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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 rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This paper represents a novel data-driven trajectory prediction methodology based on applying the Long Short-Term Memory (LSTM) prediction algorithm to the UAS-S4 Ehécatl. An LSTM model was designed as the baseline and then developed into a Staked LSTM to better capture complex and hierarchical temporal trajectory patterns. Next, the Bidirectional LSTM was developed for a better understanding of the contextual trajectories from both its past and future data points, and to provide a more comprehensive temporal perspective that could enhance its accuracy. LSTM-based models were evaluated in terms of mean absolute percentage errors. The results reveal the superiority of the Bidirectional LSTM, as it could predict UAS-S4 trajectories more accurately than the Stacked LSTM. Moreover, the developed Bidirectional LSTM was compared with other state-of-the-art deep neural networks aimed at aircraft trajectory prediction. Promising results confirmed that Bidirectional LSTM exhibits the most stable MAPE across all prediction horizons. [ABSTRACT FROM AUTHOR]

Unmanned and aerial systems as interactors among different system components for communications, have opened up great opportunities for truth data discovery in Mobile Crowd Sensing (MCS) which has not been properly solved in the literature. In this paper, an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery (UAV-ITD) scheme is proposed to obtain truth data at low-cost communications for MCS. The main innovations of the UAV-ITD scheme are as follows: (1) UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization (DMF) to discover truth data based on the trust mechanism for an Information Elicitation Without Verification (IEWV) problem in MCS. (2) This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy, which saves more communication costs than most previous data collection schemes, where they collect n or kn data samples. Finally, we conducted extensive experiments to evaluate the UAV-ITD scheme. The results show that compared with previous schemes, our scheme can reduce estimated truth error by 52.25%–96.09%, increase the accuracy of workers’ trust evaluation by 0.68–61.82 times, and save recruitment costs by 24.08%–54.15% in truth data discovery.

Abstract Manned and unmanned systems are prevalent in a wide range of aerial searching applications. For aircraft whose trajectory is not or cannot be planned on-the-fly, optimal deterministic search pattern generation is a critical area of research. Lissajous curves have recently caught attention as excellent candidates for all kinds of aerial search applications, but little fundamental research has been done to understand how best to design Lissajous pattern (LP)s for this use. This paper examines the optimization of these search patterns from analytical, numerical, and data-driven perspectives to establish the state of the field in Lissajous curves for aerial search. From an analytical perspective, it was found that the average expected distance between a Lissajous searcher and a random target on a unit square approaches 0.586 as search time increases. Furthermore, an analytical approximation for the average searcher speed was found to guarantee error of no more than 22.1%. Important outcomes from the numerical optimization of Lissajous search patterns include the development of an intuitive evaluation criterion and the conclusion that irrational frequency ratios near 0.8 typically yield highest performance. Finally, while a robust predictive model for fast pattern optimization is yet out of reach, initial results indicate that such an approach shows promise.

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]

Ra pid and accurate surveying has always attracted great interest in all scientific and industrial activities that require high-resolution topographic data. The latest automation and advancement in geomatics engineering are remote sensing solutions using Unmanned Aerial Systems (UAS) and Structure from Motion (SfM) with Multi-View Stereo (MVS) photogrammetry. This research aimed to find the influence of flight height, Ground Control Point (GCP), and software on the geometric accuracy of UAS-SfM-derived Digital Surface Models (DSMs) and orthoimages, as well as to analyze and evaluate the accuracy of UAS-SfM as a rapid and low-cost alternative to conventional survey methods. To achieve the aim of the study, aerial surveys using a fixed-wing UAS and field surveys using RTK GNSS and total station were conducted. A total of 16 photogrammetric projects were processed using different GCP configurations, and detailed statistical analysis was performed on the results. Moreover, the contribution of cross flight on bundle adjustment was investigated empirically by conducting a combined photogrammetric image processing. The analysis revealed that flight height, GCP number and distribution, and the processing software significantly affect products' quality and accuracy. Evaluation of the achieved accuracies was made based on the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy standard for digital geospatial data. The findings of this study revealed that using the optimal flight height and GCP configuration, 3D models, orthomosaics and DSMs can be rapidly reconstructed from 2D images with the quality and accuracy sufficient for most terrain analysis applications, including civil engineering projects. [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]

Copyright of Journal for Technology in Theory & Practice / Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis (TATuP) is the property of Oekom Verlag GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Drones, unmanned aerial systems, are of growing interest to organizations involved in disaster risk reduction, particularly in post-disaster emergency response. Despite the potential benefits of drones, their use is not well established and practical challenges need to be understood. This study examines the role of drones in disaster management by analyzing various applications of drones in response to the Noto Peninsula earthquake in January 2024. Drones were used on the ground in a variety of new ways, including transport of emergency supplies, restore of cellphone communications, and inspect on damaged facilities. Several issues were identified, including the need to incorporate drone capabilities into disaster management plans, develop appropriate laws and regulations, establish public-private coordination mechanisms, address technological limitations due to advances in technology, and implement training programs specifically for drone operators. Collaboration among government agencies, private organizations, and industry associations in disaster response highlighted the importance of fostering partnerships and mobilizing collective expertise in disaster management. The study concludes by highlighting the important role that drones can play in enhancing emergency response efforts and mitigating the impact of future disasters.

In this paper we advocate for the establishment of an aviation “innovative ecosystem” in Ukraine, particularly regarding the technology of unmanned aerial vehicles (UAVs). This ambitious strategy necessitates the introduction of new business models, taking advantage of new opportunities for achieving sustainable development goals in the increasingly resource-limited world. Moreover, this initiative faces a notable challenge: navigating the “double transition” of adopting both green and digital transformations across multiple domains. We believe the solution to creating an innovative ecosystem lies in harnessing the intellectual and infrastructural capabilities of top-tier research institutions – which in Ukraine include the National Aviation University (NAU) and the National Technical University “KPI.” By combining the efforts of Ukrainian higher education institutions and actively enhancing international cooperation, in particular with the Łukasiewicz Research Network – Institute of Aviation in Warsaw – we argue – Ukraine can expedite the development of “factories” of ideas and innovations by supporting the training, incubation and growth of startups, encourage the formation of micro and small high-tech enterprises and bringing their products to global and regional markets, improving the mechanisms of interaction of the startup teams with venture capital funds and investment companies.

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Building upon the existing literature on terror innovation, this research article introduces a new analytical framework that highlights four key elements of emerging technologies which facilitate their adoption by terrorist groups. We posit that technologies that are inherently ‘disruptive,’ ‘dual-use,’ ‘democratized,’ and ‘diffused’ are particularly susceptible to misuse by terrorists, and that it is the interplay between these factors that helps to drive future directions of tech-enabled terror. We draw on terror and extremist interest in and use of two key technology areas – unmanned aerial systems and additive manufacturing, specifically 3‑D‑printed firearms – as examples to highlight how the factors in the framework interact and how they help to drive innovative terror behavior. We conclude with a series of concise recommendations to mitigate harmful terrorist use of these and other disruptive technologies and limit the impact of future tech-enabled terrorist innovations.

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.

Copyright of Military Science Review / Hadtudományi Szemle is the property of National University of Public Service and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

We examined the movement patterns of American Coots relative to water temperature and heat index where a geothermally heated stream enters Utah Lake, UT, USA during winter using a thermal imager onboard an Unmanned Aerial System (UAS). Observations of Coots in different water temperatures (n = 2354) were compiled during 31 flights in January–March, 2020. Coots were actively swimming during 82.3% of the observations, fleeing during 8.1%, and sedentary in water during 9.6%. The population spent more time swimming in cooler water and were sedentary for longer in warmer water. The only instance of uniform behavior occurred during an anomalously cold period in which Coots stood huddled in the warmest available water. Using a linear model framework, we found that Coot time spent increased with water temperature when heat index decreased. Coots' swim paths deviated away from warmer water more than cooler water and more so as heat index increased. We suggest that Coots use the spring water primarily for ice-free habitat and maintain metabolic rates via a combination of thermal conductance, caloric ingestion, and exercise, but only exploit the heated water strictly for thermoregulation during anomalously cold periods. We discuss the benefits and hindrances of using UASs amidst a rapidly expanding assortment of sensors and aircraft available to researchers. Given wetlands' immediate conservation concern, ecosystem services they provide, and support of unique species assemblages, our research provides valuable insight into documenting wildlife's use of wetlands with relatively low scientific take and human interference. [ABSTRACT FROM AUTHOR]

Abstract 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.

The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This paper represents a novel data-driven trajectory prediction methodology based on applying the Long Short-Term Memory (LSTM) prediction algorithm to the UAS-S4 Ehécatl. An LSTM model was designed as the baseline and then developed into a Staked LSTM to better capture complex and hierarchical temporal trajectory patterns. Next, the Bidirectional LSTM was developed for a better understanding of the contextual trajectories from both its past and future data points, and to provide a more comprehensive temporal perspective that could enhance its accuracy. LSTM-based models were evaluated in terms of mean absolute percentage errors. The results reveal the superiority of the Bidirectional LSTM, as it could predict UAS-S4 trajectories more accurately than the Stacked LSTM. Moreover, the developed Bidirectional LSTM was compared with other state-of-the-art deep neural networks aimed at aircraft trajectory prediction. Promising results confirmed that Bidirectional LSTM exhibits the most stable MAPE across all prediction horizons.

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.

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]

The classification of three-dimensional (3D) point clouds derived through the use of cost-effective and time-efficient photogrammetric technologies can provide helpful information for applications, particularly in the mapping context. This paper presents a practical study of 3D Unmanned Aerial System (UAS) - Structure-from-Motion (SfM) point cloud classification using mainly open-source software. Following a supervised classification approach that makes use of only the dimensionality of points, the entire scene was classified into three land-cover categories: ground, high vegetation, and buildings. By applying the above-mentioned approach, the level of competence in classifying a 3D point cloud of a heterogeneous scene situated in the University of KwaZulu-Natal, South Africa, was evaluated. The resulting overall classification accuracy of 81.3%, with a Kappa coefficient of 0.70, was determined by means of a confusion matrix. The results achieved indicate the potential use of open-source software and 3D UAS-SfM point cloud classification in mapping and monitoring complex environments and in other applications that might arise. [ABSTRACT FROM AUTHOR]

In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture, such as spraying operations, mapping, or diagnostics, among others. Aerial spraying presents an inherent problem associated with the drift of small droplets caused by their entrainment in vortical structures such as tip vortices produced at the tip of rotors and wings. This problem is aggravated by other dynamic physical phenomena associated with the actual spray operation, such as liquid sloshing in the tank, GPS inaccuracies, wind gusts, and autopilot corrections, among others. This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition.

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]

More than a century has passed since the third dimension of space (airspace) has become an important place where combat and non-combat activities take place. The ability of the Air Force to carry out a wide range of content of combat operations and other activities from the airspace ensured the development of new qualitative characteristics of modern armed conflicts. The absence of physical limitations, high maneuverability and dynamism, short reaction time, as well as the high intensity of actions carried out by the Air Force, have enabled these forces to have strategic importance in armed conflicts since the Second World War. In parallel with the development of civilization and the emergence of qualitatively new technologies, rapidly improved technological solutions are being developed, which are changing the way of life of people around the world, improving it literally day by day. Each technological generation brings a new level of civilizational development, leading to the symbiosis of man as a human being and modern technical and technological means. Undoubtedly, this is also the case with the development of modern weapon systems and equipment used in armed conflicts. This is also the case with the revolutionary development of new weapon systems in air forces around the world. The development of modern weapons in the air force is best seen through the development of unmanned aerial platforms (unmanned aerial vehicles). The development of these assets is a consequence of the industrial revolution of the fourth generation and the importance of these platforms in the realization of tasks in modern armed conflicts is almost immeasurable. Namely, every conflict brings exponential development of these assets, both in technical and tactical sense. The development of these means in modern armed conflicts is a continuous process that is realized on the basis of previous experiences from the tactical use of unmanned aerial vehicles in concrete combat actions. There is no doubt that the technological progress of these means is something that in the future will enable the almost unlimited use of these aircraft in the execution of a wide variety of tasks without minimal danger to human life. This paper, whose main goal is to describe the unmanned aerial vehicle as a tool for the execution of a wide range of tasks in an unlimited war format, consists of three chapters in addition to the introduction and conclusion. In the first chapter, the theoretical determination of the unmanned aerial vehicle as a weapon-equipment system was made. In the second chapter, the use of unmanned aerial vehicles is analyzed on the examples of armed conflicts in Syria and Nagorno-Karabakh. In the third chapter, an analysis of potential aviation patterns of unmanned aerial vehicles and loitering munition (also known as a suicide drone or kamikaze drone) in combat operations was carried out.