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1
Kwak, Kyung-Hwan, Seung-Hyeop Lee, A.-Young Kim, Kwon-Chan Park, Sang-Eun Lee, Beom-Soon Han, Joohyun Lee, and Young-San Park. "Daytime Evolution of Lower Atmospheric Boundary Layer Structure: Comparative Observations between a 307-m Meteorological Tower and a Rotary-Wing UAV." Atmosphere 11, no. 11 (October 22, 2020): 1142. http://dx.doi.org/10.3390/atmos11111142.
Повний текст джерелаАнотація:
A 307-m tall meteorological tower was used to evaluate meteorological observation data obtained using a rotary-wing unmanned aerial vehicle (UAV). A comparative study between the tower and UAV observations was conducted during the daytime (06:00 to 19:00 local time (LT)) in the summer of 2017 (16–18th August). Hourly vertical profiles of air temperature, relative humidity, black carbon (BC), and ozone (O3) concentrations were obtained for up to 300 m height. Statistical metrics for evaluating the accuracy of UAV observations against the tower observation showed positive (potential temperature) and negative (relative humidity) biases, which were within acceptable ranges. The daytime evolution of the lower atmospheric boundary layer (ABL) was successfully captured by the hourly UAV observations. During the early morning, a large vertical slope of potential temperature was observed between 100 and 140 m, corresponding to the stable ABL height. The large vertical slope coincided with the large differences in BC and O3 concentrations between altitudes below and above the height. The transition from stable to convective ABL was observed at 10–11 LT, indicated by the ABL height higher than 300 m in the convective ABL. Finally, we provide several recommendations to reduce uncertainties of UAV observation.
2
Liu, Jianli, Xiaohan Liao, Huping Ye, Huanyin Yue, Yong Wang, Xiang Tan, and Dongliang Wang. "UAV Swarm Scheduling Method for Remote Sensing Observations during Emergency Scenarios." Remote Sensing 14, no. 6 (March 15, 2022): 1406. http://dx.doi.org/10.3390/rs14061406.
Повний текст джерелаАнотація:
Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency.
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Yang, Bin, Wanxue Zhu, Ehsan Eyshi Rezaei, Jing Li, Zhigang Sun, and Junqiang Zhang. "The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing." Remote Sensing 14, no. 7 (March 24, 2022): 1559. http://dx.doi.org/10.3390/rs14071559.
Повний текст джерелаАнотація:
Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.
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Gao, Sha, Shu Gan, Xiping Yuan, Rui Bi, Raobo Li, Lin Hu, and Weidong Luo. "Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface." Processes 10, no. 1 (December 21, 2021): 4. http://dx.doi.org/10.3390/pr10010004.
Повний текст джерелаАнотація:
Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions. At present, derivative products have been widely used in landslide monitoring, landscape evolution, glacier movement, volume measurement, and landscape change detection. However, there is still a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness of the results in further applications of this technological approach. In this paper, validation work was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging observations. Specifically, three repeated flights were performed on the test plot to obtain three sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was used to process and construct a 3D scene model, and based on this model the digital surface model (DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself, a window selection-based method was used to sample the point cloud set data from the three-sortie repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into account the general methodology of redundant observation error analysis for topographic surveys. At the same time, to further analyze the limits of the UAV measurement technique, possible under equivalent observation conditions with the same processing environment, a difference model (DOD) was constructed for the DSM data from three sorties, to deepen the overall characterization of the differences between the DSMs obtained from repeated observations. The results of the experimental study concluded that both the analysis of the 3D point set measurements based on window sampling and the accuracy evaluation using the difference model were generally able to achieve a centimeter level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS method for geomorphological data acquisition and 3D reconstruction.
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Chen, Xiaotong, Qin Li, Ronghao Li, Xiangyuan Cai, Jiangnan Wei, and Hongying Zhao. "UAV Network Path Planning and Optimization Using a Vehicle Routing Model." Remote Sensing 15, no. 9 (April 22, 2023): 2227. http://dx.doi.org/10.3390/rs15092227.
Повний текст джерелаАнотація:
Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications
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Amalia, Nabila, Syamsidik Syamsidik, and Nazli Ismail. "Spatio-Temporal Analysis of Ground Movement Using Unmanned Aerial Vehicle Photogrammetry in Gampong Lamkleng, Aceh Besar." International Journal of Disaster Management 6, no. 1 (June 2, 2023): 61–74. http://dx.doi.org/10.24815/ijdm.v6i1.31770.
Повний текст джерелаАнотація:
Ground movement is one of the most frequent disasters causing major damages in Indonesia. Unmanned Aerial Vehicle (UAV) has been widely used as a rapid observation method to obtain detailed characterization of ground movement. Often, active landslide area is difficult to access. This hinders close monitoring and observations of the ground movement. This study aims to demonstrate the use of UAV as tools for monitoring and observations on active ground movement area and to validate the results. For this purpose, the study was conducted at Gampong Lamkleng, Aceh Besar-Indonesia using spatio-temporal analysis by UAV photogrammetry. The UAV was chosen because it is easy to use, practical, and safe for landslide area that are relatively small and difficult to reach. Aerial photographs were processed using the Agisoft Metashape software in modeling and analyzed using Quantum GIS (QGIS) and ArcGis. The observation results show that the largest ground movement occurred in January 20 to 23, 2021 which was related to precipitation rates measured at a nearby rainfall station. The movement volume was 1,411.063 m3 and a rate of ground movement reaching 0.69 m/day due to heavy rain. The estimated value of losses is IDR 1,055,854,000. The UAV images analysis were compared to actual ground elevation measured using Real-Time Kinematic (RTK). The validation show that the accuracy based on comparison between photogrammetric and RTK measurement was at agreeable rate (99%). Otherwise, the accuracy performed on 19 check point using Root Mean Square Error analysis found that the accuracy was still very low. The low RMSE value is due to the georeferencing process using the Ground Control Point not being carried out.
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Kezoudi, Maria, Christos Keleshis, Panayiota Antoniou, George Biskos, Murat Bronz, Christos Constantinides, Maximillien Desservettaz, et al. "The Unmanned Systems Research Laboratory (USRL): A New Facility for UAV-Based Atmospheric Observations." Atmosphere 12, no. 8 (August 13, 2021): 1042. http://dx.doi.org/10.3390/atmos12081042.
Повний текст джерелаАнотація:
The Unmanned Systems Research Laboratory (USRL) of the Cyprus Institute is a new mobile exploratory platform of the EU Research Infrastructure Aerosol, Clouds and Trace Gases Research InfraStructure (ACTRIS). USRL offers exclusive Unmanned Aerial Vehicle (UAV)-sensor solutions that can be deployed anywhere in Europe and beyond, e.g., during intensive field campaigns through a transnational access scheme in compliance with the drone regulation set by the European Union Aviation Safety Agency (EASA) for the research, innovation, and training. UAV sensor systems play a growing role in the portfolio of Earth observation systems. They can provide cost-effective, spatial in-situ atmospheric observations which are complementary to stationary observation networks. They also have strong potential for calibrating and validating remote-sensing sensors and retrieval algorithms, mapping close-to-the-ground emission point sources and dispersion plumes, and evaluating the performance of atmospheric models. They can provide unique information relevant to the short- and long-range transport of gas and aerosol pollutants, radiative forcing, cloud properties, emission factors and a variety of atmospheric parameters. Since its establishment in 2015, USRL is participating in major international research projects dedicated to (1) the better understanding of aerosol-cloud interactions, (2) the profiling of aerosol optical properties in different atmospheric environments, (3) the vertical distribution of air pollutants in and above the planetary boundary layer, (4) the validation of Aeolus satellite dust products by utilizing novel UAV-balloon-sensor systems, and (5) the chemical characterization of ship and stack emissions. A comprehensive overview of the new UAV-sensor systems developed by USRL and their field deployments is presented here. This paper aims to illustrate the strong scientific potential of UAV-borne measurements in the atmospheric sciences and the need for their integration in Earth observation networks.
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Kral, Stephan, Joachim Reuder, Timo Vihma, Irene Suomi, Ewan O’Connor, Rostislav Kouznetsov, Burkhard Wrenger, et al. "Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)—The Hailuoto 2017 Campaign." Atmosphere 9, no. 7 (July 16, 2018): 268. http://dx.doi.org/10.3390/atmos9070268.
Повний текст джерелаАнотація:
The aim of the research project “Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)” is to substantially increase the understanding of the stable atmospheric boundary layer (SBL) through a combination of well-established and innovative observation methods as well as by models of different complexity. During three weeks in February 2017, a first field campaign was carried out over the sea ice of the Bothnian Bay in the vicinity of the Finnish island of Hailuoto. Observations were based on ground-based eddy-covariance (EC), automatic weather stations (AWS) and remote-sensing instrumentation as well as more than 150 flight missions by several different Unmanned Aerial Vehicles (UAVs) during mostly stable and very stable boundary layer conditions. The structure of the atmospheric boundary layer (ABL) and above could be resolved at a very high vertical resolution, especially close to the ground, by combining surface-based measurements with UAV observations, i.e., multicopter and fixed-wing profiles up to 200 m agl and 1800 m agl, respectively. Repeated multicopter profiles provided detailed information on the evolution of the SBL, in addition to the continuous SODAR and LIDAR wind measurements. The paper describes the campaign and the potential of the collected data set for future SBL research and focuses on both the UAV operations and the benefits of complementing established measurement methods by UAV measurements to enable SBL observations at an unprecedented spatial and temporal resolution.
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Revill, Andrew, Anna Florence, Alasdair MacArthur, Stephen Hoad, Robert Rees, and Mathew Williams. "Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations." Remote Sensing 12, no. 11 (June 6, 2020): 1843. http://dx.doi.org/10.3390/rs12111843.
Повний текст джерелаАнотація:
Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.
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Matsuba, Yoshinao, Takenori Shimozono, and Yoshimitsu Tajima. "OBSERVATION OF NEARSHORE WAVE-WAVE INTERACTION USING UAV." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 12. http://dx.doi.org/10.9753/icce.v36.waves.12.
Повний текст джерелаАнотація:
Infragravity waves, generated by nearshore wave-wave interaction, potentially increase the coastal hazard. Lack of detailed observation of nearshore wave fields however makes it difficult to fully understand the behavior of infragravity waves under wave-wave interactions. These days, UAVs (Unmanned Aerial Vehicles) have enabled us to easily capture the top-view images of the dynamic nearshore behavior with sufficiently high spatial and temporal resolutions. In this study, we conducted UAV-based observations of cross-shore variations of wave spectral characteristics to clarify the nearshore wave-wave interactions.
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Budianti, Noviana, Masaaki Naramoto, and Atsuhiro Iio. "Drone-Sensed and Sap Flux-Derived Leaf Phenology in a Cool Temperate Deciduous Forest: A Tree-Level Comparison of 17 Species." Remote Sensing 14, no. 10 (May 23, 2022): 2505. http://dx.doi.org/10.3390/rs14102505.
Повний текст джерелаАнотація:
Understanding the relationship between leaf phenology and physiological properties has important implications for improving ecosystem models of biogeochemical cycling. However, previous studies have investigated such relationships only at the ecosystem level, limiting the biological interpretation and application of the observed relationships due to the complex vegetation structure of forest ecosystems. Additionally, studies focusing on transpiration are generally limited compared to those on photosynthesis. Thus, we investigated the relationship between stem sap flux density (SFD) and crown leaf phenology at the individual tree level using the heat dissipation method, unmanned aerial vehicle (UAV)-based observation, and ground-based visual observation across 17 species in a cool temperate forest in Japan, and assessed the potential of UAV-derived phenological metrics to track individual tree-level sap flow phenology. We computed five leaf phenological metrics (four from UAV imagery and one from ground observations) and evaluated the consistency of seasonality between the phenological metrics and SFD using Bayesian modelling. Although seasonal trajectories of the leaf phenological metrics differed markedly among the species, the daytime total SFD (SFDday) estimated by the phenological metrics was significantly correlated with the measured ones across the species, irrespective of the type of metric. Crown leaf cover derived from ground observations (CLCground) showed the highest ability to predict SFDday, suggesting that the seasonality of leaf amount rather than leaf color plays a predominant role in sap flow phenology in this ecosystem. Among the UAV metrics, Hue had a superior ability to predict SFDday compared with the other metrics because it showed seasonality similar to CLCground. However, all leaf phenological metrics showed earlier spring increases than did sap flow in more than half of the individuals. Our study revealed that UAV metrics could be used as predictors of sap flow phenology for deciduous species in cool, temperate forests. However, for a more accurate prediction, phenological metrics representing the spring development of sap flow must be explored.
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Tamondong, A., T. Nakamura, Y. Kobayashi, M. Garcia, and K. Nadaoka. "INVESTIGATING THE EFFECTS OF RIVER DISCHARGES ON SUBMERGED AQUATIC VEGETATION USING UAV IMAGES AND GIS TECHNIQUES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-5-2020 (August 3, 2020): 93–99. http://dx.doi.org/10.5194/isprs-annals-v-5-2020-93-2020.
Повний текст джерелаАнотація:
Abstract. One of the major factors controlling the distribution and abundance of marine submerged aquatic vegetation (SAV) is light availability. Reduced water clarity due to sediment loading from rivers greatly affects the health and coverage of seagrasses and seaweeds. Monitoring SAV using unmanned aerial vehicles (UAV) has been getting attention because of its cost-effectiveness and ease of use. In this research, a low-cost UAV was utilized to assess the impacts of river discharges on SAV in Busuanga Island, Philippines. Linear regression was performed to determine the effectivity and accuracy of UAV-based percent cover estimation compared to established field survey methods of monitoring SAV. Water quality was estimated in the study area by performing spatial interpolation methods of in situ measurement of turbidity, chlorophyll, temperature, salinity, and dissolved oxygen using a multi-parameter water quality sensor. Current velocity and tidal fluctuations were monitored using bottom-mounted sensors deployed near the river mouth and in seagrass and seaweed areas with relatively good water clarities. Four stations were surveyed using automated UAV missions which were flown simultaneously with field observations. Each station surveyed has varying distances from the river mouth. Results from the classification of the UAV data and field survey show that SAV is more abundant as the distance from the river mouth increases and the turbidity decreases. Classification overall accuracies of UAV orthophotos ranging from 87.91–93.41% were achieved using Maximum Likelihood (ML) Classification. Comparison of field-based and UAV-based survey of percent cover of seagrasses show an overestimation of 1.75 times from the UAV compared to field observations.
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Chen, Chunpeng, Bo Tian, Wenting Wu, Yuanqiang Duan, Yunxuan Zhou, and Ce Zhang. "UAV Photogrammetry in Intertidal Mudflats: Accuracy, Efficiency, and Potential for Integration with Satellite Imagery." Remote Sensing 15, no. 7 (March 29, 2023): 1814. http://dx.doi.org/10.3390/rs15071814.
Повний текст джерелаАнотація:
The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration.
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Cucci, Davide Antonio, Martin Rehak, and Jan Skaloud. "Bundle adjustment with raw inertial observations in UAV applications." ISPRS Journal of Photogrammetry and Remote Sensing 130 (August 2017): 1–12. http://dx.doi.org/10.1016/j.isprsjprs.2017.05.008.
Повний текст джерела
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Brosy, Caroline, Karina Krampf, Matthias Zeeman, Benjamin Wolf, Wolfgang Junkermann, Klaus Schäfer, Stefan Emeis, and Harald Kunstmann. "Simultaneous multicopter-based air sampling and sensing of meteorological variables." Atmospheric Measurement Techniques 10, no. 8 (August 1, 2017): 2773–84. http://dx.doi.org/10.5194/amt-10-2773-2017.
Повний текст джерелаАнотація:
Abstract. The state and composition of the lowest part of the planetary boundary layer (PBL), i.e., the atmospheric surface layer (SL), reflects the interactions of external forcing, land surface, vegetation, human influence and the atmosphere. Vertical profiles of atmospheric variables in the SL at high spatial (meters) and temporal (1 Hz and better) resolution increase our understanding of these interactions but are still challenging to measure appropriately. Traditional ground-based observations include towers that often cover only a few measurement heights at a fixed location. At the same time, most remote sensing techniques and aircraft measurements have limitations to achieve sufficient detail close to the ground (up to 50 m). Vertical and horizontal transects of the PBL can be complemented by unmanned aerial vehicles (UAV). Our aim in this case study is to assess the use of a multicopter-type UAV for the spatial sampling of air and simultaneously the sensing of meteorological variables for the study of the surface exchange processes. To this end, a UAV was equipped with onboard air temperature and humidity sensors, while wind conditions were determined from the UAV's flight control sensors. Further, the UAV was used to systematically change the location of a sample inlet connected to a sample tube, allowing the observation of methane abundance using a ground-based analyzer. Vertical methane gradients of about 0.3 ppm were found during stable atmospheric conditions. Our results showed that both methane and meteorological conditions were in agreement with other observations at the site during the ScaleX-2015 campaign. The multicopter-type UAV was capable of simultaneous in situ sensing of meteorological state variables and sampling of air up to 50 m above the surface, which extended the vertical profile height of existing tower-based infrastructure by a factor of 5.
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Shimura, Tomoya, Minoru Inoue, Hirofumi Tsujimoto, Kansuke Sasaki, and Masato Iguchi. "Estimation of Wind Vector Profile Using a Hexarotor Unmanned Aerial Vehicle and Its Application to Meteorological Observation up to 1000 m above Surface." Journal of Atmospheric and Oceanic Technology 35, no. 8 (August 2018): 1621–31. http://dx.doi.org/10.1175/jtech-d-17-0186.1.
Повний текст джерелаАнотація:
AbstractSmall unmanned aerial vehicles (UAVs), also known as drones, have recently become promising tools in various fields. We investigated the feasibility of wind vector profile measurement using an ultrasonic anemometer installed on a 1-m-wide hexarotor UAV. Wind vectors measured by the UAV were compared to observations by a 55-m-high meteorological tower, over a wide range of wind speed conditions up to 11 m s−1, which is a higher wind speed range than those used in previous studies. The wind speeds and directions measured by the UAV and the tower were in good agreement, with a root-mean-square error of 0.6 m s−1 and 12° for wind speed and direction, respectively. The developed method was applied to field meteorological observations near a volcano, and the wind vector profiles, along with temperature and humidity, were measured by the UAV for up to an altitude of 1000 m, which is a higher altitude range than those used in previous studies. The wind vector profile measured by the UAV was compared with Doppler lidar measurements (collected several kilometers away from the UAV measurements) and was found to be qualitatively similar to that captured by the Doppler lidar, and it adequately represented the features of the atmospheric boundary layer. The feasibility of wind profile measurement up to 1000 m by a small rotor-based UAV was clarified over a wide range of wind speed conditions.
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Tripp, Daniel D., Elinor R. Martin, and Heather D. Reeves. "Applications of Uncrewed Aerial Vehicles (UAVs) in Winter Precipitation-Type Forecasts." Journal of Applied Meteorology and Climatology 60, no. 3 (March 2021): 361–75. http://dx.doi.org/10.1175/jamc-d-20-0047.1.
Повний текст джерелаАнотація:
AbstractTemperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type, which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds to observed ice pellets at the surface. This is in contrast to the High-Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.
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Zheng, Fengxun, Xiaofei Wang, Jiangtao Ji, Hao Ma, Hongwei Cui, Yi Shi, and Shaoshuai Zhao. "Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework." Agronomy 13, no. 4 (April 14, 2023): 1119. http://dx.doi.org/10.3390/agronomy13041119.
Повний текст джерелаАнотація:
UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing.
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Budianti, Noviana, Hiromi Mizunaga, and Atsuhiro Iio. "Crown Structure Explains the Discrepancy in Leaf Phenology Metrics Derived from Ground- and UAV-Based Observations in a Japanese Cool Temperate Deciduous Forest." Forests 12, no. 4 (April 1, 2021): 425. http://dx.doi.org/10.3390/f12040425.
Повний текст джерелаАнотація:
Unmanned aerial vehicles (UAV) provide a new platform for monitoring crown-level leaf phenology due to the ability to cover a vast area while offering branch-level image resolution. However, below-crown vegetation, e.g., understory vegetation, subcanopy trees, and the branches of neighboring trees, along with the multi-layered structure of the target crown may significantly reduce the accuracy of UAV-based estimates of crown leaf phenology. To test this hypothesis, we compared UAV-derived crown leaf phenology results against those based on ground observations at the individual tree scale for 19 deciduous broad-leaved species (55 individuals in total) characterized by different crown structures. The mean crown-level green chromatic coordinate derived from UAV images poorly explained inter- and intra-species variations in spring leaf phenology, most probably due to the consistently early leaf emergence in the below-crown vegetation. The start dates for leaf expansion and end dates for leaf falling could be estimated with an accuracy of <1-week when the influence of below-crown vegetation was removed from the UAV images through visual interpretation. However, a large discrepancy between the phenological metrics derived from UAV images and ground observations was still found for the end date of leaf expansion (EOE) and start date of leaf falling (SOF). Bayesian modeling revealed that the discrepancy for EOE increased as crown length and volume increased. The crown structure was not found to contribute to the discrepancy in SOF value. Our study provides evidence that crown structure is a pivotal factor to consider when using UAV photography to reliably estimate crown leaf phenology at the individual tree-scale.
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Avilés-Viñas, Jaime, Roberto Carrasco-Alvarez, Javier Vázquez-Castillo, Jaime Ortegón-Aguilar, Johan J. Estrada-López, Daniel D. Jensen, Ricardo Peón-Escalante, and Alejandro Castillo-Atoche. "An Accurate UAV Ground Landing Station System Based on BLE-RSSI and Maximum Likelihood Target Position Estimation." Applied Sciences 12, no. 13 (June 30, 2022): 6618. http://dx.doi.org/10.3390/app12136618.
Повний текст джерелаАнотація:
Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.
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Wang, Rui, Kan Wu, Qimin He, Yibo He, Yuanyuan Gu, and Shuang Wu. "A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data." Remote Sensing 14, no. 2 (January 10, 2022): 299. http://dx.doi.org/10.3390/rs14020299.
Повний текст джерелаАнотація:
For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.
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Sun, Yuan. "Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles." Remote Sensing 13, no. 8 (April 12, 2021): 1483. http://dx.doi.org/10.3390/rs13081483.
Повний текст джерелаАнотація:
Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.
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Kim, Min-Seong, and Byung Hyuk Kwon. "Estimation of Sensible Heat Flux and Atmospheric Boundary Layer Height Using an Unmanned Aerial Vehicle." Atmosphere 10, no. 7 (June 30, 2019): 363. http://dx.doi.org/10.3390/atmos10070363.
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In this work, sensible heat flux estimated using a bulk transfer method was validated with a three-dimensional ultrasonic anemometer or surface layer scintillometer at various sites. Results indicate that it remains challenging to obtain temperature and wind speed at an appropriate reference height. To overcome this, alternative observations using an unmanned aerial vehicle (UAV) were considered. UAV-based wind speed and sensible heat flux were indirectly estimated and atmospheric boundary layer (ABL) height was then derived using the sensible heat flux data. UAV-observed air temperature was measured by attaching a temperature sensor 40 cm above the rotary-wing of the UAV, and UAV-based wind speed was estimated using attitude data (pitch, roll, and yaw angles) recorded using the UAV’s inertial measurement unit. UAV-based wind speed was close to the automatic weather system-observed wind speed, within an error range of approximately 10%. UAV-based sensible heat flux estimated from the bulk transfer method corresponded with sensible heat flux determined using the eddy correlation method, within an error of approximately 20%. A linear relationship was observed between the normalized UAV-based sensible heat flux and radiosonde-based normalized ABL height.
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Sagan, V., M. Maimaitijiang, P. Sidike, M. Maimaitiyiming, H. Erkbol, S. Hartling, K. T. Peterson, J. Peterson, J. Burken, and F. Fritschi. "UAV/SATELLITE MULTISCALE DATA FUSION FOR CROP MONITORING AND EARLY STRESS DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 715–22. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-715-2019.
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<p><strong>Abstract.</strong> Early stress detection is critical for proactive field management and terminal yield prediction, and can aid policy making for improved food security in the context of climate change and population growth. Field surveys for crop monitoring are destructive, labor-intensive, time-consuming and not ideal for large-scale spatial and temporal monitoring. Recent technological advances in Unmanned Aerial Vehicle (UAV) and high-resolution satellite imaging with frequent revisit time have proliferated the applications of this emerging new technology in precision agriculture to address food security challenges from regional to global scales. In this paper, we present a concept of UAV and satellite virtual constellation to demonstrate the power of multi-scale imaging for crop monitoring. Low-cost sensors integrated on a UAV were used to collect RGB, multispectral, and thermal images during the growing season in a test site established near Columbia, Missouri, USA. WorldView-3 multispectral data were pan-sharpened, atmospherically corrected to reflectance and combined with UAV data for temporal monitoring of early stress. UAV thermal and multispectral data were calibrated to canopy temperature and reflectance following a rigorous georeferencing and ortho-correction. The results show that early stress can be effectively detected using multi-temporal and multi-scale UAV and satellite observation; the limitations of satellite remote sensing data in field-level crop monitoring can be overcome by using low altitude UAV observations addressing not just mixed pixel issues but also filling the temporal gap in satellite data availability enabling capture of early stress. The concept developed in this paper also provides a framework for accurate and robust estimation of plant traits and grain yield and delivers valuable insight for high spatial precision in high-throughput phenotyping and farm field management.</p>
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Xin, Yonghui, Ran Wang, Xi Wang, Xingwei Wang, Zhouxuan Xiao, and Jingyu Lin. "High-Resolution Terrain Reconstruction of Slot Canyon Using Backpack Mobile Laser Scanning and UAV Photogrammetry." Drones 6, no. 12 (December 19, 2022): 429. http://dx.doi.org/10.3390/drones6120429.
Повний текст джерелаАнотація:
Accurate terrain models are critical for studying the formation and development of slot canyons. However, for slot canyon landforms, it is challenging to generate comprehensive and high-resolution morphological data by individual observation due to the inaccessibility of steep walls on either side and the complexity of the field observation environment, such as variable-slope terrain, partial vegetation cover, and lack of satellite signal. Off-the-shelf surveying techniques, including Unmanned Aerial Vehicles (UAV) photogrammetry and Backpack Mobile Laser Scanning (BMLS), facilitate slot canyon surveys and provide better observations. This paper proposes an integrated scheme to generate comprehensive and centimeter-resolution slot canyon terrain datasets (e.g., color point clouds, Digital Elevation Models (DEM), and 3D mesh) using BMLS and fine UAV photogrammetry. The results show that the fine flight of UAVs based on a rough model can avoid collision with obstacles or flying into restricted areas, allowing users to perform tasks faster and safer. Data integration of BMLS and UAV photogrammetry can obtain accurate terrain datasets with a Root Mean Squared Error (RMSE) of point cloud registration of 0.028 m. Such high-resolution integration terrain datasets reduce local data shadows produced solely by individual datasets, providing a starting point to revealing morphological evolution and genesis of slot canyons.
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Mihailescu, Cristina, and Ioan Farcasan. "Mathematical Model for Studying the Evolution of Multi-Role Unmanned Aerial Vehicle in Turbulent Atmosphere." Applied Mechanics and Materials 325-326 (June 2013): 984–89. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.984.
Повний текст джерелаАнотація:
The paper purpose is to present some aspects regarding the control system of unmanned aerial vehicle - UAV, used for local observations, surveillance and monitoring of interest area or as a training target for anti-aircraft systems. The calculus methodology allows a numerical simulation of UAV evolution in bad atmospheric conditions by using a nonlinear model, as well as a linear one for obtaining the guidance command. The UAV model which will be presented has six DOF (degrees of freedom), and an autonomous control system. This theoretical development allows us to build the stability matrix, command matrix and the control matrix and finally to analyze the stability of autonomous UAV flight. A robust guidance system, based on Kalman filter will be evaluated for different fly conditions and the results will be presented. The flight parameters and guidance will be analyzed. The paper is inspired by national project SAMO (Autonomous Aerial Monitoring System for Interest Areas of Great Endurance). Keywords: UAV, Simulation, Control, Guidance, Endurance, Surveillance, Monitoring, Kalman filter
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Jiang, Liguang, Filippo Bandini, Ole Smith, Inger Klint Jensen, and Peter Bauer-Gottwein. "The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling." Remote Sensing 12, no. 7 (April 6, 2020): 1171. http://dx.doi.org/10.3390/rs12071171.
Повний текст джерелаАнотація:
Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance.
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Chen, Yu, Qi Dong, Xiaozhou Shang, Zhenyu Wu, and Jinyu Wang. "Multi-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method." Drones 7, no. 1 (December 23, 2022): 10. http://dx.doi.org/10.3390/drones7010010.
Повний текст джерелаАнотація:
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous surroundings. Traditional path-planning algorithms do not perform well when the environmental state is dynamic and partially observable. It is difficult for a UAV to make the correct decision with incomplete information. In this study, we proposed a multi-UAV path planning algorithm based on multi-agent reinforcement learning which entails the adoption of centralized training–decentralized execution architecture to coordinate all the UAVs. Additionally, we introduced a hidden state of the recurrent neural network to utilize the historical observation information. To solve the multi-objective optimization problem, We designed a joint reward function to guide UAVs to learn optimal policies under the multiple constraints. The results demonstrate that by using our method, we were able to solve the problem of incomplete information and low efficiency caused by partial observations and sparse rewards in reinforcement learning, and we realized kdiff multi-UAV cooperative autonomous path planning in unknown environment.
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Atkins, Jeff W., Atticus E. L. Stovall, and Xi Yang. "Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors." Drones 4, no. 3 (September 10, 2020): 56. http://dx.doi.org/10.3390/drones4030056.
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Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.
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Throneberry, Glen, Adam Takeshita, Christopher Michael Hocut, Fangjun Shu, and Abdessattar Abdelkefi. "Wake Propagation and Characteristics of a Multi-Rotor Unmanned Vehicle in Forward Flight." Drones 6, no. 5 (May 17, 2022): 130. http://dx.doi.org/10.3390/drones6050130.
Повний текст джерелаАнотація:
In this study, experimental investigations are used to explore the wake propagation and characteristics of a multi-rotor unmanned air vehicle (UAV) in a forward flight mission. Qualitative smoke visualization is used first to gain a qualitative understanding of wake characteristics above and below the body of the multi-rotor UAV which is used as guidance for quantitative particle image velocimetry (PIV) experiments which better resolve the region in the vicinity of the multi-rotor UAV body. The experimental results over a wide range of advance ratios show that as the advance ratio increases, achieved by either lower rotor speeds or higher flight speeds, the distance by which the wake propagates below the UAV is reduced. While above the UAV, the flow returns to the freestream flow closer to the body as the advance ratio increases. Therefore, this study concludes that proximity effects are reduced as the advance ratio increases. Findings from this study can be used to inform in situ sensor placement so that sensor readings are minimally affected by the wake from the multi-rotor UAV. Velocity measurement corrections are provided for sensors mounted above the UAV which can be used to improve sensor data reliability in forward flight. These results can advance autonomous sensing and increase the utility of multi-rotor UAV observations while providing designers and users further guidance to avoid proximity effects.
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Абрамов, Сергей Клавдиевич, Виктория Валерьевна Абрамова, Клавдий Данилович Абрамов, Владимир Васильевич Лукин, Василий Владимирович Бондарь та Игорь Владимирович Калужинов. "МЕТОДИКА ОПРЕДЕЛЕНИЯ ПОЛЯ ОБНАРУЖЕНИЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ НАЗЕМНЫМ НАБЛЮДАТЕЛЕМ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 3 (28 вересня 2020): 36–42. http://dx.doi.org/10.32620/reks.2020.3.04.
Повний текст джерелаАнотація:
The widespread use of small-sized unmanned aerial vehicles (UAVs) makes it urgent to control their use in the airspace over strategic infrastructure facilities. The design features of UAVs provide them with low visibility in all existing observation ranges: radar, visual, thermal, and acoustic. In this regard, for the reliable detection of such aircraft, it is necessary to use complex systems that conduct simultaneous observations in all available ranges. To optimize the location of such systems, the problem arises of determining the fields of reliable detection of UAVs for each of the means included in the system. To solve this problem for the means of visual and thermal detection based on the previously developed technique for determining the indications of the detection range based on the determination of the visually visible area of the UAV, calculated from the existing three-dimensional model of the vehicle, a new method is proposed for analyzing the characteristics of the UAV visibility for the ground observer. The application of the proposed technique is demonstrated by the example of two UAV models: ECO – with an internal combustion engine, and ELECTRA – with an electric motor. The concept of the indicatrix of the detection field is introduced, as an indicatrix of the maximum detection range, transferred from the UAV coordinate system to the observer's coordinate system by simple geometric transformations. Based on the indications of the detection field, it is possible to carry out both a direct comparative analysis of the visual visibility of UAVs and to obtain additional information from them, useful for making recommendations on the parameters of the UAV flight, in particular, the flight altitude and the direction of approach to the location of a potential observer. If there is an indicatrix of the detection field for the analyzed UAV, the calculation of the detection zone boundary can be performed automatically for any given height. This allows you to visualize information about the limits within which the UAV detection range can change depending on the direction and altitude of its flight.
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Wu, Songhua, Qichao Wang, Bingyi Liu, Jintao Liu, Kailin Zhang, and Xiaoquan Song. "UAV-borne coherent doppler lidar for marine atmospheric boundary layer observations." EPJ Web of Conferences 176 (2018): 02012. http://dx.doi.org/10.1051/epjconf/201817602012.
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A compact UAV-borne Coherent Doppler Lidar (UCDL) has been developed at the Ocean University of China for the observation of wind profile and boundary layer structure in Marine Atmospheric Boundary Layer (MABL). The design, specifications and motion-correction methodology of the UCDL are presented. Preliminary results of the first flight campaign in Hailing Island in December 2016 is discussed.
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Rogers, Kevin, and Anthony Finn. "Three-Dimensional UAV-Based Atmospheric Tomography." Journal of Atmospheric and Oceanic Technology 30, no. 2 (February 1, 2013): 336–44. http://dx.doi.org/10.1175/jtech-d-12-00036.1.
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Abstract This paper presents a method for tomographically reconstructing spatially varying three-dimensional atmospheric temperature profiles and wind velocity fields based on passive acoustic travel time measurements between a small unmanned aerial vehicle (UAV) and ground-based microphones. A series of simulations are presented to provide an indication of the performance of the technique. The parametric fields are modeled as the weighted sum of radial basis functions (RBFs) or Fourier series, which also allow local meteorological measurements made at the UAV and ground receivers to supplement any time delay observations. The technique has potential for practical applications such as boundary layer meteorology and theories of atmospheric turbulence and wave propagation through a turbulent atmosphere.
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Karpenko, Simon, Ivan Konovalenko, Alexander Miller, Boris Miller, and Dmitry Nikolaev. "UAV Control on the Basis of 3D Landmark Bearing-Only Observations." Sensors 15, no. 12 (November 27, 2015): 29802–20. http://dx.doi.org/10.3390/s151229768.
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Martins, Benjamim Hopffer, Motoyuki Suzuki, Putu Edi Yastika, and Norikazu Shimizu. "Ground Surface Deformation Detection in Complex Landslide Area—Bobonaro, Timor-Leste—Using SBAS DInSAR, UAV Photogrammetry, and Field Observations." Geosciences 10, no. 6 (June 24, 2020): 245. http://dx.doi.org/10.3390/geosciences10060245.
Повний текст джерелаАнотація:
During the past 10 years, Timor-Leste has concentrated all its efforts on infrastructure development. However, it has not achieved enough due to unexpected ground deformation in mountainous areas that is seriously affecting road constructions, etc. In order to design roads and other infrastructure under such difficult conditions, it is important to know the present and future ground conditions. Continuous monitoring is a significant methods of detecting ground deformation and providing essential information to realize an effective design. The problem arises of “How can ground deformation be monitored in extensive areas, which are generally located in mountain areas that are difficult to access?” Differential Interferometry Synthetic Aperture Radar (DInSAR) has recently been applied to monitor displacement in extensive areas. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry is useful for detecting the deformation in detail. Both methods are advantageous in that they do not require any sensors. Therefore, the combination of DInSAR and UAV photogrammetry is one of the solutions for monitoring the ground deformation in Timor-Leste. In this paper, DInSAR and UAV photogrammetry are applied to unstable ground in the Bobonaro region of Timor-Leste to find the recent ground deformation, since 2007, due to earthquakes and hard rainfall events. It is found that DInSAR is useful for screening usual and unusual ground behavior and that UAV photogrammetry is flexible to use and can detect displacements with cm accuracy after the DInSAR screening.
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Chalkley, Richard, Rich Andrew Crane, Matthew Eyre, Kathy Hicks, Kim-Marie Jackson, and Karen A. Hudson-Edwards. "A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations." Remote Sensing 15, no. 1 (December 23, 2022): 76. http://dx.doi.org/10.3390/rs15010076.
Повний текст джерелаАнотація:
Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (<50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.
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Benassai, Guido, Pietro Aucelli, Giorgio Budillon, Massimo De Stefano, Diana Di Luccio, Gianluigi Di Paola, Raffaele Montella, Luigi Mucerino, Mario Sica, and Micla Pennetta. "Rip current evidence by hydrodynamic simulations, bathymetric surveys and UAV observation." Natural Hazards and Earth System Sciences 17, no. 9 (September 12, 2017): 1493–503. http://dx.doi.org/10.5194/nhess-17-1493-2017.
Повний текст джерелаАнотація:
Abstract. The prediction of the formation, spacing and location of rip currents is a scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of numerical and experimental data, including RPAS (remotely piloted aircraft systems) observations, allowed us to detect the presence of rip currents and rip channels at the mouth of Sele River, in the Gulf of Salerno, southern Italy. The dataset used to analyze these phenomena consisted of two different bathymetric surveys, a detailed sediment analysis and a set of high-resolution wave numerical simulations, completed with Google EarthTM images and RPAS observations. The grain size trend analysis and the numerical simulations allowed us to identify the rip current occurrence, forced by topographically constrained channels incised on the seabed, which were compared with observations.
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Yogiswara, Agus Sukma, Takahiro Osawa, I. Wayan Nuarsa, and Abd Rahman As-syakur. "CARBON STOCKS ESTIMATION ON URBAN VEGETATION USING UAV-SfM PHOTOGRAMMETRY METHOD." ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) 17, no. 1 (May 25, 2023): 42. http://dx.doi.org/10.24843/ejes.2023.v17.i01.p04.
Повний текст джерелаАнотація:
Global warming and biodiversity loss are two critical issues currently debated among scientists and world policy makers. Forest retention and various reforestation and reforestation programs can play an important role in mitigating global climate change through sequestering atmospheric carbon. Forests are still the subject of discussion of the negotiations and exclude the contribution of vegetation outside the forest area. In fact, if trees outside the forest are not cut down, it can also reduce carbon emissions in the atmosphere. The lack of evidence regarding its potential and contribution to carbon stocks means that trees outside the forest have not been able to enter the negotiation.
Vegetation in urban areas are an example of tree communities outside forest that have a major contribution to carbon sequestration in the atmosphere. Urban vegetation can be found in two main locations: Urban Green Open Spaces (UGS) and Road Landscapes (RL). In Bali, especially in Denpasar City, Glodokan Tiang or Polyalthia longifolia trees are planted as road shading trees or trees in green open spaces. To prove its contribution in terms of carbon sequestration, data management and a mechanism for calculating carbon stocks are needed. Generally, the calculation of tree carbon stock consumes a lot of energy and time because it is done manually (measuring tree height and DBH). Technology of Unmanned Aerial Vehicle (UAV) can be used as an alternative to efficiently calculate the estimated of carbon stock in Urban Vegetation. The calculation uses the DBH value approach using the canopy area and tree height model (CHM) obtained from UAV data processing using the Sfm method.
UAV estimates show that the highest AGB value at Bajra Sandhi Renon Field is 201.59 kg with a stored carbon content of 94.75 kg, while on I Gusti Ngurah Rai Bypass has the highest AGB value of 215.04 kg with a stored carbon content of 101.07 kg. These results have been validated by field observations, where the results of the regression analysis at the location of Bajra Sandhi Renon and I Gusti Ngurah Rai, shows that between field observation data and estimation data with UAV there is no significant difference. While the results of the t-test: Paired Two Sample for Means at the Bajra Sandhi Renon Field and the Bypass I Gusti Ngurah Rai have a value above the significance level which proves that there is no significant difference between the carbon stock value from field observations and the carbon stock from the UAV approach.
Keywords: Carbon Stock; Urban Vegetation; UAV-Sfm.
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Yang, Suding, Xin Li, Limin Zeng, Xuena Yu, Ying Liu, Sihua Lu, Xiaofeng Huang, et al. "Development of multi-channel whole-air sampling equipment onboard an unmanned aerial vehicle for investigating volatile organic compounds' vertical distribution in the planetary boundary layer." Atmospheric Measurement Techniques 16, no. 2 (January 26, 2023): 501–12. http://dx.doi.org/10.5194/amt-16-501-2023.
Повний текст джерелаАнотація:
Abstract. To achieve near-continuous vertical observations of volatile organic compounds (VOCs) in the planetary boundary layer (PBL), multi-channel whole-air sampling equipment onboard an unmanned aerial vehicle (UAV) platform was developed in this study. The equipment consists of a multi-position solenoid valve and specially designed lightweight quartz sampling canisters. The canisters have little adsorption loss of VOCs and good inter-canister reproducibility. The 7 d recovery test shows that most VOC species (97 %) had a 1-week decay within 20 %. Online instruments for measuring O3, NO2, CO, SO2, and meteorological parameters are also integrated into the UAV platform. During one take-off and landing, the UAV platform can reach 800 m above the ground within 40 min and take whole-air samples at six heights. Vertical profiles of VOCs and trace gases during the evolution of the PBL in south-western China are successfully obtained by deploying the newly developed UAV system.
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Li, J., B. Yang, C. Chen, W. Wu, and L. Zhang. "AERIAL-TRIANGULATION AIDED BORESIGHT CALIBRATION FOR A LOW-COST UAV-LIDAR SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 245–52. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-245-2020.
Повний текст джерелаАнотація:
Abstract. The Laser-IMU boresight calibration is the precondition for an Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) system (ULS). The existing methods achieve good performance for calibrating ULSs with high-precision positioning and orientation systems (POS) (e.g., APX-15), in which, the systematic errors of the high-precision POS can be ignored, only the boresight parameters are estimated. However, these methods have difficulties in calibrating the low-cost ULSs with low-precision POS. To overcome the impact of the systematic errors of the low-precision POS on boresight calibration, an aerial-triangulation aided boresight calibration is proposed in this paper. It simultaneously estimates the laser-IMU boresight angles and system states (e.g. trajectory) by setting the point clouds derived from aerial-triangulation (AT point clouds) as the reference. Firstly, the planar voxels from the AT point clouds are extracted, due to the fact that they are more reliable in AT point clouds. Secondly, raw laser observations are matched with the extracted planar voxels to establish laser matching observations. Thirdly, a Dynamic Network (DN) is built using the GNSS observations, inertial observations, and laser matching observations to simultaneously optimize the initial laser-IMU boresight angles and the system states. All the sensor observations involved in the ULS are modeled with proper error models, which are essential for analyzing and refining the data quality of the low-cost ULS. The proposed method was tested to calibrate a low-cost ULS, KylinCloud-II, in a calibration field. It showed that the average distance between the laser point clouds and the referenced AT point clouds was decreased from 2.560m (RMSE = 3.88m) to 0.08m (RMSE = 0.99m).
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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (July 27, 2019): 60. http://dx.doi.org/10.3390/drones3030060.
Повний текст джерелаАнотація:
High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.
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Surono, Argo, Imam Ashar, and Muhamat Maariful Huda. "The UNMANNED AERIAL VEHICLE (UAV) AIRCRAFT DESIGN GALAK-24 WITH AUTONOMOUS METHOD." Jurnal Telkommil 2, Oktober (October 25, 2021): 66–74. http://dx.doi.org/10.54317/kom.v2ioktober.181.
Повний текст джерелаАнотація:
Abstract: Unmanned Aerial Vehicle is a type of aircraft that is controlled by a remote-control system via radio waves. UAV is an unmanned system (Unmanned System), which is an electro-mechanical-based system that can carry out programmed missions with the characteristics of a UAV that is able to fly without a pilot capable of controlling automatically and can run again by carrying several weapons or other tools. An autopilot is a mechanical, electrical, or hydraulic system that guides a vehicle without human intervention. The application of the Autonomous control system on the UAV is carried out by using Autonomous equipment in the form of components such as Flight Controller, GPS, Mission Planner Software and Telemetry. The number of parameters set by the observations made on the movement of the UAV when in Auto mode. The flight test used a square waypoint with a distance of 500 meters on each side. The UAV is able to fly in an Autonomous manner stably using a predetermined Waypoint. This is a pure experiment by means of tool testing and data collection that requires very high attention from the crew and results in fatigue.
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Руженцев, Микола Вікторович, Семен Сергійович Жила, Володимир Володимирович Павліков, Гліб Сергійович Черепнін, Анатолій Владиславович Попов, Володимир Віталійович Кошарський, Едуард Олексійович Церне та Дмитро Сергійович Власенко. "Теоретичні основи побудови багаточастотних радіометричних комплексів для виявлення БпЛА на тлі атмосферного випромінювання". Aerospace technic and technology, № 6 (29 листопада 2021): 74–82. http://dx.doi.org/10.32620/aktt.2021.6.08.
Повний текст джерелаАнотація:
Due to the impossibility of hiding the unmanned aerial vehicles (UAV) own radiothermal radiation or reducing its contrast against the background of atmospheric radiation, it is advisable to use highly sensitive radiometric receivers to solve the detection problem. The optimal method for complexing the results of measurements in multichannel radiometric receivers and identifying different types and classes of UAV against the sky in X, Ka, and W wave ranges under different meteorological conditions has been developed. end-to-end optimization of methods and algorithms will reveal the theoretical foundations of the construction of radiometric systems, ranging from the field of registration of electromagnetic fields to the final stages. In cloudless and clear weather, radiometric measurements in the W range will allow to obtain high-precision estimates of the spatial position of UAVs, in the X range of reliable observations in rain, snow, fog. The use of the Ka-band receiver in the radiometric complex will allow to realize the best sensitivity due to the technical achievements of domestic production in the creation of broadband radiometric receivers in this waveband. Studies of the main parameters of UAV detection have been conducted, namely, the probability of erroneous detection alarm and the probability of correct detection. The obtained theoretical results allow to determine signal processing algorithms and optimal structures of radiometric receivers, to analyze the maximum measurement error and to develop recommendations for experiments. Having received a database of radiometric contrasts, it is possible to further implement technical solutions to increase the capabilities of airspace monitoring for UAV detection. Recommendations are given for the practical choice of the UAV detection threshold to ensure the probability of correct detection is not worse than 0.9 for different angles of observation, atmospheric state, size and material of manufacture.
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Ren, Yixiang, Zhenhui Ye, Guanghua Song, and Xiaohong Jiang. "Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning." Entropy 24, no. 5 (May 1, 2022): 638. http://dx.doi.org/10.3390/e24050638.
Повний текст джерелаАнотація:
Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.
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Bolz, Katarzyna, and Gabriel Nowacki. "Air transport safety in UAV operational conditions." Journal of Civil Engineering and Transport 5, no. 1 (March 16, 2023): 9–23. http://dx.doi.org/10.24136/tren.2023.001.
Повний текст джерелаАнотація:
The article presents the possibilities of using unmanned aerial vehicles in air transport. The use of UAVs in the airspace has become widespread, despite many implemented legal regulations, there are many incidents that threaten not only aircraft during the flight, but also the airport infrastructure. The potential threats and the chances of implementing remedial measures were analyzed. An attempt to evaluate the possibility of maintaining aviation safety at an appropriate level in the conditions of UAV operational conditions has been done. The main research problem was defined as follows: How would implementing unmanned aerial vehicles into the air transport system influence the acceptable level of safety? The article uses theoretical methods such as: system analysis, analysis and synthesis in the field of literature, analogy, comparative method. In terms of empirical methods, an original diagnostic survey was carried out, based on a selected group of people related to the explored topic. In addition, the observation method was used by the feedbacks and observations of the group of air traffic controllers from civil airports (located in Poland). The article describes the current transformation of air transport, taking into account the planned modernizations. It presents the PansaUTM system as one of the countermeasures, monitoring and securing the movement in the airspace. Furthermore, the transponder issue was raised in relation to the enhancement of the UAV identification system, with a detailed explanation of the importance of the TCAS (Traffic Collision Avoidance System). Referring to the prospects for the development of air transport, the latest design concepts for cargo drones were presented. The issue of full transport autonomy of UAVs was analyzed based on the requirements of legal regulations. The comparison of benefits and threats in conjunction with the conducted empirical methods allowed for the development of conclusions confirming the research hypothesis and indicating the possibility of using remedial measures in the process of UAV evolution.
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Stoy, Paul C., Anam M. Khan, Aaron Wipf, Nick Silverman, and Scott L. Powell. "The spatial variability of NDVI within a wheat field: Information content and implications for yield and grain protein monitoring." PLOS ONE 17, no. 3 (March 22, 2022): e0265243. http://dx.doi.org/10.1371/journal.pone.0265243.
Повний текст джерелаАнотація:
Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.
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Peng, Xingshuo, Wenting Han, Jianyi Ao, and Yi Wang. "Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield." Remote Sensing 13, no. 6 (March 13, 2021): 1094. http://dx.doi.org/10.3390/rs13061094.
Повний текст джерелаАнотація:
In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.
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Ivanov, B. V., D. M. Zhuravskiy, U. V. Prokhorova, A. M. Bezgreshnov, A. V. Terekhov, M. V. Kurapov, A. S. Paramzin, and V. S. Kashkova. "The studies of the Svalbard glacial surfaces albedo by an unmanned aerial vehicle." IOP Conference Series: Earth and Environmental Science 1040, no. 1 (June 1, 2022): 012002. http://dx.doi.org/10.1088/1755-1315/1040/1/012002.
Повний текст джерелаАнотація:
Abstract Experiments related to the use of unmanned aerial vehicle (UAV) for assessing the albedo of Svalbard glaciers is described. Study area - Esmark Glacier (Isfjord Bay) and Aldegonda Glacier (Greenfjord Bay). The main purpose of the experiments is to estimate the surface albedo in the zone of the edge cracks of the outlet glacier (Esmark), where standard ground-based observations are impossible due to safety conditions, as well as to obtain spatial albedo estimates (Aldegonda) when satellite data cannot be used (overcast). The UAV (DJI Phantom 4 Pro) was retrofitted with a sensor that measures reflected solar radiation. The data on the incoming solar radiation at the surface level, measured by a similar sensor, were used to calculate the albedo. The albedo measurements were carried out along several profiles across the Aldegonda Glacier and along one profile above the Esmark Glacier, which was laid from a flat plateau (ice dome) through a zone of cracks to the open water surface of the fiord. For the first time, estimates of the surface albedo of the outlet glacier in the zone of edge cracks were obtained. Ground-based verification observations carried out on the Aldegonda glacier confirmed the results obtained by the UAV.
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Sobolewski, Michal, Norbert Grzesik, Zbigniew Koruba, and Michal Nowicki. "Fuzzy logic estimator implemented in observation-tracking device control." Aircraft Engineering and Aerospace Technology 88, no. 6 (October 3, 2016): 697–706. http://dx.doi.org/10.1108/aeat-09-2015-0206.
Повний текст джерелаАнотація:
Purpose Nowadays, various methods of observation from unmanned aerial vehicles (UAV) are being widely developed. There are many ways of increasing the amount of information retrieved from captured material. Unfortunately, hardware solutions consume a lot of energy, which is unacceptable in UAV applications, as it can have direct impact on the observing time on UAV. Those kinds of problems have been identified during the development phase of stabilizing platform in Polish Research Space Centre in Warsaw. As a result of that fact, energy saving control methods have been implemented, which estimates quality of stabilization process for the observation-tracking device (OTD). Design/methodology/approach Mathematical model has been designed and validated with real-life experiments for the purpose of optimization of stabilization and control process. Two types of controlling algorithms have been implemented: linear quadratic regulator and proportional derivative method for driving the mechanism. Based on numerical simulations of the mechanical model being controlled by the mentioned driver, it was possible to define membership functions. After the process of defuzzification, the controller predicts quality of stabilization under defined environmental working conditions. Findings An autonomous energy saving system has been created that can be implemented in many applications, where environmental conditions may change significantly. Practical implications To test the proposed fuzzy controller, OTD has been chosen as an example object of application. It is a mechanical platform which houses the optical observation system. It is designed to provide the best working conditions during flight. Originality/value That kind of decision-making unit has never been implemented before during observations which were carried out during flying of an object. That innovative controller should bring significant energy consumption savings.
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Song, Inseok, Prohim Tam, Seungwoo Kang, Seyha Ros, and Seokhoon Kim. "DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency." Electronics 12, no. 13 (July 6, 2023): 2984. http://dx.doi.org/10.3390/electronics12132984.
Повний текст джерелаАнотація:
The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.