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New technology design and its integration into a social environment in a noninvasive way are recognized as one of the main challenges of the rapidly growing technology world. The design complexity is amplified if the system is intended for a sensitive user category, e.g., persons with disabilities and aging adults. A high percentage of power wheelchairs (PWCs) users rely on caregivers or family members to charge their PWC units. In this article, a design methodology is presented that describes a system-level approach for designing a 1-kW smart wireless charger for PWCs. This article describes how power electronics, communication, automation, software, and internet technologies are integrated to deliver a functional hands-free, free-position PWC charging solution. The proposed solution utilizes a multicoil transmitter pad to achieve a free-positioning feature and dual-side control to regulate the power. The researchers adopted a human-in-the-loop approach to seek feedback from end-users and tailor the final design through multiple generations of prototypes. Moreover, some existing technical solutions regarding pad design and stray field shielding are adapted or advanced to meet the application requirements, and their effectiveness is validated on a 1-kW prototype.

Electric wheelchairs (EWs) are gradually replacing conventional wheelchairs as an assistive tool for people with disabilities who are unable to use the physical force to drive manual wheelchairs on their own. EW charging requires manipulating, plugging in, and unplugging an off-board charger, which can be challenging or impossible for people with severe disabilities. In this paper, a comprehensive solution to the problem of EW recharging is provided in the form of a smart, autonomous charging system, based on the wireless power transfer (WPT) technology. In this paper, a sufficiently large area of 0.75 m2 for a floor-charging pad involving multiple primary coils has been implemented while offering a free-positioning charging feature. A novel design methodology for primary and secondary charging pads’ optimization has been developed, taking into account various complex real-life constraints. In addition, a novel 250-W WPT controller design has been employed to maximize misalignment tolerance up to 7 cm while maintaining high system efficiency. In this paper, a novel, comprehensive, system-level organization of a master controller design has been presented which manages continuous coordination between the WPT controllers and the autonomous docking controller, records important system data, maintains safe operation of overall system, and prevents hazardous exposure to potentially harmful magnetic field beyond $15~\mu \text{T}$ . In addition, a novel design methodology for primary and secondary charging pads’ optimization has been developed, which takes into account various real-life constraints, and provides a fair comparison between different designs in a multivariable environment. The proposed design is tested in laboratory conditions and later validated in a practical environment.

Daily evapotranspiration (

In this paper, we document lessons learned from using ViSOAR Ag Explorer™ in the fields of Arkansas and Utah in the 2018-2020 growing seasons. Our insights come from creating software with fast reading and writing of 2D aerial image mosaics for platform-agnostic collaborative analytics and visualization. We currently enable stitching in the field on a laptop without the need for an internet connection. The full resolution result is then available for instant streaming visualization and analytics via Python scripting. While our software, ViSOAR Ag Explorer™ removes the time and labor software bottleneck in processing large aerial surveys, enabling a cost-effective process to deliver actionable information to farmers, we learned valuable lessons with regard to the acquisition, storage, viewing, analysis, and planning stages of aerial data surveys. Additionally, with the ultimate goal of stitching thousands of images in minutes on board a UAV at the time of data capture, we performed preliminary tests for on-board, real-time stitching and analysis on USU AggieAir sUAS using lightweight computational resources. This system is able to create a 2D map while flying and allow interactive exploration of the full resolution data as soon as the platform has landed or has access to a network. This capability further speeds up the assessment process on the field and opens opportunities for new real-time photogrammetry applications. Flying and imaging over 1500-2000 acres per week provides up-to-date maps that give crop consultants a much broader scope of the field in general as well as providing a better view into planting and field preparation than could be observed from field level. Ultimately, our software and hardware could provide a much better understanding of weed presence and intensity or lack thereof.

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.

Aerial remote sensing continues its progression into wider applications and functionality. At the same time, the field of robotics has generated many useful technologies such as the Robotics Operating System v.2 (ROS2) which allows researchers and designers to implement autonomous systems as a meta-operating system. In this paper, such a holistic autonomous data collection system (STARDOS) is shown with the goal of integration of remote sensing data collection (payload) and the robotic platform (in this case, unmanned aerial vehicles). The architecture of STARDOS is shown, along with example payload configurations and analysis use cases such as triangular greeness index (TGI) and feature extraction and matching for real-time 2d photogrammetry.

To enable power wheelchair users with limited mobility to safely and independently charge their wheelchairs.Stakeholders of multiple roles - including potential users, their caregivers, and clinicians with specifically relevant expertise - were engaged in a user-centred design process. Initial informal interviews, focus groups, online surveys, prototype demonstrations, semi-structured interviews, and expert reviews were utilized to guide development and iteratively evaluate prototypes.The resulting wireless charging system enables independent charging while also significantly increasing capacity and charging speed. Autonomous positioning and remote control features further address the particular use cases of the target population, and vital features of existing power wheelchairs are retained according to stakeholder input. Pertinent topics emerging from stakeholder input are discussed.Careful application of user-centred design principles is essential to the successful development of usable assistive technology devices, particularly for target populations with complex disabilities. The diverse perspectives of all relevant stakeholders must be considered and synthesized to produce a practical and usable solution.Implications for rehabilitationBattery charge is a constant and critical concern for power wheelchair users.Many power wheelchair users cannot independently use and monitor existing chargers.A wireless wheelchair charging system was developed to alleviate this issue.Sustained user engagement is crucial in the effective development of such assistive technology.

The concept of reversing the direction of the main propeller (and thereby the thrust) of an electric, fixed-wing unmanned aerial system (UAS) during landing maneuvers is examined in this paper. The physical performance of a propeller and motor combination is first tested using a custom thrust test bench. A two-dimensional simulation of a fixed-wing UAS is then created using a nonlinear dynamics model and the dynamics are used in a Rapidly-Exploring Random Tree (RRT) algorithm to search for feasible trajectories and their necessary control inputs. The possible usefulness of the reverse thrust maneuver is then demonstrated and shown in simulation with favorable results.

Estimation of surface energy fluxes using thermal remote sensing–based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (T(s) and T(c)) to estimate surface energy fluxes including R(n), H, LE, and G. The estimation of T(s) and T(c) components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in T(s) and T(c) estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVI(s) and NDVI(c). The observed relative error of LE corresponding to NDVI(s) uncertainty was between −1% and 2%, while for NDVI(c) uncertainty, the relative error was between −2.2% and 1.2%. However, when the combined NDVI(s) and NDVI(c) uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (u(star)), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values.

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed.

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAirTM sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity ( u * ) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

Evapotranspiration (ET) derived from remote sensing-based models represents ½ hourly to hourly value that is upscaled to a daily scale for practical applications in the fields of agricultural and water management. Several upscaling methods such as Gaussian fitting curve, sine approach, and evaporative fraction approach have been developed to extrapolate remote sensing-based ET values to daily scale by assuming constant daytime ratios (e.g., self-preservation of available energy partitioning). This simple assumption can result in uncertainties in the performance of those methods and can be violated for unstable conditions such as a cloudy day. Studies showed that, for example, diurnal variation of incoming shortwave radiation will change from a Gaussian distribution to a multimodal distribution on a cloudy day. Besides, when remote-sensing ET outputs are directly upscaled to daily scale and compared with eddy covariance measurements, a fixed footprint of eddy covariance is assumed, while the actual eddy covariance footprint is dynamic and changes with wind speed, direction and atmospheric stability. In this study, a new method is proposed to spatially and temporally simulate canopy and soil temperature for each time step (e.g., 1-hour) based on the temperature pattern recorded by IRT temperature sensors and UAV initial temperatures at the specific time of day. Next, the Two-Source Energy Balance (TSEB) model is executed for each time step of the daytime period (usually when net radiation >100 W/m^2) to calculate energy balance components. The integration of TSEB outputs over the daytime period leads to estimations of daily energy balance components. Since cloudy conditions affect temperatures recorded by IRT sensors, the proposed model is not sensitive to weather conditions. In addition, the proposed model physically simulates ET at each time step instead of directly extrapolating ET from a single remote sensing observation and model output This feature solves the limitations of comparing the direct extrapolation methods of instantaneous ET against eddy covariance measurements. The proposed approach is applied on information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) conducted since 2014 over multiple vineyards located in California. The estimated ET values from the TSEB model at hourly time steps and integrated over the daytime period are compared to eddy covariance measurements of ET. Additionally, hourly model output integrated over the daytime period compared to the different upscaling methods are presented and discussed.

Daily evapotranspiration (ETd) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (Rn) or ET-to-solar radiation (Rs), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (Rs), net radiation-to-solar radiation (Rn/Rs) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the Rs, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the Rs approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the Rs approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

Small unmanned aerial systems (sUAS) require precise inertial state information at high temporal rates for the purposes of stability and navigation as well as to provide accurate attitude estimates to scientific payloads. Common sUAS inertial navigation systems implement a single strapdown inertial measurement unit (IMU), typically made up of an orthogonal triad of linear accelerometers and a similar triad of rate gyroscopes alongside a single GPS/GNSS receiver to accomplish this task. These outputs are fused optimally, sometimes with other sensors, in an extended Kalman filter (EKF) architecture to generate reliable estimates of the vehicle states. This work develops an estimation-domain fusion strategy for combining the state estimates from multiple inertial navigation systems (INS) for the purposes of sUAS navigation and imaging. The algorithm is implemented in MATLAB and the results are presented.

Inertial sensor arrays, which in this context means sensor arrays composed of accelerometers, gyroscopes, or a combination of both, have been studied for many years and in many contexts. The literature is vast, highly redundant, and disparate and so it can be difficult to reconcile the many ways this problem has been approached. An outline of each of the different branches of the field of inertial sensor fusion is provided in order to aid newcomers and designers, particularly those who seek to apply past work to small Unmanned Aerial Systems (sUAS). Important works are noted and useful information and techniques are briefly presented.

In high-resolution imagery, shadows may cause problems in object segmentation and recognition due to their low reflectance. For instance, the spectral reflectance of shadows and water are similar, particularly in the visible band. In precision agriculture, the vegetation condition in terms of plant water use, plant water stress, and chlorophyll content can be estimated using vegetation indices. Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Enhanced Vegetation Index (EVI) are widely used vegetation indices for characterizing the condition of the vegetation. In addition, many soil indices have been developed for describing soil characteristics, such as Soil-Adjusted Vegetation Index (SAVI). However, shadows can have an influence on the performance of these vegetation and soil indices. Moreover, enhancing spatial resolution heightens the impact of shadows in the imagery. In this study, the behavior of vegetation and soil indices are evaluated using four sets of high-resolution imagery captured by the Utah State University AggieAir unmanned aerial vehicle (UAV) system. These indices were obtained from flights conducted in 2014, 2015, and 2016 over a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. Different shadow restoration methods are used to alleviate the impact of shadows in information products that might be developed from the high-resolution imagery. The histogram pattern of vegetation and soil indices before and after shadow compensation, are compared using analysis of variance (ANOVA). The results of this study indicate how shadows can affect the vegetation/soil indices and whether shadow compensation methods are able to remove the statistical difference between sunlit and shadowed vegetation/soil indices.

Current technologies employed for use of small, unmanned aerial systems (sUAS), or “drones”, for remote sensing (RS) activities that support the information needs of agricultural operations are expensive, provide relatively small geographic coverage, and typically produce data of limited scientific quality. Research is currently underway that will someday yield sense-and-avoid technologies that will enable sUAS to detect and safely avoid potential collisions with conventional and UAS air traffic. Other on-going research will result in the development of a nation-wide air traffic management system that will fully integrate UAS flights into US airspace. Together, these two research products will allow safe, long-duration sUAS flights, or flights that are “beyond-visual-line-of-sight” (BVLOS). As BVLOS technologies come on line, there will likely be a decrease in the per-acre cost of sUAS-based RS, which, in turn, will spur incentives for development of sUAS that can fly much further and cover much more area than platforms that are now in common use. A decrease in per-acre RS costs well likely result in an increased demand for RS technology in support of agricultural operations. However, adoption of BVLOS technologies that support greater access to US airspace for agricultural RS applications will present challenges to commonly used sUAS RS approaches that do not scale well as sUAS geographical coverage and flight times increase. Of particular concern are radiometric calibration of imagery and the cost and time required for generation of high-quality orthomosaics, point cloud algorithms, effects of temporal changes in surface spectral and thermal response, and delivery of time-sensitive image products to the grower. This paper examines the implications of these future challenges for sUAS RS in agriculture and offers insights into their solution that are topics of research today. In addition to examining the topics of radiometric calibration and orthorectification, other aspects, such as field protocols in the design and execution of flights and the need for additional on-board sensors, will be discussed.

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly.

Theoretically, the appearance of shadows in aerial imagery is not desirable for researchers because it leads to errors in object classification and bias in the calculation of indices. In contrast, shadows contain useful geometrical information about the objects blocking the light. Several studies have focused on estimation of building heights in urban areas using the length of shadows. This type of information can be used to predict the population of a region, water demand, etc., in urban areas. With the emergence of unmanned aerial vehicles (UAVs) and the availability of high- to super-high-resolution imagery, the important questions relating to shadows have received more attention. Three-dimensional imagery generated using UAV-based photogrammetric techniques can be very useful, particularly in agricultural applications such as in the development of an empirical equation between biomass or yield and the geometrical information of canopies or crops. However, evaluating the accuracy of the canopy or crop height requires labor-intensive efforts. In contrast, the geometrical relationship between the length of the shadows and the crop or canopy height can be inversely solved using the shadow length measured. In this study, object heights retrieved from UAV point clouds are validated using the geometrical shadow information retrieved from three sets of high-resolution imagery captured by Utah State University's AggieAir UAV system. These flights were conducted in 2014 and 2015 over a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. The results showed that, although this approach could be computationally expensive, it is faster than fieldwork and does not require an expensive and accurate instrument such as a real-time kinematic (RTK) GPS.

Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process are not accessible to researchers, each application of these machine learning approaches must repeat these training steps for any new study area. The fact that the results of this machine learning, black box approach cannot be easily transferred to different locations for extraction of soil moisture estimates is frustrating, and it can lead to inaccurate comparisons between methods or model performance. To overcome the Black-box issue, this study employed a powerful technique called genetic programming (GP), which is a combination of an evolutionary algorithm and artificial intelligence, to simulate soil moisture at different levels using high-resolution, multispectral imagery acquired with an unmanned aerial vehicle (UAV). The output of this approach is either a linear or nonlinear empirical equation that can be used by others. The performance of GP was compared with ANN and SVM modeling results. Several sets of high-resolution aerial imagery captured by the Utah State University AggieAir UAV system over two experimental pasture sites located in northern and southern Utah were used for this soil moisture estimation approach. The inputs used to train these models include the reflectance for the visible, near-infrared (NIR), and thermal bands. The results show (1) the performance of GP versus ANN and SVM and (2) the master equation provided by GP, which can be used in other locations and applications.

There is currently a growing need for scientifically accurate terrain maps of the earth. One way to develop terrain maps includes the use of LIDAR sensing and digital imagery. By fusing these sets of data, a textured digital elevation map (TDEM) can be created. The high cost of equipment and full-scale aircraft operation can be mitigated by creating a sensor package that includes both LIDAR and digital imaging that can be mounted on a low-cost, small, unmanned aerial system (sUAS). This sensor package is called a texel camera (TC) and is composed of commercial off the shelf sensors: LIDAR, digital camera, inertial navigation system, and computer processing unit. The TC is calibrated so that each data sample contains a LIDAR scan, registered digital image, aircraft attitude, position data, and timestamp. As the TC acquires data, a point cloud is created that describes the surface of the object. Each LIDAR measurement in a scan corresponds with a known pixel in the digital image. Fusing this data allows the formation of scientifically accurate TDEMs. The TC is a more cost-effective terrain mapping solution than current mobile and manual data collection methods. It is designed to fit in rotorcraft and fixedwing sUAS. Other advantages to the TC include adaptability with existing sUAS and the ability to map views from different perspectives.

Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of highresolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99– 0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral characteristics of the microbolometer camera. For this work, the MODIS UCBS Emissivity Library, NASA HyTES hyperspectral emissivity, Landsat, and Utah State University AggieAir UAV surface reflectance products are employed. The methodology is applied to a commercial vineyard agricultural setting located in Lodi, California, where HyTES, Landsat, and AggieAir UAV spatial data were collected in the 2014 growing season. Assessment of the microbolometer spectral response with regards to emissivity and emissivity modeling performance for the area of study are presented and discussed.

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level. info:eu-repo/semantics/acceptedVersion

With the increasing availability of thermal proximity sensors, UAV-borne cameras, and eddy covariance radiometers there may be an assumption that information produced by these sensors is interchangeable or compatible. This assumption is often held for estimation of agricultural parameters such as canopy and soil temperature, energy balance components, and evapotranspiration. Nevertheless, environmental conditions, calibration, and ground settings may affect the relationship between measurements from each of these thermal sensors. This work presents a comparison between proximity infrared radiometer (IRT) sensors, microbolometer thermal cameras used in UAVs, and thermal radiometers used in eddy covariance towers in an agricultural setting. The information was collected in the 2015 and 2016 irrigation seasons at a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. Information was captured at different times during diurnal cycles, and IRT and radiometer footprint areas were calculated for comparison with UAV thermal raster information. Issues such as sensor accuracy, the location of IRT sensors, diurnal temperature changes, and surface characterizations are presented.

This paper investigates the feasibility of using a Deep Learning Convolutional Neural Network (DLCNN) in inspection of concrete decks and buildings using small Unmanned Aerial Systems (sUAS). The training dataset consists of images of lab-made bridge decks taken with a point-and-shoot high resolution camera. The network is trained on this dataset in two modes: fully trained (94.7% validation accuracy) and transfer learning (97.1% validation accuracy). The testing datasets consist of 1620 sub-images from bridge decks with the same cracks, 2340 sub-images from bridge decks with similar cracks, and 3600 sub-images from a building with different cracks, all taken by sUAS. The sUAS used in the first dataset has a low-resolution camera whereas the sUAS used in the second and third datasets has a camera comparable to the point-and-shoot camera. In this study it has been shown that it is feasible to apply DLCNNs in autonomous civil structural inspections with comparable results to human inspectors when using off-the-shelf sUAS and training datasets collected with point-and-shoot handheld cameras.

For small unmanned aerial systems (sUAS), the problem of estimation of orientation angles relative to the Earths surface (attitude) is of major importance. Usually, this estimation is performed by the combination of several kinds of sensor data by a Kalman filter or complementary-style filter producing a navigation-quality solution. Kalman filters make assumptions about linearity and statistical properties of the sensor noise, which must be accurate for proper functioning of the filter. Another option, the complementary filter, is more simple, and does not make these assumptions. Although the applications of the complementary filter have been researched in depth, the complementary filter can be combined in a novel way with fractional-order filters for the purpose of small UAS flight. In this paper, a roll-pitch fractional-order estimator is shown in flight configuration (real-time code) on a small sUAS. With fewer tunable parameters, we show that the fractional-order filter is more simple to implement. Realtime results are shown which indicate adequate performance relative to an existing quaternion-based Kalman filter-based estimator.

Safety and reliability (also referred to as airworthiness) are well-known factors of Unmanned Aerial Vehicle (UAV) operations, and significant effort has been put into maintaining and improving them for everyday UAV users. Cyber-security, on the other hand, gained attention only recently. Currently, a malicious user can relatively easily disturb operation or even seize control of the most popular open-source UAVs, as a result of their poor communications security (COMSEC). Even though solutions to improve UAV's COMSEC are known, they have not been fully implemented in a user-friendly way. The contribution of the paper is an implementation of an encrypted Radio Control (RC) link that can be used with a number of popular RC transmitters. We use Galois Embedded Crypto library, an ArduinoLibs Crypto library together with openLRSng open- source radio project. We provide some lessons learned during development, implementation, and testing so users wishing to use their own encrypted link can leverage our work.

Unmanned Aerial Systems (UAS) have gained considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper presents the applications of UAS in transportation and structural engineering with emphasis on bridge inspection. A brief but thorough review of UAS applications for State Department of Transportation in the United States is provided. Potential advantages of UAS are acknowledged and the major challenges of using them for bridge inspections are determined. The feasibility of UAS in crack detection, real-time and post-processing, is studied through a case study in controlled conditions. In addition, fatigue crack detection in steel bridges is investigated using three platforms with different mounted cameras. The results of these case studies showed the possibility of using UAS for damage detection in concrete and steel bridges with comparable results with human inspections in real-time. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, cheaper, and without traffic closure. The major challenges for UAS are satisfying restrictive FAA regulations, control issues in a GPS denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time and acceptance of the collected data by bridge owners. Using UAS, with self-navigation abilities and improving image- processing algorithms to provide results near real-time could provide bridge inspectors with a useful tool to reduce costs and improve inspection quality.