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Unmanned aerial systems (UASs) are progressively being employed in an array of agricultural activities, as they come equipped with a controllable platform necessary for near real-time data acquisition. Because of these impressive advancements in geospatial technologies, the collection of important data to achieve various agricultural applications, such as crop discrimination, is increasingly being done. While they prove to be cost-effective, autonomous, and flexible in agricultural applications, a key bottleneck in such research is the lack of relevant information relating to UAV types and sensor characteristics, as well as data processing and analytical methods that are applicable for crop discrimination. As such, the study conducted a systematic review of trends, technical challenges, and opportunities. The study used PRISMA Guidelines to select, assess, and systematically review 83 articles from Web of Science, Scopus, Google Scholar, and PubMed. The findings of this review showed that rotary-wing UASs equipped with multispectral and RGB cameras were the most frequently used for crop classification. 90% of the reviewed articles noted that the deployment of these advanced technologies was predominantly in monoculture systems normally found in developed countries. Unfortunately, the potential of UASs for crop mapping and monitoring on heterogeneous smallholder farms normally practice mixed cropping systems is progressing slowly, because of technical challenges, operational costs, differing cropping systems, complex classification algorithms, environmental factors, and restrictive policies and regulations, especially in developing countries. Subsequently, research should prioritise developing simple algorithms that can accurately extract crop statistics from high-spatial resolution RGB imagery collected by UAS platforms. [ABSTRACT FROM AUTHOR]

La Playa is a significant archaeological site in Northwest Mexico that contains deposits from over 10,000 years of continuous occupation. Significantly, it contains the earliest definable Trincheras Tradition deposits as well as one of the best-preserved deposits from the Early Agricultural Period. The Proyecto La Playa, a long-term collaborative research project between archaeologists from the U.S. and Mexico, has worked at the site since 1995. One of the project priorities has been to document features in danger of the severe erosion currently affecting the site. To better document site components and support spatial analysis of the features, I used Unmanned Aerial System photogrammetry to create several geospatial datasets for La Playa. With this data I identified a part of the site that appears to have mound features in an area that has not yet been surveyed. This article documents the process of using new technologies to develop these spatial datasets. [ABSTRACT FROM AUTHOR]

One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R2 = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn. [ABSTRACT FROM AUTHOR]

The AquaFly project is a research collaboration between the Flight Systems and Control (FSC) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS) and Queen’s University Biology Department in Canada to offer a fast, reliable, and efficient method of sampling water bodies by using autonomous Unmanned Aerial Systems (UAS). To this end, two UAS are custom-designed and flown for autonomous water sampling, incorporating an octocopter configuration and a ratchet and pawl mechanism for retracting and releasing payloads. This design offers easy-to-use and safe deployment and retraction without continuous motor engagement while eliminating load swinging and the risk of instability by positioning the sampler close to the drone’s center of mass. Furthermore, the system enables autonomous water sampling in remote or inaccessible areas with pre-programmed flight paths and sampling locations, ensuring consistent data collection. The sampling mechanism allows controlled retrieval and release of payloads and further facilitates multi-drone operations to support a variety of sampling missions. This innovative UAS has the potential to revolutionize water sampling and enhance efficiency and safety in environmental monitoring, research, and resource management. Flight tests over several water bodies demonstrate successful simultaneous water sampling missions with two UAS. To the best of the authors’ knowledge, this is the first work to include multiple UAS, each equipped with a payload deployment and retraction mechanism, for autonomous water sampling. [ABSTRACT FROM AUTHOR]

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha−1) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha−1) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation. [ABSTRACT FROM AUTHOR]

Crane populations are declining worldwide, with anthropogenically exacerbated habitat loss emerging as the primary causal threat. The endangered Grey Crowned Crane (Balearica regulorum) is the least studied of the three crane species that reside in southern Africa. This data paucity hinders essential conservation planning and is primarily due to ineffective monitoring methods and this species' use of inaccessible habitats. In this study, we compared the behavioural responses of different Grey Crowned Crane social groupings to traditional on‐foot monitoring methods and the pioneering use of drones. Grey Crowned Cranes demonstrated a lower tolerance for on‐foot monitoring approaches, allowing closer monitoring proximity with drones (22.72 (95% confidence intervals ‐ 13.75, 37.52) m) than on‐foot methods (97.59 (86.13, 110.59) m) before displaying evasive behaviours. The behavioural response of flocks was minimal at flight heights above 50 m, whilst larger flocks were more likely to display evasive behaviours in response to monitoring by either method. Families displayed the least evasive behaviours to lower flights, whereas nesting birds were sensitive to the angles of drone approaches. Altogether, our findings confirm the usefulness of drones for monitoring wetland‐nesting species and provide valuable species‐specific guidelines for monitoring Grey Crowned Cranes. However, we caution future studies on wetland breeding birds to develop species‐specific protocols before implementing drone methodologies. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Systems (UAS) in remote sensing applications, specifically using the Trimble UX5 HP platform for aerial data collection over Karakol city in Kyrgyzstan. Photogrammetric technique used to identify and match common features in the overlapping aerial images to create a sparse point cloud, which were further processed to create a Digital Surface Model (DSM). A slope-based filtering algorithm was applied to the DSM data for generating a Digital Terrain Model (DTM). The normalized Digital Surface Model (nDSM) was derived from the DSM by subtracting the DTM. Object-based image analysis applied to UAS datasets for the extraction of building footprints in an urban area. The results indicate that extracted building footprints have been generated accurately with an overall completeness of 92.4% and correctness of 95,2%. [ABSTRACT FROM AUTHOR]

Cloudy skies reduce image quality obtained from unmanned aerial vehicles (UAV) due to variable lighting conditions imposed on the surface. This study provides a novel approach for identifying temporal windows of opportunity that show promise for avoiding such reductions. The total available hours for flight within a growing season were determined based on an hourly assessment of sky condition. In the study region, this represents total hours from 9 AM to 3 PM, March 1 through September 30. The most promising windows were early and late season, and early morning and late afternoon. Monthly windows were well aligned with crop decision making for emergence. An economic case study was conducted to determine the impact of sky condition on per hectare custom rate for agricultural applications of UAV. Both full-season and early-season-only operational scenarios were investigated. Use of UAV was financially favorable despite the frequent presence of poor sky conditions thought to reduce image quality (60% of total hours). A custom rate of < US$3.65 ha−1 for three flights accommodated a range of acceptable returns on investment over a 3 year period. Poor sky conditions increased the average custom rate by at most US$0.89 ha−1 in the early-season-only scenario and US$0.77 ha−1 over the full season. These rates are competitive with manned aircraft for average fields in the study region (80 ha). Although the initial perception was that clouds would reduce the potential for UAV operations when high image quality is required, economic analyses did not support this preliminary assumption. [ABSTRACT FROM AUTHOR]

The Russia-Ukraine War holds many lessons for the US Army and American policymakers and leaders on the nature and role of reconnaissance-strike complexes in modern combat, especially Ukraine's development of a battle-management system that fuses unmanned aeria l systems and satellite reconnaissance to enable the fire coordination for deep strikes into the enemy rear. In the research presented here, open-source analysis and interviews in Ukraine focus on the development and employment of reconnaissance-strike complexes with respect to deep strike and the likelihood of mutual territorial attack. [ABSTRACT FROM AUTHOR]

There are some archaeological sites with hard accessibility which remain unexplored and barely documented. The use of unmanned aerial systems (UAS) alleviates this challenge with aerial observations monitored with distant remote control. In addition to acquiring images in the visible wavelengths, other devices can be coupled on aerial platforms to inspect beyond the remaining structure of an archaeological site. For instance, thermography has proven to be of great help in the detection of buried remains due to observed temperature anomalies. This work explores the Castle of Puerta Arenas fortress to build the first aerial 3D reconstruction of this site by using RGB and thermographic images collected from a UAS. Orthomosaics have been applied to hypothesize about the original shape of the fortress, whereas 3D reconstructions have been rather applied to visualization and analysis. In this regard, the explored remains have been processed as dense point clouds in the visible and long-wave infrared spectrum, with the latter leading to the detection of hypothetical and still unknown towers. The detection of anomalies has been automatized by performing statistical analyses, globally and limited to smaller 3D voxel neighbourhoods. As a result, the studied remains have been documented and observed from an unexplored perspective, helping their conservation and dissemination, as well as suggesting future excavations. [ABSTRACT FROM AUTHOR]

Gravaglione represents one of the main swallow holes of the Canale di Pirro, low Murge, Apulia region, Italy. Here, after an intense rainstorm, a huge volume of rainwater accumulates at the surface. The drainage dynamics suggest that the Gravaglione could be part of a large, and potentially unknown, karst system. To verify this hypothesis and to acquire useful information on the possible karst environment features, an integrated aerial and geophysical multiscale and multimethod approach was applied. In particular, aerial photogrammetry, ground penetrating radar measurements and electrical resistivity tomography surveys were hence conducted and integrated to potentially detect the caves, define the subsurface volume possibly affected by karst systems and to verify the existence of links between the surficial morphology and the subsoil structure. The results provided interesting insights regarding the presence of a complex karst system extending up to 200 m b.g.l. and with a marked 3D nature. Overall, the Gravaglione case study demonstrates the geophysical approach validity and poses the basis for the development of an expeditive and low-cost high-resolution strategy for detecting and characterizing karst caves. [ABSTRACT FROM AUTHOR]

With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability to monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, and possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with a MicaSense 10-band dual camera system, six New Hampshire lakes were monitored from May to September 2022. Using the image spectral data coupled with in situ water quality data, a random forest classification algorithm was used to predict water quality categories. The analysis yielded very high overall classification accuracies for cyanobacteria cell (93%), chlorophyll-a (87%), and phycocyanin concentrations (92%). The 475 nm wavelength, normalized green-blue difference index—version 4 (NGBDI_4), and normalized green-red difference index—version 4 (NGRDI_4) indices were the most important features for these classifications. Logarithmic regressions illuminated relationships between single bands/indices with water quality data but did not perform as well as the classification algorithm approach. Ultimately, the UAS multispectral data collected in this study successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the studied NH lakes. [ABSTRACT FROM AUTHOR]

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance ( N T R ) and the normalized difference vegetation index ( N D V I ) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the N T R − N D V I trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers. [ABSTRACT FROM AUTHOR]

Standardized tree risk assessment protocols are beneficial to utility vegetation management (UVM) in that they provide the most consistent qualitative assessment of a tree's likelihood of failure, likelihood of impact, and overall risk. Yet, utility foresters do not often inspect off-right-of-way (ROW) vegetation due to constraints such as accessibility and time, which leaves many off-ROW trees unmonitored or with limited monitoring. This review focuses on the key studies addressing the application of unmanned aerial systems (UAS)-based LIDAR systems, especially in terms of UVM along electrical distribution systems. We reviewed the scientific literature in terms of the acquisition of UASbased LIDAR data and the processing of the data. The incorporation of UAS-based LIDAR will allow utility foresters to better manage both on- and off-ROW trees to better identify tree risks and thus reduce outages and increase resiliency of distribution power lines. [ABSTRACT FROM AUTHOR]

One of the most commonly used surveying techniques for landslide monitoring is a photogrammetric survey using an Unmanned Aerial System (UAS), where landslide displacements can be determined by comparing dense point clouds, digital terrain models, and digital orthomosaic maps resulting from different measurement epochs. A new data processing method for calculating landslide displacements based on UAS photogrammetric survey data is presented in this paper, whose main advantage is the fact that it does not require the production of the above-mentioned products, enabling faster and simpler displacement determination. The proposed method is based on matching features between the images from two different UAS photogrammetric surveys and calculating the displacements based only on the comparison of two reconstructed sparse point clouds. The accuracy of the method was analyzed on a test field with simulated displacements and on an active landslide in Croatia. Moreover, the results were compared with the results obtained with a commonly used method based on comparing manually tracked features on orthomosaics from different epochs. Analysis of the test field results using the presented method show the ability to determine displacements with a centimeter level accuracy in ideal conditions even with a flight height of 120 m, and on the Kostanjek landslide with a sub-decimeter level accuracy. [ABSTRACT FROM AUTHOR]

Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson's r of − 0.67 and − 0.71 ). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation. [ABSTRACT FROM AUTHOR]

This study delves into the aerodynamic behaviour of a highly flexible NACA 0012 aerofoil, drawing inspiration from avian feathers to handle a gust. We first examined unsteady flow on a rigid wing both experimentally and numerically and then explored the implications of introducing wing flexibility purely numerically. Our findings underscore the potential of composite materials in alleviating the oscillating aerodynamic forces on a wing under a streamwise gust. This behaviour is attributed to its capacity to destabilize the laminar separation bubble, fostering a more stable turbulent boundary layer. While direct avian evidence remains limited, it is postulated that in nature, such mechanisms could mitigate undesired wing flapping, optimizing energy consumption in perturbed environments. • Investigated the aerodynamics of flexible wings in surging flow conditions. • Surface flexibility, inspired by bird feathers, can help to control unsteady flow. • The use of composite materials helps alleviate unsteady aerodynamic loads. • Composite wings promote stable turbulent boundary layers, reducing unsteadiness. • Decelerating free streams tend to destabilize the boundary layer. [ABSTRACT FROM AUTHOR]

In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors' better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations. [ABSTRACT FROM AUTHOR]

It is widely believed that Unmanned Aerial Systems (UASs) are less costly than comparable manned aircraft. However, the costs per flying hour frequently used to compare unmanned and manned aircraft only cover variable costs of aircraft operation. Variable costs per flying hour may overstate UASs' cost advantage because they do not account for UASs having shorter life spans and being destroyed at higher rates than manned aircraft. In this note, we develop a lifecycle cost per flying hour that considers both acquisition and recurring costs while also accounting for UASs having shorter life spans and greater accidental destruction rates than manned aircraft. We compare unmanned United States Air Force RQ-4 to manned United States Navy P-8 costs using our methodology. While the RQ-4 has a variable cost per flying hour about 38% less than the P-8's, we find that the RQ-4's lifecycle cost per flying hour is about 17% less than the P-8's. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. [ABSTRACT FROM AUTHOR]

• UAS bridge displacement measurement to submillimetre accuracy from only three stationary points. • Euclidean homography from stable P3P algorithm. • Negligible displacement measurement error compared to standard four-point homography methods. Measuring bridge displacements using Unmanned Aerial Systems (UAS) is an emerging field offering the advantage of a remote non-contact approach for measuring bridge displacements. Unfortunately, UAS measurements contain the motion of the UAS which needs to be compensated for to get true bridge displacements. So far, UAS bridge displacement studies have compensated the unwanted motion of the UAS from measurements by tracking a minimum of 4 stationary points required as the minimal solution to calculate a Projective homography between two UAS camera views. While this has been shown to be effective, a reduction in the minimal solution would make these methods more robust to factors such as loss of stabilising points or temporary occlusion to an object entering the scene. In this paper, by combining recent advances to the Perspective-3-Point problem that make camera pose calculation from 3 points reliable and calculating a Euclidean homography instead of a Projective homography, we show in a laboratory experiment that structural displacements can successfully be made to submillimetre accuracy using only 3 points instead of the traditional 4 points. This is done with only visual information from video feed without the need for any additional sensors. [ABSTRACT FROM AUTHOR]

The use of geospatial sciences and technologies for the management of grazinglands has fostered a plethora of applications related to ecology, wildlife, vegetation science, forage productivity and quality, and animal husbandry. Some of the earliest use of remote sensing dates to the proliferation of aerial photography in the 1930s. Today, remote sensing using satellite imagery, global navigation satellite systems (GNSS), and internet-connected devices and sensors allow for real- and near real-time modeling and observation of grazingland resources. In this special issue of Remote Sensing, we introduce nine original publications focusing on varying aspects of grazingland management, such as animal health and telemetry, climate change, soil moisture, herbaceous biomass, and vegetation phenology. The work in this issue spans a diverse range of scale from satellite to unmanned aerial systems imagery, as well as ground-based measurements from mounted cameras, telemetry devices, and datalogging devices. Remote sensing-based technologies continue to evolve, allowing us to address critical issues facing grazingland management such as climate change, restoration, forage abundance and quality, and animal behavior, production, and welfare. [ABSTRACT FROM AUTHOR]

The breeding of hybrid cultivars of hemp (Cannabis sativa L.) is not well described, especially the segregation and inheritance of traits that are important for yield. A total of 23 families were produced from genetically diverse parents to investigate the inheritance of morphological traits and their association with biomass accumulation and cannabinoid yield. In addition, a novel classification method for canopy architecture was developed. The strong linear relationship between wet and dry biomass provided an accurate estimate of final dry stripped floral biomass. Of all field and aerial measurements, basal stem diameter was determined to be the single best selection criterion for final dry stripped floral biomass yield. Along with stem diameter, canopy architecture and stem growth predictors described the majority of the explainable variation of biomass yield. Within-family variance for morphological and cannabinoid measurements reflected the heterozygosity of the parents. While selfed populations suffered from inbreeding depression, hybrid development in hemp will require at least one inbred parent to achieve uniform growth and biomass yield. Nevertheless, floral phenology remains a confounding factor in selection because of its underlying influence on biomass production, highlighting the need to understand the genetic basis for flowering time in the breeding of uniform cultivars. [ABSTRACT FROM AUTHOR]

• We provide a UAV-based multispectral imagery approach objectifying FHM strategies. • A CNN was trained using 17,643 images of trees with health status and species labels. • Analysis was done at 235 plots distributed across Bavaria over three years. • The highly heterogenous dataset benefits model generalization and transferability. • We provide a pipeline to generate reference data and for DL image classification. Uncrewed Aerial Vehicles (UAVs) have emerged as a promising tool for complementing terrestrial surveys, offering unique advantages for forest health monitoring (FHM). UAVs have the potential to improve or even replace core tasks such as crown condition assessment, bridging the gap between ground-based surveys and traditional remote sensing platforms. However, present approaches have not yet fully exploited the very high temporal resolution and flexible and convenient utilization that UAVs offer even under cloudy skies. In this paper, we provide a standardized data pipeline to semi-automatically generate reference data and for monitoring forest health by merging ground-based and UAV-based data related to species-specific forest health. Furthermore, we investigated the potential of Convolutional Neural Networks (CNNs) to classify the main tree species and their crown conditions based on the reference data. Therefore, we acquired high resolution multispectral drone imagery of 235 different ICP large scale forest monitoring plots (Level-I plots) distributed across Bavaria for three consecutive years (2020–2022). Using this highly heterogeneous time-series dataset, encompassing diverse weather and lighting conditions, forest stand characteristics, and spatial distribution of study areas, we successfully classified five tree species, three genus level classes and dead trees, including the health status of the main tree species occurring in Germany. This way we managed to classify 14 distinct classes with an average macro F1-score of 0.61 using the EfficientNet CNN architecture. The highest class-specific F1-score apart from the class of dead trees (0.97) was achieved by the class of Picea abies healthy (0.80). If participating countries of the ICP Forests program adopt our approach to harmonize terrestrial and UAV-based monitoring, many ground-based tasks could be reduced or replaced, leading to significant time and cost savings. We provide standardized and open-source monitoring and analysis strategies that can be potentially extended throughout Europe. Our findings demonstrate that UAV monitoring and deep learning can modernize forest management for efficiency and sustainability. We recommend integrating drones with ground surveys in forest monitoring systems to take advantage of their benefits. [ABSTRACT FROM AUTHOR]

The flight endurance of UAV systems is an important issue that restricts the operational capabilities. Thus, different energy systems and alternative onboard energy generation systems have been tested for the UAVs. Within these systems, fuel cells provide high energy density that can increase flight endurance greatly. In this study, a PEM fuel cell – Li-Po battery hybrid system has been developed by evaluating three architecture models. In the guide of the experimental power demand data of a fixed-wing UAV, modeling and testing procedures were performed. Battery voltage and fuel cell current variations observed during the ground tests were evaluated. It has been observed that approximately 160–170 W of the 250 W power is met by the fuel cell since no preconditioning has been applied and the temperature values at which the fuel cell exhibits its optimum performance. In the case where the fuel cell could provide 7.8 An under conditions where the humidification effects were not included in the model, the required current was over 7.8 A between approximately 400-1200 s. The fuel cell and battery behavior in response to the sudden power changes and to the uncertainties corresponding to the changes in the motor power during the flight are also detailed. • A PEMFC – Li-Po battery hybrid system was developed evaluating architecture models. • Experimental data were used on the propulsion tests for a hybrid PEMFC based UAV. • Testing procedures for a hybrid PEMFC based fixed-wing mini UAV were explained. [ABSTRACT FROM AUTHOR]

Knowledge of offset vectors from vessel mounted sonars, to systems such as Inertial Measurement Units and Global Navigation Satellite Systems is crucial for accurate ocean mapping applications. Traditional survey methods, such as employing laser scanners or total stations, are used to determine professional vessel offset distances reliably. However, for vessels of opportunity that are collecting volunteer bathymetric data, it is beneficial to consider survey methods that may be less time consuming, less expensive, or which do not involve bringing the vessel into a dry dock. Thus, this article explores two alternative methods that meet this criterion for horizontally calibrating vessels. Unmanned Aircraft Systems (UASs) were used to horizontally calibrate a vessel with both Structure from Motion photogrammetry and aerial lidar while the vessel was moored to a floating dock. Estimates of the horizontal deviations from ground truth, were obtained by comparing the horizontal distances between targets on a vessel, acquired by the UAS methods, to multiple ground truth sources: a survey-grade terrestrial laser scan and fiberglass tape measurements. The investigated methods were able to achieve horizontal deviations on the order of centimeters with the use of Ground Control Points. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution (<2 cm) RGB imagery over 51 ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). A larger tile size during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but increased computational complexity and data requirements. A coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32 cm resolution). Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. The end-to-end learning capability of CNNs makes extensive preprocessing partly obsolete. The use of large and diverse datasets facilitate a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species. [ABSTRACT FROM AUTHOR]

• Energy yield analysis on string level has been combined with infrared thermography. • The average effect of thermal anomalies on the performance of strings is quantified. • This amounts to a loss of 1.16% per module substring containing a thermal anomaly. • No observed correlation between loss on string level and hot-spot temperature. • No obvious correlations between loss on string level and thermal signature category. Methods for quick and accurate detection and diagnosis of defects in PV systems are increasingly important as the global photovoltaic (PV) capacity continues to grow at a rapid pace. Two of the most used methods for defect detection involve aerial infrared thermography and data analysis of production data. In this work, we combine the two methods to analyze two utility scale PV plants, providing new understanding about the two methods. We report on the percentage and distribution of thermal anomalies of different categories and quantify their relationship with performance on string level. We find that the most important parameter for determining production losses on string level is the number of module substrings containing thermal anomalies. Due to the large variability of the effect of different thermal signatures, as well as uncertainties in the estimate of the string performance, we find no clear correlation between performance and thermal signature category or temperature. However, a whole hot cell is the thermal signature that on average has the smallest impact on the power output on string level. Finally, in our data, the performance of a string of 20 modules with 3 bypass diodes is on average reduced by 1.16 ± 0.12% per module substring containing thermal anomalies. [ABSTRACT FROM AUTHOR]

This paper considers the main goals and objectives that are solved with the help of Unmanned Aerial Vehicles. The classification of modern Unmanned Aerial Vehicles (UAVs) by weight, altitude and flight duration is shown. Basic advantages and disadvantages of the UAVs use in a stratosphere are analysed in comparison with piloted aircraft. [ABSTRACT FROM AUTHOR]

• End-to-end workflow to measure the soil surface temperature dynamics using a UAS. • Scheme to estimate the temperature from orthomosaics and weather parameters. • Random Forest regressor best estimates soil surface temperature. • Soil surface temperature estimation with a spatial resolution below 6.0 cm/pixel. Thermographies are a source of rich information, valuable in precision agriculture tasks such as crop stress assessment, plant disease analysis, and soil moisture evaluation. Traditionally, practitioners obtain soil temperature from the ground or using satellites and other airborne methods, which are costly and offer limited spatial and temporal resolution. In this paper, we introduce a method to measure soil surface temperature dynamics with the use of an unmanned aerial system (UAS). In our approach, we fuse information from thermal and multispectral cameras with ambient variables to generate estimates for soil temperature using computational intelligence models. Using the images, we produce a spatial reconstruction using structure from motion (SfM). After the multimodal registration of the resulting geo-referenced orthomosaics, we characterize the dynamics of the soil surface temperature using the differences between consecutively captured temperature orthomosaics. In our results, we are capable of estimating soil surface temperature from a UAS flying at 30 m AGL with a RMSE of 3.24∘ C ± 0.3 and 1.77∘ C ± 0.2, at one standard deviation, for two test fields with average ground sampling distances below 6.0 cm/pixel, using a Random Forest regressor. [ABSTRACT FROM AUTHOR]

On/off patch spraying based on weed maps is used in site-specific weed management. Two prerequisites for realising patch spraying are accurate weed detection and targeting of the herbicides on weed patches. There is plenty in the literature about weed detection, but little attention has been paid to the spatial accuracy of herbicide application. This study was conducted in order to assess the extent to which patch-sprayed herbicides are targeted precisely according to pre-loaded prescription maps and to evaluate a new spatial assessment method. The new method consisted of spraying with red Ponceau 4R dye, unmanned aerial vehicle (UAV) imagery and spatial image analysis based on a geographic information system (GIS). The sprayed dye was clearly visible in aereal images and the locations of the sprayed areas were compared with the locations given by the prescription maps. Four different commercial sprayers with boom section width in the range of 0.5 to 3 m and driving speed in the range of 2.5 to 8 km h−1 were used in ten experiments. All the experiments were carried out in autumn in stubble fields. The results showed that the new method was fast and reliable. The incorrectly sprayed area outside targeted areas on prescription maps averaged 81% for three different sprayers with 3 m boom sections, and 5% for a sprayer with 0.5 m boom sections (individual spray nozzle control). The target areas not sprayed within the planned weed patch areas averaged 6% of the pre-defined patch area for sprayers with 3 m boom sections, and 14% for the sprayer with 0.5 m boom sections. This study revealed that the sprayer with 0.5 m boom sections had a controller that was not quick enough at opening and closing spray nozzles at normal driving speeds. Log files from the sprayer console overestimated the sprayed area by 24% and were less accurate than the spatial analysis of the sprayed areas. [ABSTRACT FROM AUTHOR]

Understanding the severity of mid-air collisions between lightweight fixed-wing unmanned aerial systems (FW-UASs) and manned aircraft is a critical flight safety issue. Finite element models of high-density FW-UAS components were developed and validated; such components are particularly damaging during air-to-air collisions. FW-UAS motors and batteries were launched into semi-monocoque 2024-T3 targets using a compressed gas gun. Material/geometric nonlinear simulations of each impact were performed and correlated with physical test results. Predicted skin penetrations/perforations, projectile residual velocities, strain-time histories, and target forces matched experimental observations. Validated component models will be integrated into high-fidelity air-to-air collision simulations involving FW-UASs and manned aircraft. [ABSTRACT FROM AUTHOR]

In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. [ABSTRACT FROM AUTHOR]

In recent years, immersive virtual reality has been used in disciplines such as engineering, sciences, and education. Point-cloud technologies such as laser scanning and unmanned aerial systems have become important for creating virtual environments. This paper discusses creating virtual environments from 3D point-cloud data suitable for immersive and interactive virtual reality. Both laser scanning and sUAS point-clouds are utilised. These point-clouds are merged using a custom-made algorithm that identifies data gaps in the master dataset (laser scanner) and fills them with data from a slave dataset (sUAS) resulting in a more complete dataset that is used for terrain modelling and 3D modelling of objects. The terrain and 3D objects are then textured with custom-made and free textures to provide a sense of realism in the objects. The created virtual environment is a digital copy of a part of the Penn State Wilkes-Barre campus. This virtual environment will be used in immersive and interactive surveying laboratories to assess the role of immersive virtual reality in surveying engineering education. [ABSTRACT FROM AUTHOR]

• Designing one-of-a-kind experiment to monitor urban congestion with a swarm of drones. • Creating the most complete urban multimodal dataset, nicknamed pNEUMA , to study congestion. • Investigating traffic phenomena at different scales of modeling. • Developing an open science initiative with almost half a million trajectories for transportation-oriented research. The new era of sharing information and "big data" has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply "drones" have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA , to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models. [ABSTRACT FROM AUTHOR]

Recent advancements in geophysical exploration have been realized through reliably integrating unmanned aerial vehicle platforms with lightweight, high‐resolution magnetometer payloads. Unmanned aerial vehicle aeromagnetic surveys can provide a contemporary data product between the two end‐members of coverage and resolution attained using manned airborne and terrestrial magnetic surveys. This new data product is achievable because unmanned aerial vehicle platforms can safely traverse with magnetometer payloads at flight elevations closer to ground targets than manned airborne surveys, while also delivering an increased coverage rate compared to walking conventional terrestrial surveys. This is a promising new development for geophysical and mineral exploration applications, especially in variable terrains. A three‐dimensional unmanned aerial vehicle aeromagnetic survey was conducted within the Shebandowan Greenstone Belt, northwest of Thunder Bay, Ontario, Canada, in July 2017. A series of two‐dimensional grids (∼500 m × 700 m) were flown at approximate elevations of 35, 45 and 70 m above ground level using a Dà‐Jiāng Innovations multi‐rotor unmanned aerial vehicle (S900) and a GEM Systems, Inc., Potassium Vapour Magnetometer (GSMP‐35U). In total, over 48 line‐km of unmanned aerial vehicle aeromagnetic data were flown with a line spacing of 25 m. The collected aeromagnetic data were compared to a regional heliborne aeromagnetic survey flown at an elevation of approximately 85 m above the terrain, with a line spacing of 100 m, as well as a follow‐up terrestrial magnetic survey. The first vertical derivative of the gathered unmanned aerial vehicle total magnetic field data was calculated both directly between each of the different flight elevations, and indirectly by calculating the values predicted using upward continuation. This case study demonstrates that low flight elevation unmanned aerial vehicle aeromagnetic surveys can reliably collect industry standard total magnetic field measurements at an increased resolution when compared to manned airborne magnetic surveys. The enhanced interpretation potential provided by this approach also aided in delineating structural controls and hydrothermal fluid migration pathways (a pair of adjacent shear zones) related to gold mineralization on site. These structural features were not clearly resolved in the regional manned airborne magnetic data alone, further demonstrating the utility of applying high‐resolution unmanned aerial vehicle aeromagnetic surveys to mineral exploration applications. The conclusions and interpretations drawn from the unmanned aerial vehicle aeromagnetic data, coupled with historical data, were applied to make a new gold mineralization discovery on the site, assayed at 15.7 g/t. [ABSTRACT FROM AUTHOR]

• Review of current U.S. policy regulating Unmanned Aerial System (UAS) operations. • UAS operations adjacent to the roadside draw more visual attention from drivers. • UAS operations in rural roadside environments are more visually distracting. • Policy recommendations for UAS operations near roadways. • Procedural overview for localities seeking to implement UAS ordinances. Unmanned Aerial Systems (UASs), commonly known as drones, are a rapidly emerging technology with many applications across various commercial, government, and recreational users. Many of these applications have the potential to interact with roadway infrastructure, resulting in potentially risky conflicts between UAS operations and drivers on the roadway. In the United States, policy regulating UAS operations exists at the federal, state, and local levels, but there is little to no regulation specifically related to UAS operations near roadways. The purpose of this study was to evaluate if UAS operations near roadways pose a safety concern by determining if the operations visually distract drivers. In addition, this study sought to develop data-driven policy recommendations to improve the safety of drivers and UAS operators near roadways. To understand how UAS operations near roadways influence the visual attention of drivers, an experiment was designed and conducted in a high-fidelity driving simulator. Thirty participants completed the experiment in the driving simulator and their visual attention was recorded. Analysis of the visual attention results showed that UAS operations draw more visual attention from drivers when they are directly adjacent to the roadside or in a rural environment. Based on the results, a recommended policy to improve safety of UASs for operators and drivers would be to, at a minimum, restrict UAS operations within 7.6 m (25 ft) of the edge of a lane. A procedural overview for implementing legal and effective UAS policy in the United States was developed to navigate the complexities of the evolving UAS policy landscape. [ABSTRACT FROM AUTHOR]

Optical imaging systems are one of the most common sensors used for collecting data with small Unmanned Aerial Systems (sUAS). Plenty of research exists which present custom-made optical imaging systems for specific missions. However, the research commonly leaves out the explanation of design parameters and considerations taken during the design of the optical imaging system, especially the image stabilization strategy used, which is a significant issue in sUAS imaging missions. This paper surveys useful methodologies for designing a stabilized optical imaging system by presenting an overview of the important aspects that must be addressed in the designing phase and which tools and techniques are available and should be chosen according to the design requirements. [ABSTRACT FROM AUTHOR]

The success of the wilderness search and rescue missions is highly dependent on the time required to search for the lost person. The use of unmanned aerial systems may enhance search and rescue missions by supplying aerial support of the search process. There are unmanned aerial system–based solutions, which are capable of detecting the lost person using computer vision, infrared sensors, and detection of a mobile phone signal. The most pressing issue is reducing the cost of a search and rescue mission. Thus, to improve the efficiency of the resource utilization in wilderness search scenario, we consider the use of unmanned aerial system for both mobile phone detection and enabling Wi-Fi communication for the ground portion of the search and rescue team. Such an approach does not require specific additional tools (e.g. access point, specific user equipment) for communication, which reduces the cost and improves the scalability and coordination of the search and rescue mission. As a result, the article provides methods of searching the wilderness for a person using beacon signals from a mobile phone for two situations: when the distance to the source of emergency signals is unknown and when the distance is known. In addition, the voice transmission delay and the number of unmanned aircrafts are found to guaranty the quality of a call. [ABSTRACT FROM AUTHOR]

Highlights • Unmanned Aerial System (UAS) multispectral imagery was acquired at an Australian rangeland site. • Fractional vegetation cover was derived from the UAS imagery using three methods. • Object based classification achieved the best results at the 100 m plot scale (RMSE = 12–13%). • No method was able to accurately determine all three cover fractions at ultra-high resolution. Abstract Vegetation cover is a key environmental variable often mapped from satellite and aerial imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) based on multispectral imagery acquired from an Unmanned Aerial System (UAS) has several applications, including the potential to revolutionise the collection of field data for calibration/validation of satellite products. In this study, abundance maps were derived using three methods, applied to data collected in a typical Australian rangeland environment. The first method used downscaling between Landsat FVC maps and UAS images with Random Forest regression to predict bare ground, photosynthetic vegetation and non-photosynthetic vegetation cover. The second method used spectral unmixing based on endmembers identified in the multispectral imagery. The third method used an object-based classification approach to label image segments. The accuracy of all UAS FVC and Landsat FVC products were assessed using 20 field plots (100 m diameter star transects), as well as from 138 ground photo plots. The classification method performed best for all cover fractions at the 100 m plot scale (12–13% RMSE), with the downscaling approach only able to accurately predict photosynthetic cover. The downscaling and unmixing generally over-predicted non-photosynthetic vegetation associated with Chenopod shrubs. When compared with the high-resolution photo plot data, the classification method performed the worst, while the downscaling and unmixing methods achieved reasonable accuracy for the photosynthetic component only (12–13% RMSE). Multispectral UAS imagery has great potential for mapping photosynthetic vegetation cover in rangelands at ultra-high resolution, though accurately separating non-photosynthetic vegetation and bare ground was only possible when the data was scaled-up to coarser resolutions. [ABSTRACT FROM AUTHOR]