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Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use
Abstract and Figures
Unmanned Aircraft Systems (UAS) have evolved rapidly over the past decade driven primarily by military uses, and have begun finding application among civilian users for earth sensing reconnaissance and scientific data collection purposes. Among UAS, promising characteristics are long flight duration, improved mission safety, flight repeatability due to improving autopilots, and reduced operational costs when compared to manned aircraft. The potential advantages of an unmanned platform, however, depend on many factors, such as aircraft, sensor types, mission objectives, and the current UAS regulatory requirements for operations of the particular platform. The regulations concerning UAS operation are still in the early development stages and currently present significant barriers to entry for scientific users. In this article we describe a variety of platforms, as well as sensor capabilities, and identify advantages of each as relevant to the demands of users in the scientific research sector. We also briefly discuss the current state of regulations affecting UAS operations, with the purpose of informing the scientific community about this developing technology whose potential for revolutionizing natural science observations is similar to those transformations that GIS and GPS brought to the community two decades ago.
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... The use of Unmanned Aircraft Systems (UAS), also referred as unmanned aerial vehicles (UAVs), drones, and remotely piloted aircraft (RPA), is becoming increasingly popular in scientific research (Koh and Wich 2012;Marvin et al. 2016;Watts et al. 2012). The technology can cover a larger area for obtaining animal data, and several studies have used it to identify new areas of species occurrence, assess the density or distribution of a species , and monitoring changes in land cover (Koh and Wich 2012). ...
... The technology can cover a larger area for obtaining animal data, and several studies have used it to identify new areas of species occurrence, assess the density or distribution of a species , and monitoring changes in land cover (Koh and Wich 2012). Drones fall into several categories dependent on the time of flight, altitude travelled, or landing/take-off procedures (Watts et al. 2012). They also may be equipped with a variety of sensors, including RGB (optical cameras), thermal, multispectral, hyperspectral, and LiDAR, thereby enhancing their use for a variety of research purposes (Anderson and Gaston 2013;Watts et al. 2012). ...
... Drones fall into several categories dependent on the time of flight, altitude travelled, or landing/take-off procedures (Watts et al. 2012). They also may be equipped with a variety of sensors, including RGB (optical cameras), thermal, multispectral, hyperspectral, and LiDAR, thereby enhancing their use for a variety of research purposes (Anderson and Gaston 2013;Watts et al. 2012). ...
Every flight is a surprise: first records of the southern maned three-toed sloth (Bradypus crinitus: Bradypodidae) through drones Abstract: Drones (unmanned aerial vehicles, UAVs), are an advanced technology that allows the collection of large amounts of data in a short amount of time, including the detection of cryptic and arboreal animals. Here, we report the first records of the southern maned sloth Bradypus crinitus obtained with thermal cameras from a drone. As far as we know, this is the first time a sloth has been observed by a drone. We recorded four individuals of B. crinitus in the Brazilian Atlantic Forest, plus first records of Procyon cancrivorus, which were all resting in the forest canopy. The individuals' thermal responses demonstrate that the use of drones for research on these species may be a viable option to study still unexplored aspects of their ecology or conservation.
... Tactical class is also divided into subclasses: Medium Altitude Long Endurance (MALE), Endurance, Long Range, Medium Range, Short Range and Close Range. Watts, Ambrosia and Hinkley [10] classified platforms related to the scientific research sector. Their classification based on size, endurance, and capabilities divided into (i) High Altitude, Long Endurance (HALE), (ii) Medium Altitude, Long Endurance, (iii) Low Altitude, Long Endurance, (iv) Low Altitude, Short-Endurance Close, (v) Low Altitude, Short-Endurance, (vi) Vertical Take-Off & Landing (viii) Micro and (vii) Nano UAV. ...
... Terminology is a common word for the group of specific words or significances linking to a specific domain, and the study of such terms and their use [29]. Terms are words and compound words or multi-word expressions that in definite contexts are given definite significances 10 13 . Terminology of GPC Go Professional Cases (31 terms) 14 . ...
... These operations can convert the original images into the hyperspectral reflectance data of the whole tobacco fields. Radiometric correction is essential for correcting systematic error and radiation distortion (Watts et al., 2012). Considering the atmospheric gases and aerosols absorption during the image collection, methods based on the radiation transmission theory have been widely used for radiation correction, such as MODTRAN (Berk et al., 2014), 6S (Hu et al., 2013), and FLAASH (Vibhute et al., 2015). ...
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
... When the operator manually controls the micro-UAV, he should perform target-oriented navigation within dynamic physical limits and error margins [43], in which sensorimotor predictions are essential for adaptive behavior. Neuronal oscillations imply a mechanism for sampling sensory information and motor actions rhythmically, and the phase dynamics appear to be highly adaptable to temporal predictability in the environment while actively using the time processed by neurons to optimize task performance [42]. ...
The study of mental workload has attracted much interest in neuroergonomics, a frontier field of research. However, there appears no consensus on how to measure mental workload effectively because the mental workload is not only regulated by task difficulty but also affected by individual skill level reflected as mental schema. In this study, we investigated the alterations in the functional brain network induced by a 10-day simulated piloting task with different difficulty levels. Topological features quantifying global and local information communication and network organization were analyzed. It was found that during different tests, the global efficiency did not change, but the gravity center of the local efficiency of the network moved from the frontal to the posterior area; the small-worldness of the functional brain network became stronger. These results demonstrate the reconfiguration of the brain network during the development of mental schema. Furthermore, for the first two tests, the global and local efficiency did not have a consistent change trend under different difficulty levels, but after forming the developed mental schema, both of them decreased with the increase in task difficulty, showing sensitivity to the increase in mental workload. Our results demonstrate brain network reconfiguration during the motor learning process and reveal the importance of the developed mental schema for the accurate assessment of mental workload. We concluded that the efficiency of the brain network was associated with mental workload with developed mental schema.
... RGB cameras are most used due, mainly, to their low cost and ease of use (Pichon et al., 2019), while multispectral sensors can better evaluate plant health and stress status, due to their highest spectral resolution (Wang et al., 2019b). Finally, as another advantage of UAS, ultra-low altitude flying can reduce the effect of clouds on imagery and thereby improve the data quality (Watts, Ambrosia, and Hinkley, 2012;Sun et al., 2021). ...
Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.
... Selmy analyzes the use of drones in search and rescue operations.[18] Watts and others analyze drones in remote sensing and science.[19] Radovanović et al. show the application of unmanned ground vehicles in urban areas during military combat operations.[20] ...
Abstract: The development of modern weapons and military equipment as well as the need for the highest possible degree of protection of people in combat operations contributed to the development of autonomous and unmanned systems. The mentioned systems have a significant degree of application in the armed forces due to a wide range of uses. By implementing these systems in infantry units, the elements of resistance and force protection are increased to a higher level. Unmanned ground vehicles in an offensive operation can be used for activities of reconnaissance, raids, clearing and destruction of unexploded ordnance, evacuation of the injured and sick, transport, fire support, and others. The paper presents the capabilities and characteristics of various types of unmanned ground platforms and analyzes their use in combat operations and their role in protecting the forces of infantry units in an offensive operation. The advantages that these vehicles provide to infantry units are defined, and the conclusion represents the author's proposal for the implementation of unmanned ground vehicles in infantry units of the armed forces.
Rezime: Razvoj savremenog naoruţanja i vojne opreme kao i potreba za što većim stepenom zaštite ljudi u borbenim operacijama doprineo je razvoju autonomnih i bespilotnih sistema. Navedeni sistemi poseduju znaĉajan stepen primene u oruţanim snagama zbog širokog spektra upotrebe. Implementacijom ovih sistema u pešadijske jedinice povećavaju se elementi otpornost i zaštita snaga na viši nivo. Besposadna zemaljska vozila u napadnoj operaciji mogu se upotrebiti za aktivnosti izviĊanja, prepada, otkiravanja i uništavanja neeksplodiranih sredstava, evakuacije povreĊenih i obolelih, transporta, vatrene podrške, i drugih. U radu su prikazane mogućnosti i karakteristike razliĉitih vrsta besposadnih zemaljskih platformi i izvršena je analiza njhiove upotrebe u borbenim operacijama i uloga u zaštiti snaga pešadijskih jedinica u napadnoj operaciji. Definisane su prednosti koje ova vozila pruţaju pešadijskim jedinicama, a zakljuĉak predstavlja predlog autora za implementaciju zemaljskih besposadnih vozila u pešadijske jedinice oruţanih snaga. Ključne reči: bespilotni sistemi, autonomni sistemi, dron, veštaĉka inteligencija, besposadna zemaljska vozila, borbena operacija, zaštita snaga
Increasingly, unmanned aerial vehicles (UAVs) are used for surveying areas in search of a specific object, mapping, compiling an orthophotomap, or a three-dimensional terrain model, as well as for processing agricultural fields. At present, the development of algorithms for constructing a trajectory that covers a certain area and takes into account the limited energy resource on board the UAV remains an urgent task. The paper proposes an algorithm for calculating the trajectory for uniform coverage of the territory in the form of a polygon, which provides a complete study of the site by the UAV technical vision system, taking into account its parameters. The paper presents an analysis of existing algorithms for planning the coverage trajectory and proposes a new algorithm that takes into account obstacles in the surveyed area. The resulting trajectory is optimized by reducing the number of maneuvers and smoothing them out. As a result of the experiments, the flight time of the UAV along the trajectories calculated by the developed method on a site with an area of 950,000 m2 was reduced by 10% compared to the flight time along the trajectory calculated by the standard algorithm.
The increase in the use of unmanned aerial vehicles (UAVs) in marine mammal research has raised concerns, as its potential impacts remain largely unknown. To assess the effects of disturbances on harbor seals by a DJI Phantom 4 Pro, behavioral responses were recorded before, during, and after UAV approaches at two colonies. GLMMs were applied to assess proportions of seals displaying vigilance, resting, locomotion, and flushing behaviors. The proportion of time that selected individuals spent vigilant, and resting was also analyzed. Responses were evaluated as a function of UAV altitude, time of day, study site, group size, and environmental covariates. UAV presence triggered a significant increase in the proportion of seals vigilant and a decrease in resting. Greater reactions were observed at lower flying altitudes between 20 m and 10 m, although due to our study design, seals could have been reacting to lower flying altitudes and/or longer exposure times. Sound levels at different flying altitudes were investigated to aid interpretation of our results and to provide reference levels for future studies. Based on the disturbance thresholds identified, we devised recommendations on best practices to be implemented in future research on harbor seals using UAV techniques.
Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes.
This paper presents a novel approach for automatically finding conjugate points between video images collected by a small autonomous unmanned aircraft. Our approach introduces the idea of saving the resampled patch from successive least-squares matching epochs and using them as templates
for subsequent images. Tests show that this method is superior to using the first image as a template for all subsequent matching attempts. We show how the algorithm performs in terms of retention of points on successive images, distribution of points on the images, and utility when used for
bundle adjustments in comparison with the conventional method of using the first image as a template. Our proposed method is able to match points on an average of 2.7 times as many images before failure compared with using the conventional method. This leads to stronger geometrical configuration,
higher redundancy, and ultimately, significantly better bundle adjustment solutions.
Unmanned aircraft systems (UASs) are proposed as a useful alternative to manned aircraft for some aerial wildlife surveys. We described the components and current capabilities of a small UAS developed specifically for wildlife and ecological surveys that is currently in field use for a variety of applications. We also reviewed government regulations currently affecting the use of UASs in civilian airspace. Information on capabilities and regulations will be valuable for agencies and individuals interested in the potential UASs offer for monitoring wildlife populations and their habitat. Descriptions of current uses and recommendations for future employment will be helpful in implementing this technology efficiently for aerial surveys as the civilian sector begins to adopt UASs for peacetime missions.
This paper presents a practical evaluation of a direct georeferencing implementation with a quantitative analysis of the error budget of the georeferencing solution with respect to an Unmanned Aerial Remote Sensing System (UARSS). Direct georeferencing of remote sensing data using on-board navigation sensors is a critical step toward making near real-time remote sensing possible. By eliminating the need for costly and time-consuming ground control points, direct georeferencing provides the ability to fly over areas that do not have to be surveyed beforehand. This can decrease the resources required to produce usable remote sensing data by orders of magnitude. Particularly suitable are natural resource, agricultural, and infrastructure monitoring applications, where rapid and repeatable acquisition of the data directly into a Geographic Information System would be advantageous. A serious limitation of these systems is providing sufficient accuracy using low-cost and low-weight navigation sensors. This paper discusses a program for assessing the accuracy of the navigational sensors, as well as the results of a real-world implementation. A benchmarking platform for comparing the UARSS sensors to high-accuracy sensors is used for a ground-based test. Misclosures evaluated for determining the overall accuracy of the direct georeferencing system. The components of the error budget are presented, with an analysis of those elements relevant to the scope of this paper. Methods are presented for evaluating these additional components. The design goal of one meter ground accuracy is not met, and a forward-looking plan is presented for improving the performance of the system.
Project FiRE (First Response Experiment), a disaster management technology demonstration, was performed in 2001. The experiment demonstrated the use of a thermal multispectral scanning imager, integrated on an unmanned aerial vehicle (UAV), a satellite uplink/downlink image data telemetry system, and near-real-time geo-rectification of the resultant imagery for data distribution via the Internet to disaster managers. The FiRE demonstration provided geo-corrected image data over a controlled burn to a fire management community in near-real-time by means of the melding of new technologies. The use of the UAV demonstrated remotely piloted flight (thereby reducing the potential for loss of human life during hazardous missions), and the ability to “linger and stare” over the fire for extended periods of time (beyond the capabilities of human-pilot endurance). Improvements in a high-temperature calibrated thermal imaging scanner allowed “remote” operations from a UAV and provided real-time accurate fire information collection over a controlled burn. Improved bit-rate capacity telemetry capabilities increased the amount, structure, and information content of the image data relayed to the ground. The integration of precision navigation instrumentation allowed improved accuracies in geo-rectification of the resultant imagery, easing data ingestion and overlay in a GIS framework. We present a discussion of the feasibility of utilizing new platforms, improved sensor configurations, improved telemetry, and new geo-correction software to facilitate wildfire management and mitigation strategies.
This paper presents a practical evaluation of a direct georeferencing implementation with a quantitative analysis of the error budget of the georeferencing solution with respect to an Unmanned Aerial Remote Sensing System (UARSS). Direct georeferencing of remote sensing data using on-board navigation sensors is a critical step toward making near real-time remote sensing possible. By eliminating the need for costly and time-consuming ground control points, direct georeferencing provides the ability to fly over areas that do not have to be surveyed beforehand. This can decrease the resources required to produce usable remote sensing data by orders of magnitude. Particularly suitable are natural resource, agricultural, and infrastructure monitoring applications, where rapid and repeatable acquisition of the data directly into a Geographic Information System would be advantageous. A serious limitation of these systems is providing sufficient accuracy using low-cost and low-weight navigation sensors. This paper discusses a program for assessing the accuracy of the navigational sensors, as well as the results of a real-world implementation. A benchmarking platform for comparing the UARSS sensors to high-accuracy sensors is used for a ground-based test. Misclosures evaluated for determining the overall accuracy of the direct georeferencing system. The components of the error budget are presented, with an analysis of those elements relevant to the scope of this paper. Methods are presented for evaluating these additional components. The design goal of one meter ground accuracy is not met, and a forward-looking plan is presented for improving the performance of the system.
Utilization of plant above ground biomass has continued to be a critical Set difficult assessment in rangeland monitoring. Shrub size and woody structure further compound the measurement of shrub biomass utilization. This study was designed to determine the potential utility of low altitude / large scale (LA/LS) imagery in assessing shrub utilization. A near monoculture of Ceriotoides lanata (Pursh) J,T, Howell (winterfat) located in the western desert shrubland of Utah was used to evaluate this technique, Four, 3.1 by 3.1 m plots were identified and the shrubs within the plots were defoliated by hand-picking at about 10% intervals with imagery of the plots obtained between pickings. Imagery was obtained using a radio controlled airplane (drone) fitted with a 35 mm camera. Images were evaluated using image processing software and the resulting reflectance data correlated, with defoliation percentages (weight basis) for each plot. Reflectance data from images correlated highly with defoliation percentages (r(2) > 0.9). This technique of using LA/LS imagery shows promise for a quick and accurate tool in assessing utilization of shrubs.
Between 2006 and 2010, National Aeronautics and Space Administration (NASA) and the US Forest Service flew 14 unmanned airborne system (UAS) sensor missions, over 57 fires in the western US. The missions demonstrated the capabilities of a UAS platform (NASA Ikhana UAS), a multispectral sensor (autonomous modular sensor (AMS)), onboard processing and data visualization (Wildfire Collaborative Decision Environment (W-CDE)), to provide fire intelligence to management teams. Autonomous, on-board processing of the AMS sensor data allowed real-time fire product delivery to incident management teams on the wildfire events. The fire products included geo-rectified, colour-composite quick-look imagery, fire detection shape files, post-fire real-time normalized burn ratio imagery and burn area emergency response (BAER) imagery. The W-CDE was developed to allow the ingestion and visualization of AMS data and other pertinent fire-related information layers. This article highlights the technologies developed and employed, the UAS wildfire imaging missions performed and the outcomes and findings of the multi-year efforts.
We present a probabilistic technique for alignment and subsequent change detection using range sensor data. The alignment method is derived from a novel, non-rigid approach to register point clouds induced by pose-related range observations that are particularly erroneous. It allows for high scan estimation errors to be compensated distinctly, whilst considering temporally successive measurements to be correlated. Based on the alignment, changes between data sets are detected using a probabilistic approach that is capable of dif- ferentiating between likely and unlikely changes. When applied to observations containing even small dierences, it reliably identifies intentionally introduced modifications.