Altitude Sensor Research Articles

A novel efficient estimator for three‐dimensional bearings‐only source localisation with unknown sensor altitude and systematic measurement errors

AbstractA novel two‐stage profile maximum likelihood estimator is proposed to estimate the source location and the systematic errors jointly, with the aim of addressing the problem of source localisation using angle‐only measurements from a single sensor with unknown sensor altitude and systematic measurement errors. The proposed two‐stage profile maximum likelihood estimator algorithm is capable of decoupling the azimuth and elevation angle measurements while transforming the original maximum likelihood optimisation problem into two sub‐problems, that is, two‐dimensional‐projected maximum likelihood estimator and relative altitude maximum likelihood estimator. In terms of the two‐dimensional‐projected maximum likelihood estimator, an algorithm combining pseudo‐linear estimating and Kalman filtering is proposed to generate an initial estimate. Subsequently, a Gauss–Newton iterative method is developed to estimate the two‐dimensional‐projected target location and the azimuth systematic error jointly. The relative height maximum likelihood estimator is initialised using a pseudo‐linear estimator. Next, a Gauss–Newton iterative algorithm is adopted to estimate the elevation systematic error and the relative height. As indicated by the result of the simulation studies, the proposed algorithm exhibits an estimation performance close to the Cramér–Rao lower bound with unknown sensor altitude and systematic measurement errors.

The balance between spectral and spatial information to estimate straw cereal plant density at early growth stages from optical sensors

Estimating straw cereal plant density at early stages is important for field crop management and phenotyping. Usual plant density estimation methods include manual counting and image-based counting, both of which have limited throughput, due to the need for high spatial resolution images. In this study, we explored the potential of high-throughput estimations with spectral information. A large and diverse dataset was collected on micro plot field experiments, encompassing six sites, three leaf stages, and four species of straw cereals. Canopy spectral reflectance was acquired with a spectrometer, both in 0° or 45° view zenith angle, perpendicularly to the row direction. Two reflectance-based approaches were then tested. In the direct approach, density was directly estimated from reflectance using Gaussian process regression (GPR) and spectral bands selected based on Akaike’s information criterion. In the indirect approach, the green fraction derived from high spatial resolution RGB images (GF_rgb) was first estimated from reflectance using GPR and selected bands, and then linearly related to density. These reflectance-based methods were compared to a classical image-based baseline method, which estimates density directly from GF_rgb.An ablation study firstly showed the superiority of 45° observations, and the necessity to calibrate one model for each site, growth stage, and species. The band selection process recommended using no more than four bands as inputs to the GPR models. The resulting direct and indirect estimations had an overall relative error of 30 %. The image-based baseline method had a lower error of 22 % for submillimeter spatial resolutions, but it performed worse than reflectance-based methods when degrading the spatial resolution to more than 1 to 2 mm to mimic an increase in sensor altitude. These results showed that spectral information can compensate for spatial information and that spectral methods can potentially provide high-throughput and reasonably accurate estimates of straw cereal plant density.

Drone-based magnetometer prospection for archaeology

Magnetometry is one of the most efficient and successful methods of archaeological prospection. Drone-based prospecting is increasingly being used in many fields of remote sensing but with respect to magnetometry, with rather poor results. For magnetic surveys, drone prospecting comes with the problem that magnetic and mechanical disturbances originating from the aircraft decrease the quality of the measurements.Here we present the adaptation of a commercial three-axis fluxgate magnetometer setup, which can be suitable for archaeological prospection, after applying appropriate filter methods and minimising the flying altitude and speed. For approval of the system, we performed drone-based surveys in high spatial resolution (50 cm line spacing) at constant, ultra-low sensor altitudes of 45 ± 10 cm and 75 ± 10 cm. We chose an archaeological site from the roman period as survey site (3.8 ha), where high-quality ground-based caesium magnetometer data was available. This allows us to demonstrate the first detailed comparison of drone-based and ground-based magnetic survey data for archaeology. For further evaluation, the influence of the drone on the measurements is assessed, drift and sensor resolution checks are carried out, and suitable data filtering methods are evaluated.Our results show that we can detect main archaeological features such as ditches, pits, fireplaces and remnants of stone fundaments with the drone-based setup, which are the relevant structures for the majority of archaeological prospections worldwide. Further, we can prove that the data quality can significantly be increased by lowering the flight altitude and speed. We conclude that drone-based magnetometry serves above all to cover large and inaccessible areas in short time. Our findings are a first step towards the development of standards for drone-based archaeological prospection approaches.

Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

The advancement of the Internet of Things applications (technologies and enabling platforms), consisting of software and hardware (e.g., sensors, actuators, etc.), allows healthcare providers and users to analyze and measure physical environments at home or hospital. The measured physical environment parameters contribute to improving healthcare in real time. Researchers in this domain require existing representative datasets to develop machine-learning techniques to learn physical variables from the surrounding environments. The available environmental datasets are rare and need too much effort to be generated. To our knowledge, it has been noticed that no datasets are available for some countries, including Saudi Arabia. Therefore, this paper presents one of the first environmental data generated in Saudi Arabia’s environment. The advantage of this dataset is to encourage researchers to investigate the effectiveness of machine learning in such an environment. The collected data will also help utilize the machine learning and deep learning algorithms in smart home and health care applications based on the Saudi Arabia environment. Saudi Arabia has a special environment in each session, especially in the northern area where we work, where it is too hot in the summer and cold in the winter. Therefore, environmental data measurements in both sessions are important for the research community, especially those working in smart and healthcare environments. The dataset is generated based on the indoor environment from six sensors (timestamps, light, temperature, humidity, pressure, and altitude sensors). The room data were collected for 31 days in July 2022, acquiring 8910 records. The datasets include six columns of different data types that represent sensor values. During the experiment, the sensors captured the data every 5 min, storing them in a comma-separated value file. The data are already validated and publicly available at PLOMS Press and can be applied for training, testing, and validating machine learning algorithms. This is the first dataset developed by the authors for the research community for such an environment, and other datasets will follow it in different environments and places.

Three-Dimensional Airborne Electromagnetic Data Inversion With Flight Altitude Correction

The flight altitude has a large effect on the airborne electromagnetic (AEM) responses. Due to the dynamic environment of the aircraft, the recorded sensor altitudes may contain errors. Research demonstrates that the AEM responses caused by a several meters altitude errors can be larger than caused by some anomalous body. Ignoring these errors will create erroneous results in AEM data interpretation. Considering that there is not yet a published 3D AEM inversion method that takes into account the flight altitude, we develop in this paper a 3D inversion algorithm for AEM with the flight height treated as an inversion parameter. For the forward modeling we use the finite element method, while for the inversion we use the Gauss-Newton optimization method. To make our inversion works for variable flight altitudes, we propose a scheme of 3D Jacobean matrix calculation for both the resistivities and flight altitudes without much increasing the computational cost. The numerical simulation result confirms that the flight altitude really has a large effect on the AEM responses. The inversions of synthetic data show that our 3D inversion method can both recover the resistivity distribution in the underground and decrease the altitude errors recorded, while the field data inversion demonstrates that our method can deliver a better inversion model with a smaller data misfit.

Evaluation of SIF retrievals from narrow-band and sub-nanometer airborne hyperspectral imagers flown in tandem: Modelling and validation in the context of plant phenotyping

Solar-induced chlorophyll fluorescence (SIF) can be used as an indicator of crop photosynthetic activity and a proxy for vegetation stress in plant phenotyping and precision agriculture applications. SIF quantification is sensitive to the spectral resolution (SR), and its accurate retrieval requires sensors with sub-nanometer resolutions. However, for accurate SIF quantification from imaging sensors onboard airborne platforms, sub-nanometer imagers are costly and more difficult to operate than the commonly available narrow-band imagers (i.e., 4- to 6-nm bandwidths), which can also be installed on drones and lightweight aircraft. Although a few theoretical and experimental studies have evaluated narrow-band spectra for SIF quantification, there is a lack of research focused on comparing the effects of the SR on SIF from airborne hyperspectral imagers in practical applications. This study investigates the effects of SR and sensor altitude on SIF accuracy, comparing SIF quantified at the 760-nm O2-A band (SIF760) from two hyperspectral imagers with different spectral configurations (full width at half-maximum resolutions of 0.1–0.2 nm and 5.8 nm) flown in tandem on board an aircraft. SIF760 retrievals were compared from two different wheat and maize phenotyping trials grown under different nitrogen fertilizer application rates over the 2019–2021 growing seasons. SIF760 from the two sensors were correlated (R2 = 0.77–0.9, p < 0.01), with the narrow-band imager producing larger SIF760 estimates than the sub-nanometer imager (root mean square error (RMSE) 3.28–4.69 mW/m2/nm/sr). Ground-level SIF760 showed strong relationships with both sub-nanometer (R2 = 0.90, p < 0.001, RMSE = 0.07 mW/m2/nm/sr) and narrow-band (R2 = 0.88, p < 0.001, RMSE = 3.26 mW/m2/nm/sr) airborne retrievals. Simulation-based assessments of SIF760 for SRs ranging from 1 to 5.8 nm using the SCOPE model were consistent with experimental results showing significant relationships among SIF760 quantified at different SRs. Predictive algorithms of leaf nitrogen concentration using SIF760 from either the narrow-band or sub-nanometer sensor yielded similar performance, supporting the use of narrow-band resolution imagery for assessing the spatial variability of SIF in plant phenotyping, vegetation stress detection and precision agriculture contexts.

Visual-based Navigation Strategy for Autonomous Underwater Vehicles in Monitoring Scenarios

Autonomous Underwater Vehicles (AUVs) performing visual surveys aimed at the preservation of marine environments are equipped with optical sensors for image acquisition. In addition, an altitude sensor is usually installed on-board to control the distance from the seabed and avoid possible collisions. Within this context, this work proposes a navigation strategy for underwater monitoring scenarios, which fuses a single bottom-looking camera and altitude information for linear velocity estimation. This allows to exploit the payload already required by monitoring activities also for navigation purposes, thus reducing the number of sensors onboard the AUV. The linear velocity is provided by a monocular Visual Odometry (VO) technique that switches between homography and epipolar models for motion estimation and leverages altitude measurements to overcome the scale ambiguity issue. The navigation framework relies on an Extended Kalman Filter (EKF) that combines visual-based linear velocity with attitude and depth measurements for trajectory estimation. The proposed strategy has been tested on real data acquired by using Zeno AUV, equipped with bottom-looking camera, DVL, Attitude and Heading Reference System (AHRS), and depth sensor. The performance has been assessed comparing the estimated linear velocities with the DVL readings, and the VO-based estimated trajectory with that provided by a DVL-based dead-reckoning approach, yielding to a maximum absolute error of 2.16m for a reference trajectory of 166m. Given the promising results, this strategy could represent an affordable solution for underwater navigation where visibility conditions allow the use of optical sensors.

Exposure of Whimbrels to offshore wind leases during departure from and arrival to a major mid-Atlantic staging site

The United States is pursuing a diversified energy portfolio that includes offshore wind with a focus on the Atlantic Outer Continental Shelf (OCS). The Western Atlantic Flyway (WAF) supports one of the largest near-shore movement corridors of birds in the world, including several shorebird species of high conservation concern. We used satellite transmitters to examine orientation of Whimbrels (<em>Numenius phaeopus</em>) crossing the OCS and their overlap with two wind energy leases. Birds using a migratory staging site along the Delmarva Peninsula in Virginia crossed the OCS along a southeast-northwest axis. A considerable percentage (42.9%) of tracks intersected with one of the two wind leases. The juxtaposition to the Delmarva Peninsula placed wind leases southeast of the peninsula within both the departure and arrival trajectories of Whimbrels. The satellite transmitters used in this study were not equipped with altitude sensors, so we do not know if birds crossed wind leases within the rotor swept zone. Several species of shorebirds, including hundreds of thousands of individuals, make trans-Atlantic flights from three major staging sites: Delaware Bay, the lower Delmarva Peninsula, and Georgia Bight. All of these sites have wind leases positioned to their southeast. One of the most effective strategies for minimizing conflicts between birds and potential hazards is to place hazards away from critical movement corridors. More information is needed about departure and arrival patterns of shorebirds that cross the OCS to inform future lease placement.

Novel Smart Sensor Technology Platform for Border Crossing Surveillance within FOLDOUT

In this paper we introduce AIT’s Smart Sensor Technology platform, developed within the FOLDOUT project (Through-foliage detection, including in the outermost regions of the EU). The novel platform is part of FOLDOUT’s solution for ground based border surveillance, and in particular detection of foliage penetration. The platform combines a high resolution (4K) RGB camera, a thermal camera, as well as a 30x zoom (2K) NIR low-light camera. It is designed to work day and night under strongly varying weather conditions, it is transportable and can be deployed in remote areas, as well as being self-sufficient if operated by battery. The typical sensor range is about 100-200m which can be adapted per specific usage. The platforming thobe used for surveillance of dedicated areas or by providing an open interface to be integrated into whole surveillance systems in which ground-based sensors are combined with high altitude sensors to provide both close and wide area coverages. The platform provides state-of-the-art AI-based detection algorithms for object detection and classification, for both RGB and thermal images. The individual detections are further fused by establishing coincidence in time and space between the individual detectors, where sensor is geo-localized for accurate target localization and visualization on a map of the area to be monitored.

3-mm wavelength range radar altitude sensors

In this study, we analyse the specific features of design of 3-mm wavelength range radar altitude sensors based on domestic-made super high frequency modules М55323 and М55328. Some results of full-scale tests are given.

Optimizing precision agricultural operations by standardized cloud-based functions

Aim of study: An approach to integrate knowledge into the IT-infrastructure of precision agriculture (PA) is presented. The creation of operation relevant information is analyzed and explored to be processed by standardized web services and thereby to integrate external knowledge into PA. The target is to make knowledge integrable into any software solution. Area of study: The data sampling took place at the Heidfeld Hof Research Station in Stuttgart, Germany. Material and methods: This study follows the information science’s idea to separate the process from data sampling into the final actuation through four steps: data, information, knowledge, and wisdom. The process from the data acquisition, over a professional data treatment to the actual application is analyzed by methods modelled in the Unified Modelling Language (UML) for two use-cases. It was further applied for a low altitude sensor in a PA operation; a data sampling by UAV represents the starting point. Main results: For the implemented solution, the Web Processing Service (WPS) of the Open Geospatial Consortium (OGC) is proposed. This approach reflects the idea of a function as a service (FaaS), in order to develop a demand-driven and extensible solution for irregularly used functionalities. PA benefits, as on-farm processes are season oriented and a FaaS reflects the farm’s variable demands over time by origin and extends the concept to offer external know-how for the integration into specific processes. Research highlights: The standardized implementation of knowledge into PA software products helps to generate additional benefits for PA.

CONSTRUCTION AND FLIGHT CHARACTERISTICS UNNAMED AERIAL VEHICLE s

This article presents information on the design and use of unmanned aerial vehicles with adescription of the design solutions of individual elements. The purpose of this work is to analyze the designof the UAV, review the main parts, analysis and comparison of technologies for measuring altitude andstabilization of drone flight, description of UAV control processes based on modified stabilization in airspace sensors and altitude regulators. Developments in the areas of height measurement and UAVstabilization are presented and described at the current time. The general design of a UAV based on a ISSN 2221-3805. Електротехнічні та комп’ютерні системи. 2021. №34 (110)Автоматизовані електромеханічні системиquadcopter is described. The existing variants of altitude sensors, which are necessary for drones of specialdesign, are considered. 3 existing systems are described - barometric, ultrasonic and visual positioning.RADAR, LIDAR and LADAR systems are among the systems that are not found on civil drones, because theyvery have a complex design and are quite expensive for simple drones. Possibilities of their use as UAValtitude sensors, advantages and disadvantages. RADAR systems is ToF systems, which measure time offlight transmission by electromagnetic waves. LIDAR and LADAR are optical height meters, which worksimilarly systems RADAR. Two variants of drone flight stabilization, use of regulators on the basis of neuralnetworks and traditional systems with the use of PID-regulators are considered. The work of such regulatorsis described, graphs are given, which show the difference between the set and real angles of deviation of thepitch and yaw, which allows to evaluate the advantages of using each of regulators.As a future study, the development of a prototype quadcopter for the study of the considered systems ofaltitude measurement and flight stabilization is envisaged. Building a mathematical model using sensors andregulators described here to study their real real behavior in the field. Integration of LADAR, LIDAR andRADAR systems as the additional measurement systemsones for special and civil UAVs

Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy

Matched filter techniques have been widely used for retrieval of greenhouse gas enhancements from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enhancement estimation has remained largely unaltered since the introduction of quantitative matched filter retrievals. The magnitude of retrieved methane and carbon dioxide enhancements, and thereby integrated mass enhancements (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enhancement target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific unit enhancement spectra to provide target spectra that are compatible with all greenhouse gas retrieval matched filter techniques. Specifically, we use radiative transfer modeling to model four parameters that are expected to change between scenes: solar zenith angle, column water vapor, ground elevation, and sensor altitude. These parameter values are well defined, with low variation within a single scene. A benchmark dataset consisting of ten AVIRIS-NG airborne imaging spectrometer scenes was used to compare IME retrieved using a matched filter algorithm. For methane plumes, IME resulting from use of standard, generic enhancement spectra varied from −22 to +28.7% compared to scene-specific enhancement spectra. Due to differences in spectral shape between the generic and scene-specific enhancement spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences were much larger for carbon dioxide plumes, with generic enhancement spectra producing integrated mass enhancements −76.1 to −48.1% compared to scene-specific enhancement spectra. Fluxes calculated from these integrated enhancements would vary by the same percentages, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. We introduce an interpolation approach that can efficiently generate scene-specific unit enhancement spectra for given sets of parameters. Scene-specific target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.

IoT based Smart Vehicle Monitoring System : A Systematic Literature Review

This paper provides a complete over view of the current research state of Smart vehicle tracking System with GPS and cellular network. This paper consists of several review aiming to reveal the relevance and methodologies of this research area and create a foundation for future work. In this paper an advanced vehicle observation and IOT based tracking system and autopilot navigation system based on Machine Learning and neural Networking is proposed with all possible scientific validations of the model. The primary purpose of monitoring the vehicles which are moving from one place to the other in order to provide better A.I based autopilot navigation system, safety and security. The proposed method Combined the idea of Java programming, Neural networking concept with machine learning capability processing data with MediaTek mobile processor and its sophisticated features of storing data into several databases. Google Map Engine API v3 was used to display and sense the graphical images of the map and a Vision recognition server system is used to compare and represent the map API in a more realistic look. The proposed project includes the implementation of Global Positioning System (GPS), GPRS and GSM technology for vehicle tracking and monitoring on real time basic purpose using SIM module.[3] The GPS receiver installed o tracking device provides real-time Geolocation Co-ordinate of site of the vehicle; 3 adjacent GSM cellphone tower stations will continuously broadcast co-ordinate of locations and the GPRS technology with TCP based protocol sends the tracking information to the central Monitoring and Imaging server which consist of 3 child servers i)data processing sever, ii) Image and vision based server and iii)A.I. based machine learning server calculate data and minimize the information and maps with the help of Google map API and thus an decision message for next Move/driving path is generated and transmitted to Smart Controlling Device to execute the instructions and to display it in the Monitor of car display and Integrated logged-IN andriod based Google Map API version 3 app on real time basic. Hence, this system will monitor all the driving steps of the driver and provide the real time driving suggestions and feedback to the driver to ensure smooth and safe driving experience. The sensors like temperature sensor ,altitude sensor and smoke sensor send data to the neural processing Server which diagnoses the health and safety measures of the vehicles and generates a report on Car display and andriod App interface if any risk issue is found by sensors.

Ultra-sensitive pressure sensor using double stage racetrack silicon micro resonator

Three layouts of add-drop double stage racetrack microring resonators (ADSRMRR) located on the centre of the flexible silicon diaphragm were introduced as microscale optomechanical pressure sensors. The light propagation through all of the proposed ADSRMRR layouts were simulated using the signal flow graph method and the variational finite difference time domain (varFDTD) analysis. To achieve optimum transmission, a formula was derived for a critical coupling coefficient in the presence of the coupling loss factor for each coupling region. The pressure was measured from a wavelength shift in the output drop port signals considering the change in the optical length of the ADSRMRR due to the diaphragm shear stress. The pressure sensitivity of 48.5 pm/kPa, 42.4 pm/kPa and 33.2 pm/kPa with the limit of detection of 4.1 × 10−2 kPa, 4.7 × 10−2 kPa and 6.0 × 10−2 kPa were measured for the symmetric-large layout, the asymmetric and the symmetric-small layout of ADSRMRR pressure sensor, respectively. We introduced the ratio of the total optical length rather the sum of the resonant mode numbers (R-ratio) as a key factor in designing of the microscale Vernier based-pressure sensors. It is found that, using an ADSRMRR layout with a higher R-ratio provides a higher sensitivity with a lower detection limit, which are required in altitude sensors in satellites, aircraft, and weather balloons.

Design and Fabrication of Emergency Drone Recovery System

The drone market has seen a surge in the past few years and the number of consumer drones has been on the increase. With even beginners starting to fly drones, there is a serious safety issue of whether the drone will crash because of technical and unforeseen problems. It may lead to injury of people going about their day and may even cause damage to material things on the ground and naturally damage the drone itself. So, there is a need for a new system which would substantially prevent these kinds of problems. The Emergency Drone System (EDR) will be a solution and will be a versatile fit in all drones. The EDR system uses the MPU-6050 gyroscope and accelerometer sensor along with MPL3115A2 Pressure based Altitude sensor to get input on the real-time movement of the drone and analyses using the algorithm coded into the Raspberry Pi using Python programming language. Apart from this, it also inputs data from the flight controller and analyses it. The parameters such as the altitude, rate of rotation and rate of fall are calculated and based on the algorithm if any kind of anomaly is detected it is programmed to deploy the parachute which will automatically prevent the crashing of the drone. So by which the crashing of drone and damage to property is prevented and safety of people below the drone is ensured.

Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China

Airborne hyperspectral remote sensing is an important application in the ecological monitoring of the environment in mining areas, and accurate preprocessing of the original images is the key to quantitative information retrieval. The original image data need radiation correction to acquire surface reflectance data. Due to the impact of the field angle, incidental radiance, and the bidirectional reflectance distribution function (BRDF), there can be a brightness gradient between adjacent strips, which leads to radiance difference and obvious chromatic aberration of the mosaicked images. We propose a novel data correction method for seamless mosaicking of airborne hyperspectral images. Firstly, visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors are calibrated in the laboratory, and the radiation calibration model of the sensor is established by an integrating-sphere system. A correction function is then established by combining the BRDF effect and the radiation attenuation coefficients. We also normalize the exposure time, sun altitude angle, and sensor altitude angle according to the flight strip. The results showed that this method is able to eliminate the signal distortion, allowing the seamless mosaicking of 37 strip images which were taken in different date and conditions in the study area. After the atmospheric correction of the imagery was completed, the accuracy of the preprocessing results was evaluated by field-measured ASD spectroradiometer data. The coefficient of determination R2 of the results for the reflectance was greater than 0.9. The experiments show that the proposed method has a good performance in radiation accuracy, and can provide high-quality hyperspectral data for the follow-up application of the ecological monitoring of a mining area.

Development of a novel approach for recovering SIP effects from 1-D inversion of HEM data: Case study from the Alut area, northwest of Iran

We have developed a novel approach for recovering the induced polarization (IP) effects from one-dimensional (1-D) inversion of helicopter-borne electromagnetic data (HEM). To achieve this aim, we incorporate both electromagnetic and spectral induced polarization effects within the HEM data by taking the frequency dependence behaviour of resistivity/conductivity of polarizable formation into account. Furthermore, we have included the EM sensor altitude, magnetic permeability and dielectric permittivity of subsurface layers in order to conduct the full solution of forward operator. In this regard, we have calculated the magnetic permeability from the inversion of aeromagnetic data and incorporated the sensor altitude and dielectric permittivity as additional model parameters to be recovered using the inversion of the HEM data. We have carried out both forward and inverse modelling of the HEM data by implementing fast Hankel transform and hybrid algorithm including very fast simulated annealing (VFSA) and Marquardt-Levenberg (ML) strategies, respectively. This approach enables us to recover model parameters comprising of relaxation parameters of Cole-Cole model, dielectric permittivity and thickness of layered half space model and sensor altitude above the earth surface using 1-D inverse modelling of HEM data.We have tested our approach on both synthetic and field DIGHEM data. Implementing the forward solution indicates that incorporation of the IP effects may shift the frequency domain electromagnetic (FDEM) response so that it yields the sign reversal signature in the in-phase component at low frequencies. Moreover, the results of the inverse modelling of the HEM data show that the IP effect could be recovered using our approach effectively. It provides fast and cost-effective quantitative tool for recovering the IP effect from HEM data for the sake of more precise characterization of buried polarizable formations in the wide area. In addition, incorporation the IP effect along with magnetic permeability and sensor altitude provides a significant degree of improvement in the inversion and interpretation of negative responses occurred in the in-phase component at low frequencies.

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions.

Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.