenges, necessitating ongoing research and development efforts [309].

In the realm of UAV-based EM RS, the concept of the “Semi-UAV-borne EM (SUEM) System” is emerging as a promising innovation. The Semi-Airborne EM (SAEM) setup presents a promising avenue for UAV-based EM surveys [351]. In this configuration, the transmitter remains stationary on the ground while the receiver is deployed on a UAV (similar to Figure 11b), offering unique advantages over conventional AEM [352,353]. However, the payload limitations of current UAVs restrict the maximum speed of the SAEM system. Addressing challenges related to turbulence-induced shaking and motion-related noise in the dataset necessitates innovative solutions [311]. Advancements in drone technology have enabled the adaptation of SAEM systems to UAVs, reducing application and maintenance costs and facilitating multi-component surveys. Notably, MGT’s SAEM system, developed in collaboration with the geophysical industry and research organizations in Germany, exemplifies the successful integration of an EM system onto an unmanned multicopter, enabling data collection with enhanced depth penetration and accuracy. These developments mark significant progress in the evolution of SUEM systems, holding considerable potential for future UAV EM RS applications [311,352,353,354].

Very-low Frequency (VLF) EM methods are frequently mentioned in the literature, especially in the context of UAV EM applications. UAV integration with the VLF method evolved since Kipfinger’s lightweight system in 1996-97 [353,355]. Recent developments led to VLF systems on rotary-wing UAVs with a 12 kg payload capacity [356]. VLF induces primary horizontal magnetic fields and captures secondary fields in conductive subsurface formations with a receiver equipped with two coils [357,358,359]. Equation (3) calculates the vertical magnetic field component using transfer functions or “tippers” [360,361].

$$H_z\left(\omega \right)=A\left(\omega \right).H_x\left(\omega \right)+B\left(\omega \right).H_y\left(\omega \right)$$

(3)

Modern VLF instruments capture time series data for all three magnetic field components, enabling analysis in both time and frequency domains. Automated spectral analysis, like the method proposed in [362], identifies VLF transmitters. These advances highlight the potential of UAV-based VLF EM systems for large-scale geophysical surveys [356,363].

Let’s explore the data collection modes in UAV-borne EM surveys. UAV-borne EM surveys use fixed-point and continuous data collection modes [364]. In fixed-point mode, the UAV hovers at each measurement point, ensuring high-quality data but limiting data points due to energy-intensive hovering. Continuous mode collects data seamlessly during low-level flight, offering substantial data volumes similar to grasshopper mode [125]. Post-processing ensures data quality comparable to fixed-point mode [365].

This section discusses three key aspects of UAV-based EM surveys: compatible EM sensors for UAVs, UAV platforms used in EM surveys, and the development of UAV-based EM systems.

• EM Sensors: Various EM instruments have been utilized in UAV-borne surveys, including GEM-3D, MPV, MPV-II, Pedemis, High-Frequency EMI, Dualem-1S, EM38, Profiler 400-EMP, CMD MiniExplorer, US Army’s drone-mounted EM induction sensor, CAS & Jilin University single-component sensor and others [311,349,366,367]. Among these, the GEM-2UAV is prominent, weighing 3 kg and operating at ten frequencies (25 Hz to 96 kHz). It requires a GNSS antenna, WinGEM software, and consumes 20 W during surveys, offering configurable operational modes and data logging initiated through a control unit [348,349,368].
• UAV Platforms: UAV platforms for carrying EM instruments are categorized into four types: multi-rotor [347,349,352], fixed-wing [73,369], helicopters [356], and airships/balloons [370]. The choice of platform depends on factors like survey objectives and the size of the EM instrument.
• UAV-borne EM Survey Systems: Building on the information provided in the preceding two bullets, Table 16 offers a thorough overview of the UAV-borne EM systems that have been developed.

Table 16. Review of state-of-the-art UAV-borne EM survey systems.

Table 16. Review of state-of-the-art UAV-borne EM survey systems.

Sys.	Platform Type	UAV Name/Model	EM Instruments	References
1	Multi-rotor	MTOW octocopter	Miniaturized induction coil triple	[352,353]
2	Multi-rotor	X825 octocopter	Metronix SHFT-02e induction coil triple	[351]
3	Multi-rotor	SibGIS hexacopter	A measuring system with an inductive sensor	[201,347,371,372]
4	Multi-rotor	SibGIS hexacopter	A grounded transmitter line spanning 2.2 km serves as the origin of the current pulses, coupled with an airborne PDI-50 receiver loop on the UAV.	[373]
5	Helicopter	Aeroscout Scout B1-100	The Super High-Frequency Induction Coil Triple sensor, in conjunction with the ADU07 data logging module, both developed by MGT.	[356]
6	Multi-rotor	DJI Matrice 600 Pro	GEM-2UAV CSEM sensor	[349]
7	Fixed-wing	VTOL Mother-Goose	Louhi portable EM transmitter and three-component receiver	[309]
8	Unmanned VTOL airship	Quaddirigible (filled with helium)	A VLF-type EM survey system	[370]
9	Multi-rotor	ZION CH940 and LAB6106 multicopters	GEM-2	[348]
10	Multi-rotor	Hexacopter	A coil wound with enameled copper wire, comprising 25 turns and a diameter of 25 cm, designed for the generation of a magnetic field.	[374]
11	Hexacopter and fixed-wing	SGU’s fixed-wing VLF	Three orthogonally mounted induction coil sensors and a data acquisition system with up to 1 MHz continuous data sampling of the EM components.	[369]
12	Multi-rotor	Hexacopter	The drone-borne TEM system utilized a central loop device.	[365]
13	Not specified	Not specified	D-GREATEM system	[375]
14	Fixed-wing	Silver Fox UAV	A sensing coil towed behind the UAV	[73]
15	Multi-rotor	Hexacopter	A measurement setup using an inductive sensor (receiving loop) is tethered by a UAV, while a galvanically grounded power transmitter is positioned on the ground and linked to a pulse generator.	[376]
16	Unmanned helicopter	Tianxiang V-750	A SAEM system, designed by the CAS, encompasses a single-component sensor, transmitter, and receiver, all equipped with vibration isolation.	[311]
17	Multi-rotor	Hexacopter	A SAEM system, engineered by Jilin University, consists of a robust ground-based transmitter generating high-power signals and a single-component sensor.	[311]
18	Multi-rotor	DJI Matrice 600	The Geophex multi-coil, CMD MiniExplorer EM instruments, and GEM-2	[366]
19	Multi-rotor	DJI Wind4 quadcopter	Geophex GEM-2UAV	[377]
20	Fixed-wing	A long-range drone	UAV-borne gravity and EM sensors	[158,160]
21	Octocopter	System name: MGT-GEO Radio EM	The sensor system weighs 6.5 grams, operates in a frequency range of 1-524 kHz, encompasses channels for Hx, Hy, and Hz, boasts a sample rate of up to 524 kHz, achieves synchronization through GPS, and utilizes compact flash disk storage media.	[366]
22	Rotary-wing	DroneSAM	A low-frequency hybrid geophysical system integrating a ground active source transmitter system with a drone for slow-flying, low-level data acquisition of TEM and magnetometric resistivity data.	[378]
23	Rotary-wing	Multi-rotor (DronEM)	Drone for EM fields Measurements (DronEM) is outfitted with a Selective Electric Triaxial Probe and is capable of scanning the EM spectrum ranging from 10 MHz to 3 GHz at altitudes up to 200 m.	[379]
24	Rotary-wing	Not specified	UAV VLF EM System: two VLF UAV sensor coils with cables accompanied by other instruments.	[380]
25	Rotary-wing	Octo/helicopter	3-component EM sensor (induction coil DEEP) and fluxgate magnetometer.	[381]
26	Rotary-wing	Hexacopter	Time-domain EM system suspended beneath the UAV	[10]

In this part an overview is provided on the data integration, processing and inversion principles, and methods applied. The UAV-based EM data processing, following methodologies by [366,382], involves several key steps (Figure 12). Initially, raw datasets from various sources, including drone, EM instrument data, and additional sources (e.g., LiDAR, if available) are integrated, synchronized, and preprocessed for interpretation [383]. Noise filtering enhances data quality by removing high-frequency noise using a low-pass filter. Data segmentation categorizes raw EM sensor data into non-informative, grid/profile, and vertical sounding segments, facilitating anomaly analysis [366]. Inversion iteratively updates resistivity models by comparing field data with synthetic data, employing methods like Layered Constrained Inversion and Spatially Constrained Inversion [384,385,386]. Validation involves addressing noise sources and correlating EM data with geological maps and boreholes to discern soil characteristics and validate results. Additional geophysical data and hand drilling may be utilized for validation when alternative datasets are unavailable. A comprehensive review of UAV-borne EM survey geoscientific applications is summarized in Table 17.

3.2.2. Unmanned Aerial GPR

Over the past decade, UAV-based radar imaging has seen significant advancement, with various radar technologies, unmanned platforms, and payload configurations showcased [390,391,392,393,394,395]. Early research by [396,397] laid the foundation, leading to tests with high-frequency radars at P, X, and C bands [398,399,400], despite limited penetration capabilities. In [401] multi-frequency GPR for rotary-wing UAVs was explored, sparking a surge in contactless GPR research with UAV platforms. The convergence of UAVs and radar technology offers all-weather data recording and buried object detection, driving interest across scientific and industrial sectors [402,403,404,405]. Applications range from landmine detection to environmental monitoring, highlighting UAV-based GPR’s promising future in geophysical surveys and underground exploration [406,407,408]. UAV-based GPR systems are categorized as prototypes explicitly designed for UAVs or conventional GPR devices adapted for UAVs, with systems further classified based on whether antennas contact the ground, leading to ground-coupled and air-launched GPR systems [409,410,411].

Two approaches exist for assembling payloads in the UAV-GPR systems: independent and integrated designs (see Figure 13). In the independent approach, the UAV and payload are separate subsystems, while in the integrated method, they are developed collaboratively. The independent setup allows for compatibility with various UAV platforms but requires a separate interface for integration. Conversely, the integrated architecture simplifies subsystem synchronization and enables high-accuracy geo-referencing for navigation without redundant sensors. This approach is preferred for UAV systems with advanced sensors or specialized flight modes [24]. Visual examples include [412], for independent architecture, and [392], for integrated architecture.

In this part, the observation modes in UAV-borne GPR are reviewed. The scanning strategies employed in UAV-borne GPR are of significant importance. These systems are categorized into three main types based on observation modes and antenna orientation relative to the Earth’s surface (see Figure 14) [392,413].

Figure 13. Payload assembly architectures in UAV-GPR systems: (a) Independent-payload and (b) Integrated-payload architectures. The principles were borrowed from [24,414], with the flowcharts being reconfigured.

Figure 13. Payload assembly architectures in UAV-GPR systems: (a) Independent-payload and (b) Integrated-payload architectures. The principles were borrowed from [24,414], with the flowcharts being reconfigured.

Figure 14. Observation modes in UAV-borne GPR survey (reconfigured based on [24]).

Figure 14. Observation modes in UAV-borne GPR survey (reconfigured based on [24]).

Down-looking GPR (DLGPR) configurations position antennas perpendicular to the surface, offering advantages in detecting deeper targets while potentially obscuring shallow ones due to reflections at the air-soil interface [24,409,415,416,417]. Forward-looking GPR (FLGPR) orientations minimize clutter by reducing reflections from the air-ground interface through oblique antenna incidence [24,416,418,419]. Side-looking GPR (SLGPR) configurations utilize tilted antennas to mitigate specular reflection from the air-soil interface, often moving laterally or following circular paths for data capture [392,420,421,422,423].

The scanning architecture choice depends on the target depth and scenario. FLGPR or SLGPR configurations suit shallow targets, minimizing clutter and enhancing detection. DLGPR systems excel for deeper targets, despite increased clutter, offering extended dynamic range. DLGPR and FLGPR imaging capabilities are compared, with FLGPR optimizing transverse magnetic wave penetration, while DLGPR provides enhanced resolution but increased clutter [413,415].

UAV-based GPR methods are divided into fully-airborne and semi-airborne setups. In fully airborne configurations, both antennas are on one UAV or split between two UAVs for transmission and reception. Semi-airborne setups involve a ground-based vehicle with the transmission antenna and a UAV with the reception antenna, operating in down-looking mode (see Figure 15).

Data processing in UAV-borne GPR, inspired by air-launched systems (e.g., [416]), involves two categories: Standard methods and Advanced algorithms (see Figure 16). Standard methods filter noise and correct motion errors, while advanced algorithms aim for high-resolution imaging [395].

In standard processing, data undergoes positioning management and preprocessing, including background removal [394], dewow [425], and clutter elimination. Techniques such as time-gating [427], average subtraction [428,429], and SVD-based filtering [430] are employed for clutter removal. Ground profile retrieval and height correction are also essential for data accuracy [426].

Focusing methods aim to enhance image resolution and interpretability by transforming diffraction hyperbolas into distinct bright spots [431,432]. Figure 17 depicts a UAV-mounted DL-GPR surveying a region of interest, capturing backscattered radar signals along its flight trajectory (Γ) across the angular frequency range $\mathsf{\Omega }=[{\omega }_{min},{\omega }_{max}]$. Each measured point (m) in the volumetric subsurface domain (D) is defined by the position vector ${\overrightarrow{r}}_m=x_m\widehat{i}+y_m\widehat{j}+z_m\widehat{k}$, representing various buried targets. The radar imaging process employs a simplified linear scattering model [433]. Equation (4) defines the scattered field at each point, with ${\mathrm{E}}_{\mathrm{s}}$ representing the scattered field, ${\mathrm{E}}_{\mathrm{i}}$ denoting the incident field within domain D, χ(r) characterizing the unknown contrast function at any point $\mathrm{r}$ in D, G corresponding to Green’s function, and k is the propagation constant.

$$E_s\left(r_m,\omega \right)=k^2{\iiint }_{D}G\left(r_m,r,\omega \right)E_i(r,\omega )\chi (r)dr$$

(4)

Various algorithms were proposed to address the UAV-GPR imaging challenge, discussed as follows:

• Migration Techniques: Migration techniques like Kirchhoff’s wave-equation and phase-shift migration (PSM) algorithms, commonly used in GPR, are applied in UAV-based GPR data processing for efficient analysis [435,436,437,438,439]. However, PSM requires data interpolation to a regular grid, which may pose challenges in irregular survey trajectories. Newer approaches like Piecewise SAR (P-SAR) address these limitations by considering the reflection and transmission coefficients of EM waves through diverse subsurface layers [440].
• Back-projection (BP) or Delayand-and-Sum (DAS) Method: UAV-based GPR often employs beam-forming or SAR-like Back-projection (BP) or Delay-and-Sum (DAS) methods due to non-rectilinear measurement trajectories [414]. They integrate radar echoes from the flight path to generate a reflectivity map using Equation (5).

$$\chi \left(r\right)={\int }_{\mathsf{\Gamma }}{\int }_{\mathsf{\Omega }}{E}_s\left(r_m,\omega \right)\left(G\left(r_m,r,\omega \right)E_i(r,\omega )\right)\mathrm{*}d{r}_{m}d\omega$$

(5)

Where * represents the conjugation operator.

DAS involves summation of measurements at focal points across the target area [441]. Reflectivity $\mathsf{\rho }\left(\mathrm{r}\right)$ is determined using scattered field data from N acquisition points and M discrete frequencies (Equation (6)), considering phase shifts from wave propagation (Equation (7)).

$$\mathsf{\rho }\left(\mathrm{r}\right)={\sum }_{n=1}^N{\sum }_{m=1}^M{E}_s(r_n,f_m)e^{+j2({\varphi }_0+{\varphi }_1)}$$

(6)

$${\varphi }_0+{\varphi }_1=k_{0,m}{‖r^{\prime }-r_n‖}_2$$

(7)

Where $r_n$ represents the position where the n-th measurement was acquired, $f_m$ stands for the m-th discrete frequency (it is related to angular frequency), and ${\varphi }_0$ and ${\varphi }_1$ correspond to the phase shifts resulting from wave propagation. $k_{0,m}$ represents the free-space wavenumber for the m-th discrete frequency, while $r_{i,n}$ signifies the refraction point on the air-ground interface. The refraction point position ($r_{i,n}$) is determine using Snell’s law [441].

DAS, suitable for irregular flight trajectories, are comparable to PSM techniques in performance for UAV-based GPR processing. They find application in various UAV-based GPR studies [392,394,397,406,414,428,429,442].

• Microwave Tomography (MT): MT focusing algorithms, using inverse filtering techniques, aim to solve the EM inverse scattering problem [432,443]. Unlike SAR-like methods, MT directly inverts the linear integral equation [24,395], addressing the imaging problem through a solution to the linear inverse problem (Equation (8)).

$$E_s=L_{\chi }$$

(8)

$\mathrm{L}:{\mathcal{L}}^2\left(\mathrm{D}\right)\to {\mathcal{L}}^2(\mathsf{\Gamma }\times \mathsf{\Omega })$ maps unknown to data space, both being square-integrable function spaces. Regularization, often through truncated SVD, stabilizes the ill-posed inverse problem [444]. MT’s resilience to noise surpasses SAR-like methods, promoting its widespread adoption in UAV-based GPR imaging across various data collection scenarios and environments [24,395,425].

• Full-waveform Inversion (FWI) or Integral Equation (IE)-based Methods: FWI or IE GPR processing links radar backscattered fields with soil EM properties [404]. It estimates soil conductivity and permittivity by minimizing a cost function comparing the model and observed data [24]. In UAV-based GPR, FWI estimates soil permittivity, facilitating SWC mapping [395,406,434].

The mentioned methods reconstruct 2D images or 3D models of the subsurface and potential buried targets. These results can be refined using automatic target detection and recognition techniques, from traditional CFAR detectors [445] to advanced DL-based methods [446,447].

In this part the state-of-the-art UAV-compatible GPR antennas are reviewed. UAV-borne GPR systems rely on antennas to convert guided waves [424,448]. Modern UAV-compatible antennas include Vivaldi-like antennas, Archimedean spiral antennas, and helix antennas, as reported in [24,393,413,414,422], detailed in Table 18. Antennas in UAV-based GPR systems prioritize weight, dimensions, and radiation performance, often favoring horn-like or planar designs. These antennas are commonly featured in UAV-based GPR systems developed by [402,406,413,422,426,449,450]. Table 19 provides a comprehensive inspection of cutting-edge UAV-based radar and GPR systems, extracting detailed technical specifications from published works.

In this part, applications of the UAV-borne GPR survey are reviewed. Table 20 provides an outline of the key application domains in which the previously discussed UAV-borne GPR prototypes find utility.

3.2.3. Unmanned Aerial LiDARgrammetry

A UAV-based LiDAR system consists of an aerial section with a UAV platform and sensors, including compact LiDAR, GNSS receiver, and IMU, while the ground section comprises a control terminal, flight control system, and ground control points [469,470,471]. LiDAR emits laser pulses to the Earth’s surface, measuring distances based on pulse travel time. Integration of timing, LiDAR orientation, and location data determines point cloud accuracy [469]. For further details, interested readers are referred to [472,473].

When discussing UAV-compatible LiDAR sensors, it is important to distinguish between their mechanisms: Mechanical Scanning-based Sensors, Solid-state Technique-based Sensors, and Solid-state Hybrid Sensors [214,470,474,475]. The cutting-edge survey-grade UAV LiDAR sensors include Riegl VUX-1 UAV, Riegl mini VUX-1 UAV, Riegl mini VUX-1DL, Riegl VUX-240, Velodyne Puck LITE VLP-16, Quanergy M8, and Livox Mid-40. For more information, readers are referred to the relevant references (e.g., [470]).

To provide an overview of data processing and corrections in UAV-borne LiDARgrammetry, a comprehensive workflow comprising three main parts was proposed in [470]: point cloud production, point cloud refinement, and DEM generation. The initial phase involves calibration [476] and a combination of laser data and trajectory for initial point cloud generation [477]. Point cloud refinement includes removal of LiDAR point noises and outliers [478], as well as ground point classification [479]. Finally, DEM generation encompasses ground point filtering, followed by visualization and analysis. For further details, interested readers are referred to the provided reference.

Alongside the applications of UAV-borne LiDARgrammetry in urban mapping [28], this RS method are increasingly used in geoscientific studies for mapping inaccessible terrain and generating 3D models of geological features like cliffs, coasts, and volcanoes. This section explores UAV-borne LiDARgrammetry applications in geoscience, emphasizing its distinct utility from airborne LiDARgrammetry despite shared feasibility (Table 21). The applications mainly focus on capturing the surface geometry of the Earth’s exterior.

4. Discussions and Conclusions

References

1. Windley, F.; Albritton, C. Earth sciences. Encyclopedia Britannica 2024. [Google Scholar]
2. Lowrie, W.; Fichtner, A. Fundamentals of geophysics; Cambridge university press: 2020.
3. Fossen, H. Structural geology; Cambridge university press: 2016.
4. Vanicek, P.; Krakiwsky, E.J. Geodesy: the concepts; Elsevier: 2015.
5. Holt-Jensen, A. Geography: history and concepts. 2018.
6. Hasan, M.; Shang, Y.; Yi, X.; Shao, P.; He, M. Determination of rock mass integrity coefficient using a non-invasive geophysical approach. Journal of Rock Mechanics and Geotechnical Engineering 2023, 15, 1426–1440. [Google Scholar] [CrossRef]
7. Nabighian, M.N.; Grauch, V.; Hansen, R.; LaFehr, T.; Li, Y.; Peirce, J.W.; Phillips, J.D.; Ruder, M. The historical development of the magnetic method in exploration. Geophysics 2005, 70, 33ND–61ND. [Google Scholar] [CrossRef]
8. Hinze, W.J.; Von Frese, R.R.; Von Frese, R.; Saad, A.H. Gravity and magnetic exploration: Principles, practices, and applications; Cambridge University Press: 2013.
9. Nabighian, M.N. Electromagnetic methods in applied geophysics: Voume 1, theory; Society of Exploration Geophysicists: 1988.
10. Xing, K.; Li, S.; Qu, Z.; Gao, M.; Gao, Y.; Zhang, X. UAV Time-Domain Electromagnetic System and a Workflow for Subsurface Targets Detection. Remote Sensing 2024, 16, 330. [Google Scholar] [CrossRef]
11. McCay, A.T.; Harley, T.L.; Younger, P.L.; Sanderson, D.C.; Cresswell, A.J. Gamma-ray spectrometry in geothermal exploration: State of the art techniques. Energies 2014, 7, 4757–4780. [Google Scholar] [CrossRef]
12. Conyers, L.B. Ground-penetrating radar for archaeology; Rowman & Littlefield: 2023.
13. Zhao, D.; Liu, X.; Wang, Z.; Gou, T. Seismic anisotropy tomography and mantle dynamics. Surveys in Geophysics 2023, 44, 947–982. [Google Scholar] [CrossRef]
14. Perrone, A.; Lapenna, V.; Piscitelli, S. Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews 2014, 135, 65–82. [Google Scholar] [CrossRef]
15. Foley, C.P.; Leslie, K.; Binks, R.; Lewis, C.; Murray, W.; Sloggett, G.; Lam, S.; Sankrithyan, B.; Savvides, N.; Katzaros, A. Field trials using HTS SQUID magnetometers for ground-based and airborne geophysical applications. IEEE transactions on applied superconductivity 1999, 9, 3786–3792. [Google Scholar] [CrossRef]
16. Siemon, B.; Costabel, S.; Voß, W.; Meyer, U.; Deus, N.; Elbracht, J.; Günther, T.; Wiederhold, H. Airborne and ground geophysical mapping of coastal clays in Eastern Friesland, Germany. Geophysics 2015, 80, WB21–WB34. [Google Scholar] [CrossRef]
17. Planke, S.; Svensen, H.; Myklebust, R.; Bannister, S.; Manton, B.; Lorenz, L. Geophysics and remote sensing. Physical Geology of Shallow Magmatic Systems: Dykes, Sills and Laccoliths 2018, 131-146.
18. Kaloshin, I.; Kuznetsov, V.; Skripachev, V.; Surovceva, I. Сapabilities evaluation of spaceborne scientific equipment for geophysical applications. In Proceedings of the MATEC Web of Conferences, 2017; p. 01024.
19. Cummings, A.R.; McKee, A.; Kulkarni, K.; Markandey, N. The rise of UAVs. Photogrammetric Engineering & Remote Sensing 2017, 83, 317–325. [Google Scholar]
20. Stewart, R.R.; Chang, L.; Sudarshan, S.; Becker, A.; Alfataierge, E. A New Buzz in the Air: Seismic Drones. Geophysical Society of Houston P 2016, 22. [Google Scholar]
21. Levell, J.; Clow, A.; van Duijn, B.; Franken, P.; Campman, X. Drones for deploying seismic nodes: for those hard to reach places. In Proceedings of the 80th EAGE Conference and Exhibition 2018, 2018; pp. 1–5.
22. Zheng, Y.; Li, S.; Xing, K.; Zhang, X. Unmanned aerial vehicles for magnetic surveys: A review on platform selection and interference suppression. Drones 2021, 5, 93. [Google Scholar] [CrossRef]
23. Luo, K.; Cao, J.; Wang, C.; Cai, S.; Yu, R.; Wu, M.; Yang, B.; Xiang, W. First unmanned aerial vehicle airborne gravimetry based on the CH-4 UAV in China. Journal of Applied Geophysics 2022, 206, 104835. [Google Scholar] [CrossRef]
24. López, Y.Á.; Garcia-Fernandez, M.; Alvarez-Narciandi, G.; Andrés, F.L.-H. Unmanned aerial vehicle-based ground-penetrating radar systems: A review. IEEE Geoscience and Remote Sensing Magazine 2022, 10, 66–86. [Google Scholar] [CrossRef]
25. Altfelder, S.; Preugschat, B.; Matos, M.; Kandzia, F.; Wiens, B.; Eshmuradov, O.; Kunze, C. Upscaling ground-based backpack gamma-ray spectrometry to spatial resolution of UAV-based gamma-ray spectrometry for system validation. Journal of Environmental Radioactivity 2024, 273, 107382. [Google Scholar] [CrossRef]
26. Sudarshan, S.K.; Huang, L.; Li, C.; Stewart, R.; Becker, A.T. Seismic surveying with drone-mounted geophones. In Proceedings of the 2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016; pp. 1354–1359.
27. Gerke, M. Developments in UAV-photogrammetry. Journal of Digital Landscape Architecture 2018, 3, 262–272. [Google Scholar]
28. Kovanič, Ľ.; Topitzer, B.; Peťovský, P.; Blišťan, P.; Gergeľová, M.B.; Blišťanová, M. Review of photogrammetric and Lidar applications of UAV. Applied Sciences 2023, 13, 6732. [Google Scholar] [CrossRef]
29. Mohamad, N.; Ahmad, A.; Din, A.H.M. A Review of UAV Photogrammetry Application in Assessing Surface Elevation Changes. Journal of Information System and Technology Management7 2022, 195–204. [Google Scholar] [CrossRef]
30. Zheng, J.; Yao, W.; Lin, X.; Ma, B.; Bai, L. An accurate digital subsidence model for deformation detection of coal mining areas using a UAV-based LiDAR. Remote Sensing 2022, 14, 421. [Google Scholar] [CrossRef]
31. Pranckutė, R. Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 2021, 9, 12. [Google Scholar] [CrossRef]
32. Singh, V.K.; Singh, P.; Karmakar, M.; Leta, J.; Mayr, P. The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics 2021, 126, 5113–5142. [Google Scholar] [CrossRef]
33. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj 2021, 372. [Google Scholar]
34. Fingas, M.F.; Brown, C.E. Review of oil spill remote sensing. Spill Science & Technology Bulletin 1997, 4, 199–208. [Google Scholar]
35. Shahsavani, H. An aeromagnetic survey carried out using a rotary-wing UAV equipped with a low-cost magneto-inductive sensor. International Journal of Remote Sensing 2021, 42, 8805–8818. [Google Scholar] [CrossRef]
36. Caron, R.M.; Samson, C.; Straznicky, P.; Ferguson, S.; Sander, L. Aeromagnetic surveying using a simulated unmanned aircraft system. Geophysical Prospecting 2014, 62, 352–363. [Google Scholar] [CrossRef]
37. Gavazzi, B.; Le Maire, P.; Munschy, M.; Dechamp, A. Fluxgate vector magnetometers: A multisensor device for ground, UAV, and airborne magnetic surveys. The Leading Edge 2016, 35, 795–797. [Google Scholar] [CrossRef]
38. Dai, J.; Huang, K.; Xu, C.; Zhang, S.; Yi, Z.; Meng, L.; Xiao, Q.; Liu, C.; Zhang, C. UAV magnetic measurement system for regional geomagnetic survey. In Proceedings of the IOP Conference Series: Earth and Environmental Science, 2019; p. 032019.
39. Jiang, D.; Zeng, Z.; Zhou, S.; Guan, Y.; Lin, T. Integration of an aeromagnetic measurement system based on an unmanned aerial vehicle platform and its application in the exploration of the Ma’anshan magnetite deposit. IEEE Access 2020, 8, 189576–189586. [Google Scholar] [CrossRef]
40. Mu, Y.; Zhang, X.; Xie, W.; Zheng, Y. Automatic detection of near-surface targets for unmanned aerial vehicle (UAV) magnetic survey. Remote Sensing 2020, 12, 452. [Google Scholar] [CrossRef]
41. Døssing, A.; Lima Simoes da Silva, E.; Martelet, G.; Maack Rasmussen, T.; Gloaguen, E.; Thejll Petersen, J.; Linde, J. A high-speed, light-weight scalar magnetometer bird for km scale UAV magnetic surveying: On sensor choice, bird design, and quality of output data. Remote Sensing 2021, 13, 649. [Google Scholar] [CrossRef]
42. Lev, E.; Arie, M. Unmanned airborne magnetic and VLF investigations: Effective geophysical methodology for the near future. Positioning 2011, 2011. [Google Scholar]
43. Krishna, V.; Lima Simões da Silva, E.; Døssing, A. Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV). Geoscientific Instrumentation, Methods and Data Systems 2021, 10, 25–34. [Google Scholar]
44. Walker, B. Developing high sensitivity magnetometers for unmanned aircraft. 2016.
45. AirBIRD & AirGRAD UAV Systems. Available online: https://www.gemsys.ca/uav-systems/.
46. MagArrow II UAV-Enabled Magnetometer. Available online: https://www.geometrics.com/product/magarrow/.
47. Jackisch, R.; Madriz, Y.; Zimmermann, R.; Pirttijärvi, M.; Saartenoja, A.; Heincke, B.H.; Salmirinne, H.; Kujasalo, J.-P.; Andreani, L.; Gloaguen, R. Drone-borne hyperspectral and magnetic data integration: Otanmäki Fe-Ti-V deposit in Finland. Remote sensing 2019, 11, 2084. [Google Scholar] [CrossRef]
48. Zheng, Y.; Zhang, X.; Liu, H.; Xing, K. A Fast Position Estimation Method of Near-Surface Targets Detection for Unmanned Aerial Vehicle Magnetic Surveys. In Proceedings of the Advances in Intelligent Automation and Soft Computing, 2022; pp. 936–945.
49. Cherkasov, S.; Kapshtan, D. Unmanned aerial systems for magnetic survey. Drones Appl 2018, 135–148. [Google Scholar]
50. Macharet, D.G.; Perez-Imaz, H.I.; Rezeck, P.A.; Potje, G.A.; Benyosef, L.C.; Wiermann, A.; Freitas, G.M.; Garcia, L.G.; Campos, M.F. Autonomous aeromagnetic surveys using a fluxgate magnetometer. Sensors 2016, 16, 2169. [Google Scholar] [CrossRef]
51. Malehmir, A.; Dynesius, L.; Paulusson, K.; Paulusson, A.; Johansson, H.; Bastani, M.; Wedmark, M.; Marsden, P. The potential of rotary-wing UAV-based magnetic surveys for mineral exploration: A case study from central Sweden. The Leading Edge 2017, 36, 552–557. [Google Scholar] [CrossRef]
52. Nikulin, A.; de Smet, T.S. A UAV-based magnetic survey method to detect and identify orphaned oil and gas wells. The Leading Edge 2019, 38, 447–452. [Google Scholar] [CrossRef]
53. Parshin, A.V.; Morozov, V.A.; Blinov, A.V.; Kosterev, A.N.; Budyak, A.E. Low-altitude geophysical magnetic prospecting based on multirotor UAV as a promising replacement for traditional ground survey. Geo-spatial information science 2018, 21, 67–74. [Google Scholar] [CrossRef]
54. Cunningham, M. Aeromagnetic surveying with unmanned aircraft systems. Carleton University, 2016.
55. Cunningham, M.; Samson, C.; Wood, A.; Cook, I. Aeromagnetic surveying with a rotary-wing unmanned aircraft system: A case study from a zinc deposit in Nash Creek, New Brunswick, Canada. Pure and Applied Geophysics 2018, 175, 3145–3158. [Google Scholar] [CrossRef]
56. Parvar, K. Development and evaluation of unmanned aerial vehicle (UAV) magnetometry systems. Queen’s University (Canada), 2016.
57. Acoustic and magnetic signatures management naval forces. Available online: https://www.ecagroup.com/en/defence-security/acoustic-magnetic-signatures-management-naval-forces/.
58. Stoll, J.; Moritz, D. Unmanned aircraft systems for rapid near surface geophysical measurements. In Proceedings of the 75th EAGE Conference & Exhibition-Workshops, 2013; pp. cp–349-00062.
59. Flying robots in combination with onboard geophysical sensors. Available online: https://www.mgt-geo.com/technology.html/.
60. Walter, C.; Braun, A.; Fotopoulos, G. Spectral analysis of magnetometer swing in high-resolution UAV-borne aeromagnetic surveys. In Proceedings of the 2019 IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS), 2019; pp. 1–4.
61. QIAO, Z.; MA, G.; ZHOU, W.; YU, P.; ZHOU, S.; WANG, T.; TANG, S.; DAI, W.; MENG, Z.; ZHANG, Z. Research on the comprehensive compensation of aeromagnetic system error of multi-rotor UAV. Chinese Journal of Geophysics 2020, 63, 4604–4612. [Google Scholar]
62. de Smet, T.S.; Nikulin, A.; Romanzo, N.; Graber, N.; Dietrich, C.; Puliaiev, A. Successful application of drone-based aeromagnetic surveys to locate legacy oil and gas wells in Cattaraugus county, New York. Journal of Applied Geophysics 2021, 186, 104250. [Google Scholar] [CrossRef]
63. Nikulin, P.D.; deSmet, P.D.; Puliaiev, A.; Zhurakhov, V.; Fasullo, S.; Chen, G.; Spiegel, I.; Cappuccio, K. Automated UAS aeromagnetic surveys to detect MBRL unexploded ordnance. The Journal of Conventional Weapons Destruction 2020, 24, 13. [Google Scholar]
64. Le Maire, P.; Bertrand, L.; Munschy, M.; Diraison, M.; Géraud, Y. Aerial magnetic mapping with an unmanned aerial vehicle and a fluxgate magnetometer: A new method for rapid mapping and upscaling from the field to regional scale. Geophysical prospecting 2020, 68, 2307–2319. [Google Scholar] [CrossRef]
65. Gailler, L.; Labazuy, P.; Régis, E.; Bontemps, M.; Souriot, T.; Bacques, G.; Carton, B. Validation of a new UAV magnetic prospecting tool for volcano monitoring and geohazard assessment. Remote Sensing 2021, 13, 894. [Google Scholar] [CrossRef]
66. Pisciotta, A.; Vitale, G.; Scudero, S.; Martorana, R.; Capizzi, P.; D’Alessandro, A. A lightweight prototype of a magnetometric system for unmanned aerial vehicles. Sensors 2021, 21, 4691. [Google Scholar] [CrossRef]
67. Walter, C.; Braun, A.; Fotopoulos, G. High-resolution unmanned aerial vehicle aeromagnetic surveys for mineral exploration targets. Geophysical Prospecting 2020, 68, 334–349. [Google Scholar] [CrossRef]
68. Lum, C.; Rysdyk, R.; Pongpunwattana, A. Autonomous airborne geomagnetic surveying and target identification. In Infotech@ Aerospace; 2005; p. 7039.
69. Dion-Ortega, A. Abitibi Géophysique lance le tout premier drone magnétométrique. Montreal, QC, Canada. Retrieved November 2015, 9, 2015. [Google Scholar]
70. ScanEagle – Mini-UAV (Unmanned Aerial Vehicle). Available online: https://www.naval-technology.com/projects/scaneagle-uav/.
71. Funaki, M.; Hirasawa, N. Outline of a small unmanned aerial vehicle (Ant-Plane) designed for Antarctic research. Polar Science 2008, 2, 129–142. [Google Scholar] [CrossRef]
72. Funaki, M.; Higashino, S.-I.; Sakanaka, S.; Iwata, N.; Nakamura, N.; Hirasawa, N.; Obara, N.; Kuwabara, M. Small unmanned aerial vehicles for aeromagnetic surveys and their flights in the South Shetland Islands, Antarctica. Polar Science 2014, 8, 342–356. [Google Scholar] [CrossRef]
73. Barnard, J. The use of unmanned aircraft in oil, gas and mineral exploration and production activities. In Proceedings of the Proceedings of the 23rd Bristol International UAV Systems Conference, 2008.
74. Glen, J.M.; Egger, A.E.; Ippolito, C.; Athens, N. Correlation of geothermal springs with sub-surface fault terminations revealed by high-resolution, UAV-acquired magnetic data. In Proceedings of the Proceedings of the 38th Workshop on Geothermal Reservoir Engineering: Stanford Geothermal Program Workshop Report SGP-TR-198, 2013; pp. 1233–1242.
75. Li, W.; Qin, X.; Gan, X. The IGGE UAV aero magnetic and radiometric survey system. In Proceedings of the Near Surface Geoscience 2014-20th European Meeting of Environmental and Engineering Geophysics, 2014; pp. 1–5.
76. Jackisch, R.; Lorenz, S.; Kirsch, M.; Zimmermann, R.; Tusa, L.; Pirttijärvi, M.; Saartenoja, A.; Ugalde, H.; Madriz, Y.; Savolainen, M. Integrated geological and geophysical mapping of a carbonatite-hosting outcrop in siilinjärvi, finland, using unmanned aerial systems. Remote Sensing 2020, 12, 2998. [Google Scholar] [CrossRef]
77. UAV based magnetic survey system. Available online: https://radai.fi/uav-based-magnetic-survey-system/.
78. JU, X.; NIU, H.-b.; GUO, H.; HAN, S.; WANG, Y.-z.; LÜ, C.; ZHENG, Q. Safety analysis and quality evaluation of the aeromagnetic measurement system of CH-4 UAV. Progress in Geophysics 2020, 35, 1565–1571. [Google Scholar]
79. Gradient Magnetometer UAV. Available online: https://www.gemsys.ca/wp-content/themes/gemsystems/pdf/GEM_MONARCH_UAV.pdf.
80. Parshin, A.; Savin, A.; Morozov, V.; Badmayev, M.-J. Development of low-cost unmanned aerogeophysical system based on light VTOL aircraft. In Proceedings of the International Conference on Aviamechanical Engineering and Transport (AviaENT 2019), 2019; pp. 271–276.
81. Hansen, C.R.D. Magnetic signature characterization of a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). 2018.
82. Industrial drone TD100 series. Available online: https://www.aeroexpo.online/prod/brican-flight-systems/product-181537-19049.html.
83. Drone Magnetic Survey for Mineral Exploration in Gansu. Available online: https://www.jouav.com/case-study/drone-magnetic-survey-for-minning.html.
84. Koyama, T.; Kaneko, T.; Ohminato, T.; Yanagisawa, T.; Watanabe, A.; Takeo, M. An aeromagnetic survey of Shinmoe-dake volcano, Kirishima, Japan, after the 2011 eruption using an unmanned autonomous helicopter. Earth, Planets and Space 2013, 65, 657–666. [Google Scholar] [CrossRef]
85. Hashimoto, T.; Koyama, T.; Kaneko, T.; Ohminato, T.; Yanagisawa, T.; Yoshimoto, M.; Suzuki, E. Aeromagnetic survey using an unmanned autonomous helicopter over Tarumae Volcano, northern Japan. Exploration Geophysics 2014, 45, 37–42. [Google Scholar] [CrossRef]
86. Pei, Y.; Liu, B.; Hua, Q.; Liu, C.; Ji, Y. An aeromagnetic survey system based on an unmanned autonomous helicopter: Development, experiment, and analysis. International journal of remote sensing 2017, 38, 3068–3083. [Google Scholar] [CrossRef]
87. Eck, C.; Imbach, B. Aerial magnetic sensing with an UAV helicopter. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2012, 38, 81–85. [Google Scholar] [CrossRef]
88. GEM Hawk Available online: https://www.gemsys.ca/wp-content/themes/gemsystems/pdf/GEM_UAV_Solutions-GEM%20HAWK_Feb2017_web.pdf.
89. Versteeg, R.; McKay, M.; Anderson, M.; Johnson, R.; Selfridge, B.; Bennett, J. Feasibility study for an Autonomous UAV-Magnetometer system. Idaho National Laboratory 2007, 1–53. [Google Scholar]
90. McKay, M.D.; Anderson, M.O. Development of Autonomous Magnetometer Rotorcraft for Wide Area Assessment; Idaho National Lab.(INL), Idaho Falls, ID (United States): 2011.
91. Xi, Y.; Lu, N.; Zhang, L.; Li, J.; Zhang, F.; Wu, S.; Liao, G.; Beng, F.; Huang, W. Integration and application of an aeromagnetic survey system based on unmanned helicopter platform. Geophysical and Geochemical Exploration 2019, 43, 125–131. [Google Scholar]
92. ZHANG, F.-m.; WEN, J.-l.; ZHAO, X.-h.; FANG, W.-l. Development and application of aeromagnetic measurement system for unmanned helicopter. Progress in Geophysics 2019, 34, 1694–1699. [Google Scholar]
93. Xu, Q.; Liu, L.; Huang, S.; Wu, F. Research and test of compensation method for cesium optical pump aeromagnetic system of domestic UAV helicopter. J. Nav. Inst. Aeronaut. Eng 2020, 35, 141–148. [Google Scholar]
94. Noriega, G. Performance measures in aeromagnetic compensation. The Leading Edge 2011, 30, 1122–1127. [Google Scholar] [CrossRef]
95. Dou, Z.; Han, Q.; Niu, X.; Peng, X.; Guo, H. An adaptive filter for aeromagnetic compensation based on wavelet multiresolution analysis. IEEE Geoscience and Remote Sensing Letters 2016, 13, 1069–1073. [Google Scholar] [CrossRef]
96. Accomando, F.; Vitale, A.; Bonfante, A.; Buonanno, M.; Florio, G. Performance of two different flight configurations for drone-borne magnetic data. Sensors 2021, 21, 5736. [Google Scholar] [CrossRef] [PubMed]
97. Leliak, P. Identification and evaluation of magnetic-field sources of magnetic airborne detector equipped aircraft. IRE Transactions on Aerospace and Navigational Electronics 1961, 95–105. [Google Scholar] [CrossRef]
98. Tuck, L. Characterization and compensation of magnetic interference resulting from unmanned aircraft systems. Carleton University, 2019.
99. Reeves, C. Aeromagnetic surveys: principles, practice and interpretation; Geosoft Washington (DC): 2005; Volume 155.
100. Samson, C.; Straznicky, P.; Laliberté, J.; Caron, R.; Ferguson, S.; Archer, R. Designing and building an unmanned aircraft system for aeromagnetic surveying. In Proceedings of the SEG International Exposition and Annual Meeting, 2010; pp. SEG–2010-1167.
101. Wood, A.; Cook, I.; Doyle, B.; Cunningham, M.; Samson, C. Experimental aeromagnetic survey using an unmanned air system. The Leading Edge 2016, 35, 270–273. [Google Scholar] [CrossRef]
102. Nasyrtdinov, B.; Latipov, R.; Khassanov, D.; Popov, M.; Usmanov, A. Assessment of the impact of unmanned aerial vehicles with different engine types on the MMPOS-1 magnetometer. International Multidisciplinary Scientific GeoConference: SGEM 2020, 20, 475–482. [Google Scholar]
103. Anderson, D.; Pita, A. Geophysical surveying with georanger uav. In Infotech@ Aerospace; 2005; p. 6952.
104. Gee, J.S.; Cande, S.C.; Kent, D.V.; Partner, R.; Heckman, K. Mapping geomagnetic field variations with unmanned airborne vehicles. Eos, Transactions American Geophysical Union 2008, 89, 178–179. [Google Scholar] [CrossRef]
105. Rodgers, M.; Malservisi, R.; Connor, C.; Wang, P.; Connor, L.; Van Alphen, R.; Vallée, M.; Hastings, M.S.; McIlrath, J. From beaches to volcanoes: UAV applications in geoscience using high-resolution topography and aerial magnetic surveys. In Proceedings of the AGU Fall Meeting Abstracts, 2019; pp. EP11C–2127.
106. Durfeld, R. GEOPHYSICAL REPORT AIRBORNE MAGNETIC SURVEY ON THE BIRCH PROPERTY; 2012.
107. Wells, M. Attenuating magnetic interference in a UAV system. Carleton University, 2008.
108. Forrester, R.W. Magnetic signature control strategies for an unmanned aircraft system. Carleton University, 2011.
109. LI, Z.; GAO, S.; WANG, X. New method of aeromagnetic surveys with rotorcraft UAV in particular areas. Chinese Journal of Geophysics 2018, 61, 3825–3834. [Google Scholar]
110. Calou, P.; Munschy, M. Airborne magnetic surveying with a drone and determination of the total magnetization of a dipole. IEEE Transactions on Magnetics 2020, 56, 1–9. [Google Scholar] [CrossRef]
111. Hammack, R.; Veloski, G.; Schlagenhauf, M.; Lowe, R.; Zorn, A.; Wylie, L. Using drone-mounted geophysical sensors to map legacy oil and gas infrastructure. In Proceedings of the Unconventional Resources Technology Conference, 20–22 July 2020, 2020; pp. 3517–3528.
112. Nikulin, A.; de Smet, T.S. UAV-based aeromagnetic surveys for orphaned well location: Emerging best practices. The Leading Edge 2023, 42, 817–823. [Google Scholar] [CrossRef]
113. de Smet, T.S.; Nikulin, A.; Balrup, N.; Graber, N. Successful integration of UAV aeromagnetic mapping with terrestrial methane emissions surveys in orphaned well remediation. Remote Sensing 2023, 15, 5004. [Google Scholar] [CrossRef]
114. Luoma, S.; Zhou, X. Construction of a fluxgate magnetic gradiometer for integration with an unmanned aircraft system. Remote Sensing 2020, 12, 2551. [Google Scholar] [CrossRef]
115. Fernandez Romero, S.; Morata Barrado, P.; Rivero Rodriguez, M.A.; Vazquez Yañez, G.A.; De Diego Custodio, E.; Michelena, M.D. Vector magnetometry using remotely piloted aircraft systems: An example of application for planetary exploration. Remote Sensing 2021, 13, 390. [Google Scholar] [CrossRef]
116. Schmidt, V.; Becken, M.; Schmalzl, J. A UAV-borne magnetic survey for archaeological prospection of a Celtic burial site. First Break 2020, 38, 61–66. [Google Scholar] [CrossRef]
117. Porras, D.; Carrasco, J.; Carrasco, P.; Alfageme, S.; Gonzalez-Aguilera, D.; Lopez Guijarro, R. Drone magnetometry in mining research. An application in the study of Triassic Cu–Co–Ni mineralizations in the Estancias Mountain Range, Almería (Spain). Drones 2021, 5, 151. [Google Scholar] [CrossRef]
118. Kim, B.; Lee, S.; Park, G.; Cho, S.-J. Development of an unmanned airship for magnetic exploration. Exploration Geophysics 2021, 52, 462–467. [Google Scholar] [CrossRef]
119. Efrem, R.; Coutu, A.; Saeedi, S. Suspended Magnetometer Survey for Mineral Data Acquisition with Vertical Take-off and Landing Fixed-wing Aircraft. arXiv preprint arXiv:2402.11797 2024.
120. Parvar, K.; Braun, A.; Layton-Matthews, D.; Burns, M. UAV magnetometry for chromite exploration in the Samail ophiolite sequence, Oman. Journal of Unmanned Vehicle Systems 2017, 6, 57–69. [Google Scholar] [CrossRef]
121. Cunningham, M.; Samson, C.; Laliberté, J.; Goldie, M.; Wood, A.; Birkett, D. Inversion of magnetic data acquired with a rotary-wing unmanned aircraft system for gold exploration. Pure and Applied Geophysics 2021, 178, 501–516. [Google Scholar] [CrossRef]
122. Yoo, L.-S.; Lee, J.-H.; Lee, Y.-K.; Jung, S.-K.; Choi, Y. Application of a drone magnetometer system to military mine detection in the demilitarized zone. Sensors 2021, 21, 3175. [Google Scholar] [CrossRef]
123. Kolster, M.E.; Wigh, M.D.; Lima Simoes da Silva, E.; Bjerg Vilhelmsen, T.; Døssing, A. High-speed magnetic surveying for unexploded ordnance using UAV systems. Remote Sensing 2022, 14, 1134. [Google Scholar] [CrossRef]
124. Wang, Y.M.D., T. . Measuring Earth’s gravity field from the air. Available online: https://eos.org/science-updates/measuring-earths-gravity-field-from-the-air (accessed on 10 January).
125. Francke, J. Developing UAV - Mounted Geophysical Sensor Arrays; July 2020.
126. Alkan, R.M.; Erol, S.; Ozulu, I.M.; Ilci, V. Accuracy comparison of post-processed PPP and real-time absolute positioning techniques. Geomatics, Natural Hazards and Risk 2020, 11, 178–190. [Google Scholar] [CrossRef]
127. Nordin, A.F.; Jamil, H.; Isa, M.N.; Mohamed, A.; Tahir, S.H.; Musta, B.; Forsberg, R.; Olesen, A.V.; Nielsen, J.E.; Abd Majid, A.K. Geological mapping of Sabah, Malaysia, using airborne gravity survey. Borneo Science, The Journal of Science and Technology 2016, 37, 14–27. [Google Scholar]
128. Termens, A. airborne gravimetry. Available online: https://www.slideshare.net/slideshow/airborne-gravimetry/75797480 (accessed on May 9).
129. Dransfield, M. Requirements for airborne gravity gradient terrain corrections. ASEG Extended Abstracts 2010, 2010, 1–4. [Google Scholar] [CrossRef]
130. Kaub, L.; Seruge, C.; Chopra, S.D.; Glen, J.M.; Teodorescu, M. Developing an autonomous unmanned aerial system to estimate field terrain corrections for gravity measurements. The Leading Edge 2018, 37, 584–591. [Google Scholar] [CrossRef]
131. Dransfield, M.; Zeng, Y. Airborne gravity gradiometry: Terrain corrections and elevation error. Geophysics 2009, 74, I37–I42. [Google Scholar] [CrossRef]
132. Navigation, I. iCORUS. Available online: https://www.imarnavigation.de/downloads/iCORUS.pdf.
133. Jensen, T.E.; Olesen, A.V.; Forsberg, R.; Olsson, P.-A.; Josefsson, Ö. New results from strapdown airborne gravimetry using temperature stabilisation. Remote sensing 2019, 11, 2682. [Google Scholar] [CrossRef]
134. Grover, G.; Forsberg, R.; Jensen, T.; Müller, F.; Hoss, M.; Bourne, T.; Wright, O. World’s First Fixed Wing UAV Gravity Data Collection Flight. Less cost & less carbon. In Proceedings of the Second EAGE Workshop on Unmanned Aerial Vehicles, 2021; pp. 1–3.
135. Lin, C.; Chiang, K.; Kuo, C. Integration of INS and GNSS for gravimetric application with UAS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, 42, 263–268. [Google Scholar] [CrossRef]
136. Middlemiss, R.P.; Bramsiepe, S.G.; Douglas, R.; Hough, J.; Paul, D.J.; Rowan, S.; Hammond, G.D. Field tests of a portable MEMS gravimeter. Sensors 2017, 17, 2571. [Google Scholar] [CrossRef]
137. Passey, E.; Hammond, G.; Bramsiepe, S.; Prasad, A.; Middlemiss, R.; Paul, D.; Walker, R.; Noack, A.; Anastasiou, K. Development of a MEMs gravimeter for drone-based field surveys. In Proceedings of the EGU General Assembly Conference Abstracts, 2020; p. 20906.
138. Byrne, M. New Gravimeter Technology Will Enable High-Res Drone-Based Gravity Mapping. Available online: https://www.vice.com/en/article/8q8xak/gravimeter-technology-drone-based-gravity-mapping-geologists-volcano-detection.
139. Pike, W.; Standley, I.; Calcutt, S.; Mukherjee, A. A broad-band silicon microseismometer with 0.25 NG/rtHz performance. In Proceedings of the 2018 IEEE Micro Electro Mechanical Systems (MEMS), 2018; pp. 113–116.
140. Tang, S.; Liu, H.; Yan, S.; Xu, X.; Wu, W.; Fan, J.; Liu, J.; Hu, C.; Tu, L. A high-sensitivity MEMS gravimeter with a large dynamic range. Microsystems & nanoengineering 2019, 5, 45. [Google Scholar]
141. Du, Z.; Topham, A.; Lofts, J.J.C.; Seshia, A.A. Brazil Pre-Salt, Santos Basin: Feasibility study for the application of borehole gravity to improve reservoir monitoring. In Proceedings of the Offshore Technology Conference, 2020; p. D031S034R001.
142. Zhao, C.; Pandit, M.; Sobreviela, G.; Steinmann, P.; Mustafazade, A.; Zou, X.; Seshia, A. A resonant MEMS accelerometer with 56ng bias stability and 98ng/Hz 1/2 noise floor. Journal of Microelectromechanical Systems 2019, 28, 324–326. [Google Scholar] [CrossRef]
143. Anastasiadis, K. Gravimeters prospecting for aerial surveys using drones. 2016.
144. Middlemiss, R.; Samarelli, A.; Paul, D.; Hough, J.; Rowan, S.; Hammond, G. Measurement of the Earth tides with a MEMS gravimeter. Nature 2016, 531, 614–617. [Google Scholar] [CrossRef]
145. RG-1 REMOTE OPERATING GRAVITY METER. Available online: https://scintrexltd.com/product/rg-1-remote-operating-gravity-meter/.
146. Deurloo, R.; Bastos, M.; Geng, Y.; Yan, W. Evaluation of low-and medium-cost IMUs for Airborne Gravimetry with UAVs. In Proceedings of the AGU Fall Meeting Abstracts, 2011; pp. G12A–01.
147. okue, S.; Ogura, Y.; Morikawa, H.; Matsuda, S.; Yokoi, I.; Suda, H.; Kima, S.; Kusumoto, S.; Noguchi, T.; Komazawa, M. Developments of airborne survey on an unmanned helicopter for quick observation of gravity and magnetic. In Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013, 2013; pp. 1363–1368.
148. Morikawa, H.; Tokue, S.; Ogura, Y.; Matsuda, S.; Saeki, M.; Ohsawa, E.; Suzuki, T.; Yokoi, I.; Kusumoto, S.; Noguchi, T. A development of airborne survey of gravity and magnetics on an unmanned helicopter and its data processing. In Proceedings of the Japan Geoscience Meeting, 2013.
149. Becker, D. Advanced calibration methods for strapdown airborne gravimetry; Technische Universität Darmstadt: 2016.
150. DRONES FOR GEOPHYSICS TAKING OFF. Available online: https://brasilminingsite.com.br/drones-for-geophysics-taking-off/.
151. Lin, C.-A. The Performance Analysis of an UAV Borne Vector Gravimetry System. In Proceedings of the Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), 2016; pp. 857–871.
152. Ball, M. Aurora Centaur UAS Used in NOAA Gravity Measurement Program. Available online: https://www.unmannedsystemstechnology.com/2017/03/aurora-uas-centaur-used-noaa-gravity-measurement-program/ (accessed on 28 Mar).
153. Alaoui-Sosse, S.; Pastor, P.; Durand, P.; Medina, P.; Gavart, M.; Darrozes, J.; Lothon, M. Development and qualification of instrumented unmanned planes for turbulence observations in the atmospheric surface layer. 2018.
154. Hammond, G. MEMS Gravimeters; Institute for Gravitational Research SUPA, University of Glasgow: 2018.
155. Wang, Y.; Cao, J.; Yu, R.; Xiang, W. Airborne gravimetry and error compensation based on the undulating flight of UAV. In Proceedings of the International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 2021; pp. 435–445.
156. BAS-200 of Russian Helicopters was the first ever UAV at MAKS-2021. Available online: https://www.helihub.com/2021/07/26/bas-200-of-russian-helicopters-was-the-first-ever-uav-at-maks-2021/ (accessed on 26 Jul).
157. New BAS-200 Drone for Geophysical Explorations in Arctic. Available online: https://www.helihub.com/2022/02/21/new-bas-200-drone-for-geophysical-explorations-in-arctic/ (accessed on 21 Feb).
158. Sediles Martinez, A.J. Unmanned Aerial Vehicles (UAVs) As a Non-invasive Optimization Tool for the Exploration and Management of Raw Materials. 2022.
159. DroneSOM – Drone Geophysics and Self-Organizing Maps. Available online: https://www.gtk.fi/en/research-project/dronesom/.
160. DroneSOM – more precise, efficient, and environmentally friendly mineral exploration. Available online: https://dronesom.com/.
161. Koomans, R.; Limburg, H. Drone-borne gamma-ray spetrometry–a dream come true. 2020.
162. Limburg, H.; van der Veeke, S.; Koomans, R. Towards drone-borne gammaray mapping of soils. First Break 2019, 37, 55–61. [Google Scholar] [CrossRef]
163. Van Der Veeke, S.; Koomans, R.; Van Egmond, F.; Limburg, J. A drone as platform for airborne gamma-ray surveys to characterize soil and monitor contaminations. In Proceedings of the 24th European Meeting of Environmental and Engineering Geophysics, 2018; pp. 1–5.
164. Royo, P.; Vargas, A.; Guillot, T.; Saiz, D.; Pichel, J.; Rabago, D.; Duch, M.A.; Grossi, C.; Luchkov, M.; Dangendorf, V. The Mapping of Alpha-Emitting Radionuclides in the Environment Using an Unmanned Aircraft System. Remote Sensing 2024, 16, 848. [Google Scholar] [CrossRef]
165. Gabrlik, P.; Lazna, T. Simulation of gamma radiation mapping using an unmanned aerial system. IFAC-PapersOnLine 2018, 51, 256–262. [Google Scholar] [CrossRef]
166. Geelen, S.; Camps, J.; Olyslaegers, G.; Ilegems, G.; Schroeyers, W. Radiological surveillance using a fixed-wing uav platform. Remote Sensing 2022, 14, 3908. [Google Scholar] [CrossRef]
167. Koomans, R.L., Han; van der Veeke, Steven. THE USE OF GAMMA-RAY SPECTROMETERS IN AIRBORNE SURVEYS: Soil Mapping with Drones. 2022.
168. MacFarlane, J.; Payton, O.; Keatley, A.; Scott, G.; Pullin, H.; Crane, R.; Smilion, M.; Popescu, I.; Curlea, V.; Scott, T. Lightweight aerial vehicles for monitoring, assessment and mapping of radiation anomalies. Journal of environmental radioactivity 2014, 136, 127–130. [Google Scholar] [CrossRef]
169. van der Veeke, S.; Limburg, J.; Koomans, R.; Söderström, M.; van der Graaf, E. Optimizing gamma-ray spectrometers for UAV-borne surveys with geophysical applications. Journal of Environmental Radioactivity 2021, 237, 106717. [Google Scholar] [CrossRef]
170. Erdi-Krausz, G.; Matolin, M.; Minty, B.; Nicolet, J.; Reford, W.; Schetselaar, E. Guidelines for radioelement mapping using gamma ray spectrometry data: also as open access e-book; International Atomic Energy Agency (IAEA): 2003.
171. Parshin, A.; Morozov, V.; Snegirev, N.; Valkova, E.; Shikalenko, F. Advantages of gamma-radiometric and spectrometric low-altitude geophysical surveys by unmanned aerial systems with small scintillation detectors. Applied Sciences 2021, 11, 2247. [Google Scholar] [CrossRef]
172. Kazemeini, M.; Vargas, J.; Barzilov, A.; Yim, W. Gamma ray measurements using unmanned aerial systems. Use of Gamma Radiation Techniques in Peaceful Applications 2019, 85. [Google Scholar]
173. Borbinha, J.; Romanets, Y.; Teles, P.; Corisco, J.; Vaz, P.; Carvalho, D.; Brouwer, Y.; Luís, R.; Pinto, L.; Vale, A. Performance analysis of geiger–müller and cadmium zinc telluride sensors envisaging airborne radiological monitoring in NORM sites. Sensors 2020, 20, 1538. [Google Scholar] [CrossRef]
174. Briechle, S.; Sizov, A.; Tretyak, O.; Antropov, V.; Molitor, N.; Krzystek, P. UAV-based detection of unknown radioactive biomass deposits in Chernobyl’s Exclusion Zone. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, 42, 163–169. [Google Scholar] [CrossRef]
175. Šálek, O.; Matolín, M.; Gryc, L. Mapping of radiation anomalies using UAV mini-airborne gamma-ray spectrometry. Journal of environmental radioactivity 2018, 182, 101–107. [Google Scholar] [CrossRef]
176. Parshin, A.; Grebenkin, N.; Morozov, V.; Shikаlenko, F. Research Note: First results of a low-altitude unmanned aircraft system gamma survey by comparison with the terrestrial and aerial gamma survey data. Geophysical prospecting 2018, 66, 1433–1438. [Google Scholar] [CrossRef]
177. Parshin, A.; Budyak, A.; Chebokchinov, I.; Sapunov, V.; Bulnayev, A.; Morozov, V. Complex UAS-geophysical surveys at the first stages of geological prospecting: Case in the western Sayan (Russia). In Proceedings of the First EAGE Workshop on Unmanned Aerial Vehicles, 2019; pp. 1–5.
178. Kurvinen, K.; Smolander, P.; Pöllänen, R.; Kuukankorpi, S.; Kettunen, M.; Lyytinen, J. Design of a radiation surveillance unit for an unmanned aerial vehicle. Journal of environmental radioactivity 2005, 81, 1–10. [Google Scholar] [CrossRef] [PubMed]
179. Heikonen, K.; Saaristo, T. You and Your TETRA RADIO, TETRA as a Tool for Public Safety. 2002.
180. Kunze, C.; Preugschat, B.; Arndt, R.; Kandzia, F.; Wiens, B.; Altfelder, S. Development of a UAV-based gamma spectrometry system for natural radionuclides and field tests at central Asian Uranium legacy sites. Remote Sensing 2022, 14, 2147. [Google Scholar] [CrossRef]
181. Widodo, S.; Abimanyu, A.; Apribra, R. Development of drone mounted aerial gamma monitoring system for environmental radionuclide surveillance in BATAN. In Proceedings of the Journal of Physics: Conference Series, 2020; p. 012126.
182. Van Egmond, F.; Van Der Veeke, S.; Knotters, M.; Koomans, R.; Walvoort, D.; Limburg, J. Mapping soil texture with a gamma-ray spectrometer: comparison between UAV and proximal measurements and traditional sampling: validation study; 2352-2739; WOT Natuur & Milieu: 2018.
183. Pirttijärvi, M.; Oy, R. Radai’s UAV based radiometric measurements at Rautuvaara mine in Kolari. Radai Survey Report (http://Www. Radai. Fi) 2016.
184. Hendriks, P.; Limburg, J.; De Meijer, R. Full-spectrum analysis of natural γ-ray spectra. Journal of Environmental Radioactivity 2001, 53, 365–380. [Google Scholar] [CrossRef]
185. Grasty, R.; Glynn, J.; Grant, J. The analysis of multichannel airborne gamma-ray spectra. Geophysics 1985, 50, 2611–2620. [Google Scholar] [CrossRef]
186. Minty, B.R.; McFadden, P.; Kennett, B.L. Multichannel processing for airborne gamma-ray spectrometry. Geophysics 1998, 63, 1971–1985. [Google Scholar] [CrossRef]
187. Van der Graaf, E.; Limburg, J.; Koomans, R.; Tijs, M. Monte Carlo based calibration of scintillation detectors for laboratory and in situ gamma ray measurements. Journal of environmental radioactivity 2011, 102, 270–282. [Google Scholar] [CrossRef]
188. Billings, S.D.; Minty, B.R.; Newsam, G.N. Deconvolution and spatial resolution of airborne gamma-ray surveys. Geophysics 2003, 68, 1257–1266. [Google Scholar] [CrossRef]
189. Pfitzner, K.; Ryan, B.; Martin, P. Airborne gamma survey of the Sleisbeck mine area; January 2003.
190. Kock, P.; Samuelsson, C. Comparison of airborne and terrestrial gamma spectrometry measurements-evaluation of three areas in southern Sweden. Journal of Environmental Radioactivity 2011, 102, 605–613. [Google Scholar] [CrossRef]
191. Xia, J.; Song, B.; Gu, Y.; Li, Z.; Xu, J.; Ge, L.; Zhang, Q.; Zeng, G.; Liu, Q.; Yang, X. Application of advanced spectral-ratio radon background correction in the UAV-borne gamma-ray spectrometry. Nuclear Engineering and Technology 2023, 55, 2927–2934. [Google Scholar] [CrossRef]
192. Van der Veeke, S.; Limburg, J.; Koomans, R.; Söderström, M.; de Waal, S.; van der Graaf, E. Footprint and height corrections for UAV-borne gamma-ray spectrometry studies. Journal of Environmental Radioactivity 2021, 231, 106545. [Google Scholar] [CrossRef] [PubMed]
193. Lowdon, M.; Martin, P.G.; Hubbard, M.W.; Taggart, M.P.; Connor, D.T.; Verbelen, Y.; Sellin, P.; Scott, T.B. Evaluation of scintillator detection materials for application within airborne environmental radiation monitoring. Sensors 2019, 19, 3828. [Google Scholar] [CrossRef] [PubMed]
194. Woodbridge, E.; Connor, D.T.; Verbelen, Y.; Hine, D.; Richardson, T.; Scott, T.B. Airborne gamma-ray mapping using fixed-wing vertical take-off and landing (VTOL) uncrewed aerial vehicles. Frontiers in Robotics and AI 2023, 10. [Google Scholar] [CrossRef] [PubMed]
195. Prokesch, M. CdZnTe for gamma and x-ray applications. Solid-state radiation detectors: Technology and applications 2015, 17-48.
196. Aleotti, J.; Micconi, G.; Caselli, S.; Benassi, G.; Zambelli, N.; Bettelli, M.; Calestani, D.; Zappettini, A. Haptic teleoperation of UAV equipped with gamma-ray spectrometer for detection and identification of radio-active materials in industrial plants. Factories of the Future: The Italian Flagship Initiative 2019, 197-214.
197. Hartman, J.; Barzilov, A.; Novikov, I. Remote sensing of neutron and gamma radiation using aerial unmanned autonomous system. In Proceedings of the 2015 IEEE nuclear science symposium and medical imaging conference (NSS/MIC), 2015; pp. 1–4.
198. Barzilov, A.; Kazemeini, M. Unmanned Aerial System Integrated Sensor for Remote Gamma and Neutron Monitoring. Sensors 2020, 20, 5529. [Google Scholar] [CrossRef]
199. Molnar, A.; Domozi, Z.; Lovas, I. Drone-based gamma radiation dose distribution survey with a discrete measurement point procedure. Sensors 2021, 21, 4930. [Google Scholar] [CrossRef]
200. Sanada, Y.; Torii, T. Aerial radiation monitoring around the Fukushima Dai-ichi nuclear power plant using an unmanned helicopter. Journal of environmental radioactivity 2015, 139, 294–299. [Google Scholar] [CrossRef]
201. Parshin, A.; Gatilov, M.; Davidenko, Y.; Bashkeev, A.; Snegirev, N.; Milgunov, A. Using the “triad” of UAV-TEM, UAV-magnetic prospecting and UAV-gamma-spectrometry: Case of prospecting for blind ore deposits. In Proceedings of the NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, 2022; pp. 1–5.
202. Mochizuki, S.; Kataoka, J.; Tagawa, L.; Iwamoto, Y.; Okochi, H.; Katsumi, N.; Kinno, S.; Arimoto, M.; Maruhashi, T.; Fujieda, K. First demonstration of aerial gamma-ray imaging using drone for prompt radiation survey in Fukushima. Journal of Instrumentation 2017, 12, P11014. [Google Scholar] [CrossRef]
203. UAV Radiation Monitoring Solutions. Available online: https://www.aretasaerial.com/sites/default/files/Aretas-Aerial-UAV-Radiation-Monitoring-030817.pdf.
204. Čerba, Š.; Lüley, J.; Vrban, B.; Osuský, F.; Nečas, V. Unmanned radiation-monitoring system. IEEE Transactions on Nuclear Science 2020, 67, 636–643. [Google Scholar] [CrossRef]
205. Gong, P.; Tang, X.-B.; Huang, X.; Wang, P.; Wen, L.-S.; Zhu, X.-X.; Zhou, C. Locating lost radioactive sources using a UAV radiation monitoring system. Applied radiation and isotopes 2019, 150, 1–13. [Google Scholar] [CrossRef]
206. Molnar, A. Three-Dimensional Detection of Gamma Radiation and Polluting Gases Using Quadrocopters. Herald of Dagestan State Technical University Technical Sciences 2020, 47, 102–116. [Google Scholar] [CrossRef]
207. Pöllänen, R.; Toivonen, H.; Peräjärvi, K.; Karhunen, T.; Ilander, T.; Lehtinen, J.; Rintala, K.; Katajainen, T.; Niemelä, J.; Juusela, M. Radiation surveillance using an unmanned aerial vehicle. Applied radiation and isotopes 2009, 67, 340–344. [Google Scholar] [CrossRef] [PubMed]
208. Preugschat, B.; Ibs-von Seht, M.; Kunze, C.; Arndt, R.; Kandzia, F.; Wiens, B.; Altfelder, S.; Walther, C. Drone-Based Investigation of Uranium Mining Legacies-Recent Developments in the DUB-GEM Project. In Proceedings of the EGU General Assembly Conference Abstracts, 2022; pp. EGU22–12872.
209. Adão, T.; Hruška, J.; Pádua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J.J. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote sensing 2017, 9, 1110. [Google Scholar] [CrossRef]
210. Olson, D.; Anderson, J. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal 2021, 113, 971–992. [Google Scholar] [CrossRef]
211. Berra, E.; Peppa, M. Berra, E.; Peppa, M. Advances and challenges of UAV SFM MVS photogrammetry and remote sensing: Short review. In Proceedings of the 2020 ieee latin american grss & isprs remote sensing conference (lagirs), 2020; pp. 533–538.
212. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of photogrammetry and remote sensing 2014, 92, 79–97. [Google Scholar] [CrossRef]
213. Greenwood, W.W.; Lynch, J.P.; Zekkos, D. Applications of UAVs in civil infrastructure. Journal of infrastructure systems 2019, 25, 04019002. [Google Scholar] [CrossRef]
214. Zhang, Z.; Zhu, L. A review on unmanned aerial vehicle remote sensing: Platforms, sensors, data processing methods, and applications. Drones 2023, 7, 398. [Google Scholar] [CrossRef]
215. Paraforos, D.S.; Sharipov, G.M.; Heiß, A.; Griepentrog, H.W. Position Accuracy Assessment of a UAV-mounted Sequoia+ Multispectral Camera Using a Robotic Total Station. Agriculture 2022, 12, 885. [Google Scholar] [CrossRef]
216. Felipe-García, B.; Hernández-López, D.; Lerma, J.L. Analysis of the ground sample distance on large photogrammetric surveys. Applied Geomatics 2012, 4, 231–244. [Google Scholar] [CrossRef]
217. Logie, G.S.; Coburn, C.A. An investigation of the spectral and radiometric characteristics of low-cost digital cameras for use in UAV remote sensing. International Journal of Remote Sensing 2018, 39, 4891–4909. [Google Scholar] [CrossRef]
218. Kelcey, J.; Lucieer, A. Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2012, 39, 393–398. [Google Scholar] [CrossRef]
219. DadrasJavan, F.; Samadzadegan, F.; Seyed Pourazar, S.H.; Fazeli, H. UAV-based multispectral imagery for fast Citrus Greening detection. Journal of Plant Diseases and Protection 2019, 126, 307–318. [Google Scholar] [CrossRef]
220. Jiménez-Jiménez, S.I.; Ojeda-Bustamante, W.; Marcial-Pablo, M.d.J.; Enciso, J. Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy. ISPRS International Journal of Geo-Information 2021, 10, 285. [Google Scholar] [CrossRef]
221. Niethammer, U.; James, M.; Rothmund, S.; Travelletti, J.; Joswig, M. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Engineering Geology 2012, 128, 2–11. [Google Scholar] [CrossRef]
222. Lucieer, A.; Jong, S.M.d.; Turner, D. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography 2014, 38, 97–116. [Google Scholar] [CrossRef]
223. Lindner, G.; Schraml, K.; Mansberger, R.; Hübl, J. UAV monitoring and documentation of a large landslide. Applied Geomatics 2016, 8, 1–11. [Google Scholar] [CrossRef]
224. Turner, D.; Lucieer, A.; De Jong, S.M. Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing 2015, 7, 1736–1757. [Google Scholar] [CrossRef]
225. Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an unmanned aerial vehicle-based digital imaging system to derive a 3D point cloud for landslide scarp recognition. Remote sensing 2016, 8, 95. [Google Scholar] [CrossRef]
226. Fernández, T.; Pérez, J.L.; Cardenal, J.; Gómez, J.M.; Colomo, C.; Delgado, J. Analysis of landslide evolution affecting olive groves using UAV and photogrammetric techniques. Remote Sensing 2016, 8, 837. [Google Scholar] [CrossRef]
227. Peppa, M.; Mills, J.; Moore, P.; Miller, P.; Chambers, J. Accuracy assessment of a UAV-based landslide monitoring system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, 41, 895–902. [Google Scholar] [CrossRef]
228. Ardi, N.; Iryanti, M.; Asmoro, C.; Nurhayati, N.; Agustine, E. Mapping landslide potential area using fault fracture density analysis on unmanned aerial vehicle (UAV) image. In Proceedings of the IOP Conference Series: Earth and Environmental Science, 2018; p. 012010.
229. Ma, S.; Xu, C.; Shao, X.; Zhang, P.; Liang, X.; Tian, Y. Geometric and kinematic features of a landslide in Mabian Sichuan, China, derived from UAV photography. Landslides 2019, 16, 373–381. [Google Scholar] [CrossRef]
230. Rossi, G.; Tanteri, L.; Tofani, V.; Vannocci, P.; Moretti, S.; Casagli, N. Multitemporal UAV surveys for landslide mapping and characterization. Landslides 2018, 15, 1045–1052. [Google Scholar] [CrossRef]
231. Barlow, J.; Gilham, J.; Ibarra Cofrã, I. Kinematic analysis of sea cliff stability using UAV photogrammetry. International Journal of Remote Sensing 2017, 38, 2464–2479. [Google Scholar] [CrossRef]
232. Karantanellis, E.; Marinos, V.; Vassilakis, E.; Christaras, B. Object-based analysis using unmanned aerial vehicles (UAVs) for site-specific landslide assessment. Remote Sensing 2020, 12, 1711. [Google Scholar] [CrossRef]
233. Godone, D.; Allasia, P.; Borrelli, L.; Gullà, G. UAV and structure from motion approach to monitor the maierato landslide evolution. Remote Sensing 2020, 12, 1039. [Google Scholar] [CrossRef]
234. Xu, Q.; Li, W.-l.; Ju, Y.-z.; Dong, X.-j.; Peng, D.-l. Multitemporal UAV-based photogrammetry for landslide detection and monitoring in a large area: a case study in the Heifangtai terrace in the Loess Plateau of China. Journal of mountain science 2020, 17, 1826–1839. [Google Scholar] [CrossRef]
235. Wang, Z.; Huang, T.; Bao, X.; Ma, S.; Sun, C.; Liu, G. Study on extraction of landslide information based on UAV survey. In Proceedings of the IOP Conference Series: Earth and Environmental Science, 2021; p. 012042.
236. Nikolakopoulos, K.G.; Kyriou, A.; Koukouvelas, I.K.; Tomaras, N.; Lyros, E. UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece. Remote Sensing 2023, 15, 2870. [Google Scholar] [CrossRef]
237. Gasperini, D.; Allemand, P.; Delacourt, C.; Grandjean, P. Potential and limitation of UAV for monitoring subsidence in municipal landfills. International Journal of Environmental Technology and Management 2014, 17, 1–13. [Google Scholar] [CrossRef]
238. Vrublová, D.; Kapica, R.; Jiránková, E.; Strus, A. Documentation of landslides and inaccessible parts of a mine using an unmanned UAV system and methods of digital terrestrial photogrammetry. GeoScience Engineering 2015, 61, 8. [Google Scholar] [CrossRef]
239. Suh, J.; Choi, Y. Mapping hazardous mining-induced sinkhole subsidence using unmanned aerial vehicle (drone) photogrammetry. Environmental Earth Sciences 2017, 76, 1–12. [Google Scholar] [CrossRef]
240. Haddon, E. Modeling Land-surface Deformation and Subsidence with UAV Photogrammetry. Available online: https://eros.usgs.gov/doi-remote-sensing-activities/2018/usgs/modeling-land-surface-deformation-and-subsidence-uav-photogrammetry.
241. Ignjatović Stupar, D.; Rošer, J.; Vulić, M. Investigation of unmanned aerial vehicles-based photogrammetry for large mine subsidence monitoring. Minerals 2020, 10, 196. [Google Scholar] [CrossRef]
242. Galeana-Pérez, V.M.; Chávez-Alegría, O.; Medellin-Aguilar, G. On the measure of land subsidence throughout DEM and orthomosaics using GPS and UAV. Ingeniería, investigación y tecnología 2021, 22, 0–0. [Google Scholar] [CrossRef]
243. Tan, H.; Yu, X.; Zhu, M.; Guo, Z.; Chen, H. Deformation monitoring and spatiotemporal evolution of mining area with unmanned aerial vehicle and D-InSAR technology. Mobile Information Systems 2022, 2022. [Google Scholar] [CrossRef]
244. Zhang, Y.; Lian, X.; Ge, L.; Liu, X.; Du, Z.; Yang, W.; Wu, Y.; Hu, H.; Cai, Y. Surface subsidence monitoring induced by underground coal mining by combining DInSAR and UAV photogrammetry. Remote Sensing 2022, 14, 4711. [Google Scholar] [CrossRef]
245. Alamsyah, D.C. Simulating The Use of UAV Photogrammetry For Monitoring Land Subsidence at Urban Area. Syntax Literate; Jurnal Ilmiah Indonesia 2022, 7, 6656–6673. [Google Scholar] [CrossRef]
246. Kim, N.; Macciotta, R.; Jun, B. Time series analysis of slope displacements using UAV photogrammetry and its relationship with rainfall intensity. Landslides 2024, 1–17. [Google Scholar] [CrossRef]
247. Zhao, J.; Yang, X.; Zhang, Z.; Niu, Y.; Zhao, Z. Mine Subsidence Monitoring Integrating DS-InSAR with UAV Photogrammetry Products: Case Studies on Hebei and Inner Mongolia. Remote Sensing 2023, 15, 4998. [Google Scholar] [CrossRef]
248. Fu, Y.; Wu, Y.; Yin, X.; Zhang, Y. Mapping mining-induced ground fissures and their evolution using UAV photogrammetry. Frontiers in Earth Science 2023, 11, 1260913. [Google Scholar] [CrossRef]
249. Vasterling, M.; Schloemer, S.; Fischer, C.; Ehrler, C. Thermal Imaging of Subsurface Coal Fires by means of an Unmanned Aerial Vehicle (UAV) in the Autonomous Province Xinjiang, PRC. In Proceedings of the EGU general assembly conference abstracts, 2010; p. 8664.
250. Harvey, M.C.; Luketina, K. Thermal infrared cameras and drones: a match made in heaven for cost-effective geothermal exploration, monitoring and development. In Proceedings of the 37th New Zealand Geothermal Workshop, 2015.
251. Harvey, M.; Rowland, J.; Luketina, K. Drone with thermal infrared camera provides high resolution georeferenced imagery of the Waikite geothermal area, New Zealand. Journal of Volcanology and Geothermal Research 2016, 325, 61–69. [Google Scholar] [CrossRef]
252. Nishar, A.; Richards, S.; Breen, D.; Robertson, J.; Breen, B. Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): A case study of the Wairakei–Tauhara geothermal field, Taupo, New Zealand. Renewable Energy 2016, 86, 1256–1264. [Google Scholar] [CrossRef]
253. Lee, E.; Yoon, H.; Hyun, S.P.; Burnett, W.C.; Koh, D.C.; Ha, K.; Kim, D.j.; Kim, Y.; Kang, K.m. Unmanned aerial vehicles (UAVs)-based thermal infrared (TIR) mapping, a novel approach to assess groundwater discharge into the coastal zone. Limnology and Oceanography: Methods 2016, 14, 725–735. [Google Scholar] [CrossRef]
254. Chio, S.-H.; Lin, C.-H. Preliminary study of UAS equipped with thermal camera for volcanic geothermal monitoring in Taiwan. Sensors 2017, 17, 1649. [Google Scholar] [CrossRef] [PubMed]
255. Cherkasov, S.V.; Farkhutdinov, A.M.; Rykovanov, D.P.; Shaipov, A.A. The use of unmanned aerial vehicle for geothermal exploitation monitoring: Khankala field example. Journal of Sustainable Development of Energy, Water and Environment Systems 2018, 6, 351–362. [Google Scholar] [CrossRef]
256. Kraaijenbrink, P.D.; Shea, J.M.; Litt, M.; Steiner, J.F.; Treichler, D.; Koch, I.; Immerzeel, W.W. Mapping surface temperatures on a debris-covered glacier with an unmanned aerial vehicle. Frontiers in Earth Science 2018, 6, 64. [Google Scholar] [CrossRef]
257. Bjornsson, G.; Grimsson, G.; Sigurdsson, A.; Laenen, V.S. Thermal mapping of Icelandic geothermal surface manifestations with a drone. In Proceedings of the Proceedings of 44th Workshop on Geothermal Reservoir Engineering, 2019; pp. 1–8.
258. Dugdale, S.J.; Kelleher, C.A.; Malcolm, I.A.; Caldwell, S.; Hannah, D.M. Assessing the potential of drone-based thermal infrared imagery for quantifying river temperature heterogeneity. Hydrological Processes 2019, 33, 1152–1163. [Google Scholar] [CrossRef]
259. Walter, T.R.; Jousset, P.; Allahbakhshi, M.; Witt, T.; Gudmundsson, M.T.; Hersir, G.P. Underwater and drone based photogrammetry reveals structural control at Geysir geothermal field in Iceland. Journal of Volcanology and Geothermal Research 2020, 391, 106282. [Google Scholar] [CrossRef]
260. Silvestri, M.; Marotta, E.; Buongiorno, M.F.; Avvisati, G.; Belviso, P.; Bellucci Sessa, E.; Caputo, T.; Longo, V.; De Leo, V.; Teggi, S. Monitoring of surface temperature on Parco delle Biancane (Italian geothermal area) using optical satellite data, UAV and field campaigns. Remote Sensing 2020, 12, 2018. [Google Scholar] [CrossRef]
261. Casas-Mulet, R.; Pander, J.; Ryu, D.; Stewardson, M.J.; Geist, J. Unmanned aerial vehicle (UAV)-based thermal infra-red (TIR) and optical imagery reveals multi-spatial scale controls of cold-water areas over a groundwater-dominated riverscape. Frontiers in Environmental Science 2020, 8, 64. [Google Scholar] [CrossRef]
262. Bertalan, L.; Holb, I.; Pataki, A.; Négyesi, G.; Szabó, G.; Szalóki, A.K.; Szabó, S. UAV-based multispectral and thermal cameras to predict soil water content–A machine learning approach. Computers and Electronics in Agriculture 2022, 200, 107262. [Google Scholar] [CrossRef]
263. Muanza, P.; Jónsdóttir, I.; Kristinsson, S.; Einarsson, G.; Björnsson, G. Geothermal mapping and remote sensing of thermal anomalies at Grændalur area, Hveragerði, SW Iceland.
264. Bahri, R.A.; Noviandy, T.R.; Suhendra, R.; Idroes, G.M.; Yanis, M.; Yandri, E.; Nizamuddin, N.; Irvanizam, I. Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu’um Geothermal Manifestation Exploration Security. Leuser Journal of Environmental Studies 2023, 1, 25–33. [Google Scholar] [CrossRef]
265. Hassan-Esfahani, L.; Torres-Rua, A.; Jensen, A.; McKee, M. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing 2015, 7, 2627–2646. [Google Scholar] [CrossRef]
266. Ge, X.; Wang, J.; Ding, J.; Cao, X.; Zhang, Z.; Liu, J.; Li, X. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 2019, 7, e6926. [Google Scholar] [CrossRef] [PubMed]
267. Lu, F.; Sun, Y.; Hou, F. Using UAV visible images to estimate the soil moisture of steppe. Water 2020, 12, 2334. [Google Scholar] [CrossRef]
268. Ge, X.; Ding, J.; Jin, X.; Wang, J.; Chen, X.; Li, X.; Liu, J.; Xie, B. Estimating agricultural soil moisture content through UAV-based hyperspectral images in the arid region. Remote Sensing 2021, 13, 1562. [Google Scholar] [CrossRef]
269. Sedano-Cibrián, J.; Pérez-Álvarez, R.; de Luis-Ruiz, J.M.; Pereda-García, R.; Salas-Menocal, B.R. Thermal water prospection with UAV, low-cost sensors and GIS. Application to the Case of La Hermida. Sensors 2022, 22, 6756. [Google Scholar] [CrossRef]
270. Ismail, A.; Rashid, A.S.A.; Sa’ari, R.; Mustaffar, M.; Abdullah, R.A.; Kassim, A.; Yusof, N.M.; Abd Rahaman, N.; Apandi, N.M.; Kalatehjari, R. Application of UAV-based photogrammetry and normalised water index (NDWI) to estimate the rock mass rating (RMR): A case study. Physics and Chemistry of the Earth, Parts A/B/C 2022, 127, 103161. [Google Scholar] [CrossRef]
271. Zhang, Y.; Han, W.; Zhang, H.; Niu, X.; Shao, G. Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms. Journal of Hydrology 2023, 617, 129086. [Google Scholar] [CrossRef]
272. Park, S.; Choi, Y. Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review. Minerals 2020, 10, 663. [Google Scholar] [CrossRef]
273. Vasuki, Y.; Holden, E.-J.; Kovesi, P.; Micklethwaite, S. Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Computers & Geosciences 2014, 69, 22–32. [Google Scholar]
274. Jakob, S.; Zimmermann, R.; Gloaguen, R. The need for accurate geometric and radiometric corrections of drone-borne hyperspectral data for mineral exploration: Mephysto—A toolbox for pre-processing drone-borne hyperspectral data. Remote Sensing 2017, 9, 88. [Google Scholar] [CrossRef]
275. Madjid, M.; Vandeginste, V.; Hampson, G.; Jordan, C.; Booth, A. Drones in carbonate geology: Opportunities and challenges, and application in diagenetic dolomite geobody mapping. Marine and Petroleum Geology 2018, 91, 723–734. [Google Scholar] [CrossRef]
276. Kirsch, M.; Lorenz, S.; Zimmermann, R.; Tusa, L.; Möckel, R.; Hödl, P.; Booysen, R.; Khodadadzadeh, M.; Gloaguen, R. Integration of terrestrial and drone-borne hyperspectral and photogrammetric sensing methods for exploration mapping and mining monitoring. Remote Sensing 2018, 10, 1366. [Google Scholar] [CrossRef]
277. Heincke, B.; Jackisch, R.; Saartenoja, A.; Salmirinne, H.; Rapp, S.; Zimmermann, R.; Pirttijärvi, M.; Sörensen, E.V.; Gloaguen, R.; Ek, L. Developing multi-sensor drones for geological mapping and mineral exploration: Setup and first results from the MULSEDRO project. GEUS Bulletin 2019, 43. [Google Scholar] [CrossRef]
278. Jackisch, R.; Madriz, Y.; Zimmermann, R.; Pirttijärvi, M.; Saartenoja, A.; Salmirinne, H.; Heincke, B.H.; Gloaguen, R. Integration of UAS-borne hyperspectral remote-sensing and geophysics in mineral exploration under sub-polar conditions in Finland. In Proceedings of the Geophysical Research Abstracts, 2019.
279. Flores, H.; Lorenz, S.; Jackisch, R.; Tusa, L.; Contreras, I.C.; Zimmermann, R.; Gloaguen, R. UAS-based hyperspectral environmental monitoring of acid mine drainage affected waters. Minerals 2021, 11, 182. [Google Scholar] [CrossRef]
280. Cramer, A.S.; Calvin, W.M.; McCoy, S.W.; Breitmeyer, R.J.; Haagsma, M.; Kratt, C. Mapping potentially acid generating material on abandoned mine lands using remotely piloted aerial systems. Minerals 2021, 11, 365. [Google Scholar] [CrossRef]
281. Pérez-Álvarez, R.; Sedano-Cibrián, J.; de Luis-Ruiz, J.M.; Fernández-Maroto, G.; Pereda-García, R. Mining exploration with UAV, low-cost thermal cameras and GIS tools—application to the specific case of the complex sulfides hosted in Carbonates of Udías (Cantabria, Spain). Minerals 2022, 12, 140. [Google Scholar] [CrossRef]
282. Sinaice, B.B.; Owada, N.; Ikeda, H.; Toriya, H.; Bagai, Z.; Shemang, E.; Adachi, T.; Kawamura, Y. Spectral angle mapping and AI methods applied in automatic identification of Placer deposit magnetite using multispectral camera mounted on UAV. Minerals 2022, 12, 268. [Google Scholar] [CrossRef]
283. Jackisch, R.; Heincke, B.H.; Zimmermann, R.; Sørensen, E.V.; Pirttijärvi, M.; Kirsch, M.; Salmirinne, H.; Lode, S.; Kuronen, U.; Gloaguen, R. Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland. Solid Earth Discussions 2021, 2021, 1–51. [Google Scholar] [CrossRef]
284. Salgado, L.; López-Sánchez, C.A.; Colina, A.; Baragaño, D.; Forján, R.; Gallego, J. Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning. Environmental Pollution 2023, 333, 122066. [Google Scholar] [CrossRef]
285. Eskandari, A.; Hosseini, M.; Nicotra, E. Application of Satellite Remote Sensing, UAV-Geological Mapping, and Machine Learning Methods in the Exploration of Podiform Chromite Deposits. Minerals 2023, 13, 251. [Google Scholar] [CrossRef]
286. McLeod, T.; Samson, C.; Labrie, M.; Shehata, K.; Mah, J.; Lai, P.; Wang, L.; Elder, J. Using video acquired from an unmanned aerial vehicle (UAV) to measure fracture orientation in an open-pit mine. Geomatica 2013, 67, 173–180. [Google Scholar] [CrossRef]
287. Wang, Q.; Wu, L.; Chen, S.; Shu, D.; Xu, Z.; Li, F.; Wang, R. Accuracy evaluation of 3d geometry from low-attitude uav collections a case at zijin mine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, 40, 297–300. [Google Scholar] [CrossRef]
288. Cho, S.-J.; Bang, E.-S.; Kang, I.-M. Construction of precise digital terrain model for nonmetal open-pit mine by using unmanned aerial photograph. Economic and Environmental Geology 2015, 48, 205–212. [Google Scholar] [CrossRef]
289. Lee, S.; Choi, Y. Topographic survey at small-scale open-pit mines using a popular rotary-wing unmanned aerial vehicle (drone). Tunnel and underground space 2015, 25, 462–469. [Google Scholar] [CrossRef]
290. Chen, J.; Li, K.; Chang, K.-J.; Sofia, G.; Tarolli, P. Open-pit mining geomorphic feature characterisation. International Journal of Applied Earth Observation and Geoinformation 2015, 42, 76–86. [Google Scholar] [CrossRef]
291. Blistan, P.; Kovanič, Ľ.; Zelizňaková, V.; Palková, J. Using UAV photogrammetry to document rock outcrops. Acta Montanistica Slovaca 2016, 21. [Google Scholar]
292. Ge, L.; Li, X.; Ng, A.H.-M. UAV for mining applications: A case study at an open-cut mine and a longwall mine in New South Wales, Australia. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016; pp. 5422–5425.
293. Rossi, P.; Mancini, F.; Dubbini, M.; Mazzone, F.; Capra, A. Combining nadir and oblique UAV imagery to reconstruct quarry topography: methodology and feasibility analysis. European Journal of Remote Sensing 2017, 50, 211–221. [Google Scholar] [CrossRef]
294. Mitchell, J.; Marshall, J.A. Design of a novel auto-rotating uav platform for underground mine cavity surveying. 2017.
295. Freire, G.; Cota, R. Capture of images in inaccessible areas in an underground mine using an unmanned aerial vehicle. In Proceedings of the UMT 2017: Proceedings of the First International Conference on Underground Mining Technology, 2017.
296. Turner, R.; Bhagwat, N.; Galayda, L.; Knoll, C.; Russell, E.; MacLaughlin, M. Geotechnical characterization of underground mine excavations from UAV-captured photogrammetric & thermal imagery. In Proceedings of the ARMA US Rock Mechanics/Geomechanics Symposium, 2018; pp. ARMA–2018-2508.
297. Beretta, F.; Rodrigues, A.; Peroni, R.; Costa, J. Automated lithological classification using UAV and machine learning on an open cast mine. Applied Earth Science 2019, 128, 79–88. [Google Scholar] [CrossRef]
298. Katuruza, M.; Birch, C. The use of unmanned aircraft system technology for highwall mapping at Isibonelo Colliery, South Africa. Journal of the Southern African Institute of Mining and Metallurgy 2019, 119, 291–295. [Google Scholar] [CrossRef]
299. Stead, D.; Donati, D.; Wolter, A.; Sturzenegger, M. Application of remote sensing to the investigation of rock slopes: Experience gained and lessons learned. ISPRS international journal of geo-information 2019, 8, 296. [Google Scholar] [CrossRef]
300. Raj, P. Use of Drones in an Underground Mine for Geotechnical Monitoring. The University of Arizona, 2019.
301. Turner, R.M.; MacLaughlin, M.M.; Iverson, S.R. Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery. Engineering geology 2020, 266, 105470. [Google Scholar] [CrossRef]
302. Honarmand, M.; Shahriari, H. Geological mapping using drone-based photogrammetry: An application for exploration of vein-type Cu mineralization. Minerals 2021, 11, 585. [Google Scholar] [CrossRef]
303. Wakeford, Z.E.; Chmielewska, M.; Hole, M.J.; Howell, J.A.; Jerram, D.A. Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy. The Photogrammetric Record 2019, 34, 445–466. [Google Scholar] [CrossRef]
304. Gracchi, T.; Tacconi Stefanelli, C.; Rossi, G.; Di Traglia, F.; Nolesini, T.; Tanteri, L.; Casagli, N. UAV-based multitemporal remote sensing surveys of volcano unstable flanks: a case study from stromboli. Remote Sensing 2022, 14, 2489. [Google Scholar] [CrossRef]
305. Granados-Bolaños, S.; Quesada-Román, A.; Alvarado, G.E. Low-cost UAV applications in dynamic tropical volcanic landforms. Journal of Volcanology and Geothermal Research 2021, 410, 107143. [Google Scholar] [CrossRef]
306. Swift, C.M. Fundamentals of the electromagnetic method. Electromagnetic methods in applied geophysics 1988, 1, 5–10. [Google Scholar]
307. Frequency-Domain Electromagnetics. Available online: https://olsonengineering.com/methods/geophysical-methods/electromagnetics/frequency-domain-em/.
308. Ward, S.H.; Hohmann, G.W. Electromagnetic theory for geophysical applications. In Electromagnetic methods in applied geophysics: Voume 1, theory; Society of Exploration Geophysicists: 1988; pp. 130–311.
309. Pirttijärvi, M.; Saartenoja, A.; Korkeakangas, P. Drone-based electromagnetic survey system for geophysical applications. Open Research Europe 2022, 2, 3. [Google Scholar] [CrossRef]
310. Korus, J. Combining hydraulic head analysis with airborne electromagnetics to detect and map impermeable aquifer boundaries. Water 2018, 10, 975. [Google Scholar] [CrossRef]
311. Wu, X.; Xue, G.; Fang, G.; Li, X.; Ji, Y. The development and applications of the semi-airborne electromagnetic system in China. Ieee Access 2019, 7, 104956–104966. [Google Scholar] [CrossRef]
312. Fountain, D. years of airborne EM–focus on the last decade: AEM 2008. In Proceedings of the 5th International Conference on Airborne Electromagnetics, conference abstracts, 2008.
313. Legault, J.M. Airborne electromagnetic systems–state of the art and future directions. CSEG Recorder 2015, 40, 38–49. [Google Scholar]
314. McNeil, J.; Labson, V. Geological mapping using VLF radio field. Electromagnetic methods in applied geophysics 1991, 2. [Google Scholar]
315. Smith, R.; Fountain, D.; Allard, M. The MEGATEM fixed-wing transient EM system applied to mineral exploration: a discovery case history. First Break 2003, 21. [Google Scholar] [CrossRef]
316. Lane, R.; Green, A.; Golding, C.; Owers, M.; Pik, P.; Plunkett, C.; Sattel, D.; Thorn, B. An example of 3D conductivity mapping using the TEMPEST airborne electromagnetic system. Exploration Geophysics 2000, 31, 162–172. [Google Scholar] [CrossRef]
317. Sørensen, K.I.; Auken, E. SkyTEM-A new high-resolution helicopter transient electromagnetic system. Exploration Geophysics 2004, 35, 191–199. [Google Scholar] [CrossRef]
318. Vallee, M.A.; Smith, R.S.; Keating, P. Metalliferous mining geophysics—State of the art after a decade in the new millennium. Geophysics 2011, 76, W31–W50. [Google Scholar] [CrossRef]
319. Lin, J.; Chen, J.; Liu, F.; Zhang, Y. The helicopter time-domain electromagnetic technology advances in China. Surveys in Geophysics 2021, 42, 585–624. [Google Scholar] [CrossRef]
320. Thomson, S.; Fountain, D.; Watts, T. Airborne geophysics–evolution and revolution. In Proceedings of the Proceedings of Exploration, 2007; pp. 19–37.
321. Doll, W.E.; Gamey, T.J.; Holladay, J.S.; Sheehan, J.R.; Norton, J.; Beard, L.P.; Lee, J.L.; Hanson, A.E.; Lahti, R.M. Results of a high-resolution airborne TEM system demonstration for unexploded ordnance detection. Geophysics 2010, 75, B211–B220. [Google Scholar] [CrossRef]
322. Lo, B.; Zang, M. Numerical modeling of Z-TEM (airborne AFMAG) responses to guide exploration strategies. In SEG Technical Program Expanded Abstracts 2008; Society of Exploration Geophysicists: 2008; pp. 1098–1102.
323. Holtham, E.; Oldenburg, D.W. Three-dimensional forward modelling and inversion of Z-TEM data. In SEG Technical Program Expanded Abstracts 2008; Society of Exploration Geophysicists: 2008; pp. 564–568.
324. Vovenko, T.; Moilanen, E.; Volkovitsky, A.; Karshakov, E. New abilities of quadrature EM systems. In Proceedings of the 6th International AEM Conference & Exhibition, 2013; pp. cp–383-00067.
325. Abedi, M.; Fournier, D.; Devriese, S.G.; Oldenburg, D.W. Integrated inversion of airborne geophysics over a structural geological unit: A case study for delineation of a porphyry copper zone in Iran. Journal of Applied Geophysics 2018, 152, 188–202. [Google Scholar] [CrossRef]
326. Delsman, J.R.; Van Baaren, E.S.; Siemon, B.; Dabekaussen, W.; Karaoulis, M.C.; Pauw, P.S.; Vermaas, T.; Bootsma, H.; De Louw, P.G.; Gunnink, J.L. Large-scale, probabilistic salinity mapping using airborne electromagnetics for groundwater management in Zeeland, the Netherlands. Environmental research letters 2018, 13, 084011. [Google Scholar] [CrossRef]
327. Steuer, A.; Smirnova, M.; Becken, M.; Schiffler, M.; Günther, T.; Rochlitz, R.; Yogeshwar, P.; Mörbe, W.; Siemon, B.; Costabel, S. Comparison of novel semi-airborne electromagnetic data with multi-scale geophysical, petrophysical and geological data from Schleiz, Germany. Journal of Applied Geophysics 2020, 182, 104172. [Google Scholar] [CrossRef]
328. Rankka, K.; Andersson-Sköld, Y.; Hultén, C.; Larsson, R.; Leroux, V.; Dahlin, T. Quick clay in Sweden. 2004.
329. Pfaffhuber, A.A.; Persson, L.; Lysdahl, A.O.; Kåsin, K.; Anschütz, H.; Bastani, M.; Bazin, S.; Löfroth, H. Integrated scanning for quick clay with AEM and ground-based investigations. First Break 2017, 35. [Google Scholar] [CrossRef]
330. Auken, E.; Boesen, T.; Christiansen, A.V. A review of airborne electromagnetic methods with focus on geotechnical and hydrological applications from 2007 to 2017. Advances in geophysics 2017, 58, 47–93. [Google Scholar]
331. Pfaffhuber, A.A.; Lysdahl, A.O.; Sørmo, E.; Skurdal, G.H.; Thomassen, T.; Anschütz, H.; Scheibz, J. Delineating hazardous material without touching—AEM mapping of Norwegian alum shale. First Break 2017, 35. [Google Scholar] [CrossRef]
332. Pedersen, J.B.; Schaars, F.W.; Christiansen, A.V.; Foged, N.; Schamper, C.; Rolf, H.; Auken, E. Mapping the fresh-saltwater interface in the coastal zone using high-resolution airborne electromagnetics. First Break 2017, 35. [Google Scholar] [CrossRef]
333. Siemon, B.; Steuer, A. Airborne geophysical investigation of groundwater resources in Northern Sumatra after the Tsunami of 2004; INTECH Open Access Publisher: 2011.
334. Minsley, B.; Wellman, T.; Walvoord, M.A.; Revil, A. Sensitivity of airborne geophysical data to sublacustrine and near-surface permafrost thaw. Cryosphere 2015, 9. [Google Scholar] [CrossRef]
335. Pastick, N.J.; Jorgenson, M.T.; Wylie, B.K.; Minsley, B.J.; Ji, L.; Walvoord, M.A.; Smith, B.D.; Abraham, J.D.; Rose, J.R. Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats Ecoregion, Central Alaska. Permafrost and Periglacial Processes 2013, 24, 184–199. [Google Scholar] [CrossRef]
336. Mikucki, J.A.; Auken, E.; Tulaczyk, S.; Virginia, R.; Schamper, C.; Sørensen, K.; Doran, P.; Dugan, H.; Foley, N. Deep groundwater and potential subsurface habitats beneath an Antarctic dry valley. Nature communications 2015, 6, 6831. [Google Scholar] [CrossRef]
337. Podgorski, J.E.; Auken, E.; Schamper, C.; Vest Christiansen, A.; Kalscheuer, T.; Green, A.G. Processing and inversion of commercial helicopter time-domain electromagnetic data for environmental assessments and geologic and hydrologic mapping. Geophysics 2013, 78, E149–E159. [Google Scholar] [CrossRef]
338. Siemon, B.; Ibs-von Seht, M.; Steuer, A.; Deus, N.; Wiederhold, H. Airborne electromagnetic, magnetic, and radiometric surveys at the German North Sea coast applied to groundwater and soil investigations. Remote Sensing 2020, 12, 1629. [Google Scholar] [CrossRef]
339. Airborne Electromagnetic (AEM) Survey 2022. Available online: https://www.usgs.gov/centers/upper-midwest-water-science-center/science/airborne-electromagnetic-aem-survey-2022 (accessed on January 26).
340. Høyer, A.-S.; Jørgensen, F.; Sandersen, P.; Viezzoli, A.; Møller, I. 3D geological modelling of a complex buried-valley network delineated from borehole and AEM data. Journal of Applied Geophysics 2015, 122, 94–102. [Google Scholar] [CrossRef]
341. Finn, C.A.; Deszcz-Pan, M.; Anderson, E.D.; John, D.A. Three-dimensional geophysical mapping of rock alteration and water content at Mount Adams, Washington: Implications for lahar hazards. Journal of Geophysical Research: Solid Earth 2007, 112. [Google Scholar] [CrossRef]
342. Legault, J.; Orta, M.; Kaminski, V.; Prikhodko, A.; Kumar, H. Helicopter Electromagnetic (VTEM™ AND ZTEM™) Applications for Gold Exploration. In Proceedings of the Kegs-Pdac Symposium, Toronto, Canadá, 2010.
343. Mahmoodi, O.; Morelli, R. Using magnetic and electromagnetic data processing to map sub-Phanerozoic basement features in the Flin Flon area. Summary of Investigations 2017, 2, 2017-2014.2012. [Google Scholar]
344. Fraser, D.C. Magnetite ore tonnage estimates from an aerial electromagnetic survey. Geoexploration 1973, 11, 97–105. [Google Scholar] [CrossRef]
345. Roach, I.C. The Frome airborne electromagnetic survey, South Australia. ASEG Extended Abstracts 2012, 2012, 1–3. [Google Scholar] [CrossRef]
346. Crowe, M.; Heinson, G.; Dhu, T. Magnetotellurics and Airborne Electromagnetics? a combined method for assessing basin structure and exploring for unconformity related uranium. ASEG extended abstracts 2013, 2013, 1–5. [Google Scholar] [CrossRef]
347. Parshin, A.; Bashkeev, A.; Davidenko, Y.; Persova, M.; Iakovlev, S.; Bukhalov, S.; Grebenkin, N.; Tokareva, M. Lightweight unmanned aerial system for time-domain electromagnetic prospecting—The next stage in applied UAV-Geophysics. Applied Sciences 2021, 11, 2060. [Google Scholar] [CrossRef]
348. Mitsuhata, Y.; Ueda, T.; Kamimura, A.; Kato, S.; Takeuchi, A.; Aduma, C.; Yokota, T. Development of a drone-borne electromagnetic survey system for searching for buried vehicles and soil resistivity mapping. Near Surface Geophysics 2022, 20, 16–29. [Google Scholar] [CrossRef]
349. Vilhelmsen, T.B.; Døssing, A. Drone-towed CSEM system for near-surface geophysical prospecting: On instrument noise, temperature drift, transmission frequency and survey setup. EGUsphere 2022, 2022, 1–32. [Google Scholar]
350. Spies, B.R.; Frischknecht, F.C. Electromagnetic sounding. 1991.
351. Kotowski, P.O.; Becken, M.; Thiede, A.; Schmidt, V.; Schmalzl, J.; Ueding, S.; Klingen, S. Evaluation of a semi-airborne electromagnetic survey based on a multicopter aircraft system. Geosciences 2022, 12, 26. [Google Scholar] [CrossRef]
352. Stoll*, J.B.; Noellenburg, R.; Kordes, T.; Becken, M.; Tezkan, B.; Yogeshwar, P.; Bergers, R.; Matzander, U. Semi-Airborne electromagnetics using a multicopter. In Proceedings of the International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Xi’an, China, May 19–22, 2019, 2019; pp. 363–366.
353. Stoll, J.B.; Kordes, T.; Noellenburg, R. Semi-Airborne Electromagnetics Using a Multicopter. Available online: https://www.per.co.id/2021/09/07/semi-airborne-electromagnetics-using-a-multicopter/.
354. Di, Q.; Xue, G.; Yin, C.; Li, X. New methods of controlled-source electromagnetic detection in China. Science China Earth Sciences 2020, 63, 1268–1277. [Google Scholar] [CrossRef]
355. Kipfinger, R. Unmanned airborne vehicle (UAV): Flight testing and evaluation of two-channel E-field very low frequency (VLF) instrument. United States Geological Survey 1998, 1–4. [Google Scholar]
356. Eröss, R.; Tezkan, B.; Stoll, J.B.; Bergers, R. Interpretation of very low frequency measurements carried out with an unmanned aerial system by 2D conductivity models. Journal of Environmental and Engineering Geophysics 2017, 22, 83–94. [Google Scholar] [CrossRef]
357. Telford, W.M.; Geldart, L.P.; Sheriff, R.E. Applied geophysics; Cambridge university press: 1990.
358. Tezkan, B. A review of environmental applications of quasi-stationary electromagnetic techniques. Surveys in Geophysics 1999, 20, 279–308. [Google Scholar] [CrossRef]
359. Pedersen, L.B.; Persson, L.; Bastani, M.; Byström, S. Airborne VLF measurements and mapping of ground conductivity in Sweden. Journal of applied geophysics 2009, 67, 250–258. [Google Scholar] [CrossRef]
360. Vozoff, K. The magnetotelluric method in the exploration of sedimentary basins. Geophysics 1972, 37, 98–141. [Google Scholar] [CrossRef]
361. Pedersen, L.B.; Qian, W.; Dynesius, L.; Zhang, P. An airborne tensor VLF system. From concept to realization 1. Geophysical Prospecting 1994, 42, 863–883. [Google Scholar] [CrossRef]
362. Eröss, R. Very Low Frequency Measurements carried out with an Unmanned Aircraft System. Universität zu Köln, 2015.
363. Harris, F.J. On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE 1978, 66, 51–83. [Google Scholar] [CrossRef]
364. Macnae, J. Low Noise, Lightweight EM and AMT sensors for Unmanned Airborne Systems (UAS). 2021.
365. Qi, Z.-p.; Li, X.; Li, H.; Zhang, Y.-y.; Zhou, J. Drone-borne transient electromagnetic system and its application in UXO detection. In Proceedings of the SEG International Exposition and Annual Meeting, 2018; pp. SEG–2018-2997180. [Google Scholar]
366. Karaoulis, M.; Ritsema, I.; Bremmer, C.; De Kleine, M.; Oude Essink, G.; Ahlrichs, E. Drone-Borne Electromagnetic (DR-EM) Surveying in The Netherlands: Lab and Field Validation Results. Remote Sensing 2022, 14, 5335. [Google Scholar] [CrossRef]
367. Barrowes, B.E.; Glaser, D.R.; Quinn, B.G.; Prishvin, M.; Shubitidze, F.; RESEARCH, C.R.; States, E.L.H.N.H.U. Unmanned Aerial Systems Electromagnetic Induction Sensor Development: Evaluation of Commercial-off-the-Shelf Unmanned Aerial System Motor Interference and Mitigation in Airborne Electromagnetic Induction Sensors; US Army Engineer Research and Development Center, Cold Regions Research and …: 2019.
368. Tang, P.; Chen, F.; Jiang, A.; Zhou, W.; Wang, H.; Leucci, G.; de Giorgi, L.; Sileo, M.; Luo, R.; Lasaponara, R. Multi-frequency electromagnetic induction survey for archaeological prospection: Approach and results in Han Hangu Pass and Xishan Yang in China. Surveys in Geophysics 2018, 39, 1285–1302. [Google Scholar] [CrossRef]
369. Bastani, M.; Johansson, H.; Paulusson, A.; Paulusson, K.; Dynesius, L. Unmanned Aerial Vehicles (UAV) and ground-based electromagnetic (EM) systems. First Break 2020, 38, 87–89. [Google Scholar]
370. Bobarika, I.; Parshin, A. Lightweight UAV Variant of Unconventional Design as a Carrier for Performing Complex Airborne Geophysical Surveying. In Proceedings of the First EAGE Workshop on Unmanned Aerial Vehicles; 2019; pp. 1–4. [Google Scholar]
371. Parshin, A.; Iakovlev, S.; Davidenko, Y.A.; Bashkeev, A.; Vinokurov, V.; Bukhalov, S. Two Variants of Lightweight Unmanned Systems for Low-Altitude Electromagnetic Soundings. In Proceedings of the Engineering and Mining Geophysics 2021; 2021; pp. 1–6. [Google Scholar]
372. Parshin, A.; Davidenko, Y.; Yakovlev, S.; Vinokurov, V.; Bashkeev, A. Lightweight TEM and VLF systems for low-altitude UAV-based geophysical. In Proceedings of the NSG2021 27th European Meeting of Environmental and Engineering Geophysics; 2021; pp. 1–5. [Google Scholar]
373. Hallbauer-Zadorozhnaya, V.D., Yu. A.; Parshin, A.V.; Stettler, E. Drone based experimental TEM surveys over Lake Baikal and a uranium occurrence. In Proceedings of the 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022.
374. Moreno, W.F.; Nogueira, F.C.; Shiguemori, E.H.; Kux, H.J. Electromagnetic Sensor Onboard Drones for the Detectin of Land Mines. 2018.
375. Morifuji, Y.; Kubota, K.; Tanaka, S.; Suenaga, H.; Jomori, A.; Jomori, A.; Toyama, T. Drone electromagnetic survey for slope subsurface structure investigation—Flight along steep slope. In Proceedings of the Proceedings of the 14th SEGJ International Symposium, Tokyo, Japan, 18–21 October 2021, 2021; pp. 38–41.
376. Davydenko, Y.; Tereshkin, S.; Bashkeev, A.; Iakovlev, S.; Shkirya, M.; Parshin, A.; Persova, M. The Results of Experimental and Methodological Work With the New Uav-Tem Technology on Lake Baikal. In Proceedings of the NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics; 2022; pp. 1–5. [Google Scholar]
377. Bjerg, T.; da Silva, E.L.S.; Døssing, A. Investigation of UAV noise reduction for electromagnetic induction surveying. In Proceedings of the NSG2020 3rd Conference on Geophysics for Mineral Exploration and Mining; 2020; pp. 1–5. [Google Scholar]
378. Unmanned Aerial Vehicle Geophysics. Available online: https://discogeo.com/expertise/uav/.
379. DronEM (Drone for Electromagnetic fields Measurements). Available online: https://www.geo-k.co/2017/05/29/dronem-drone-for-electromagnetic-fields-measurements/.
380. UAV VLF-EM SYSTEM: Resistivity Mapping Solution. Available online: https://www.gemsys.ca/uav-vlf/.
381. Electromagnetic Drone. Available online: https://www.per.co.id/services/geophysical/drone-electromagnetic/.
382. Siemon, B.; Christiansen, A.V.; Auken, E. A review of helicopter-borne electromagnetic methods for groundwater exploration. Near Surface Geophysics 2009, 7, 629–646. [Google Scholar] [CrossRef]
383. Frischknecht, F.C. Field about an oscillating magnetic dipole over a two-layer earth and application to ground and airborne electromagnetic surveys. Quart. Colorado School of Mines 1967, 62, 1–370. [Google Scholar]
384. Auken, E.; Christiansen, A.V. Layered and laterally constrained 2D inversion of resistivity data. Geophysics 2004, 69, 752–761. [Google Scholar] [CrossRef]
385. Minsley, B.J. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophysical Journal International 2011, 187, 252–272. [Google Scholar] [CrossRef]
386. Viezzoli, A.; Christiansen, A.V.; Auken, E.; Sørensen, K. Quasi-3D modeling of airborne TEM data by spatially constrained inversion. Geophysics 2008, 73, F105–F113. [Google Scholar] [CrossRef]
387. Bosch, F.; Müller, I. Improved karst exploration by VLF-EM-gradient survey: comparison with other geophysical methods. Near Surface Geophysics 2005, 3, 299–310. [Google Scholar] [CrossRef]
388. Johmori, A.; Johmori, A.; Kondou, T.; Yuuki, Y.; Kawase, M. Development of grounded electrical-source airborne transient electromagnetics (GREATEM) system using a multicopter. In Proceedings of the Proceedings of the 138th Society of Exploration Geophysicists of Japan Conference, Tokyo, 2018; pp. 120–123.
389. Sun, H.; Zhang, N.; Li, D.; Liu, S.; Ye, Q. The first semi-airborne transient electromagnetic survey for tunnel investigation in very complex terrain areas. Tunnelling and Underground Space Technology 2023, 132, 104893. [Google Scholar] [CrossRef]
390. Li, C.J.; Ling, H. High-resolution, downward-looking radar imaging using a small consumer drone. In Proceedings of the 2016 IEEE International Symposium on Antennas and Propagation (APSURSI); 2016; pp. 2037–2038. [Google Scholar]
391. Ludeno, G.; Catapano, I.; Renga, A.; Vetrella, A.R.; Fasano, G.; Soldovieri, F. Assessment of a micro-UAV system for microwave tomography radar imaging. Remote Sensing of Environment 2018, 212, 90–102. [Google Scholar] [CrossRef]
392. Schreiber, E.; Heinzel, A.; Peichl, M.; Engel, M.; Wiesbeck, W. Advanced buried object detection by multichannel, UAV/drone carried synthetic aperture radar. In Proceedings of the 2019 13th European Conference on Antennas and Propagation (EuCAP); 2019; pp. 1–5. [Google Scholar]
393. Jenssen, R.O.R.; Eckerstorfer, M.; Jacobsen, S. Drone-mounted ultrawideband radar for retrieval of snowpack properties. IEEE Transactions on Instrumentation and Measurement 2019, 69, 221–230. [Google Scholar] [CrossRef]
394. Catapano, I.; Gennarelli, G.; Ludeno, G.; Noviello, C.; Esposito, G.; Renga, A.; Fasano, G.; Soldovieri, F. Small multicopter-UAV-based radar imaging: Performance assessment for a single flight track. Remote Sensing 2020, 12, 774. [Google Scholar] [CrossRef]
395. Noviello, C.; Esposito, G.; Catapano, I.; Soldovieri, F. Multilines imaging approach for mini-UAV radar imaging system. IEEE Geoscience and Remote Sensing Letters 2021, 19, 1–5. [Google Scholar] [CrossRef]
396. Giret, R.; Jeuland, H.; Enert, P. A study of a 3D-SAR concept for a millimeter wave imaging radar onboard an UAV. In Proceedings of the First European Radar Conference, 2004. EURAD., 2004; pp. 201–204.
397. Weib, M.; Ender, J. A 3D imaging radar for small unmanned airplanes-ARTINO. In Proceedings of the European Radar Conference, 2005. EURAD 2005., 2005; pp. 209–212.
398. Zaugg, E.; Long, D.; Edwards, M.; Fladeland, M.; Kolyer, R.; Crocker, I.; Maslanik, J.; Herzfeld, U.; Wallin, B. Using the microASAR on the NASA SIERRA UAS in the characterization of Arctic sea ice experiment. In Proceedings of the 2010 IEEE Radar Conference; 2010; pp. 271–276. [Google Scholar]
399. Remy, M.A.; de Macedo, K.A.; Moreira, J.R. The first UAV-based P-and X-band interferometric SAR system. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium; 2012; pp. 5041–5044. [Google Scholar]
400. Aguasca, A.; Acevo-Herrera, R.; Broquetas, A.; Mallorqui, J.J.; Fabregas, X. ARBRES: Light-weight CW/FM SAR sensors for small UAVs. Sensors 2013, 13, 3204–3216. [Google Scholar] [CrossRef] [PubMed]
401. Amiri, A.; Tong, K.; Chetty, K. Feasibility study of multi-frequency Ground Penetrating Radar for rotary UAV platforms. 2012.
402. Colorado, J.; Perez, M.; Mondragon, I.; Mendez, D.; Parra, C.; Devia, C.; Martinez-Moritz, J.; Neira, L. An integrated aerial system for landmine detection: SDR-based Ground Penetrating Radar onboard an autonomous drone. Advanced Robotics 2017, 31, 791–808. [Google Scholar] [CrossRef]
403. Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Las Heras, F.; Gonzalez-Valdes, B.; Rodriguez-Vaqueiro, Y.; Pino, A.; Arboleya-Arboleya, A. GPR system onboard a UAV for non-invasive detection of buried objects. In Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting; 2018; pp. 1967–1968. [Google Scholar]
404. Wu, K.; Rodriguez, G.A.; Zajc, M.; Jacquemin, E.; Clément, M.; De Coster, A.; Lambot, S. A new drone-borne GPR for soil moisture mapping. Remote Sensing of Environment 2019, 235, 111456. [Google Scholar] [CrossRef]
405. Burr, R.; Schartel, M.; Grathwohl, A.; Mayer, W.; Walter, T.; Waldschmidt, C. UAV-borne FMCW InSAR for focusing buried objects. IEEE Geoscience and Remote Sensing Letters 2021, 19, 1–5. [Google Scholar] [CrossRef]
406. Šipoš, D.; Gleich, D. A lightweight and low-power UAV-borne ground penetrating radar design for landmine detection. Sensors 2020, 20, 2234. [Google Scholar] [CrossRef]
407. Vergnano, A.; Franco, D.; Godio, A. Drone-borne ground-penetrating radar for snow cover mapping. Remote Sensing 2022, 14, 1763. [Google Scholar] [CrossRef]
408. Saponaro, A.; Dipierro, G.; Cannella, E.; Panarese, A.; Galiano, A.M.; Massaro, A. A UAV-GPR fusion approach for the characterization of a quarry excavation area in Falconara Albanese, Southern Italy. Drones 2021, 5, 40. [Google Scholar] [CrossRef]
409. Rosen, E.M.; Ayers, E. Assessment of down-looking GPR sensors for landmine detection. In Proceedings of the Detection and Remediation Technologies for Mines and Minelike Targets X; 2005; pp. 423–434. [Google Scholar]
410. Rosen, E.M.; Rotondo, F.S.; Ayers, E. Testing and evaluation of forward-looking GPR countermine systems. In Proceedings of the Detection and Remediation Technologies for Mines and Minelike Targets X; 2005; pp. 901–911. [Google Scholar]
411. Diamanti, N.; Annan, A.P. Air-launched and ground-coupled GPR data. In Proceedings of the 2017 11th European Conference on Antennas and Propagation (EUCAP); 2017; pp. 1694–1698. [Google Scholar]
412. Ristmae, T.; Dionysiou, D.D.; Koutsokeras, M.; Douklias, A.; Ouzounoglou, E.; Amditis, A.; Fotopoulos, A.; Diles, G.; Linardatos, P.; Smanis, K. The CURSOR Search and Rescue (SaR) Kit: an innovative solution for improving the efficiency of Urban SaR Operations. In Proceedings of the ISCRAM; 2021; pp. 867–880. [Google Scholar]
413. Garcia-Fernandez, M.; López, Y.Á.; Andrés, F.L.-H. Airborne multi-channel ground penetrating radar for improvised explosive devices and landmine detection. IEEE Access 2020, 8, 165927–165943. [Google Scholar] [CrossRef]
414. Fernández, M.G.; López, Y.Á.; Arboleya, A.A.; Valdés, B.G.; Vaqueiro, Y.R.; Andrés, F.L.-H.; García, A.P. Synthetic aperture radar imaging system for landmine detection using a ground penetrating radar on board a unmanned aerial vehicle. IEEE Access 2018, 6, 45100–45112. [Google Scholar] [CrossRef]
415. Garcia-Fernandez, M.; Morgenthaler, A.; Alvarez-Lopez, Y.; Las Heras, F.; Rappaport, C. Bistatic landmine and IED detection combining vehicle and drone mounted GPR sensors. Remote Sensing 2019, 11, 2299. [Google Scholar] [CrossRef]
416. Catapano, I.; Gennarelli, G.; Ludeno, G.; Noviello, C.; Esposito, G.; Soldovieri, F. Contactless ground penetrating radar imaging: State of the art, challenges, and microwave tomography-based data processing. IEEE Geoscience and Remote Sensing Magazine 2021, 10, 251–273. [Google Scholar] [CrossRef]
417. Noviello, C.; Gennarelli, G.; Esposito, G.; Ludeno, G.; Fasano, G.; Capozzoli, L.; Soldovieri, F.; Catapano, I. An overview on down-looking UAV-based GPR systems. Remote Sensing 2022, 14, 3245. [Google Scholar] [CrossRef]
418. Comite, D.; Ahmad, F.; Amin, M.G.; Dogaru, T. Forward-looking ground-penetrating radar: Subsurface target imaging and detection: A review. IEEE Geoscience and Remote Sensing Magazine 2021, 9, 173–190. [Google Scholar] [CrossRef]
419. Comite, D.; Ahmad, F.; Dogaru, T.; Amin, M.G. Adaptive detection of low-signature targets in forward-looking GPR imagery. IEEE Geoscience and Remote Sensing Letters 2018, 15, 1520–1524. [Google Scholar] [CrossRef]
420. Chandra, M.; Tanzi, T. On the Design of a side-looking Drone-borne GPR and its Physical Basis. In Proceedings of the URSI. AT-RASC 2018. Second URSI Atlantic Radio Science Meeting-2018. 2018. [Google Scholar]
421. Burr, R.; Schartel, M.; Mayer, W.; Walter, T.; Waldschmidt, C. UAV-based polarimetric synthetic aperture radar for mine detection. In Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium; 2019; pp. 9208–9211. [Google Scholar]
422. chartel, M.; Burr, R.; Bähnemann, R.; Mayer, W.; Waldschmidt, C. An experimental study on airborne landmine detection using a circular synthetic aperture radar. arXiv preprint arXiv:2005.02600 2020.
423. Grathwohl, A.; Hinz, P.; Burr, R.; Steiner, M.; Waldschmidt, C. Experimental study on the detection of avalanche victims using an airborne ground penetrating synthetic aperture radar. In Proceedings of the 2021 IEEE Radar Conference (RadarConf21); 2021; pp. 1–6. [Google Scholar]
424. Karthikeyan, R.; Chandramouli, A.; Srivatsun, G. Ground Penetrating Radar (GPR) Antenna Design: A Comparative Study. International Journal of Engineering and Advanced Technology (IJEAT) 2018, 8, 168–176. [Google Scholar]
425. Noviello, C.; Esposito, G.; Fasano, G.; Renga, A.; Soldovieri, F.; Catapano, I. Small-UAV radar imaging system performance with GPS and CDGPS based motion compensation. Remote Sensing 2020, 12, 3463. [Google Scholar] [CrossRef]
426. García-Fernández, M.; Álvarez-Narciandi, G.; López, Y.Á.; Andrés, F.L.-H. Improvements in GPR-SAR imaging focusing and detection capabilities of UAV-mounted GPR systems. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 189, 128–142. [Google Scholar] [CrossRef]
427. Vuksanovic, B.; Bostanudin, N.; Hidzir, H.; Parchizadeh, H. Discarding Unwanted Features from GPR Images Using2DPCA and ICA Techniques. International Journal of Information and Electronics Engineering 2013, 3, 317. [Google Scholar] [CrossRef]
428. Gonzalez-Diaz, M.; Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Las-Heras, F. Improvement of GPR SAR-based techniques for accurate detection and imaging of buried objects. IEEE Transactions on Instrumentation and measurement 2019, 69, 3126–3138. [Google Scholar] [CrossRef]
429. Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Las Heras, F. Autonomous airborne 3D SAR imaging system for subsurface sensing: UWB-GPR on board a UAV for landmine and IED detection. Remote Sensing 2019, 11, 2357. [Google Scholar] [CrossRef]
430. Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Arboleya-Arboleya, A.; Las-Heras, F.; Rodriguez-Vaqueiro, Y.; Gonzalez-Valdes, B.; Pino-Garcia, A. SVD-based clutter removal technique for GPR. In Proceedings of the 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting; 2017; pp. 2369–2370. [Google Scholar]
431. Daniels, D.J. Ground penetrating radar; Iet: 2004; Volume 1.
432. Persico, R. Introduction to ground penetrating radar: inverse scattering and data processing; John Wiley & Sons: 2014.
433. Curlander, J.C.; McDonough, R.N. Synthetic aperture radar; Wiley, New York: 1991; Volume 11.
434. Catapano, I.; Noviello, C.; Soldovieri, F. Down-Looking Airborne Radar Imaging Performance: The Multi-Line and Multi-Frequency. Remote Sensing 2021, 13, 4897. [Google Scholar] [CrossRef]
435. Schneider, W.A. Integral formulation for migration in two and three dimensions. Geophysics 1978, 43, 49–76. [Google Scholar] [CrossRef]
436. Gazdag, J. Wave equation migration with the phase-shift method. Geophysics 1978, 43, 1342–1351. [Google Scholar] [CrossRef]
437. Marpaung, D.H.; Lu, Y. A comparative study of migration algorithms for UWB GPR images in SISO-SAR and MIMO-array configurations. In Proceedings of the 2014 15th International Radar Symposium (IRS); 2014; pp. 1–4. [Google Scholar]
438. Özdemir, C.; Demirci, Ş.; Yiğit, E.; Yilmaz, B. A review on migration methods in B-scan ground penetrating radar imaging. Mathematical Problems in Engineering 2014, 2014. [Google Scholar] [CrossRef]
439. Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Gonzalez-Valdes, B.; Rodriguez-Vaqueiro, Y.; Arboleya-Arboleya, A.; Las Heras, F. Recent advances in high-resolution Ground Penetrating Radar on board an Unmanned Aerial Vehicle. In Proceedings of the 2019 13th European Conference on Antennas and Propagation (EuCAP); 2019; pp. 1–5. [Google Scholar]
440. Fallahpour, M.; Case, J.T.; Ghasr, M.T.; Zoughi, R. Piecewise and Wiener filter-based SAR techniques for monostatic microwave imaging of layered structures. IEEE Transactions on Antennas and Propagation 2013, 62, 282–294. [Google Scholar] [CrossRef]
441. Johansson, E.M.; Mast, J.E. Three-dimensional ground-penetrating radar imaging using synthetic aperture time-domain focusing. In Proceedings of the Advanced Microwave and Millimeter-Wave Detectors; 1994; pp. 205–214. [Google Scholar]
442. Burr, R.; Schartel, M.; Schmidt, P.; Mayer, W.; Walter, T.; Waldschmidt, C. Design and Implementation of a FMCW GPR for UAV-based Mine Detection. In Proceedings of the 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM); 2018; pp. 1–4. [Google Scholar]
443. Catapano, I.; Gennarelli, G.; Ludeno, G.; Soldovieri, F.; Persico, R. Ground-penetrating radar: operation principle and data processing. Wiley encyclopedia of electrical and electronics engineering 1999, 1–23. [Google Scholar]
444. Bertero, M.; Boccacci, P.; De Mol, C. Introduction to inverse problems in imaging; CRC press: 2021.
445. Gader, P.; Lee, W.-H.; Wilson, J.N. Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening. IEEE Transactions on Geoscience and Remote Sensing 2004, 42, 2522–2534. [Google Scholar] [CrossRef]
446. Hou, F.; Lei, W.; Li, S.; Xi, J. Deep learning-based subsurface target detection from GPR scans. IEEE sensors journal 2021, 21, 8161–8171. [Google Scholar] [CrossRef]
447. Alpdemir, M.N.; Sezgin, M. A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection. Neural Computing and Applications 2024, 36, 8199–8219. [Google Scholar] [CrossRef]
448. Maruddani, B.; Sandi, E.; Salam, M.F.N. Design and Implementation of Low-cost Wideband Vivaldi Antenna for Ground Penetrating Radar. KnE Social Sciences 2019, 498–506-498–506.
449. Cerquera, M.R.P.; Montaño, J.D.C.; Mondragón, I.; Canbolat, H. UAV for landmine detection using SDR-based GPR technology. Robots Operating in Hazardous Environments 2017, 26–55. [Google Scholar]
450. Roussi, C.; Xique, I.; Burns, J.; Hart, B. Buried object imaging using a small UAS-based GPR. In Proceedings of the Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019; pp. 146–154.
451. Fernández, M.G.; Narciandi, G.Á.; Arboleya, A.; Antuña, C.V.; Andrés, F.L.-H.; López, Y.Á. Development of an airborne-based GPR system for landmine and IED detection: Antenna analysis and intercomparison. IEEE Access 2021, 9, 127382–127396. [Google Scholar] [CrossRef]
452. Yarlequé, M.A.; Alvarez, S.; Martínez, H.J.; Canelo, A.C. FMCW GPR radar for archaeological applications: First analytical and measurement results. In Proceedings of the 2017 Xxxiind General Assembly and Scientific Symposium of the International Union of Radio Science (Ursi Gass); 2017; pp. 1–3. [Google Scholar]
453. Dill, S.; Schreiber, E.; Engel, M.; Heinzel, A.; Peichl, M. A drone carried multichannel Synthetic Aperture Radar for advanced buried object detection. In Proceedings of the 2019 IEEE Radar Conference (RadarConf); 2019; pp. 1–6. [Google Scholar]
454. Valence, E.; Baraer, M.; Rosa, E.; Barbecot, F.; Monty, C. Drone-based ground-penetrating radar (GPR) application to snow hydrology. The Cryosphere 2022, 16, 3843–3860. [Google Scholar] [CrossRef]
455. Wu, S.; Wang, L.; Zeng, X.; Wang, F.; Liang, Z.; Ye, H. UAV-Mounted GPR for Object Detection Based on Cross-Correlation Background Subtraction Method. Remote Sensing 2022, 14, 5132. [Google Scholar] [CrossRef]
456. Jenssen, R.O.R.; Jacobsen, S.K. Measurement of snow water equivalent using drone-mounted ultra-wide-band radar. Remote Sensing 2021, 13, 2610. [Google Scholar] [CrossRef]
457. Jenssen, R.O.R.; Jacobsen, S. Drone-mounted UWB snow radar: technical improvements and field results. Journal of Electromagnetic Waves and Applications 2020, 34, 1930–1954. [Google Scholar] [CrossRef]
458. Prager, S.; Moghaddam, M. Application of ultra-wideband synthesis in software defined radar for UAV-based landmine detection. In Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium; 2019; pp. 10115–10118. [Google Scholar]
459. Li, C.; Li, Z.; Huang, W.; Zhang, B.; Deng, Y.; Li, G. Morphology dynamics of ice cover in a river bend revealed by the UAV-GPR and sentinel-2. Remote Sensing 2023, 15, 3180. [Google Scholar] [CrossRef]
460. Bandini, F.; Kooij, L.; Mortensen, B.K.; Caspersen, M.B.; Thomsen, L.G.; Olesen, D.; Bauer-Gottwein, P. Mapping inland water bathymetry with ground penetrating radar (GPR) on board unmanned aerial systems (UASs). Journal of Hydrology 2023, 616, 128789. [Google Scholar] [CrossRef]
461. Svedin, J.; Bernland, A.; Gustafsson, A.; Claar, E.; Luong, J. Small UAV-based SAR system using low-cost radar, position, and attitude sensors with onboard imaging capability. International Journal of Microwave and Wireless Technologies 2021, 13, 602–613. [Google Scholar] [CrossRef]
462. Altdorff, D.; Schliffke, N.; Riedel, M.; Schmidt, V.; van der Kruk, J.; Vereecken, H.; Stoll, J.; Becken, M. UAV-borne electromagnetic induction and ground-penetrating radar measurements: a feasibility test. Water Resour. Res 2014, 42, W11403. [Google Scholar]
463. Building a ground penetrating radar for drones with Red Pitaya. Available online: https://content.redpitaya.com/blog/building-a-ground-penetrating-radar-with-red-pitaya.
464. García-Fernández, M.; Álvarez-Narciandi, G.; López, Y.Á.; Las-Heras, F. Array-based ground penetrating synthetic aperture radar on board an unmanned aerial vehicle for enhanced buried threats detection. IEEE Transactions on Geoscience and Remote Sensing 2023. [Google Scholar] [CrossRef]
465. Linck, R.; Kaltak, A. Drone radar: A new survey approach for Archaeological Prospection. In Proceedings of the Proceedings of the 3th International Conference on Archaeological Prospection, Sligo, Ireland, 2019; pp. 268–271.
466. Francke, J.; Dobrovolskiy, A. Challenges and opportunities with drone-mounted GPR. In Proceedings of the First International Meeting for Applied Geoscience & Energy; 2021; pp. 3043–3047. [Google Scholar]
467. Linna, P.; Halla, A.; Narra, N. Ground-penetrating radar-mounted drones in agriculture. In Proceedings of the New Developments and Environmental Applications of Drones: Proceedings of FinDrones 2020, 2022; pp. 139–156.
468. Molina, C.M.; Wisniewski, K.D.; Salamanca, A.; Saumett, M.; Rojas, C.; Gómez, H.; Baena, A.; Pringle, J.K. Monitoring of simulated clandestine graves of victims using UAVs, GPR, electrical tomography and conductivity over 4-8 years post-burial to aid forensic search investigators in Colombia, South America. Forensic Science International 2024, 355, 111919. [Google Scholar] [CrossRef]
469. Guan, S.; Zhu, Z.; Wang, G. A review on UAV-based remote sensing technologies for construction and civil applications. Drones 2022, 6, 117. [Google Scholar] [CrossRef]
470. Zhou, W.; Chen, F.; Guo, H.; Hu, M.; Li, Q.; Tang, P.; Zheng, W.; Liu, J.a.; Luo, R.; Yan, K. UAV Laser scanning technology: A potential cost-effective tool for micro-topography detection over wooded areas for archaeological prospection. International Journal of Digital Earth 2020, 13, 1279–1301. [Google Scholar] [CrossRef]
471. Riemersma, G. Improving UAV LiDAR survey accuracy using Ground Control Targets. Available online: https://www.routescene.com/resources/ground-control-targets-for-uav-lidar-accuracy/#:~:text=Setting%20Ground%20Control%20Points%20(GCP,the%20accuracy%20of%20the%20survey. (accessed on January 10).
472. Ravi, R.; Shamseldin, T.; Elbahnasawy, M.; Lin, Y.-J.; Habib, A. Bias impact analysis and calibration of UAV-based mobile LiDAR system with spinning multi-beam laser scanner. Applied Sciences 2018, 8, 297. [Google Scholar] [CrossRef]
473. Del Savio, A.A.; Luna Torres, A.; Chicchón Apaza, M.A.; Vergara Olivera, M.A.; Llimpe Rojas, S.R.; Urday Ibarra, G.T.; Reyes Ñique, J.L.; Macedo Arevalo, R.I. Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud. Applied Sciences 2022, 12, 12838. [Google Scholar] [CrossRef]
474. Fernandez-Diaz, J.C.; Carter, W.E.; Shrestha, R.L.; Glennie, C.L. Now you see it… now you don’t: Understanding airborne mapping LiDAR collection and data product generation for archaeological research in Mesoamerica. Remote Sensing 2014, 6, 9951–10001. [Google Scholar] [CrossRef]
475. Abediasl, H.; Hashemi, H. Monolithic optical phased-array transceiver in a standard SOI CMOS process. Optics express 2015, 23, 6509–6519. [Google Scholar] [CrossRef]
476. Habib, A.; Bang, K.I.; Kersting, A.P.; Chow, J. Alternative methodologies for LiDAR system calibration. Remote Sensing 2010, 2, 874–907. [Google Scholar] [CrossRef]
477. Kaňuk, J.; Gallay, M.; Eck, C.; Zgraggen, C.; Dvorný, E. Technical report: unmanned helicopter solution for survey-grade LiDAR and hyperspectral mapping. Pure and Applied Geophysics 2018, 175, 3357–3373. [Google Scholar] [CrossRef]
478. Meng, X.; Currit, N.; Zhao, K. Ground filtering algorithms for airborne LiDAR data: A review of critical issues. Remote Sensing 2010, 2, 833–860. [Google Scholar] [CrossRef]
479. Chen, Z.; Gao, B.; Devereux, B. State-of-the-art: DTM generation using airborne LIDAR data. Sensors 2017, 17, 150. [Google Scholar] [CrossRef] [PubMed]
480. Chang, K.-J.; Hsieh, Y.-C.; Chan, Y.-C.; Huang, M.-J. UAS LiDAR data processing, quality assessment and geosciences prospects. In Proceedings of the Geophysical Research Abstracts; 2019. [Google Scholar]
481. VanValkenburgh, P.; Cushman, K.; Butters, L.J.C.; Vega, C.R.; Roberts, C.B.; Kepler, C.; Kellner, J. Lasers without lost cities: Using drone lidar to capture architectural complexity at Kuelap, Amazonas, Peru. Journal of Field Archaeology 2020, 45, S75–S88. [Google Scholar] [CrossRef]
482. MacDonell, C.J.; Williams, R.D.; Maniatis, G.; Roberts, K.; Naylor, M. Consumer-grade UAV solid-state LiDAR accurately quantifies topography in a vegetated fluvial environment. Earth Surface Processes and Landforms 2023, 48, 2211–2229. [Google Scholar] [CrossRef]
483. Demers, M. UAV Magnetic and Lidar surveys on the Miakadow Property; Eagle Geosciences: October 12 2021.
484. Esin, A.İ.; Akgul, M.; Akay, A.O.; Yurtseven, H. Comparison of LiDAR-based morphometric analysis of a drainage basin with results obtained from UAV, TOPO, ASTER and SRTM-based DEMs. Arabian Journal of Geosciences 2021, 14, 1–15. [Google Scholar] [CrossRef]
485. Luo, D.; Lin, H.; Jin, Z.; Zheng, H.; Song, Y.; Feng, L.; Guo, Q. Applications of UAV digital aerial photogrammetry and LiDAR in geomorphology and land cover research. Journal of Earth Environment 2019, 10, 213–226. [Google Scholar]
486. Finley, T.; Salomon, G.; Nissen, E.; Stephen, R.; Cassidy, J.; Menounos, B. Preliminary results and structural interpretations from drone lidar surveys over the Eastern Denali fault, Yukon. Yukon Exploration and Geology 2021, 83–105. [Google Scholar]
487. Using UAV LiDAR mapping to monitor landslides safely. Available online: https://www.routescene.com/case-studies/uav-lidar-mapping-landslides-safely/ (accessed on April 28).
488. Jones, L.; Hobbs, P. The application of terrestrial LiDAR for geohazard mapping, monitoring and modelling in the British Geological Survey. Remote Sensing 2021, 13, 395. [Google Scholar] [CrossRef]
489. Liu, C.; Li, W.; Lei, W.; Liu, L.; Wu, H. Architecture planning and geo-disasters assessment mapping of landslide by using airborne LiDAR data and UAV images. In Proceedings of the International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications; 2011; pp. 468–474. [Google Scholar]
490. Casagli, N.; Frodella, W.; Morelli, S.; Tofani, V.; Ciampalini, A.; Intrieri, E.; Raspini, F.; Rossi, G.; Tanteri, L.; Lu, P. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning. Geoenvironmental Disasters 2017, 4, 1–23. [Google Scholar] [CrossRef]
491. Kowalski, A.; Wajs, J.; Kasza, D. MONITORING OF ANTHROPOGENIC LANDSLIDE ACTIVITY WITH COMBINED UAV AND LIDAR-DERIVED DEMS-A CASE STUDY OF THE CZERWONY WAWOZ LANDSLIDE (SW POLAND, WESTERN SUDETES). Acta Geodynamica et Geromaterialia 2018, 15, 117–130. [Google Scholar] [CrossRef]
492. Hussain, Y.; Schlögel, R.; Innocenti, A.; Hamza, O.; Iannucci, R.; Martino, S.; Havenith, H.-B. Review on the geophysical and UAV-based methods applied to landslides. Remote Sensing 2022, 14, 4564. [Google Scholar] [CrossRef]
493. França Pereira, F.; Sussel Gonçalves Mendes, T.; Jorge Coelho Simões, S.; Roberto Magalhães de Andrade, M.; Luiz Lopes Reiss, M.; Fortes Cavalcante Renk, J.; Correia da Silva Santos, T. Comparison of LiDAR-and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm. Landslides 2023, 20, 579–600. [Google Scholar] [CrossRef]
494. Petrovič, D.; Kozmus Trajkovski, K.; Grigillo, D. Use of LiDAR drone data for O-mapping and change detection in mountainous areas. Abstracts of the ICA 2023, 6, 200. [Google Scholar] [CrossRef]
495. Wang, K.; Yao, L.; Lin, J. Ground Surface Deformation Detection from Far Satellite SAR to UAV LiDAR and Terrestrial Lidar. In Proceedings of the 5th Asia Pacific Meeting on Near Surface Geoscience & Engineering; 2023; pp. 1–5. [Google Scholar]
496. Yongting, Z.; Youning, X.; Wei, L.; Caiyong, W.; Qiaomin, L.; Xueru, Y.; Jianhong, B. Technical methods for colliery subsidence disaster monitoring using UAV LiDAR: a case study of the Maliantai colliery, Ningdong coal base, Ningxia. Geological Bulletin of China 2018, 37, 2270–2277. [Google Scholar]
497. Jóźków, G.; Walicka, A.; Borkowski, A. Monitoring terrain deformations caused by underground mining using UAV data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2021, 43, 737–744. [Google Scholar] [CrossRef]
498. Dong, Y.; Wang, D.; Liu, F.; Wang, J. A new data processing method for high-precision mining subsidence measurement using airborne LiDAR. Frontiers in Earth Science 2022, 10, 858050. [Google Scholar] [CrossRef]
499. Yang, Q.; Tang, F.; Wang, F.; Tang, J.; Fan, Z.; Ma, T.; Su, Y.; Xue, J. A new technical pathway for extracting high accuracy surface deformation information in coal mining areas using UAV LiDAR data: An example from the Yushen mining area in western China. Measurement 2023, 218, 113220. [Google Scholar] [CrossRef]
500. An, S.; Yuan, L.; Xu, Y.; Wang, X.; Zhou, D. Ground subsidence monitoring in based on UAV-LiDAR technology: a case study of a mine in the Ordos, China. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 2024, 10, 57. [Google Scholar] [CrossRef]
501. Abellán, A.; Oppikofer, T.; Jaboyedoff, M.; Rosser, N.J.; Lim, M.; Lato, M.J. Terrestrial laser scanning of rock slope instabilities. Earth surface processes and landforms 2014, 39, 80–97. [Google Scholar] [CrossRef]
502. Akturk, E.; Altunel, A.O. Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain. Measurement 2019, 136, 382–386. [Google Scholar] [CrossRef]
503. Sturzenegger, M.; Willms, D.; Pate, K.; Johnston, B. Experience using terrestrial remote sensing techniques for rock slope performance assessment. In Proceedings of the Slope Stability 2013: Proceedings of the 2013 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, 2013; pp. 775–782.
504. Fernández-Lozano, J.; Gutiérrez-Alonso, G. The use of UAVs (unmanned air vehicles) in geology. In Proceedings of the Conference Paper. Available at: The Future of Mining in South Africa: Sunset or Sunrise; 2016. [Google Scholar]
505. Lin, M.-L.; Chen, Y.-C.; Tseng, Y.-H.; Chang, K.-J.; Wang, K.-L. Investigation of geological structures using UAV LiDAR and its effects on the failure mechanism of deep-seated landslide in Lantai Area, Taiwan. Applied Sciences 2021, 11, 10052. [Google Scholar] [CrossRef]
506. Application of UAV Lidar in Mining Survey. Available online: https://www.dronefromchina.com/new/Application-of-UAV-Lidar-in-Mining-Survey.html.
507. Li, T.; Zhang, B.; Xiao, W.; Cheng, X.; Li, Z.; Zhao, J. UAV-based photogrammetry and LiDAR for the characterization of ice morphology evolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4188–4199. [Google Scholar] [CrossRef]
508. García-López, S.; Vélez-Nicolás, M.; Zarandona-Palacio, P.; Curcio, A.; Ruiz-Ortiz, V.; Barbero, L. UAV-borne LiDAR revolutionizing groundwater level mapping. Science of the Total Environment 2023, 859, 160272. [Google Scholar] [CrossRef] [PubMed]
509. Du, M.; Li, H.; Roshanianfard, A. Design and experimental study on an innovative UAV-LiDAR topographic mapping system for precision land levelling. Drones 2022, 6, 403. [Google Scholar] [CrossRef]
510. James, M.R.; Carr, B.; D’Arcy, F.; Diefenbach, A.; Dietterich, H.; Fornaciai, A.; Lev, E.; Liu, E.; Pieri, D.; Rodgers, M. Volcanological applications of unoccupied aircraft systems (UAS): Developments, strategies, and future challenges. Volcanica 2020, 3, 67–114. [Google Scholar] [CrossRef]
511. Chen, Z.; Chen, Y.; Shi, T.; Chen, X.; Pan, X.; Lei, J.; Wu, T.; Li, Y.; Liu, Q.; Liu, X. Estimation of Soil Organic Carbon in Tropical Rainforest Regions by Combining Uav Hyperspectral and Lidar Data. Available at SSRN 4547030.
512. Pouladi, N.; Gholizadeh, A.; Khosravi, V.; Borůvka, L. Digital mapping of soil organic carbon using remote sensing data: A systematic review. Catena 2023, 232, 107409. [Google Scholar] [CrossRef]
513. Rey, J.; Martínez, J.; Mendoza, R.; Hidalgo, C.; Florez Rodríguez, C. Combining Geophysical Techniques (Eri, Ip, Tdem and Gpr) for the Characterization of Mining Waste. Available at SSRN 4127252.
514. Oliveira, R.J.; Caldeira, B.; Teixidó, T.; Borges, J.F.; Bezzeghoud, M. Geophysical data fusion of ground-penetrating radar and magnetic datasets using 2D wavelet transform and singular value decomposition. Frontiers in Earth Science 2022, 10, 1011999. [Google Scholar] [CrossRef]
515. Ghezzi, A. A New Approach to data integration in Archaeological Geophysics. 2020.
516. Khalil, M.H.; Hassan, N.A. Ground magnetic, GPR, and dipole-dipole resistivity for landfill investigation. International Journal of Geosciences 2016, 7, 828. [Google Scholar] [CrossRef]
517. Ukaegbu, I.K.; Gamage, K.A.; Aspinall, M.D. Integration of Ground-Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects. Sensors 2019, 19, 2743. [Google Scholar] [CrossRef]
518. Coelho, B.Z.; Karaoulis, M. Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations. Advanced Engineering Informatics 2022, 53, 101671. [Google Scholar] [CrossRef]
519. Tian, G.; Shaikh, S.A.; Lizan, A.; Chen, R.; Wang, Y. Geophysical Data Fusions for Resolution Improvement. In Proceedings of the International Conference on Engineering Geophysics, Al Ain, United Arab Emirates, 15-18 November 2015, 2015; pp. 42–42.
520. Grandjean, G.; Malet, J.-P.; Bitri, A.; Méric, O. Geophysical data fusion by fuzzy logic for imaging the mechanical behaviour of mudslides. Bulletin de la société géologique de France 2007, 178, 127–136. [Google Scholar] [CrossRef]
521. Casa, R.; Castaldi, F.; Pascucci, S.; Basso, B.; Pignatti, S. Geophysical and hyperspectral data fusion techniques for in-field estimation of soil properties. Vadose Zone Journal 2013, 12, vzj2012–0201. [Google Scholar] [CrossRef]
522. Bakuła, K.; Lejzerowicz, A.; Pilarska-Mazurek, M.; Ostrowski, W.; Górka, J.; Biernat, P.; Czernic, P.; Załęgowski, K.; Kleszczewska, K.W.; Węzka, K. Sensor integration and application of low-sized mobile mapping platform equipped with lidar, gpr and photogrammetric sensors. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2022, 43. [Google Scholar] [CrossRef]
523. Zhang, D.; Wu, Z.; Shi, D.; Li, J.; Lu, Y. Integration of Terrestrial Laser Scanner (TLS) and Ground Penetrating Radar (GPR) to Characterize the Three-Dimensional (3D) Geometry of the Maoyaba Segment of the Litang Fault, Southeastern Tibetan Plateau. Remote Sensing 2022, 14, 6394. [Google Scholar] [CrossRef]
524. Lv, P.; Xue, G.; Chen, W.; Song, W. Application of the transfer learning method in multisource geophysical data fusion. Journal of Geophysics and Engineering 2023, 20, 361–375. [Google Scholar] [CrossRef]
525. Afshar, A.; Norouzi, G.; Moradzadeh, A.; Riahi, M.; Porkhial, S. Curie point depth, geothermal gradient and heat-flow estimation and geothermal anomaly exploration from integrated analysis of aeromagnetic and gravity data on the Sabalan Area, NW Iran. Pure and Applied Geophysics 2017, 174, 1133–1152. [Google Scholar] [CrossRef]
526. Olierook, H.K.; Scalzo, R.; Kohn, D.; Chandra, R.; Farahbakhsh, E.; Clark, C.; Reddy, S.M.; Müller, R.D. Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models. Geoscience Frontiers 2021, 12, 479–493. [Google Scholar] [CrossRef]
527. Kuras, A.; Heincke, B.H.; Salehi, S.; Mielke, C.; Köllner, N.; Rogass, C.; Altenberger, U.; Burud, I. Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland. Remote Sensing 2022, 14, 4877. [Google Scholar] [CrossRef]
528. Leblanc, G.; Lee, M.; Morris, W. A simple adaptable data fusion methodology for geophysical exploration. Exploration Geophysics 2012, 43, 190–197. [Google Scholar] [CrossRef]
529. Lamri, T.; Djemaï, S.; Hamoudi, M.; Zoheir, B.; Bendaoud, A.; Ouzegane, K.; Amara, M. Satellite imagery and airborne geophysics for geologic mapping of the Edembo area, Eastern Hoggar (Algerian Sahara). Journal of African Earth Sciences 2016, 115, 143–158. [Google Scholar] [CrossRef]
530. Honarmand, M. Application of airborne geophysical and ASTER data for hydrothermal alteration mapping in the Sar-Kuh Porphyry Copper Area, Kerman Province, Iran. Open Journal of Geology 2016, 6, 1257–1268. [Google Scholar] [CrossRef]
531. Agapiou, A.; Lysandrou, V.; Sarris, A.; Papadopoulos, N.; Hadjimitsis, D.G. Fusion of satellite multispectral images based on ground-penetrating radar (GPR) data for the investigation of buried concealed archaeological remains. Geosciences 2017, 7, 40. [Google Scholar] [CrossRef]
532. Jackisch, R. Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration. 2022.
533. Martelet, G.; Gloaguen, E.; Døssing, A.; Lima Simoes da Silva, E.; Linde, J.; Rasmussen, T.M. Airborne/UAV multisensor surveys enhance the geological mapping and 3d model of a pseudo-skarn deposit in Ploumanac’h, French Brittany. Minerals 2021, 11, 1259. [Google Scholar] [CrossRef]
534. Ronchi, D.; Limongiello, M.; Demetrescu, E.; Ferdani, D. Multispectral UAV Data and GPR Survey for Archeological Anomaly Detection Supporting 3D Reconstruction. Sensors 2023, 23, 2769. [Google Scholar] [CrossRef] [PubMed]
535. Lee, J.; Lee, H.; Ko, S.; Ji, D.; Hyeon, J. Modeling and Implementation of a Joint Airborne Ground Penetrating Radar and Magnetometer System for Landmine Detection. Remote Sensing 2023, 15, 3813. [Google Scholar] [CrossRef]
536. Masini, N.; Sogliani, F.; Sileo, M.; Abate, N.; Danese, M.; Vitale, V.; Lasaponara, R.; Piro, S. Fusion and integration of heterogeneous close range remote sensing and geophysical data. The case of Grumentum. In Proceedings of the Journal of Physics: Conference Series, 2022; p. 012018.
537. Drone-Based Magnetic and Electromagnetic Measurements in Sweden: A Few Case Studies. Available online: https://seg.org/calendar_events/drone-based-magnetic-and-electromagnetic-measurements-in-sweden-a-few-case-studies/.
538. Mu, Y.; Xie, W.; Zhang, X. The joint UAV-borne magnetic detection system and cart-mounted time domain electromagnetic system for UXO detection. Remote Sensing 2021, 13, 2343. [Google Scholar] [CrossRef]
539. Šašak, J.; Gallay, M.; Kaňuk, J.; Hofierka, J.; Minár, J. Combined use of terrestrial laser scanning and UAV photogrammetry in mapping alpine terrain. Remote Sensing 2019, 11, 2154. [Google Scholar] [CrossRef]
540. Mao, Z.; Hu, S.; Wang, N.; Long, Y. Precision evaluation and fusion of topographic data based on UAVs and TLS surveys of a loess landslide. Frontiers in Earth Science 2021, 9, 801293. [Google Scholar] [CrossRef]
541. Sankey, J.B.; Sankey, T.T.; Li, J.; Ravi, S.; Wang, G.; Caster, J.; Kasprak, A. Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sensing of Environment 2021, 253, 112223. [Google Scholar] [CrossRef]
542. Guan, Y.; Grote, K. Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods. Remote Sensing 2023, 16, 61. [Google Scholar] [CrossRef]
543. Blanco-Sacristán, J.; Panigada, C.; Gentili, R.; Tagliabue, G.; Garzonio, R.; Martín, M.P.; Ladron de Guevara, M.; Colombo, R.; Dowling, T.P.; Rossini, M. UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust. Earth Surface Processes and Landforms 2021, 46, 2466–2484. [Google Scholar] [CrossRef]
544. Laugier, E.J.; Casana, J. Integrating satellite, UAV, and ground-based remote sensing in archaeology: An exploration of pre-modern land use in Northeastern Iraq. Remote Sensing 2021, 13, 5119. [Google Scholar] [CrossRef]