Remotely Piloted Aircraft Systems Research Articles

The critical impact of remote pilot modelling in evaluation of detect-and-avoid systems explained for ACAS Xu

Detect-and-avoid (DAA) systems for remotely piloted aircraft systems (RPAS) can provide remain well clear (RWC) guidance as well as shorter term resolution advisories (RAs) for collision avoidance, which are both provided in the vertical and horizontal planes. Simulation-based studies for large sets of encounter scenarios are used in the development and evaluation of DAA systems, which encompass safety and operational acceptability of the DAA supported operations. Given the key role of the remote pilot (RP) in responding to RWC guidance and RAs, a RP model is an essential element in such simulations. This paper describes the development of a RP model for evaluation of encounter scenarios involving the ACAS Xu DAA system. The model describes RP situation awareness (perception, comprehension, projection) as basis for decision-making, modes for responding to RAs and/or RWC guidance, response delays, response strengths, and the flight control actions. The RP model includes deterministic and stochastic settings. It is integrated in a simulation environment for encounters of manned and/or unmanned aircraft, the involved DAA and airborne collision avoidance systems, the surveillance and communication systems, and the human operators. Simulation results are provided for a set of encounters between pairs of RPAS both having ACAS Xu for various configurations of the RP model, and for cases with and without sensor errors. The results show that there can be large differences between the results of deterministic and Monte Carlo simulations, indicating that limited sensor errors can have a large impact on the nonlinear system dynamics. Furthermore it is shown that deadlock conditions can exist where the RPAS show oscillatory behaviour and do not manage to effectively pass each other, dependent on the encounter geometry and RP model settings. It is advised to perform a broad sensitively study for RP performance and to study extending the scope of DAA systems to include guidance for efficiently returning to mission without triggering new conflicts.

Drone-based infrared thermography to measure the intranasal temperature of baleen whales

ABSTRACT Traditional methods for quantifying the internal temperature of marine mammals require handling live animals, which is not practical for free-swimming baleen whales. Developing a less invasive, more repeatable method would significantly improve our understanding of whale health and thermal physiology. Infrared thermography (IRT) devices compatible with remotely piloted aircraft systems (RPAS) have facilitated qualitative assessments of heat signatures from marine mammals at sea, but absolute temperatures derived using this approach are rare. The goal of this study was to develop a precise empirical method for estimating intranasal temperatures of baleen whales using RPAS-based IRT. We conducted controlled field experiments and flights over North Atlantic right whales (Eubalaena glacialis, NARWs) to develop and test the methodology. Two approaches were evaluated to estimate intranasal temperatures from IRT sensor intensities: a three-point empirical line regression calibrated per flight using known-temperature objects and a generalized linear model incorporating environmental variables. Controlled field experiments demonstrated that the former approach had a median bias of −0.6°C (interquartile range: 1.5ºC), while the latter approach had unexplained negative proportional bias with increasing true temperature of the target object. After accounting for bias, the former approach yielded an average intranasal temperature of 26.9 ± 1.7°C for 21 unique NARWs. The anatomy of the mysticete upper respiratory tract and physiological heat conservation strategies may explain why estimates were low compared to internal temperatures measured from baleen whales using other techniques (30–39ºC). Variability within whales was less than ± 2°C, supporting the use of these methods to monitor the health of individuals over time. However, variability among whales was greater (up to 7ºC). Improvements in our understanding of whale physiology and respiratory mechanics and advancements in RPAS-based IRT calibrations could make this technology more reliable for assessing individual body temperatures and monitoring populations in the future.

The Environmental Legal Framework of Mexican Caribbean Dunes: A Retrospective Case Study of Vegetation and Coastal Dune Loss in the Sian Ka’an Biosphere Reserve

The Mexican Caribbean coastal dune is protected by national and international environmental legislation. However, through permits, concessions and authorizations for changes in land use, the coastal dune has been fragmented or suppressed, mainly for touristic activities, causing a decline in protective and ecological ecosystem services. In this study, we evaluated the strength and weakness of Mexican legislation to protect the Caribbean coastal dune ecosystem and estimated the historical and current effects on coastal dune vegetation and dune geomorphology, associated with legal allowances of land use change in the Sian Ka’an Biosphere Reserve (SKBR). Legislation at the federal, state and local level were critically reviewed, and with remote sensing techniques and the Remotely Piloted Aircraft System (RPAS), we conducted a case study in the SKBR to estimate coastal dune vegetation alteration trends during the period 2011–2020 and modifications on the dune geomorphology associated with land use change allowances. At the federal (four laws), state (eight laws) and local (nine Local and Territorial Planning Programs (POEL and POET) levels, we found a lack of consensus and alignment between regulations, starting with a lack of definition of ecosystems subject to protection. For coastal dunes, none of them consider topography, ecological function and a way to identify it in the field, making the surveillance highly complex and favoring land use changes, the removal of vegetation and dune geomorphology alteration. Remote sensing techniques showed that areas with land use authorizations exhibit negative vegetation cover trends (Mann–Kendall <−0.4), indicating a decline in vegetation cover density that is mostly anthropogenically induced. The RPAS analysis demonstrated drastic alterations to complete elimination of the coastal dune geomorphology in areas with land use change. In the Mexican Caribbean, the loss of coastal dune and associated ecosystem by the lack of congruent legislation threatens the environmental stability of the coastal areas.

A Systematic Approach towards the Integration of Initial Airworthiness Regulatory Requirements in Remotely Piloted Aircraft System Conceptual Design Methodologies

The regulatory framework of Remotely Piloted Aircraft Systems (RPASs) has recently experienced an extraordinary evolution. This article seeks to improve the integration of certification considerations in RPAS conceptual design approaches so as to enhance the safety, certifiability and competitiveness of their resulting designs. The first part of the research conducts a two-stage analysis of contemporary regulations related to an RPAS’s initial airworthiness. In the first stage, the broad international regulation paradigm is evaluated attending to a set of criteria that are tightly related to both airworthiness and design considerations. The second stage keeps the most promising documents from a design–integration standpoint, which are assessed according to their applicability considering both design and operational aspects. The results of this analysis provide insights regarding the main issues in airworthiness design criteria extraction and integration in design methodologies. To aid the designer in surmounting these challenges, a flexible procedure named DECEX is developed. Considering the documents and findings from the survey, and attending to the scope of the design methodology being developed, it aids in establishing a complete regulatory document corpus and in comparing and extracting the applicable airworthiness design criteria. Two case studies for different RPAS types are conducted to demonstrate its application.

Lessons from the implementation of Threat and Error Management: A case study of the Australian general aviation

Threat and Error Management (TEM) is one of recent safety initiatives to further improve the overall aviation safety level. In response to the recommendations of the International Civil Aviation Organisation (ICAO) for TEM to be introduced to all pilot training syllabi, the Civil Aviation Safety Authority (CASA) in Australia mandated TEM as an additional assessment item for various levels of flight tests and ground examinations in 2009. The current study adopted a two-pronged approach: A case study to highlight the importance of TEM training, followed by gaining an in-depth appreciation as to how TEM was implemented and its effects of the use of TEM training. A semi-structured interview of five highly experienced general aviation Flight Examiners (FE) was conducted, and thematic analysis was performed for the interview data from which four main themes emerged: 1) Impracticality; 2) Lack of guidance and support; 3) TEM implementation; and 4) TEM in practice. All participants shared the same view that TEM was not implemented well. It is expected that the findings from this study add to the body of knowledge to better inform and provide guidance and reference for National Aviation Authorities (NAA) to develop an implementation plan when considering the implementation of TEM training in their regulatory framework. Our study highlights a notable disconnect in CASA’s operational workforce’s grasp of TEM, impacting trust in the general aviation sector in its significance and application. It should be also noted that since our study, to address this and foster innovation in safety projects, CASA has initiated comprehensive strategies such as workforce planning and actively engaging in Technical Working Groups (TWGs) to harness community expertise for safety improvements. Nevertheless, the findings have the potential to provide information regarding pilot attitudes to future safety initiatives in other rapidly growing sectors such as Recreational Aviation (RA) and Remotely Piloted Aircraft System (RPAS).

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Micro-Credentialing and Digital Badges in Developing RPAS Knowledge, Skills, and Other Attributes

This study explores the potential of micro-credentialing and digital badges in developing and validating the knowledge, skills, and other attributes (KSaOs) required for diverse Remotely Piloted Aircraft Systems (RPAS) operations. The rapid proliferation of drone usage has outpaced the development of necessary KSaOs for safe and efficient drone operations. This research aims to bridge this gap by identifying the unique and specific KSaOs required for different types of drone operations and examining how micro-credentialing and digital badges can provide tangible evidence of these KSaOs. The study also investigates the potential benefits and challenges of implementing digital badges in the RPAS sector and how these challenges can be addressed. Furthermore, it explores how digital badges can contribute to the standardization and recognition of RPAS competencies across different national regulatory bodies. The methodology involves observational studies of publicly available videos of drone operations, with a focus on agriculture spraying operations. The findings highlight the importance of both generic and specific KSaOs in RPAS operations and suggest that digital badges may provide an effective means of evidencing mastery of these competencies. This research contributes to the ongoing discourse on drone regulation and competency development, offering practical insights for regulators, training providers, and drone operators.

Data Acquisition and Lessons Learnt from Geophysical Remotely Piloted Aircraft System (RPAS) Surveys in Northern Canada

Data Acquisition and Lessons Learnt from Geophysical Remotely Piloted Aircraft System (RPAS) Surveys in Northern Canada

Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas

The recent expansion of wild Pacific oysters already had negative repercussions on sites in Europe and has raised further concerns over their potential harmful impact on the balance of biomes within protected areas. Monitoring their colonisation, especially at early stages, has become an urgent ecological issue. Current efforts to monitor wild Pacific oysters rely on “walk-over” surveys that are highly laborious and often limited to specific areas of easy access. Remotely Piloted Aircraft Systems (RPAS), commonly known as drones, can provide an effective tool for surveying complex terrains and detect Pacific oysters. This study provides a novel workflow for automated detection, counting and mapping of individual Pacific oysters to estimate their density per square meter by using Convolutional Neural Networks (CNNs) applied to drone imagery. Drone photos were collected at low tides and altitudes of approximately 10 m across a variety of cases of rocky shore and mudflats scenarios. Using object detection, we compared how different Convolutional Neural Networks (CNNs) architectures including YOLOv5s, YOLOv5m, TPH-YOLOv5 and FR-CNN performed in the detection of Pacific oysters over the surveyed areas. We report the precision of our model at 88% with a difference in performance of 1% across the two sites. The workflow presented in this work proposes the use of grid maps to visualize the density of Pacific oysters per square meter towards ecological management and the creation of time series to identify trends.

Using machine learning to count Antarctic shag (Leucocarbo bransfieldensis) nests on images captured by remotely piloted aircraft systems

Using 51 orthomosaics of 11 breeding locations of the Antarctic shag (Leucocarbo bransfieldensis), we propose a method for automating counting of shag nests. This is achieved by training an object detection model based on the “You Only Look Once” (YOLO) architecture and identifying nests on sections of the orthomosaic, which are later combined with predictions for the entire orthomosaic. Our results show that the current use of Remotely Piloted Aircraft Systems (RPAS) to collect images of areas with shag colonies, combined with machine learning algorithms, can provide reliable and fast estimates of shag nest counts (F1 score > 0.95). By using data from only two shag colonies for training, we show that models can be obtained that generalise well to images of both spatially and temporally distinct colonies. The proposed practical application opens the possibility of using aerial imagery to perform large-scale surveys of Antarctic islands in search of undiscovered shag colonies. We discuss the conditions for optimal performance of the model as well as its limitations. The code, data and trained model allowing for full reproducibility of the results are available at https://github.com/Appsilon/Antarctic-nests.

Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

Identification of unique electromagnetic signatures from GLRaV-3 infected grapevine leaves in different stages of virus development.

Grapevine leafroll associated virus 3 (GLRaV-3) is one of the most common viruses that adversely affect grape production quality around the world. Grapevine virus is usually detected by polymerase chain reaction (PCR) tests, but the main diagnostic challenges with the PCR are uneven distribution of symptoms, low virus titers in early stage, and the tests are time consuming and expensive. In the absence of effective control and treatment for grapevine virus, the recommendation is to remove infected vines before they become a source of transmission and a rapid and early detection method for viruses is urgently needed. This study investigated how GLRaV-3 infection affects spectral behavior of grapevine leaves and the use of remote sensing tools to identify changes caused by the biotic stress. A Real-Time RT-qPCR test was performed to select representative vines with healthy and GLRaV-3 infection, and changes in electromagnetic (EM) spectrum of leaves were observed with a portable hyperspectral spectrometer and a multispectral sensor on a remotely piloted aircraft system (RPAS). The results of this study suggest that foliar EM reflectance may differ depending on the presence or absence of visible symptoms of infected leaves and different stages of virus infection progress could be indicated by changes in the relative rate of reflectance around 700 nm. This research also confirmed that the development of a narrow-band hyperspectral index for the detection of grapevine virus is feasible, further research is recommended in a much more comprehensive investigation including a combination of hyperspectral sensor and chlorophyll fluorescence sensors, interpolation of suspect wavebands into ratios or indices, larger sample sizes and a more controlled study environment.

Método para inspeção de sistemas de proteção de descargas atmosféricas: Plantas de armazenamento de grãos

ABSTRACT Brazil has the highest incidence of atmospheric discharges, on average of 77.8 million hitting the country yearly. Companies providing post-harvest services have a high monetary value concentrated in their predominantly metallic structures. Atmospheric discharge protection systems (ADPS) are used in this context to protect the facilities and people, composed of several elements that require visual inspection with a certain periodicity, an activity framed as working at height. This research aimed to establish a methodology for employing a Remotely Piloted Aircraft System (RPAS) in the visual analysis of the operational condition of external atmospheric discharge protection systems components located in grain storage structures to collect information during the preliminary risk analysis and planning of activities at height. A survey of the current literature related to the subject was carried out, in addition to the norms, inspection agencies, and operational prerequisites of this type of equipment in the national territory. Finally, a method was created and validated based on the inspection checklist with the main points for checking, which generates a score that represents the urgency level of intervention, applied in a unit located in western Paraná, Brazil, on 11/28/2022. This procedure eliminated the risk of workers falling during the planning and inspection stage of the structures, which helped the qualified professional to issue the technical inspection report more quickly. The disadvantage is related to the dependence on favorable weather conditions.

VOLUMETRIC QUANTIFICATION OF EUCALYPTUS SP. STANDS THROUGH AERIAL PHOTOGRAPHS OBTAINED BY RPAS

Volumetric quantification of Eucalyptus sp. stands through aerial photographs obtained by RPAS. The data collection for forest management is necessary to carry out the inventory, being a procedure that requires field work, high cost and time. Therefore, the objective of this work was to develop dendrometric equations based on aerial images of RPAS (Remotely Piloted Aircraft System) combined with the manipulation of vector data, and to estimate the forest production of Eucalyptus sp., in the southern region of the state of Rondônia. The extraction of the variables crown projection area (ACRPAS) and total height (hRPAS) were derived from RPAS images, these have been validated from data obtained in the field by means of forest inventory and by standing trees rigorous scaling. Diametric and volumetric equations were adjusted according to the variables obtained by RPAS. The total height (h) of the scaled trees and hRPAS did not show significant differences (X²= 31.013). When comparing the estimates obtained by the v equation with their respective values of the scaling, it is verified that the v equation is accurate for estimates of the variable under study (X²= 0.8077). The results indicated success in tree detection, even with the difficulties in delimiting the crowns due to occlusion. Despite the challenges arising from flight altitudes below 250 m, the estimates of diameter, height, and individual volume using metrics derived from RPAS products were accurate, consistent with those obtained in the traditional forest inventory. The use of RPAS for volumetric quantification of forest stands in Rondônia represents an innovative approach in the sector, despite the challenges encountered.

RPAS operations in urban airflow: Efficient modelling of representative wind speed variations along a flight path through a flow field with changing turbulence characteristics

For remotely piloted aircraft systems (RPAS) operating in the urban environment, the flight path will most likely pass through the turbulent airflow of building wakes. To attenuate or reject instabilities caused by the turbulent airflow, control systems strategies are being developed in preparation for increased demand on the smaller urban air mobility vehicles. In this paper, results from urban airflow measurements of four 1:300 scaled Canadian city model flow fields are examined to provide detailed understanding of flow characteristics that can be found in typical Canadian cities. The results show common patterns for the range of inner city wind speed and turbulence intensity but that within the urban boundary layer (UBL) the variation of flow characteristics does not have a gradient with respect to altitude and that flow characteristics are highly variable along representative flight paths. For the purpose of simulation of RPAS response to the complex urban airflow, methods for using real urban flows data as inputs to simulations is presented. Demonstration of an Advancing Fourier Series (AFS) method, using the measured city data, shows successful preservation of the spatial- and temporal-correlations for reconstruction of a flow time series offering a novel, efficient and flexible approach for implementation of a time-based simulation of RPAS response in complex flow environments such as the urban environment.

Seasonal Phragmites australis classification in Long Point National Wildlife Area wetlands using a remotely piloted aircraft system and random forest machine learning

This study produced a high-accuracy remotely piloted aircraft system (RPAS) imagery classification method for identifying the invasive reed Phragmites australis ( Cav.) Trin. Ex Steud subsp. australis using random forest (RF) machine learning. RPAS imagery was collected in the spring and fall of 2019 using a fixed-wing RPAS equipped with a visible spectrum camera (eBee X, S.O.D.A. 3D; senseFly) in Long Point, Ontario, Canada. Imagery was used to produce separate early and late season classifications and a bi-temporal classification which used imagery from both dates. The overall accuracy achieved for each was 97%, 96%, and 91%, respectively. Digital surface models (DSMs) were the most important variable for identifying Phragmites in all classifications due to their greater height when compared to surrounding herbaceous vegetation. The bi-temporal classification, which utilized change in DSM value during the growing season, resulted in an estimated 47.8% new growth of Phragmites and appeared to capture sparse growth better than traditional classification differencing alone. This study highlights the promising use of high-resolution DSMs produced from RPAS imagery to classify invasive Phragmites and monitor within-year patch expansions.

Employment of RPA (Remotely Piloted Aircraft) in Identifying Imperfections in the Asphalt Pavement of the Transoeste Bus Rapid Transit (BRT) System

The Brazilian Road network is constantly used by the population and its maintenance is an essential charge to ensure the performance of the pavement. Therefore, the verification of asphalt conditions is extremely relevant to analyze their structural and functional qualities. It is noteworthy that this verification is usually performed by technicians manually being the results slow and often with impalpable and partial analysis. Given this, we see the need to study the benefits of obtaining this information indirectly without the need for inloco man. In this work we will focus on the use of Remotely Piloted Aircraft System to obtain the images that will enable the analysis of asphalt conditions. The place where the study will take place is the corridor of BRT Transoeste in Rio de Janeiro, which connects the region of Barra da Tijuca to Santa Cruz and Campo Grande, passing through Guaratiba and Recreio dos Bandeirantes. The following procedures will be adopted: Flight Planning with RPAS observing GSD Ground Sample Distance advocated by the scientific literature, field support using GNSS NTRIP technology, processing of images obtained with the use of Agisoft software and generating Orthorectified Orthophoto. Finally, the main objective of the work is to verify the potential of the metric quality of the images from RPAS in identifying damage to the asphalt surface

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43–90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1–2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review

Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately leading to increased profitability in crop production. This technology is becoming a reality in coffee farming, an essential commodity in the global economic balance, mainly due to academic attention and applicability. This study presents a bibliometric analysis focused on using RPASs in coffee farming to structure the existing academic literature and reveal trends and insights into the research topic. For this purpose, searches were conducted over the last 20 years (2002 to 2022) in the Web of Science and Scopus scientific databases. Subsequently, bibliometric analysis was applied using Biblioshiny for Bibliometrix software in R (version 2022.07.1), with emphasis on the temporal evolution of research on the topic, performance analysis highlighting key publications, journals, researchers, institutions, countries, and the scientific mapping of co-authorship, keywords, and future trends/possibilities. The results revealed 42 publications on the topic, with the pioneering studies being the most cited. Brazilian researchers and institutions (Federal University of Lavras) have a strong presence in publications on the subject and in journals focusing on technological applications. As future trends and possibilities, the employment of technology optimizes the productivity and profitability studies of coffee farming for the timely and efficient application of aerial imaging.