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Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively. [ABSTRACT FROM AUTHOR]

Watermelon is one of the most commonly grown vegetable crops worldwide due to the economic and nutritional importance of its fruits. The yield and quality of watermelon fruits are affected by constant attacks from pests, diseases, and weeds throughout all phenological stages of the crop. Labor shortages and unevenness of pesticide applications using backpack and tractor sprayers are significant challenges. The objective of this study was to evaluate the effect of different spray nozzles (XR110015 and MGA60015) and application rates (8, 12, and 16 L ha−1) on droplet distribution on different targets in watermelon plants using an unmanned aerial vehicle. Water-sensitive papers were used as targets to analyze the droplet coverage, deposition, density, and volume median diameter. Data were collected from targets placed on the leaf adaxial and abaxial sides, fruit, apical bud, and stem of each plant. The mean droplet coverage and density increased as the application rate was increased, with no significant interaction between the factors or statistical difference between spray nozzles, except for the leaf abaxial side. No significant differences were found for the variables analyzed at application rates of 12 and 16 L ha−1, whereas significant differences were observed at 8 L ha−1. The use of unmanned aerial vehicles in watermelon crops is efficient; however, further studies should be conducted to evaluate their effectiveness in pest control and compare them with other application methods. [ABSTRACT FROM AUTHOR]

Copyright of Ciência Florestal (01039954) is the property of Ciencia Florestal 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.)

Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences. HIGHLIGHTS Photogrammetry datasets useful for calibrating 1D numerical river ice jam models.; Remotely sensed digital elevation models of grounded ice can provide estimates of ice jam thickness.; Remotely sensed large-scale orthomosaics provide insights into ice jam formation and evolution.;

The use of unmanned aerial vehicles (UAVs) for pesticide application has increased substantially. However, there is a lack of technical information regarding the optimal operational parameters. The aim of this study was to evaluate the quality of pesticide application on a soybean crop using a UAV employing different spray nozzles. The experiments were conducted using a completely randomized design with four treatments and eight repetitions. The trial was conducted in a soybean growing area during the soybean reproductive stage (1.1 m tall). The treatments included aerial application (rate: 10 L hm−2) using an Agras MG1-P UAV with XR 11001 (flat fan), AirMix 11001 (air-induction flat fan), and COAP 9001 (hollow cone spray) nozzles; for comparison, ground application (rate of 100 L hm−2) using a constant pressure knapsack sprayer with an XR 110015 (flat fan) nozzle was performed. The deposition was evaluated by quantifying a tracer (brilliant blue) using spectrophotometry and analyzing the droplet spectrum using water-sensitive paper. Furthermore, the application quality was investigated using statistical process control methodology. The best deposition performance was exhibited by the application via UAV using the COAP 9001 and AirMix 11001 nozzles. For all the treatments, the process remained under statistical control, indicating commendable adherence to quality standards. The aerial application provided greater penetration of the spray into the crop canopy. With the use of the UAV, the coverage on the water-sensitive paper was

Watermelon is one of the most commonly grown vegetable crops worldwide due to the economic and nutritional importance of its fruits. The yield and quality of watermelon fruits are affected by constant attacks from pests, diseases, and weeds throughout all phenological stages of the crop. Labor shortages and unevenness of pesticide applications using backpack and tractor sprayers are significant challenges. The objective of this study was to evaluate the effect of different spray nozzles (XR110015 and MGA60015) and application rates (8, 12, and 16 L ha−1) on droplet distribution on different targets in watermelon plants using an unmanned aerial vehicle. Water-sensitive papers were used as targets to analyze the droplet coverage, deposition, density, and volume median diameter. Data were collected from targets placed on the leaf adaxial and abaxial sides, fruit, apical bud, and stem of each plant. The mean droplet coverage and density increased as the application rate was increased, with no significant interaction between the factors or statistical difference between spray nozzles, except for the leaf abaxial side. No significant differences were found for the variables analyzed at application rates of 12 and 16 L ha−1, whereas significant differences were observed at 8 L ha−1. The use of unmanned aerial vehicles in watermelon crops is efficient; however, further studies should be conducted to evaluate their effectiveness in pest control and compare them with other application methods.

Sugarcane planting is an important and growing activity in Brazil. Thereupon, several techniques have been developed over the years to maximize crop productivity and profit, amongst them, processing of sugarcane field images. In this sense, this research aims to identify and analyze crop rows and measure their gaps from aerial images of sugarcane fields. For this, a small Remotely Piloted Aircraft captured the images, generating orthomosaics of the areas for analysis. Then, each orthomosaic is classified with the K-Nearest Neighbor algorithm to segment regions of interest. Planting row orientation is estimated using the RGB gradient filter. Morphological operations and computational geometry models are then used to detect and map rows and gaps along the planting row segment. To evaluate the results, crop rows are mapped and compared to manually taken measurements. Our technique obtained an error smaller than 2% when compared to gap length in crop rows from an orthomosaic with the area of 8.05 ha (ha). The proposed approach can map the positioning of the automatically generated row segments appropriately onto manually created segments. Moreover, our method also achieved similar results when confronted with a manual technique for differing growth stages (40 and 80 days after harvest) of the sugarcane crop. The proposed method presents a great potential to be adopted in sugarcane planting monitoring.

ABSTRACT: This article presents an experimental study to verify the feasibility of using the emerging UAV technology in the mapping of fishing grounds exploited by artisanal fishing in Sepetiba Bay, in the south of the state of Rio de Janeiro-Brazil, aiming to better understand the functionality and information generated by the equipment, and whether the data can help improve local fisheries management and also provide information for the implementation of simplified traceability for regional fish. The study area included four locations, Ilha da Madeira, Vila Geny, Coroa Grande and Ponte Preta, all located in Sepetiba Bay, located in the municipality of Itaguaí, in the southern region of the state of Rio de Janeiro. The aerial mapping was carried out in September, October and November 2017, between 10 am and 12 pm (GMT-3), employing a Phantom 3 Professional UAV (DJI, Shenzhen, China), Anac Brasil registration number PP-011092014. The onboard camera a DJI 4K Edition Sony Exmor R Model IMX117: 7.81mm equipped with a CMOS sensor dimension of 6.2 mm, 4000 x 3000 12 megapixel resolution, with an f/2.8 lens and field of view (FOV) of 94, having a focal length of 14mm. Images were collected perpendicularly to the main plan. Flight height was set at 80 m, flight speed was determined based on the overlap of 80%, dimension of area to be mapped, and battery life. Hence, the operator utilized a average flight speed of 25 km/h. ground sample distance (GSD) was 1.20 cm/px. and an overlap of 80% between images was established to avoid possible failures and orthomosaic information gaps. For each fishery mapped, standard software processing was carried out, which consists of: aligning the photos, creating a point cloud, creating a digital elevation model, and finally the orthomosaic itself. Once processed, the orthomosaics were exported from Agis Soft Photo Scan Professional to Google Earth (Google Inc.), where it is possible to make a more comprehensive assessment of fisheries in a much more practical and faster way. A total of four flights were carried out, one for each study area, averaging 10 minutes each. As a result, the technology proved to be viable to apply, since it made it possible to identify and map the areas defined for this research, generating data such as distance and, characteristics of the place, which could be used to track the fish caught and landed, in addition to enabling the monitoring of vessels and fishing, and thus improve the management of local fisheries.

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

The use of unmanned aerial vehicles (UAVs) for pesticide application has increased substantially. However, there is a lack of technical information regarding the optimal operational parameters. The aim of this study was to evaluate the quality of pesticide application on a soybean crop using a UAV employing different spray nozzles. The experiments were conducted using a completely randomized design with four treatments and eight repetitions. The trial was conducted in a soybean growing area during the soybean reproductive stage (1.1 m tall). The treatments included aerial application (rate: 10 L hm−2) using an Agras MG1-P UAV with XR 11001 (flat fan), AirMix 11001 (air-induction flat fan), and COAP 9001 (hollow cone spray) nozzles; for comparison, ground application (rate of 100 L hm−2) using a constant pressure knapsack sprayer with an XR 110015 (flat fan) nozzle was performed. The deposition was evaluated by quantifying a tracer (brilliant blue) using spectrophotometry and analyzing the droplet spectrum using water-sensitive paper. Furthermore, the application quality was investigated using statistical process control methodology. The best deposition performance was exhibited by the application via UAV using the COAP 9001 and AirMix 11001 nozzles. For all the treatments, the process remained under statistical control, indicating commendable adherence to quality standards. The aerial application provided greater penetration of the spray into the crop canopy. With the use of the UAV, the coverage on the water-sensitive paper was <1%; moreover, the AirMix 11001 and XR 110015 nozzles had the lowest drift potential. [ABSTRACT FROM AUTHOR]

Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences. [ABSTRACT FROM AUTHOR]

Copyright of Nativa is the property of Revista Nativa 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.)

The yield of coffee plants depends on several factors, and the leaf nutrition is one of the most important. The form of application of leaf fertilizers used is expensive considering the application efficacy and the efficacy of adherence and absorption of nutrients. The objective of this work was to evaluate the effect of configurations of operational parameters of centrifugal nozzle rotation and operational height on the application efficacy and efficacy of absorption of leaf fertilizers in Conilon coffee plants. The coverage, density, and deposition of spray droplets were efficacy parameters evaluated through water-sensitive paper tags and artificial targets. The efficacy of the macronutrients applied was confirmed through leaf analyses at 15 and 30 days after application. The application efficacy with a height of flight of 2.0 m and rotation of 10,000 rpm was the most adequate, with coverage of 12.4% and density of droplets of 127 droplets cm−2. The application of foliar fertilizers by UAV provided a distribution in newer leaves in which the absorption of macronutrients was greater, and the flight height of 2.0 m and rotation of the centrifugal nozzle of 10,000 rpm resulted in the best efficacy parameters. [ABSTRACT FROM AUTHOR]

Remotely piloted aircrafts (RPAs) are becoming well-known for monitoring forest restoration (FR). Canopy cover percentage on a restoration site is a major indicator for the early phases of FR. We present a protocol to evaluate canopy cover using low-cost RPA and a friendly computational environment. We tested the protocol in six FR projects in the Brazilian Atlantic Forest. After processing the images automatically to generate the digital surface model and the digital terrain model, the canopy cover is measured in QGIS by considering height thresholds in the canopy height model. The results had F1 values higher than 0.9, which confirms a robust methodology that does not require specific computer science or remote sensing skills. Thus, low-cost RPA proved to be accurate and user-friendly in measuring the canopy cover, which improves monitoring during most FR stages. Such finding plays an important role in FR policies and future works must study a user-friendly protocol for measuring tree height via low-cost drones.

Papaya production and export is increasingly expanding in the world market due to the nutritional importance of the fruit. Phytosanitary issues, labor shortages, and unevenness in land-based costal and motorized applications compromise crops, the environment, and humankind. The purpose of this study was to evaluate the efficiency of droplet distribution using an unmanned aerial vehicle, with different application rates (12.0, 15.0, and 18.0 L ha−1) and spray nozzles (XR110015 and MGA015) in the upper (UL), middle (ML), and lower (LL) layers, and on papaya fruit clusters (BF). Water-sensitive paper labels and artificial targets were used to assess the efficiency. Coverage, density, droplet distribution, and droplet diameter were influenced by the application rates in the following order: 18.0 > 15.0 > 12.0 L ha−1, showing concentrated droplet distribution in the respective layers: UL > ML > LL > BF. The 18.0 L ha−1 rate increased the variables examined, and the droplet coverage on the UL using the XR110015 nozzle was 6.56 times greater than that found on the LL and BF. The MGA015 nozzle presented better results in the LL and BF in all variables analyzed. The UAVs were efficient in applying to the papaya crop and further studies should be carried out in order to confirm the efficacy of plant protection products applied using this technology. [ABSTRACT FROM AUTHOR]

During the Network for studying Entrainment and microPHysics of cLouds using Adaptive Exploration (NEPHELAE)–ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field campaign in January and February 2020 in Barbados, remotely piloted aircraft (RPA) were implemented to characterise the structures of trade‐wind cumulus for a total of 40 flights. Two observation methods were tested: one making racetracks to get statistics on the cumulus clouds and a new sampling strategy using sensor‐driven adaptive sampling to track an individual cloud autonomously throughout its lifetime. It appears from the statistics that there are two types of cohabiting cloud population, with small‐diameter clouds (transect lengths less than 500 m) being less buoyant than larger clouds (transect lengths greater than 500 m). Also, this statistical study shows that cumulus clouds do not have an adiabatic core. These results are compared with individual clouds tracked by adaptive sampling, which also show that the core of the cumulus clouds is diluted by the environment. A comparison with high‐resolution large‐eddy simulations shows that these numerical studies tend to underestimate mixing in the whole cloud. [ABSTRACT FROM AUTHOR]

Background: Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals. Methods: We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. Review findings: We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review. Conclusions: Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images. [ABSTRACT FROM AUTHOR]

Coffee production and marketing is one of the main global commercial activities, but crop yields depend on several factors, among which plant health. The objective of this study was to evaluate the efficiency of spray droplet deposition in coffee crops grown in a mountain region, associated to the efficacy of the control of fungal diseases. The application efficiency, using an unmanned aerial vehicle (UAV), and the efficacy of the products applied were tested. Water-sensitive paper tags were used to analyze the application efficiency; agronomic efficiency, vegetative vigor, yield, and physiological parameters were used to determine the fungicide efficacy. Droplet coverage in the upper canopy layer using a pneumatic sprayer (28.70%) was 4.11-fold higher than that found in the same layer for application using a UAV (6.98%) at the rate of 15 L ha−1. The highest droplet depositions by using a UAV were found for the rate of 15 L ha−1: 1.60, 1.04, and 0.43 µL cm−2 in the upper, middle, and lower layers, respectively; the deposition in the upper layer with application using a pneumatic sprayer was 42.67 µL cm−2, and therefore, a 26.7-fold higher deposition. The results denote that the control of fungal diseases through fungicide applications using a UAV is efficient for mountain coffee crops. [ABSTRACT FROM AUTHOR]

Solid waste causes big problems to the environment and public health when disposed of inadequately. Law 14,026/2020 updated the legal framework for sanitation and amended Federal Law 12,305/2012, which establishes the National Policy for Solid Waste, and is regulated by Federal Decree 10,936/2022, which addresses waste management. The stability of waste massifs in landfills can be compromised by vertical and horizontal displacements. To ensure landfill environments’ physical and economic safety, operational measurements for monitoring such displacements are essential. Due to technological advances, new equipment and topographic survey techniques have emerged. Among them, the use of the remotely piloted aircraft (RPA) is an alternative for fast and effective monitoring, which can support strategic planning in landfills. The aim of the study was to identify displacements in waste massifs of the municipal sanitary landfill of Lages - SC using the RPA. The aerial survey was performed using the RVJET and Phantom 4 Advanced platforms with ground control points. Subsequently, orthomosaics and digital elevation models were produced. The identification of instability points in the landfill was performed. High displacement velocity was identified only at the massifs in operation. The landfill presented uniform surface settlement intensity and steady deformation velocity, not exceeding the warning level. This study calculated the displacement velocity with longer intervals between measurements than those currently used in traditional methods. Despite this, it was possible to build a database and carry out analysis by visual inspections and variations in the elevation of the massifs for preventive and corrective control of the landfill. RPA demonstrated to be adequate to monitor landfills. However, this assessment must be complemented with other instrumental analyses to understand waste massifs’ behavior over time.

United States Air Force remotely piloted aircraft (RPA) aircrew and support personnel perform critical missions in support of military operations around the world. The high value placed on the work of these individuals has resulted in increased workload and heightened risk of adverse psychological health outcomes. The impact of this can be detrimental to the health of the individual as well as the success of the RPA mission. To address these concerns, a combat-to-reconstitution policy was developed to provide additional downtime and recovery to RPA personnel, and recently piloted in some units within the RPA community. A total of 397 RPA personnel, of which 26% ( n  = 103) indicated their schedule was either internal or external reconstitution, completed an anonymous occupational health assessment sometime between November 2021 and July 2022. Responses included a mixture of both positive and negative experiences during the implementation of reconstitution, along with recommendations for improvements. Certain reconstitution-related sources of stress (e.g. work overload, lack of resources), were found to increase risk of burnout or psychological distress. Overall, this preliminary study provides a foundational landscape for the scientific and operational understanding of reconstitution as an operational intervention to improve recovery.

Unmanned aerial vehicles (UAVs), or drones, are being utilized by wildlife biologists to monitor populations of birds and mammals. However, the reaction of wildlife to drones varies by species, so a preliminary evaluation must be conducted to determine if the target species can be detected via drone footage and to determine how the target species will react to drone presence. Lek‐mating prairie‐chickens (Tympanuchus spp.) seem amenable to drone‐based surveys because they are relatively large and display in groups on elevated sites with sparse vegetation. The goal of our study was to determine what flight characteristics maximize prairie‐chicken detection on drone video footage, to document the birds' reactions to the drones, and to compare how the noise produced by the drones compares to ambient sounds. We tested 3 sizes of rotary‐winged drones, flown at 3 heights, with 3 different camera angles by flying over known prairie‐chicken leks. We determined that >65% of prairie‐chickens present were detected using video footage at a height of 100 m with a 10° camera angle. Drones of different sizes had similar detectability. However, observers in blinds adjacent to leks routinely detected more birds than were detected on drone footage (detection on drone footage was 39.7 ± 9.6% of birds present). Prairie‐chickens flushed in 96% of trials when a drone was flown over display locations. Time to return to the lek following drone disturbance was similar to prairie‐chickens' reactions to natural predators. Prairie‐chickens flushed when drone sound levels were comparable to ambient noise. Developing guidelines for the ethical use of drones in wildlife research will require quantifying the amount of taxa‐specific disturbance caused by drones. Our study begins to close this knowledge gap by documenting disturbance to upland, lek‐mating grouse of conservation concern, and suggests that the focal species' reaction to aerial predators may provide clues as to how that species may react to drones. [ABSTRACT FROM AUTHOR]

Over the past two decades, the world has witnessed the rapid development and proliferation of small remotely piloted aircraft systems, colloquially known as small drones or, simply, drones for the purpose of this article. Open source communities, such as ArduPilot (ArduPilot, 2021) and PX4 (Dronecode, 2021) have made autopilot technology readily available to hobbyists and companies alike. Every few months, a drone manufacturer announces a new sensor, distance, flight time record, or larger payload capacity. Drone operators tout new applications, customers, and business models. [ABSTRACT FROM AUTHOR]

Abstract Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour‐intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone‐acquired thermal orthomosaics to monitor the abundance of grey‐headed flying‐foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semi‐automated methods for counting flying‐foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying‐foxes manually counted in drone‐acquired thermal imagery and the true abundance of flying‐foxes in single roost trees, as obtained from direct on‐ground observation. This indicated that the number of flying‐foxes observed in thermal imagery accurately reflected the true abundance of flying‐foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying‐foxes manually counted was highly repeatable between the same‐day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying‐foxes manually counted in drone‐acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning‐enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi‐automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying‐fox roosts and will aid in the conservation and management of this globally threatened group of flying‐mammals, as well as other homeothermic arboreal‐roosting species.

Sugarcane is one of the main crops in the world due to its economic value promoted by the sale of its derivatives, such as bioethanol and sugar. In order to achieve greater economic performance and productivity in the sugarcane field, several digital image processing studies have been conducted on sugarcane field images. However, mapping and measuring gaps in the planting rows are still being performed manually on-site to determine whether to replant the entire area or only the gaps. High cost of time and manpower is required to perform the manual measurement. Based on that, the aim of this study is to present a novel method to detect crop rows and measure gaps in crop fields. Our method is also able to deal with curved crop rows, which is a real problem and substantially limits numerous solutions in practical applications. The proposed method is evaluated using a mosaic of real scene image that was prepared with the support of a small remotely piloted aircraft. Experimental tests showed a low relative error of approximately 1.65% compared to manual mapping in the planting regions, even for regions with gaps in the curved crop rows. It means that our proposal can identify and measure crop rows accurately, which enables automated inspections with high-precision measurements. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental 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.)

This document presents the results of a proof of concept for describing with more detail the social and complementary infrastructure around the tertiary roads of the Taminango region in the department of Nariño, Colombia. A dataset with samples of free satellite images from Google Maps and OpenStreetMaps was obtained. Then, a supervised deep learning algorithm with FCN (Fully Convolutional Network) topology is applied for the points of interest labeling process and the identification of the state of the roads using Keras and TensorFlow. Subsequently, a system consisting of a desktop application and a mobile application that integrates the functionalities of the trained algorithm through an intuitive interface and simple logic that stimulates interaction with the consultant is proposed. The desktop application includes a GUI designed in Python for tagging points of interest. The mobile application was developed with Flutter and comprises a database with documentation of the routes and road network in the region. It includes an augmented reality system in Vuforia Engine and Unity with virtual content developed in Blender and SolidWorks; A 3D model of the map of the region has been recreated for easier interaction and visualization of the points of interest and the status of the studied roads. In addition, complementary information was collected through remotely piloted aircraft for data acquisition in environments difficult to access, and through the community participation for the description and identification of areas not visible on official maps or statistics. This study addresses a method for the classification and identification of state of tertiary road network of the studied region, as well as labeling points of interest for the efficient management of resources for the development of new infrastructure there.

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Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour‐intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone‐acquired thermal orthomosaics to monitor the abundance of grey‐headed flying‐foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semi‐automated methods for counting flying‐foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying‐foxes manually counted in drone‐acquired thermal imagery and the true abundance of flying‐foxes in single roost trees, as obtained from direct on‐ground observation. This indicated that the number of flying‐foxes observed in thermal imagery accurately reflected the true abundance of flying‐foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying‐foxes manually counted was highly repeatable between the same‐day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying‐foxes manually counted in drone‐acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning‐enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi‐automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying‐fox roosts and will aid in the conservation and management of this globally threatened group of flying‐mammals, as well as other homeothermic arboreal‐roosting species. [ABSTRACT FROM AUTHOR]

Simulations are increasingly popular in employee selection and training. While face valid and engaging, the attributes being assessed are often poorly understood. This study evaluated the extent to which a multitasking assessment based on concurrent memorization, math, visual monitoring, and listening tasks predicted simulated unmanned aircraft vehicle (UAV) mission performance in a military trainee sample (N = 368). Performance was based on accuracy of mission planning, information recall during "Lost Link" conditions, and success in rescuing stranded allies while monitoring the aircraft's resources. Although scores on the multitasking assessment were only weakly related to performance of pre-flight mission planning tasks completed under static conditions, multitasking was strongly related to overall simulated UAV mission performance, including execution of tasks requiring attending to multiple, dynamic sources of information and shifting attention among concurrent processes and demands. Further, multi-tasking demonstrated substantial incremental validity beyond the traditional measures of cognitive ability that have been used for decades within the US military. Implications, limitations, and recommendations for selection and classification and future research are discussed. [ABSTRACT FROM AUTHOR]

Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.

The usage of Remotely Piloted Aircraft (RPA) for infrared (IR) imaging of PV systems for health status monitoring of PV modules has been identified as a cost-effective approach which offers 10–15 fold lower inspection times than conventional techniques. It provides easy access to PV systems irrespective of plant capacity and location of installation and is capable of performing a rapid analysis of the system. Reliable IR image is important for accurate assessment to detect and locate defects and defective modules effectively and analyze the impact on power generation. This paper describes the importance of RPA based IR imaging for identifying solar PV systems defects. It also addresses the opportunities that can be achieved through the adoption of RPA based IR imaging technology which can add value to social, economic and environmental performance of the PV systems. [ABSTRACT FROM AUTHOR]

Abstract We trialed the collection of blow samples using a waterproof electric multirotor (quadcopter) drone from two free‐ranging dolphin species, the abundant and approachable bottlenose dolphin (Tursiops aduncus) and the less common and boat shy humpback dolphin (Sousa sahulensis). This drone was fast, maneuverable, and quiet compared to other drones commonly used in studies of cetaceans and relative to their hearing thresholds. We were successful in collecting blow samples from four individual dolphins (three bottlenose dolphins and one humpback dolphin) in two groups. The success of obtaining samples was dependent on the individual dolphin's activity. We were successful in sampling when dolphins were resting and socializing but found that socializing dolphins were not predictable in their surfacing and direction and therefore do not recommend drone sampling socializing dolphins. The suitability and preference of the sampling technique over biopsy sampling is highly dependent on the dolphin activity. We also attempted to extract DNA from the blow samples with the aim of assessing the feasibility of using blow sampling by drone for population genetic studies. We were unsuccessful in extracting DNA and recommend that others attempting to sample dolphin blow with a drone should prioritize collecting a larger volume of blow that may yield adequate concentrations of DNA to be amplified. Blow sample volume could potentially be increased by sampling with more absorbent materials.

Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted.

A feature of modern aviation risk management is the lack of a unified approach to flight safety risk assessment, which is manifested by the fact that different risk measures are used in practice. An effective approach to provide a comprehensive assessment of aviation risks has not yet been proposed. Aviation activities are subject to specific risks, due to their sectoral identity and flight safety features. The article deals with the implementation of a combined approach to preventive management of aviation risks, which requires identification and monitoring of risks under uncertainty. [ABSTRACT FROM AUTHOR]

Highlights • The aim of this article is to develop a new CMS to detect dust in PV panels. • The CMS is embedded in an unmanned aerial vehicle. • The CMS employs a radiometric sensor, and the results are validated by thermography. • The approach is based on the emissivity that is produced by the surface. • Several scenarios were conducted using real solar PV panels. Abstract The photovoltaic solar energy industry is expanding, and there is therefore a need to increase and improve its maintainability, operating costs, availability, reliability, safety, life cycle, etc. The aim of this article is to design, develop and check a new condition monitoring system to detect dust in solar photovoltaic panels. The condition monitoring system uses a radiometric sensor connected to an Arduino platform. This novel approach is based on emissivity analysis produced over a surface and characterized with a low emissivity value when dust appears. A thermographic camera is also employed to validate the results provided by the radiometric sensor. The system is designed to be embedded in an unmanned aerial vehicle. Radiometric data is sent and analysed, Internet of Things is employed, and thermograms are stored for further processing. Several scenarios with a real solar panel are used in the experiments, in which the angles and distances of the sensors and surface conditions are studied. An analysis of the radiometric sensor provides accuracy results, and the presence of dust is identified in all scenarios. [ABSTRACT FROM AUTHOR]

The article analyzes the existing methods of determining the workload level. On the basis of the analyzed material new methodological bases for calculating the workload level of external pilots of remotely piloted aircraft were created. While creating new methodological basis, all the relevant parameters that have an impact on the workload value were defined and grouped. The formulas for calculating these parameters were derived. [ABSTRACT FROM AUTHOR]

Highlights • 6.15% of participants met PTSD symptom criteria as outlined in DSM-5. • RPA operators 31–35 and 36–40 ha d greater odds of meeting symptom criteria. • Operators working 51+ hours also had greater odds of meeting symptom criteria. • Number of events in which bystanders were killed was a significant predictor. • Sense of shared responsibility for death of a bystander was also a predictor. Abstract The prevalence and expression of post-traumatic stress disorder (PTSD) symptoms among United States Air Force remotely piloted aircraft (RPA; commonly referred to as "drones") warfighters exposed to battlefield trauma via remote, electronic warfare is relevant and critical to the effective delivery of mental health care for this population. RPA warfighters (n = 715) with real-time exposure to at least one traumatic event participated in an online survey. Measures included the PTSD Checklist for DSM-5 (PCL-5) and survey of exposure to traumatic events during the course of operational combat missions. A total of 6.15% met PTSD symptom criteria; those in the age ranges of 31–35 and 36–40 and those working 51 or more hours per week had greater odds of meeting symptom criteria. For combat-related events, the number of events in which RPA warfighters witnessed civilian bystanders being killed by enemy forces or felt shared responsibility for the injury or death of bystanders were also significant predictors, regardless of whether the risk was anticipated or unanticipated. The results of this study suggest that specific types of exposure and participation in missions with specific outcomes, albeit via electronic, remote means, are associated with an increased risk for meeting PTSD symptom criteria. [ABSTRACT FROM AUTHOR]

Abstract This work considers the modern technologies of Remotely Piloted Aircraft (RPA) design and manufacturing. A theoretical research and computer modeling of the aerodynamic properties of separate RPA parts and the aircraft in general have been performed. [ABSTRACT FROM AUTHOR]

Testing of untethered subscale models, often referred to as subscale flight testing, has traditionally had a relatively minor, yet relevant use in aeronautical research and development. As recent advances in electronics, rapid prototyping and unmanned-vehicle technologies expand its capabilities and lower its cost, this experimental method is seeing growing interest across academia and the industry. However, subscale models cannot meet all similarity conditions required for simulating full-scale flight. This leads to a variety of approaches to scaling and to other alternative applications. Through a literature review and analysis of different scaling strategies, this study presents an overall picture of how subscale flight testing has been used in recent years and synthesises its main issues and practical limitations. Results show that, while the estimation of full-scale characteristics is still an interesting application within certain flight conditions, subscale models are progressively taking a broader role as low-cost technology-testing platforms with relaxed similarity constraints. Different approaches to tackle the identified practical challenges, implemented both by the authors and by other organisations, are discussed and evaluated through flight experiments.