Journal articles on the topic 'Unmanned Aerial System Imagery Technology advances'
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1
Marcaccio, J. V., C. E. Markle, and P. Chow-Fraser. "UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 249–56. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-249-2015.
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With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < $3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km<sup>2</sup>) wetlands, or portions of larger wetlands throughout a year.
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Zhao, Biquan, Jiating Li, P. Stephen Baenziger, Vikas Belamkar, Yufeng Ge, Jian Zhang, and Yeyin Shi. "Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management." Agronomy 10, no. 11 (November 12, 2020): 1762. http://dx.doi.org/10.3390/agronomy10111762.
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Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show different spectral and morphological signatures have not been investigated much. This study investigated and compared the performance of models trained using aerial imagery collected at two growth stages of winter wheat with different canopy phenotypes. Specifically, three CNN-based models were trained with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. Results show that the multi-stage model trained by images from both growth stages outperformed the models trained by images from individual growth stages on all testing data. The mean accuracy of the multi-stage model was 89.23% for both growth stages, while the mean of the other two models were 52.32% and 84.9%, respectively. This study demonstrates the importance of diversity of training data in big data analytics, and the feasibility of developing a universal decision support system for wheat lodging detection and mapping multi-growth stages with high-resolution remote sensing imagery.
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Chang, Anjin, Jinha Jung, Murilo M. Maeda, Juan A. Landivar, Henrique D. R. Carvalho, and Junho Yeom. "Measurement of Cotton Canopy Temperature Using Radiometric Thermal Sensor Mounted on the Unmanned Aerial Vehicle (UAV)." Journal of Sensors 2020 (August 19, 2020): 1–7. http://dx.doi.org/10.1155/2020/8899325.
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Canopy temperature is an important variable directly linked to a plant’s water status. Recent advances in Unmanned Aerial Vehicle (UAV) and sensor technology provides a great opportunity to obtain high-quality imagery for crop monitoring and high-throughput phenotyping (HTP) applications. In this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing. Raw thermal imagery collected over a cotton field was converted to surface temperature using the Software Development Kit (SDK) provided by the sensor company. Canopy temperature map was generated using Structure from Motion (SfM), and Thermal Stress Index (TSI) was calculated for the test site. UAV temperature measurements were compared to ground measurements acquired by net radiometers and thermocouples. Temperature differences between UAV and ground measurements were less than 5%, and UAV measurements proved to be more stable. The proposed UAV system was successful in showing temperature differences between the cotton genotype. In conclusion, the system described in this study could possibly be used to monitor crop water status in a field setting, which should prove helpful for precision agriculture and crop research.
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Chávez, José Luis, Alfonso F. Torres-Rua, Wayne E. Woldt, Huihui Zhang, Christopher C. Robertson, Gary W. Marek, Dong Wang, Derek M. Heeren, Saleh Taghvaeian, and Christopher M. U. Neale. "A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western U.S." Applied Engineering in Agriculture 36, no. 4 (2020): 423–36. http://dx.doi.org/10.13031/aea.13941.
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Highlights Unmanned aerial systems (UAS) are able to provide data for precision irrigation management. Improvements are needed regarding UAS platforms, sensors, processing software, and regulations. Integration of multi-scale imagery into scientific irrigation scheduling tools are needed for technology adoption. Abstract . Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible to use sUAS technology to produce maps of actual crop coefficients, actual crop evapotranspiration, and soil water deficits, for irrigation management, the technology and regulations need to evolve further to facilitate a successful wide adoption and application. Improvements and standards are needed in terms of cameras’ spectral (bands) ranges, radiometric resolutions and associated calibrations, fuel/power technology for longer missions, better imagery processing software, and easier FAA approval of higher altitudes flight missions among other issues. Furthermore, the sUAS technology would play a larger role in irrigated agriculture when integrating multi-scale data (sUAS, ground-based or proximal, satellite) and soil water sensors is addressed, including the need for advances on processing large amounts of data from multiple and different sources, and integration into scientific irrigation scheduling (SIS) systems for convenience of decision making. Desirable technological innovations, and features of the next generation of UAS platforms, sensors, software, and methods for irrigated agriculture, are discussed. Keywords: Agricultural water management, Irrigation prescription mapping, Irrigation scheduling, Precision irrigation, Remote sensing, Sensors, Spatial crop evaOotranspiration, Unmanned aerial systems.
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Jin, Xiuliang, Zhenhai Li, and Clement Atzberger. "Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”." Remote Sensing 12, no. 6 (March 13, 2020): 940. http://dx.doi.org/10.3390/rs12060940.
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High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
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Neumann, John L., and Paula J. Durlach. "Human Factors and Trainability of Piloting a Simulated Micro Unmanned Aerial Vehicle." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 23 (September 2005): 2055–59. http://dx.doi.org/10.1177/154193120504902312.
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In 2004, the U.S. Army Research Institute's (ARI) Simulator Systems Research Unit began studies involving the training requirements for operators of a micro-unmanned aerial vehicle (MAV). Our research involved the use of a touch-screen operator control interface developed for the DARPA MAV Advanced Technology Demonstration. This control system allowed operators to plan and run autonomous flight missions or to tele-operate a simulated MAV around a static synthetic environment. An initial study focused primarily on the usability of the system. Extensive heuristic evaluations were conducted by seven volunteers with backgrounds in human factors and military training systems. Each evaluator completed a self-paced training session including exercises that tested their ability to perform various control functions. Lack of immediate feedback from touch-screen inputs and missing or obscure status information formed the basis of several of the usability issues. Manually piloting the MAV presented the most difficulty to operators. As such, a second study was conducted that focused specifically on manual control tasks. In this study, participants were trained on manual control of the MAV, and then completed four increasingly difficult missions, requiring them to pilot the vehicle through the synthetic environment. This experiment was designed to compare the effect of supplemental sensor imagery on mission performance. During Study 1, operators could choose to view a sensor image taken from a fixed camera pointed 15 degrees below horizontal or straight down (90 degrees), but only one view was available at a time. During Study 2, operators were provided with three sensor views simultaneously. The 15-degree view was presented in a primary sensor window, and two additional views were displayed in smaller windows below it. The camera angle of one of these supplemental views was the manipulated independent variable — 30, 60, or 90 degrees from horizontal. The remaining window always contained an overhead satellite view (downward view from 5000 feet above the MAV). Data were collected on time to complete each mission, the number of physical interactions each user made with the interface, SME ratings, workload, and usability. Results indicated that mission requirements had a greater effect on performance and workload ratings than the camera angle of the supplemental view, though the camera angle of the supplemental view did affect mission time required to capture images of designated target buildings. Session averages of workload, usability, mission completion time, and SME rating were significantly inter-correlated. Higher SME ratings were associated with lower participant ratings of workload, shorter mission completion times, and higher usability ratings.
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Sultonov, Furkat, Jun-Hyun Park, Sangseok Yun, Dong-Woo Lim, and Jae-Mo Kang. "Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery." Applied Sciences 12, no. 4 (February 13, 2022): 1953. http://dx.doi.org/10.3390/app12041953.
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Automatic road extraction from unmanned aerial vehicle (UAV) imagery has been one of the major research topics in the area of remote sensing analysis due to its importance in a wide range of applications such as urban planning, road monitoring, intelligent transportation systems, and automatic road navigation. Thanks to the recent advances in Deep Learning (DL), the tedious manual segmentation of roads can be automated. However, the majority of these models are computationally heavy and, thus, are not suitable for UAV remote-sensing tasks with limited resources. To alleviate this bottleneck, we propose two lightweight models based on depthwise separable convolutions and ConvMixer inception block. Both models take the advantage of computational efficiency of depthwise separable convolutions and multi-scale processing of inception module and combine them in an encoder–decoder architecture of U-Net. Specifically, we substitute standard convolution layers used in U-Net for ConvMixer layers. Furthermore, in order to learn images on different scales, we apply ConvMixer layer into Inception module. Finally, we incorporate pathway networks along the skip connections to minimize the semantic gap between encoder and decoder. In order to validate the performance and effectiveness of the models, we adopt Massachusetts roads dataset. One incarnation of our models is able to beat the U-Net’s performance with 10× fewer parameters, and DeepLabV3’s performance with 12× fewer parameters in terms of mean intersection over union (mIoU) metric. For further validation, we have compared our models against four baselines in total and used additional metrics such as precision (P), recall (R), and F1 score.
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Niu, Yaxiao, Liyuan Zhang, Huihui Zhang, Wenting Han, and Xingshuo Peng. "Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery." Remote Sensing 11, no. 11 (May 28, 2019): 1261. http://dx.doi.org/10.3390/rs11111261.
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The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.
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Ulhaq, Anwaar, and Douglas Pinto Sampaio Gomes. "Editorial for the Special Issue “Advances in Object and Activity Detection in Remote Sensing Imagery”." Remote Sensing 14, no. 8 (April 12, 2022): 1844. http://dx.doi.org/10.3390/rs14081844.
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Advances in data collection and accessibility, such as unmanned aerial vehicle (UAV) technology, the availability of satellite imagery, and the increasing performance of deep learning models, have had significant impacts on solving various remote sensing problems and proposing new applications ranging from vegetation and wildlife monitoring to crowd monitoring [...]
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Ułanowicz, Leszek, and Ryszard Sabak. "Unmanned aerial vehicles supporting imagery intelligence using the structured light technology." Archives of Transport 58, no. 2 (June 30, 2021): 35–45. http://dx.doi.org/10.5604/01.3001.0014.8796.
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One of the possible tasks for unmanned aerial vehicles (UAVs) is field capturing of object images. The field capturing of object images (scenes) is possible owing to the UAV equipped with photographic cameras, TV cameras, infrared camer-as or synthetic aperture radars (SAR). The result of the recognition is a metric mapping of space, i.e. 2D flat images. In order to increase the quality of image recognition, it is necessary to search for and develop stereoscopic visualization with the possibility of its mobile use. A pioneering approach presented in the research paper is using a UAV with an imagery intelligence system based on structured light technology for air reconnaissance of object over a selected area or in a given direction in the field. The outcome of imagery intelligence is a three-dimensional (3D imaging) information on the geometry of an observed scene. The visualization with a stereoscopic interface proposed in the work allows for a natural perception of the depth of the scene and mutual spatial relationships, as well as seeing which objects are closer and which are further. The essence of the article is to present the application of three-dimensional vision measurement technology on UAVs. The paper presents an analysis of the possibilities of using UAVs for image recognition and a method of image recognition based on the technology of structural lighting using the method of projection of Gray’a fringes and codes. The designed image recognition system based on the structural lighting technology is described. It also discusses task modules forming a measuring head, i.e., projection, detection and calculation modules, and the exchange of control or measurement data between imaging system components. It presents the results of tests on the possibility of rapidly acquiring images using a UAV. The test results and the analyses indicate that using a UAV with an imaging technology based on structural light can contribute to improving the abilities to detect, identify, locate and monitor objects at close range, within a selected direction outdoors or indoors.
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Schenkel, Jared, Paul Taele, Daniel Goldberg, Jennifer Horney, and Tracy Hammond. "Identifying Potential Mosquito Breeding Grounds: Assessing the Efficiency of UAV Technology in Public Health." Robotics 9, no. 4 (November 11, 2020): 91. http://dx.doi.org/10.3390/robotics9040091.
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Human ecology has played an essential role in the spread of mosquito-borne diseases. With standing water as a significant factor contributing to mosquito breeding, artificial containers disposed of as trash—which are capable of holding standing water—provide suitable environments for mosquito larvae to develop. The development of these larvae further contributes to the possibility for local transmission of mosquito-borne diseases in urban areas such as Zika virus. One potential solution to address this issue involves leveraging unmanned aerial vehicles that are already systematically becoming more utilized in the field of geospatial technology. With higher pixel resolution in comparison to satellite imagery, as well as having the ability to update spatial data more frequently, we are interested in investigating the feasibility of unmanned aerial vehicles as a potential technology for efficiently mapping potential breeding grounds. Therefore, we conducted a comparative study that evaluated the performance of an unmanned aerial vehicle for identifying artificial containers to that of conventionally utilized GPS receivers. The study was designed to better inform researchers on the current viability of such devices for locating a potential factor (i.e., small form factor artificial containers that can host mosquito breeding grounds) in the local transmission of mosquito-borne diseases. By assessing the performance of an unmanned aerial vehicle against ground-truth global position system technology, we can determine the effectiveness of unmanned aerial vehicles on this problem through our selected metrics of: timeliness, sensitivity, and specificity. For the study, we investigated these effectiveness metrics between the two technologies of interest in surveying a study area: unmanned aerial vehicles (i.e., DJI Phantom 3 Standard) and global position system-based receivers (i.e., Garmin GPSMAP 76Cx and the Garmin GPSMAP 78). We first conducted a design study with nine external participants, who collected 678 waypoint data and 214 aerial images from commercial GPS receivers and UAV, respectively. The participants then processed these data with professional mapping software for visually identifying and spatially marking artificial containers between the aerial imagery and the ground truth GPS data, respectively. From applying statistical methods (i.e., two-tailed, paired t-test) on the participants’ data for comparing how the two technologies performed against each other, our data analysis revealed that the GPS method performed better than the UAV method for the study task of identifying the target small form factor artificial containers.
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Uche, U. E., and S. T. Audu. "UAV for Agrochemical Application: A Review." Nigerian Journal of Technology 40, no. 5 (May 13, 2022): 795–809. http://dx.doi.org/10.4314/njt.v40i5.5.
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Unmanned aerial vehicles (UAVs) are tools for mechanized agriculture: they are used to alleviate maladies in a variety of fields through commercial, scientific, agricultural, and infrastructure enhancement. The purpose of the paper is to illuminate knowledge on mechanized agriculture using unmanned aircraft systems for pesticides and fertilizer application in obstacle rich farm. Various journal papers were reviewed to ascertain the state-of-the-art in agricultural unmanned aerial vehicles. X-rayed are unmanned aerial vehicle agrochemicals spraying architecture and efficacy, deployment and control strategies, obstacle sensing and avoidance systems, development/studies, and the limitations of the technology. The review shows that great strides have been made to develop agricultural unmanned aerial vehicles that can autonomously identify obstacle type, realize desired avoidance actions, and carry out variable rate agrochemical application. It is however noted that studies should continue on developing protocols and standard operation procedure, more human friendly interface platform, power technology, higher payload, real time quality imagery and robust mechanical features as well as enhanced sense and avoidance technology to meet the requirement of agricultural unmanned aerial vehicle for real time autonomous actions, flight endurance, low speed and low altitude. The paper therefore addressed the lack of awareness and absence of dedicated education on precision agriculture in the farming sector that has since ensured that its adoption level as a preferred system of farming remains very low in Nigeria despite the many benefits of unmanned aircraft vehicle farming technology
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Wysocki, Krzysztof, and Martyna Niewińska. "Counteracting imagery (IMINT), optoelectronic (EOIMINT) and radar (SAR) intelligence." Scientific Journal of the Military University of Land Forces 204, no. 2 (June 15, 2022): 222–44. http://dx.doi.org/10.5604/01.3001.0015.8975.
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The development of military technique and technology forces necessary changes in military reconnaissance using advanced methods of contemporary battlefield imaging. This paper addresses the topic of imagery intelligence as an essential source for gaining information about the deployment and quantity of means and forces of a potential enemy. Currently, armies of the world are equipped with modern imagery intelligence systems that make it possible to collect, process and analyse the collected data on enemy’s troops and the environment in which the enemy operates. The purpose of the study is to present the proper role of camouflage undertakings that make it possible to counteract imagery, optoelectronic and radar intelligence. The increasing capabilities in this problem area mean that in the near future intelligence tasks will be carried out not only by ground, space or naval systems, but primarily by reconnaissance aircraft and unmanned aerial systems. In accordance with the problem indicated in the topic, the paper brings closer the possibilities of counteracting imagery intelligence from the theoretical and practical perspective. In addition, it presents the latest camouflage solutions employed both in the Polish Armed Forces and other selected armies. At the end of the paper, the authors formulate the most important conclusions that constitute a generalisation of the results of studies presented in different parts of the publication.
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Matese, Alessandro. "Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”." Forests 11, no. 4 (April 5, 2020): 406. http://dx.doi.org/10.3390/f11040406.
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Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. This special issue (SI) collects nine papers reporting research on different forestry applications using UAV imagery. The special issue covers seven Red-Green-Blue (RGB) sensor papers, three papers on multispectral imagery, and one further paper on hyperspectral data acquisition system. Several data processing and machine learning methods are presented. The special issue provides an overview regarding potential applications to provide forestry characteristics in a timely, cost-efficient way. With the fast development of sensors technology and image processing algorithms, the forestry potential applications will growing fast, but future work should consider the consistency and repeatability of these novel techniques.
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Filipovych, V., R. Shevchuk, and А. Mychak. "Satellite Imagery Application for Searching Buried Intrusive Structures." Science and Innovation 18, no. 2 (April 30, 2022): 59–65. http://dx.doi.org/10.15407/scine18.02.059.
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Introduction. At the current stage of information technology development, methods for remote sensing have been increasingly used for mineral exploration.Problem Statement. Significant capital intensity of geological works for intrusive bodies search when the crystalline basement is overlapped by a thin sedimentary cover requires the implementation of advanced methods that, on the one hand, allow reducing the costs of exploration and, on the other hand, enable increasing theaccuracy of objects identification.Purpose. The development of methodological framework for the application of remote sensing data to identify prospective areas in search of buried intrusive bodies.Materials and Methods. Medium (Landsat, Sentinel) and high (WorldView) resolution optical satellite imagery data in the thermal infrared and visible ranges of the electromagnetic radiation spectrum; radar satellite data (SRTM), multispectral aerial survey data obtained by unmanned aerial vehicles; methods for structuralinterpretation, digital terrain model analysis, results of field thermometry have been used in this research.Results. A few prospective sites for the search for buried intrusions within the Hubkivska and AnastasivskoBolyarska squares of the Novohrad-Volynskyi block of the Ukrainian Shield, regardless of the geophysical data, have been identified. These objects were later confirmed by detailed geomagnetic surveying and drilling. Withinthe detected thermal anomalies, several small (60—120 m long and 30—50 m wide) dikes have been detected. Four of the 5 wells drilled have confirmed the presence of dike bodies, and 1 well enters the fracture zone. In other areas, where detailed geophysical survey was carried out within the detected thermal anomalies, new dike bodieshave been discovered.Conclusions. The developed technique may be used as an additional tool in geological prospecting.
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Tan, C. H., M. Ng, D. S. B. Shaiful, S. K. H. Win, W. J. Ang, S. K. Yeung, H. B. Lim, M. N. Do, and S. Foong. "A smart unmanned aerial vehicle (UAV) based imaging system for inspection of deep hazardous tunnels." Water Practice and Technology 13, no. 4 (December 1, 2018): 991–1000. http://dx.doi.org/10.2166/wpt.2018.105.
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Abstract Inspection of deep tunnel networks is extremely challenging due to their inaccessibility, and them being an unknown and potentially hazardous environment. Unmanned aerial vehicles (UAVs) provide a viable alternative for access, and are unaffected by debris or sewer flow. However, commercial UAVs are designed for high altitude aerial imagery and are not appropriate for short-range detailed imaging of tunnel surfaces. In addition, autonomous flight is usually achieved using GPS, which is not available underground. This paper presents the design and development of a smart UAV platform, Surveyor with Intelligent Rotating Lens (SWIRL), customized for autonomous operation in tunnels. It can capture high resolution images for subsequent image processing, and defect detection and classification. An innovative rotating system enables undistorted imaging of the tunnel's inner circumference surface using a single camera. The proposed location method using limited data resulted in substantial unit weight and power consumption reductions, compared to existing systems, making more than 35 minutes of autonomous flight possible.
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Stöcker, Claudia, Serene Ho, Placide Nkerabigwi, Cornelia Schmidt, Mila Koeva, Rohan Bennett, and Jaap Zevenbergen. "Unmanned Aerial System Imagery, Land Data and User Needs: A Socio-Technical Assessment in Rwanda." Remote Sensing 11, no. 9 (May 1, 2019): 1035. http://dx.doi.org/10.3390/rs11091035.
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Unmanned Aerial Systems (UAS) are emerging as a tool for alternative land tenure data acquisition. Even though UAS appear to represent a promising technology, it remains unclear to what extent they match the needs of communities and governments in the land sector. This paper responds to this question by undertaking a socio-technical study in Rwanda, aiming to determine the match between stakeholders’ needs and the characteristics of the UAS data acquisition workflow and its final products as valuable spatial data for land administration and spatial planning. A needs assessment enabled the expression of a range of land information needs across multiple levels and stakeholder sectors. Next to the social study, three different UAS were flown to test not only the quality of data but the possibilities of the use of this technology within the current institutional environment. A priority list of needs for cadastral and non-cadastral information as well as insights into operational challenges and data quality measures of UAS-based data products are presented. It can be concluded that UAS can have a significant contribution to match most of the prioritized needs in Rwanda. However, the results also reveal that structural and capacity conditions currently undermine this potential.
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Prahara, Adhi, Son Ali Akbar, and Ahmad Azhari. "Texton Based Segmentation for Road Defect Detection from Aerial Imagery." International Journal of Artificial Intelligence Research 4, no. 2 (January 5, 2021): 107. http://dx.doi.org/10.29099/ijair.v4i2.179.
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Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.
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Michez, Adrien, Kevin Morelle, François Lehaire, Jérome Widar, Manon Authelet, Cédric Vermeulen, and Philippe Lejeune. "Use of unmanned aerial system to assess wildlife (Sus scrofa) damage to crops (Zea mays)." Journal of Unmanned Vehicle Systems 4, no. 4 (December 1, 2016): 266–75. http://dx.doi.org/10.1139/juvs-2016-0014.
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Damage caused by ungulates to agricultural areas is difficult to evaluate because the real extent of the damage remains usually poorly described and potentially leads to conflicts. Recent advances in unmanned aerial systems (UAS) provide new versatile mapping and quantification possibilities in a wide range of applications. We used crop fields (Zea mays) damaged by wild boar (Sus scrofa) and compared the extent of the damage by means of three methods: (i) traditional ground-based assessment; (ii) UAS orthoimages with operator delineation; and (iii) UAS crop height model with automatic delineation based on height threshold. We showed for the first time that UAS can be applied for assessing damage of ungulates to agriculture. The two methods using UAS imagery provide coherent and satisfactory results and tend to underestimate the damage area when compared to in-use ground-based field expertise. However, we suggest that performance of UAS should further be tested in variable conditions to assess the broad application of this tool. Our study describes the potential of UAS as a tool for estimating more accurately the damage area and subsequently the compensation costs for wildlife damage. The proposed approach can be used in support of local and regional policies for the definitions of compensation for farmers.
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Liu, Han, Randy Dahlgren, Royce Larsen, Scott Devine, Leslie Roche, Anthony O’ Geen, Andy Wong, Sarah Covello, and Yufang Jin. "Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite." Remote Sensing 11, no. 5 (March 12, 2019): 595. http://dx.doi.org/10.3390/rs11050595.
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Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.
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Kim, Eun-Ju, Sook-Hyun Nam, Jae-Wuk Koo, and Tae-Mun Hwang. "Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea." Water 13, no. 14 (July 13, 2021): 1930. http://dx.doi.org/10.3390/w13141930.
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The purpose of this study is to compare the spectral indices for a two-dimensional river algae map using an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) hybrid system. The UAV and USV hybrid systems can overcome the limitation of not being able to effectively compare images of the same region obtained at different times and under different seasonal conditions, when using a method of comparing and analyzing with absolute values in remote sensing. Radiometric correction was performed to minimize the interference that could distort the analysis results of the UAV imagery, and the images were taken under weather conditions that would minimally affect them. Three spectral indices, namely, normalized difference vegetation index (NDVI), normalized green–red difference index (NGRDI), green normalized difference vegetation index (GNDVI), and normalized difference red edge index (NDRE) were compared for the chlorophyll-a images. In field application and correlational analysis, the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). As a result of comparing the chlorophyll-a concentration with the in-situ chlorophyll-a imagery by UAV, we obtained the RMSE of NDVI at 2.25, and the RMSE of GNDVI at 3.41.
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Mishra, Ishan, Aayush Kumar, and Vanshaj Malhotra. "Conceptual Design of an Unmanned Aerial Vehicle for Mars Exploration." European Journal of Engineering and Technology Research 6, no. 5 (August 15, 2021): 111–17. http://dx.doi.org/10.24018/ejers.2021.6.5.2528.
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Significant technology advances have enabled planetary exploration aircraft to be considered as a viable science platform. These systems fill in a unique planetary science measurement gap, that of the regional scale, near-surface observation while providing a new perspective for planetary discovery. Exploration of Mars using UAV (Unmanned Aerial Vehicle) has been planned for over 25 years by leading space organizations such as NASA. Recent efforts have been able to produce some mature mission and flight system concepts, ready for flight project implementation. There are however si0gnificant numbers of challenges associated with getting an airplane to fly through the thin, carbon dioxide-rich Martian atmosphere. Traditional aircraft design expertise does not always apply to this sort of vehicle, and geometric, aerodynamic, and mission restrictions result in a restricted viable design space. This paper presents the conceptual approach that was taken to design a UAV capable of performing a VTOL (Vertical Take-Off and Landing) in the atmosphere of Mars. The UAV was designed to participate in the International Planetary Aerial Systems Challenge 2021. The UAV could carry a science payload of up to 5 kg (weight on mars).
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Mishra, Ishan, Aayush Kumar, and Vanshaj Malhotra. "Conceptual Design of an Unmanned Aerial Vehicle for Mars Exploration." European Journal of Engineering and Technology Research 6, no. 5 (August 15, 2021): 111–17. http://dx.doi.org/10.24018/ejeng.2021.6.5.2528.
Full textAbstract:
Significant technology advances have enabled planetary exploration aircraft to be considered as a viable science platform. These systems fill in a unique planetary science measurement gap, that of the regional scale, near-surface observation while providing a new perspective for planetary discovery. Exploration of Mars using UAV (Unmanned Aerial Vehicle) has been planned for over 25 years by leading space organizations such as NASA. Recent efforts have been able to produce some mature mission and flight system concepts, ready for flight project implementation. There are however si0gnificant numbers of challenges associated with getting an airplane to fly through the thin, carbon dioxide-rich Martian atmosphere. Traditional aircraft design expertise does not always apply to this sort of vehicle, and geometric, aerodynamic, and mission restrictions result in a restricted viable design space. This paper presents the conceptual approach that was taken to design a UAV capable of performing a VTOL (Vertical Take-Off and Landing) in the atmosphere of Mars. The UAV was designed to participate in the International Planetary Aerial Systems Challenge 2021. The UAV could carry a science payload of up to 5 kg (weight on mars).
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Zhou, G. Q., and W. Q. Di. "EXTRACTION OF HOUSES FROM POINT CLOUD LIDAR: PROBLEMS AND CHALLENGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 831–37. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-831-2020.
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Abstract. Although many efforts have been made on the extraction of houses from LiDAR (Light Detection and Ranging) and/or aerial imagery and/or their fusion, little investigation using co-registration between the orthoimage map and LiDAR on the basis of geodetic coordinates as element for house extraction. For this reason, this paper first overviews the advances of LiDAR and investigates the advantages and disadvantages of LiDAR system vs. traditional photogrammetry, and then indicates that LiDAR technology has not yet resolved all existing problems that traditional photogrammetry remained so far, such as texture information, LiDAR point cloud density. A comprehensive comparison in extraction of houses (feature information) from LiDAR data set and from aerial imagery are also presented. It has been widely accepted and admitted that full automation for extraction of houses (feature information in city area) from LiDAR point cloud has still been difficult. Therefore, this paper proposes a human-computer interaction operation for houses extraction through combination of LiDAR point cloud and the orthorectified high-resolution aerial imagery. The real data is utilized for validation of the proposed method.
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Williams, Jeff, Kevin Hand, and Christian Haselwimmer. "Unmanned Air Systems: Technology and Regulatory Advances for the Oil Spill Response Community." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 2017120. http://dx.doi.org/10.7901/2169-3358-2017.1.000120.
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Field testing small unmanned air systems (UAS) in marine oil spill response exercises began in 2006. Soon afterward there were multiple credible examples where uas's could complement the traditional roles which manned aircraft filled for oil spill observation. Testing stopped abruptly in 2007 when the U.S. Federal Aviation Administration changed rules for the commercial use of uas's. Testing resumed in 2013 after the U.S. Congress mandated that the FAA finalize operating rules for uas commercial use. Exercise tests validated oil spill observation by uas's when an experienced aerial oil spill observer confirmed that properly equipped uas platforms and cameras could offer results equal to manned aircraft flights. Today there are a much wider variety of uas's and increasingly more capable sensors which can be utilized for creating highly detailed maps or data collection for geographic information system applications such as the National Oceanic and Atmospheric Administration (NOAA) Environmental Response Management Application (ERMA). Radio technology advances have also improved the ability to transfer video/data over greater distance and faster speeds than initial tests. Mobile ad hoc networks of multiple radios can transfer uas data streams beyond line of sight and connect with the internet for even broader distribution. This same network can also be used by responders in the field to exchange video, voice and location data and be linked real time with command post map displays and data feeds creating a true common operating picture across the entire response effort. From an organizational perspective, uas's are not discussed in the 2014 USCG Incident Management Handbook. Despite this however, their activities need coordinated with manned aircraft through Air Operations for regulations and safety. Staging them at airports serves little purpose given their flexibility and small size. Better utilization would be achieved placing the uas and operators near the command posts or at staging sites alongside the boats or vehicles they would work from. Their unique differences would also support creating a UAS Group Supervisor in Air Operations to clarify their requirements and tasking. The Situation Unit would typically be the best central receiving point for incoming data and from there aerial observers and data specialists can route video / data to operations, gis users and display operators managing the common operating picture. Additional topics for final presentation:*See and avoid capabilities*Automatic Dependent Surveillance–Broadcast (ADS-B) transmitters/receivers*Night flights approval*New operator regulations not requiring pilot's license
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Montgomery, Sarah M., Brandi B. Karisch, and Joby Czarnecki. "107 Comparison of Vegetative Indices Using an Unmanned Aerial Vehicle (UAV) and Satellite Imagery in Heifers Grazing Annual Ryegrass." Journal of Animal Science 100, Supplement_1 (March 8, 2022): 29. http://dx.doi.org/10.1093/jas/skac028.056.
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Abstract Grazing management is heading towards a more digitized world where producers can use technology such as satellite imagery, drone imagery and global positioning system (GPS) data sets for matching real-time forage availability to livestock nutritional needs. The objective of this study was to compare vegetative indices between unmanned aerial vehicles (UAV) and satellite imagery to observe changes in forage mass in relation to environment and grazing pressure. This project took place at the H. H. Leveck Animal Research Center located in Starkville, MS where nine, two-hectare pastures planted in Marshall annual ryegrass (Lolium multiflorum) were utilized. Each pasture was monitored with a satellite and UAV over a 76-day grazing period with images taken every 14-days. Images from the UAV were captured at 121.9 m above ground level using a DJI inspire 2 rotor wing fitted with a MicaSense RedEdge camera (MicaSense®; Seattle,WA). Satellite images were captured using a constellation of satellites from Planet (Plant Labs Inc.; San Francisco, CA). Images were processed using Pix4D (Pix4D SA; Prilly, Switzerland) and analyzed in ArcGIS (ArcGIS; Esri; Redlands, CA). Normalized vegetation index (NDVI) was calculated for both imagery platforms. In addition, forage samples were collected for future analysis every 14-days to calculate biomass and observe forage nutritive value. Each UAV image was resampled using cubic convolution to match spatial resolution of the satellite image. Raster calculator in ArcGIS was utilized to calculate NDVI values between images. Reported NDVI value data were analyzed in the NPAR1WAY procedure of SAS® software, Version 9.4 (SAS Institute, Cary, NC, 2013). using a significance level of α = 0.05. Greenness of vegetation was significantly greater (P< 0.0001) for the UAV compared to the satellite imagery with NDVI being 0.80 compared to 0.78 respectively. In conclusion, vegetation observed by UAV imagery reports greater density of green vegetation.
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Fourlas, George K., and George C. Karras. "A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles." Machines 9, no. 9 (September 13, 2021): 197. http://dx.doi.org/10.3390/machines9090197.
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The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.
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Kainz, Ondrej, Marek Gera, Miroslav Michalko, and František Jakab. "Experimental Solution for Estimating Pedestrian Locations from UAV Imagery." Applied Sciences 12, no. 19 (September 21, 2022): 9485. http://dx.doi.org/10.3390/app12199485.
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This research describes an experimental solution used for estimating the positions of pedestrians from video recordings. Additionally, clustering algorithms were utilized to interpret the data. The system employs the You Only Look Once (YOLO) algorithm for object detection. The detection algorithm is applied to video recordings provided by an unmanned aerial vehicle (UAV). An experimental method for calculating the pedestrian’s geolocation is proposed. The output of the calculation, i.e., the data file, can be visualized on a map and analyzed using cluster analyses, including K-means, DBSCAN, and OPTICS algorithms. The experimental software solution can be deployed on a UAV or other computing devices. Further testing was performed to evaluate the suitability of the selected algorithms and to identify optimal use cases. This solution can successfully detect groups of pedestrians from video recordings and it provides tools for subsequent cluster analyses.
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Bash, Eleanor A., and Brian J. Moorman. "Surface melt and the importance of water flow – an analysis based on high-resolution unmanned aerial vehicle (UAV) data for an Arctic glacier." Cryosphere 14, no. 2 (February 12, 2020): 549–63. http://dx.doi.org/10.5194/tc-14-549-2020.
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Abstract. Models of glacier surface melt are commonly used in studies of glacier mass balance and runoff; however, with limited data available, most models are validated based on ablation stakes and data from automatic weather stations (AWSs). The technological advances of unmanned aerial vehicles (UAVs) and structure from motion (SfM) have made it possible to measure glacier surface melt in detail over larger portions of a glacier. In this study, we use melt measured using SfM processing of UAV imagery to assess the performance of an energy balance (EB) and enhanced temperature index (ETI) melt model in two dimensions. Imagery collected over a portion of the ablation zone of Fountain Glacier, Nunavut, on 21, 23, and 24 July 2016 was previously used to determine distributed surface melt. An AWS on the glacier provides some measured inputs for both models as well as an additional check on model performance. Modelled incoming solar radiation and albedo derived from UAV imagery are also used as inputs for both models, which were used to estimate melt from 21 to 24 July 2016. Both models estimate total melt at the AWS within 16 % of observations (4 % for ETI). Across the study area the median model error, calculated as the difference between modelled and measured melt (EB = −0.064 m, ETI = −0.050 m), is within the uncertainty of the measurements. The errors in both models were strongly correlated to the density of water flow features on the glacier surface. The relation between water flow and model error suggests that energy from surface water flow contributes significantly to surface melt on Fountain Glacier. Deep surface streams with highly asymmetrical banks are observed on Fountain Glacier, but the processes leading to their formation are missing in the model assessed here. The failure of the model to capture flow-induced melt would lead to significant underestimation of surface melt should the model be used to project future change.
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Kulhavy, David, I.-Kuai Hung, Daniel R. Unger, Reid Viegut, and Yanli Zhang. "Measuring Building Height Using Point Cloud Data Derived from Unmanned Aerial System Imagery in an Undergraduate Geospatial Science Course." Higher Education Studies 11, no. 1 (January 5, 2021): 105. http://dx.doi.org/10.5539/hes.v11n1p105.
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The use of Unmanned Aerial Systems (UAS), also known as drones is increasing in geospatial science curricula within the United States. Within the Arthur Temple College of Forestry and Agriculture (ATCOFA) at Stephen F. Austin State University, Texas, seniors in the geospatial science program complete capstone projects to evaluate current geospatial technology to investigate complex ecological, social and environmental issues. Under the umbrella of a student initiated and designed senior project, students designed a study to estimate height of buildings with UAS data incorporating UAS data, LP360 and ArcScene programs, and Pictometry web-based interface. Results from a statistical analysis of the data confirm that geospatial science height estimation techniques can provide accurate estimates of height remotely. The independence of the students completing the project with UAS data for LP360 and ArcScene estimations, and utilizing Pictometry as an on-onscreen measuring tool, point to the need to integrate remote sensing, statistical analysis and synthesis of data into undergraduate geospatial science curricula. This reinforces the hands-on learning approach within ATCOFA and provides guidance to integrate the use of UAS in natural resource education.
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Leckie, Donald G. "Advances in remote sensing technologies for forest surveys and management." Canadian Journal of Forest Research 20, no. 4 (April 1, 1990): 464–83. http://dx.doi.org/10.1139/x90-063.
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Canadian forest management has had a long history of developing and implementing remote sensing technology and is a major user of remote sensing. Despite difficulties in developing and implementing new digital remote sensing techniques, several key developments in Canadian forest management and in remote sensing and computer technology make the development and implementation of new remote sensing techniques at this time feasible and appropriate. Integration of different remote sensing technologies, remote sensing data with other information sources through geographic information systems, and remote sensing interpretations with forest management systems and practices are critical. Current capabilities and new advances in remote sensing technology for forest survey (excluding forest damage assessment) are discussed. Satellite imagery is a cost-effective tool for broad forest type mapping. New satellite systems improve this capability, but their major impact will be in inventories for new clear-cut and burned areas. Advances in linear array imager technology and lidar systems may lead to development of an end to end inventory mapping system. This system would provide an alternative to aerial photography and current mapping methods and could revolutionize the way forests are inventoried. Imaging spectrometry is a new technology with applications in damage assessment, but as yet has limited potential for assisting in other forest surveys. Spaceborne imaging radar systems are being developed for the 1990s. These systems can produce imagery under cloudy conditions. Their major impact on forestry will be to provide an alternative to visible-infrared satellite data for inventory update.
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Ponte, Salvatore, Gennaro Ariante, Umberto Papa, and Giuseppe Del Core. "An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles." Electronics 9, no. 7 (July 19, 2020): 1175. http://dx.doi.org/10.3390/electronics9071175.
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Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.
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Moreno, Jose Luis, Jose Fernando Ortega, Miguel Ángel Moreno, and Rocío Ballesteros. "Using an unmanned aerial vehicle (UAV) for lake management: ecological status, lake regime shift and stratification processes in a small Mediterranean karstic lake." Limnetica 41, no. 2 (June 15, 2022): 1. http://dx.doi.org/10.23818/limn.41.21.
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High-resolution remote sensing imagery by unmanned aerial vehicles (UAVs) has been used as a tool for the environmental management of natural resources. Monitoring programmes that evaluate the ecological status of water bodies according to the Water Framework Directive (WFD) involve significant costs and sampling efforts that can be reduced by using UAVs. UAV imagery was used to measure some metrics of the “macrophytes and phytobenthos” biological quality element (BQE), which is required to assess the ecological status of European lakes; e.g. the percentage cover of hydrophytes and helophytes. Eight UAV flights took place during an annual cycle (July 2016 to July 2017) in a small karstic lake located in southeast Spain. Limnolog¬ical surveys of physicochemical (temperature, conductivity, dissolved oxygen, pH) and biological (pigments) parameters were simultaneously performed to correctly interpret the UAV images. For each flight performance, an orthomosaic of georeferenced RGB images was obtained, and the different features of interest were monitored and quantified by an automated identification and classification system (the LAIC software). The UAV images allowed us to not only evaluate the lake’s ecological status by measuring macrophyte metrics, but to also detect relevant ecological events for environmental management. A gradual burial process of charophyte meadows by the proliferation of periphytic cyanobacterial was detected in an early state by UAV images. Stratification processes, such as hypolimnetic sulphur bacteria blooms or metalimnetic white colloidal layers, were also ob¬served by UAV imaging. We conclude that UAV imagery is a useful tool for environmental lake management.
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Gupta, Anunay, Tanzina Afrin, Evan Scully, and Nita Yodo. "Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities." Future Transportation 1, no. 2 (September 1, 2021): 326–50. http://dx.doi.org/10.3390/futuretransp1020019.
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The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.
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Elsheshtawy, Amr M., and Larisa A. Gavrilova. "Improving Linear Projects Georeferencing to Create Digital Models Using UAV Imagery." E3S Web of Conferences 310 (2021): 04001. http://dx.doi.org/10.1051/e3sconf/202131004001.
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Global Positioning System (GPS) on Unmanned Aerial Vehicles (UAV) platform relies on Micro Electro Mechanical Systems (MEMS) technology with a precision of 10 m at shooting time at UAV camera stations positions. Nonetheless, obstacles to the GPS signal at the finest flight altitude can prevent accurate camera stations positions retrieval. In this research, three different georeferencing techniques were compared with geometric precision. The first is Direct Georeferencing (DG), which mainly depends on using Navigation GPS onboard without using any Ground Control Points (GCPs). The second is Indirect Georeferencing (IG), which mainly depends on three GCPs used to assist Aero-Triangulation (AT). The third is Modified technique depends on the same three GCPs used in the second method and enhanced location of camera stations usage of the Linear Relation Model (LR Model). The study area was in the south of the Moscow Region, Russia. Threeimaging strips have been taken using the DJI PHANTOM 4 PRO UAV. The accuracy assessment was carried out using image-derived coordinates and checkpoints (CPs) residuals. This study emphasizes that the Modified methodology using enhanced camera stations positions gave better accuracy than using the drone GPS camera stations positions.
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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprsarchives-xl-3-w4-77-2016.
Full textAbstract:
Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial technology to accomplish direct georeferencing of the platform and/or the imaging payload. The Direct Mapping Solution for Unmanned Aerial Vehicles (DMS-UAV) is a complete and ready-to-integrate OEM solution for Direct Georeferencing (DG) on unmanned aerial platforms. Designed as a solution for systems integrators to create mapping payloads for UAVs of all types and sizes, the DMS produces directly georeferenced products for any imaging payload (visual, LiDAR, infrared, multispectral imaging, even video). Additionally, DMS addresses the airframe’s requirements for high-accuracy position and orientation for such tasks as precision RTK landing and Precision Orientation for Air Data Systems (ADS), Guidance and Control. <br><br> This paper presents results using a DMS comprised of an Applanix APX-15 UAV with a Sony a7R camera to produce highly accurate orthorectified imagery without Ground Control Points on a Microdrones md4-1000 platform conducted by Applanix and Avyon. APX-15 UAV is a single-board, small-form-factor GNSS-Inertial system designed for use on small, lightweight platforms. The Sony a7R is a prosumer digital RGB camera sensor, with a 36MP, 4.9-micron CCD producing images at 7360 columns by 4912 rows. It was configured with a 50mm AF-S Nikkor f/1.8 lens and subsequently with a 35mm Zeiss Sonnar T* FE F2.8 lens. Both the camera/lens combinations and the APX-15 were mounted to a Microdrones md4-1000 quad-rotor VTOL UAV. The Sony A7R and each lens combination were focused and calibrated terrestrially using the Applanix camera calibration facility, and then integrated with the APX-15 GNSS-Inertial system using a custom mount specifically designed for UAV applications. The mount is constructed in such a way as to maintain the stability of both the interior orientation and IMU boresight calibration over shock and vibration, thus turning the Sony A7R into a metric imaging solution. <br><br> In July and August 2015, Applanix and Avyon carried out a series of test flights of this system. The goal of these test flights was to assess the performance of DMS APX-15 direct georeferencing system under various scenarios. Furthermore, an examination of how DMS APX-15 can be used to produce accurate map products without the use of ground control points and with reduced sidelap was also carried out. Reducing the side lap for survey missions performed by small UAVs can significantly increase the mapping productivity of these platforms. <br><br> The area mapped during the first flight campaign was a 250m x 300m block and a 775m long railway corridor in a rural setting in Ontario, Canada. The second area mapped was a 450m long corridor over a dam known as Fryer Dam (over Richelieu River in Quebec, Canada). Several ground control points were distributed within both test areas. <br><br> The flight over the block area included 8 North-South lines and 1 cross strip flown at 80m AGL, resulting in a ~1cm GSD. The flight over the railway corridor included 2 North-South lines also flown at 80m AGL. Similarly, the flight over the dam corridor included 2 North-South lines flown at 50m AGL. The focus of this paper was to analyse the results obtained from the two corridors. <br><br> Test results from both areas were processed using Direct Georeferencing techniques, and then compared for accuracy against the known positions of ground control points in each test area. The GNSS-Inertial data collected by the APX-15 was post-processed in Single Base mode, using a base station located in the project area via POSPac UAV. For the block and railway corridor, the basestation’s position was precisely determined by processing a 12-hour session using the CSRS-PPP Post Processing service. Similarly, for the flight over Fryer Dam, the base-station’s position was also precisely determined by processing a 4-hour session using the CSRS-PPP Post Processing service. POSPac UAV’s camera calibration and quality control (CalQC) module was used to refine the camera interior orientation parameters using an Integrated Sensor Orientation (ISO) approach. POSPac UAV was also used to generate the Exterior Orientation parameters for images collected during the test flight. <br><br> The Inpho photogrammetric software package was used to develop the final map products for both corridors under various scenarios. The imagery was first imported into an Inpho project, with updated focal length, principal point offsets and Exterior Orientation parameters. First, a Digital Terrain/Surface Model (DTM/DSM) was extracted from the stereo imagery, following which the raw images were orthorectified to produce an orthomosaic product.
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Mian, O., J. Lutes, G. Lipa, J. J. Hutton, E. Gavelle, and S. Borghini. "ACCURACY ASSESSMENT OF DIRECT GEOREFERENCING FOR PHOTOGRAMMETRIC APPLICATIONS ON SMALL UNMANNED AERIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W4 (March 17, 2016): 77–83. http://dx.doi.org/10.5194/isprs-archives-xl-3-w4-77-2016.
Full textAbstract:
Efficient mapping from unmanned aerial platforms cannot rely on aerial triangulation using known ground control points. The cost and time of setting ground control, added to the need for increased overlap between flight lines, severely limits the ability of small VTOL platforms, in particular, to handle mapping-grade missions of all but the very smallest survey areas. Applanix has brought its experience in manned photogrammetry applications to this challenge, setting out the requirements for increasing the efficiency of mapping operations from small UAVs, using survey-grade GNSS-Inertial technology to accomplish direct georeferencing of the platform and/or the imaging payload. The Direct Mapping Solution for Unmanned Aerial Vehicles (DMS-UAV) is a complete and ready-to-integrate OEM solution for Direct Georeferencing (DG) on unmanned aerial platforms. Designed as a solution for systems integrators to create mapping payloads for UAVs of all types and sizes, the DMS produces directly georeferenced products for any imaging payload (visual, LiDAR, infrared, multispectral imaging, even video). Additionally, DMS addresses the airframe’s requirements for high-accuracy position and orientation for such tasks as precision RTK landing and Precision Orientation for Air Data Systems (ADS), Guidance and Control. <br><br> This paper presents results using a DMS comprised of an Applanix APX-15 UAV with a Sony a7R camera to produce highly accurate orthorectified imagery without Ground Control Points on a Microdrones md4-1000 platform conducted by Applanix and Avyon. APX-15 UAV is a single-board, small-form-factor GNSS-Inertial system designed for use on small, lightweight platforms. The Sony a7R is a prosumer digital RGB camera sensor, with a 36MP, 4.9-micron CCD producing images at 7360 columns by 4912 rows. It was configured with a 50mm AF-S Nikkor f/1.8 lens and subsequently with a 35mm Zeiss Sonnar T* FE F2.8 lens. Both the camera/lens combinations and the APX-15 were mounted to a Microdrones md4-1000 quad-rotor VTOL UAV. The Sony A7R and each lens combination were focused and calibrated terrestrially using the Applanix camera calibration facility, and then integrated with the APX-15 GNSS-Inertial system using a custom mount specifically designed for UAV applications. The mount is constructed in such a way as to maintain the stability of both the interior orientation and IMU boresight calibration over shock and vibration, thus turning the Sony A7R into a metric imaging solution. <br><br> In July and August 2015, Applanix and Avyon carried out a series of test flights of this system. The goal of these test flights was to assess the performance of DMS APX-15 direct georeferencing system under various scenarios. Furthermore, an examination of how DMS APX-15 can be used to produce accurate map products without the use of ground control points and with reduced sidelap was also carried out. Reducing the side lap for survey missions performed by small UAVs can significantly increase the mapping productivity of these platforms. <br><br> The area mapped during the first flight campaign was a 250m x 300m block and a 775m long railway corridor in a rural setting in Ontario, Canada. The second area mapped was a 450m long corridor over a dam known as Fryer Dam (over Richelieu River in Quebec, Canada). Several ground control points were distributed within both test areas. <br><br> The flight over the block area included 8 North-South lines and 1 cross strip flown at 80m AGL, resulting in a ~1cm GSD. The flight over the railway corridor included 2 North-South lines also flown at 80m AGL. Similarly, the flight over the dam corridor included 2 North-South lines flown at 50m AGL. The focus of this paper was to analyse the results obtained from the two corridors. <br><br> Test results from both areas were processed using Direct Georeferencing techniques, and then compared for accuracy against the known positions of ground control points in each test area. The GNSS-Inertial data collected by the APX-15 was post-processed in Single Base mode, using a base station located in the project area via POSPac UAV. For the block and railway corridor, the basestation’s position was precisely determined by processing a 12-hour session using the CSRS-PPP Post Processing service. Similarly, for the flight over Fryer Dam, the base-station’s position was also precisely determined by processing a 4-hour session using the CSRS-PPP Post Processing service. POSPac UAV’s camera calibration and quality control (CalQC) module was used to refine the camera interior orientation parameters using an Integrated Sensor Orientation (ISO) approach. POSPac UAV was also used to generate the Exterior Orientation parameters for images collected during the test flight. <br><br> The Inpho photogrammetric software package was used to develop the final map products for both corridors under various scenarios. The imagery was first imported into an Inpho project, with updated focal length, principal point offsets and Exterior Orientation parameters. First, a Digital Terrain/Surface Model (DTM/DSM) was extracted from the stereo imagery, following which the raw images were orthorectified to produce an orthomosaic product.
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Fanta-Jende, P., D. Steininger, F. Bruckmüller, and C. Sulzbachner. "A VERSATILE UAV NEAR REAL-TIME MAPPING SOLUTION FOR DISASTER RESPONSE – CONCEPT, IDEAS AND IMPLEMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 429–35. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-429-2020.
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Abstract. In recent years, the proliferation and further development of unmanned aerial vehicles (UAVs) led to a great number of key technologies, advances and opportunities especially in the realm of time-critical applications. UAVs as a platform provide a unique combination of flexibility, affordability and sensor technology which enables the design of cost-effective and intriguing services particularly for disaster response. This contribution presents a concept for UAV-based near real-time mapping system for disaster relief to provide decision-making support for first responders particularly for possible disaster scenarios in Austria. We outline our system concept and its respective architecture, discuss requirements from a stakeholder perspective as well as legal regulations and initiatives at an EU level. In the methodology section of this paper, the preliminary data processing pipeline with respect to the near real-time orthomosaic generation and the semantic segmentation network are presented. Lastly, first experimental results of the pipeline are shown, and further advances are discussed.
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Brook, Anna, Seham Hamzi, Dar Roberts, Charles Ichoku, Nurit Shtober-Zisu, and Lea Wittenberg. "Total Carbon Content Assessed by UAS Near-Infrared Imagery as a New Fire Severity Metric." Remote Sensing 14, no. 15 (July 29, 2022): 3632. http://dx.doi.org/10.3390/rs14153632.
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The ash produced by forest fires is a complex mixture of organic and inorganic particles with many properties. Amounts of ash and char are used to roughly evaluate the impacts of a fire on nutrient cycling and ecosystem recovery. Numerous studies have suggested that fire severity can be assessed by measuring changes in ash characteristics. Traditional methods to determine fire severity are based on in situ observations, and visual approximation of changes in the forest floor and soil which are both laborious and subjective. These measures primarily reflect the level of consumption of organic layers, the deposition of ash, particularly its depth and color, and fire-induced changes in the soil. Recent studies suggested adding remote sensing techniques to the field observations and using machine learning and spectral indices to assess the effects of fires on ecosystems. While index thresholding can be easily implemented, its effectiveness over large areas is limited to pattern coverage of forest type and fire regimes. Machine learning algorithms, on the other hand, allow multivariate classifications, but learning is complex and time-consuming when analyzing space-time series. Therefore, there is currently no consensus regarding a quantitative index of fire severity. Considering that wildfires play a major role in controlling forest carbon storage and cycling in fire-suppressed forests, this study examines the use of low-cost multispectral imagery across visible and near-infrared regions collected by unmanned aerial systems to determine fire severity according to the color and chemical properties of vegetation ash. The use of multispectral imagery data might reduce the lack of precision that is part of manual color matching and produce a vast and accurate spatio-temporal severity map. The suggested severity map is based on spectral information used to evaluate chemical/mineralogical changes by deep learning algorithms. These methods quantify total carbon content and assess the corresponding fire intensity that is required to form a particular residue. By designing three learning algorithms (PLS-DA, ANN, and 1-D CNN) for two datasets (RGB images and Munsell color versus Unmanned Aerial System (UAS)-based multispectral imagery) the multispectral prediction results were excellent. Therefore, deep network-based near-infrared remote sensing technology has the potential to become an alternative reliable method to assess fire severity.
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Ariateja, Dananjaya, Uvi Desi Fatmawati, and Iqbal Ahmad Dahlan. "Instrumentasi Pemantauan Perairan Berbasis Telemetri Pada Prototipe Unmanned Surface Vehicle (USV)." JTEV (Jurnal Teknik Elektro dan Vokasional) 7, no. 2 (August 3, 2021): 200. http://dx.doi.org/10.24036/jtev.v7i2.113096.
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Technological advances, especially in the field of remote control, both automatic and non-automatic, are very rapid. This can be seen from the technological capabilities that can work on the land, air, and water. Indonesia as one of the largest archipelagic countries in the world that have borders such as land, air, and vast seas must have this technology to anticipate the potential for problems that endanger citizens and the state. So far, Indonesia has reached this technology, for example, drone technology as a border monitoring mission through the air and aerial photography purposes. Ground robots are used to defuse remote-controlled bombs. To complete this, the researchers conducted research related to a remotely controlled prototype of an unmanned water vehicle. The research conducted discusses the manufacture of prototypes of unmanned surface vehicles and control stations that can communicate with each other. This prototype is controlled by the Arduino Nano microcontroller module, while the main control system uses a desktop-based application that is run via a laptop. Based on the test results, sending sensor data to Arduino and to the control station via the RF-module media went well. The transmission distance of transmitting sensor data and navigation control reaches approximately 250 meters, while the transmission distance of IP camera images reaches approximately 9 meters.
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Eberhard, Lucie A., Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler. "Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping." Cryosphere 15, no. 1 (January 5, 2021): 69–94. http://dx.doi.org/10.5194/tc-15-69-2021.
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Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.
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Mashraqi, Aisha, Yousef Asiri, Abeer Algarni, and Hanaa Abu-Zinadah. "Drone imagery forest fire detection and classification using modified deep learning model." Thermal Science 26, Spec. issue 1 (2022): 411–23. http://dx.doi.org/10.2298/tsci22s1411m.
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With the progression of information technologies, unmanned aerial vehicles (UAV) or drones are more significant in remote monitoring the environment. One main application of UAV technology relevant to nature monitoring is monitoring wild animals. Among several natural disasters, Wildfires are one of the deadliest and cause damage to millions of hectares of forest lands or resources which threatens the lives of animals and people. Drones present novel features and convenience which include rapid deployment, adjustable and wider viewpoints, less human intervention, and high maneuverability. With the effective enforcement of deep learning in many applications, it is used in the domain of forest fire recognition for enhancing the accuracy of forest fire detection through extraction of deep semantic features from images. This article concentrates on the design of the drone imagery forest fire detection and classification using modified deep learning (DIFFDC-MDL) model. The presented DIFFDC-MDL model aims in the detection and classification of forest fire in drone imagery. To accomplish this, the presented DIFFDC-MDL model designs a modified MobileNet-v2 model to generate feature vectors. For forest fire classification, a simple recurrent unit model is applied in this study. In order to further improve the classification outcomes, shuffled frog leap algorithm is used. The simulation outcome analysis of the DIFFDC-MDL system was tested utilizing a database comprising fire and non-fire samples. The extensive comparison study referred that the improvements of the DIFFDC-MDL system over other recent algorithms.
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Cromwell, Connor, Jesse Giampaolo, Joseph Hupy, Zachary Miller, and Aishwarya Chandrasekaran. "A Systematic Review of Best Practices for UAS Data Collection in Forestry-Related Applications." Forests 12, no. 7 (July 20, 2021): 957. http://dx.doi.org/10.3390/f12070957.
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Recent advancements in unmanned aerial systems and GPS technology, allowing for centimeter precision without ground-based surveys, have been groundbreaking for applications in the field of forestry. As this technology becomes integrated into forest management approaches, it is important to consider the implementation of proper safety and data collection strategies. The creation of such documentation is beneficial, because it allows for those aspiring to create a UAS program to learn from others’ experiences, without bearing the consequences of past blunders associated with the development of these practices. When establishing a UAS program, it is pertinent to deeply research the necessary equipment, create documentation that establishes operational norms, and develop standards for in-field operations. Regarding multispectral vs. RGB sensor payloads, the sensor selection should be based upon what type of information is desired from the imagery acquired. It is also important to consider the methods for obtaining the most precise geolocation linked to the aerial imagery collected by the sensor. While selecting the proper UAS platform and sensor are key to establishing a UAS operation, other logistical strategies, such as flight crew training and operational planning, are equally important. Following the acquisition of proper equipment, further preparations must be made in order to ensure safe and efficient operations. The creation of crew resource management and safety management system documentation is an integral part of any successful UAS program. Standard operating procedure documents for individual tasks and undertakings are also a necessity. Standardized practices for the scheduling, communication, and management of the UAS fleet must also be formulated. Once field operations are set in motion, the continuous improvement of the documentation and best practices is paramount.
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Yoo, Seungho, and Woonghee Lee. "Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs." Sensors 21, no. 23 (December 4, 2021): 8111. http://dx.doi.org/10.3390/s21238111.
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Supported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might not be provided in densely populated areas, as the satellites coverage is broad but its resource capacity is limited. To offload the traffic of the densely populated area, we present an adaptable aerial access network (AAN), composed of low-Earth orbit (LEO) satellites and federated reinforcement learning (FRL)-enabled unmanned aerial vehicles (UAVs). Using the proposed system, UAVs could operate with relatively low computation resources than centralized coverage management systems. Furthermore, by utilizing FRL, the system could continuously learn from various environments and perform better with the longer operation times. Based on our proposed design, we implemented FRL, constructed the UAV-aided AAN simulator, and evaluated the proposed system. Base on the evaluation result, we validated that the FRL enabled UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances. In the evaluations, our proposed AAN system provided about 3.25 times more communication resources and had 5.1% lower latency than the satellite-only AAN.
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Pereira, Luísa Gomes, Paulo Fernandez, Sandra Mourato, Jorge Matos, Cedric Mayer, and Fábio Marques. "Quality Control of Outsourced LiDAR Data Acquired with a UAV: A Case Study." Remote Sensing 13, no. 3 (January 26, 2021): 419. http://dx.doi.org/10.3390/rs13030419.
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Over the last few decades, we witnessed a revolution in acquiring very high resolution and accurate geo-information. One of the reasons was the advances in photonics and LiDAR, which had a remarkable impact in applications requiring information with high accuracy and/or elevated completeness, such as flood modelling, forestry, construction, and mining. Also, miniaturization within electronics played an important role as it allowed smaller and lighter aerial cameras and LiDAR systems to be carried by unmanned aerial vehicles (UAV). While the use of aerial imagery acquired with UAV is becoming a standard procedure in geo-information extraction for several applications, the use of LiDAR for this purpose is still in its infancy. In several countries, companies have started to commercialize products derived from LiDAR data acquired using a UAV but not always with the necessary expertise and experience. The LIDAR-derived products’ price has become very attractive, but their quality must meet the contracted specifications. Few studies have reported on the quality of outsourced LiDAR data acquired with UAV and the problems that need to be handled during production. There can be significant differences between the planning and execution of a commercial project and a research field campaign, particularly concerning the size of the surveyed area, the volume of the acquired data, and the strip processing. This work addresses the quality control of LiDAR UAV data through outsourcing to develop a modelling-based flood forecast and alert system. The contracted company used the Phoenix Scout-16 from Phoenix LiDAR Systems, carrying a Velodyne VLP-16 and mounted on a DJI Matrice 600 PRO Hexacopter for an area of 560 ha along a flood-prone area of the Águeda River in Central Portugal.
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Kabir, Rabiul Hasan, and Kooktae Lee. "Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory." Applied Sciences 11, no. 9 (April 29, 2021): 4070. http://dx.doi.org/10.3390/app11094070.
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This paper addresses a wildlife monitoring problem using a team of unmanned aerial vehicles (UAVs) with the optimal transport theory. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work. In all different simulations, it is shown that the proposed scheme significantly outperforms other UAV-based wildlife monitoring strategies in terms of the target detection rate up to 3.6 times.
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Nikulin, Alex, and Timothy S. de Smet. "A UAV-based magnetic survey method to detect and identify orphaned oil and gas wells." Leading Edge 38, no. 6 (June 2019): 447–52. http://dx.doi.org/10.1190/tle38060447.1.
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Recent advances in autonomous unmanned aerial vehicle (UAV) technology, along with successful efforts to miniaturize total field magnetometers, offer a unique opportunity to test low-cost UAV-mounted systems for wide-area high-resolution magnetic surveys. Modern UAV platforms capable of flying at low altitudes and collecting dense aerial surveys, coupled with sensitive and compact instruments, allow identification of anthropogenic targets previously identifiable only in ground magnetometer surveys. We present results of a proof-of-concept study focused on developing and field testing a UAV-based magnetometer system to detect and identify abandoned and unmarked oil and gas wells in an area of historical hydrocarbon exploration and development in New York state. Our results indicate that magnetic anomalies associated with metal casing of vertical wells are pronounced considerably above background levels both at the surface and up to 50 m above-ground elevation. We determine that a detection altitude of 40 m is optimal to avoid any canopy interference while recording magnetic data at the highest signal-to-noise ratio. This methodology makes rapid detection and identification of unmarked wells possible and, in turn, allows for future sustainable development of these areas.
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Saddik, Amine, Rachid Latif, and Abdelhafid El Ouardi. "Low-Power FPGA Architecture Based Monitoring Applications in Precision Agriculture." Journal of Low Power Electronics and Applications 11, no. 4 (September 30, 2021): 39. http://dx.doi.org/10.3390/jlpea11040039.
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Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on a monitoring algorithm for agricultural fields, an electronic architecture based on a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. We focused our study on our own dataset of agricultural fields to validate the results. The fields studied in our case are in the Guelmin-Oued noun region in the south of Morocco. These fields are divided into two areas, with a total surface of 3.44 Ha2 for the first field and 3.73 Ha2 for the second. The images were collected using a DJI-type unmanned aerial vehicle and an RGB camera. Performance evaluation showed that the system could process up to 86 fps versus 12 fps or 20 fps in C/C++ and OpenMP implementations, respectively. Software optimizations have increased the performance to 107 fps, which meets real-time constraints.
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LeBoeuf, John. "648 Practical Applications of Site-specific Management—An Industry Perspective." HortScience 34, no. 3 (June 1999): 559D—559. http://dx.doi.org/10.21273/hortsci.34.3.559d.
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The initial surge of interest in precision agriculture technologies exhibited by innovators and early adopters involved in crop production appears to have crossed over an important threshold and made a significant development. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified. Access to accurate information pertaining to practical applications of site-specific management would be expected to motivate more producers to incorporate technology uses with crop production. This next group of producers has been watching technology developments as they preferred to avoid risk and wait for identifiable benefits. Waiting for detailed case studies involving high value fruit and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected, especially in the fresh-market industry that places quality attributes high on the list of desired features. Practical applications of technology that pertain to manageable factors will be the impetus to implementation of site-specific management. High resolution remote sensing imagery from digital aerial and satellite sensors has been used to identify plant stress, direct plant tissue and soil sampling efforts to identifiable soil variability, and provide valuable information for analysis and interpretation of crop growth. Examples of remote sensing imagery that has provided valuable in season progress reports will be identified. Imagery can then be used in a geographic information system along with field maps based on soil properties and physical characteristics determined by on-the-go tractors using various sensors. The focus will be on practice, not theory, as seen from an industry perspective.
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Ballaria, D., D. Orellana, E. Acostaa, A. Espinoza, and V. Morocho. "UAV MONITORING FOR ENVIROMENTAL MANAGEMENT IN GALAPAGOS ISLANDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1105–11. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1105-2016.
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In the Galapagos Islands, where 97% of the territory is protected and ecosystem dynamics are highly vulnerable, timely and accurate information is key for decision making. An appropriate monitoring system must meet two key features: on one hand, being able to capture information in a systematic and regular basis, and on the other hand, to quickly gather information on demand for specific purposes. The lack of such a system for geographic information limits the ability of Galapagos Islands’ institutions to evaluate and act upon environmental threats such as invasive species spread and vegetation degradation. In this context, the use of UAVs (unmanned aerial vehicles) for capturing georeferenced images is a promising technology for environmental monitoring and management. This paper explores the potential of UAV images for monitoring degradation of littoral vegetation in Puerto Villamil (Isabela Island, Galapagos, Ecuador). Imagery was captured using two camera types: Red Green Blue (RGB) and Infrarred Red Green (NIR). First, vegetation presence was identified through NDVI. Second, object-based classification was carried out for characterization of vegetation vigor. Results demonstrates the feasibility of UAV technology for base-line studies and monitoring on the amount and vigorousness of littoral vegetation in the Galapagos Islands. It is also showed that UAV images are not only useful for visual interpretation and object delineation, but also to timely produce useful thematic information for environmental management.