Single Flight Research Articles

Solving slot allocation problem with multiple ATFM measures by using enhanced meta-heuristic algorithm

In Air Traffic Flow Management (ATFM), one key issue is to determine the Calculated Take Off Time (CTOT) for each flight. Due to the limited slot time resources, one single flight’s slot decision could be influenced by plenty of other flights.Few existing approaches in the previous literatures consider the interaction among various flights, so that the possible solution achieved will perform low stability and performance, especially under the condition of multiple measures. Aiming to solve the resource and flights coupling problems, in this paper, we present one heuristic method based on multi-dimension flight priority, with novel proposed methods of slot division and flight ranking. Meanwhile, in order to improve the optimality and flexibility of solutions, Estimation of Distribution Algorithm (EDA) is adopted. As one meta-heuristic algorithm, EDA is used to optimize the assignment sequence based on probability model, by which to provide an enhanced approach to solve slot allocation optimization problems. The numerical experiments have been conducted, the comparison results show that our approach has high calculation performance compared with conventional meta-heuristic algorithm-based approach, and better stability and applicability for re-allocation under the dynamic situation.

Swarm-Based 4D Path Planning For Drone Operations in Urban Environments

Drones have a wide range of applications in urban environments as they can both enhance people's daily activities and commercial activities through various operations and deployments. With the increasing number of drones, flight safety and efficiency become the main concern, and effective drone operations can make a difference. Accordingly, 4D path planning for drone operations is the focus of this paper, and the swarm-based method is proposed to solve this complicated optimization problem. Under the framework of ‘AirMatrix’, the problem is solved in two levels, i.e., 3D path planning for a single drone and conflict resolution among drones. In the multi-path planning level, multiple alternative flight paths for each drone are generated to increase the acceptance rate of a flight request. The constraints on a single flight path and two different flight paths are considered. The goal is to obtain several different short flight paths as alternatives. A clustering improved ant colony optimization CIACO) algorithm is employed to solve the multi-path planning problem. The crowding mechanism is used in clustering, and some improvements are made to strengthen the global and local search ability in the early and later phases of iterations. In the task scheduling level, the conflicts between two drones are defined in two circumstances. One is for the time interval of passing the same path point, another one is for the right-angle collision between two drones. A three-layer fitness function is proposed to maximize the number of permitted flights according to the safety requirement, in which the airspace utilization and the operators’ requests are both considered. A ‘cross-off’ strategy is developed to calculate the fitness value, and a ‘distributed-centralized’ strategy is applied considering the task priorities of drones. A genetic algorithm GA)-based task scheduling algorithm is also developed according to the characteristic of the established model. Simulation results demonstrate that 4D flight path of each drone can be generated by the proposed swarmed-based algorithms, and safe and efficient drone operations in a specific airspace can be ensured.

Improving single flyover noise prediction for subsonic aircraft

Current ICAO Doc 9911 provides the algorithm to calculate aircraft noise levels for any kind of airport flight scenario. The essential difference exists between the measured and calculated sound levels, especially for single flight noise events. Doc 9911 recommends using this method for equivalent sound levels L and noise indices L first of all. A number of national noise regulations still require for single noise event assessment. An article analyzes a number of reasons to explain the inaccuracy of noise event calculations. For example, the differences between calculated balanced flight parameters (thrust and velocity first of all) and supervised in real flights may influence the accuracy first of all. Statistical data was gathered to make more general view on these differences and some proposal to use them in calculations has being proved. Also, the assumptions of the ICAO Doc 9911 method may contribute to the inaccuracy of calculations. Among them are the homogeneous atmosphere for sound propagation, generalized for overall fleet noise directivity pattern, etc. Ground effect model defines the values for aircraft absent in operation currently. The first assumption provides a conflict with flight path calculations for varied atmosphere parameters with height over the surface.

Analyzing the Carbon Footprint of an Intravitreal Injection.

PurposeTo estimate the carbon footprint of a single intravitreal injection in a hospital-based intravitreal service.MethodsGreenhouse gas emissions attributable to the delivery of an intravitreal injection were calculated using a hybrid lifecycle analysis technique. Data were collected regarding procurement of materials, patient travel, and building energy use.ResultsCarbon emissions associated with a single intravitreal injection, excluding the anti-VEGF agent, were 13.68 kg COeq. This equates to 82,100 kg COeq annually for our service. Patient travel accounted for the majority of emissions at 77%, with procurement accounting 19% for and building energy usage for 4% of total emissions. The omission of items considered dispensable from injection packs would reduce carbon emissions by an estimated 0.56 kg per injection – an annual saving of 3,360 kg COeq for our service. Similar savings, if extrapolated to a country the size of the United Kingdom, could yield annual carbon savings of 450,000 kg COeq. For context, a single one-way economy transatlantic flight produces 480 kg COeq per person.ConclusionWasteful practice in healthcare increases greenhouse gas production and drives climate change. The healthcare sector should be a leader in sustainable practice promotion and changes to high volume procedures have the largest impact on emissions. Long-acting agents offer the greatest future potential for meaningful reductions.

Flight schedule adjustment for hub airports using multi-objective optimization

Abstract Based on the concept of “passengers self-help hubbing,” we build a flight schedule optimization model where maximizing the number of feasible flight connections, indicating transfer opportunities, as one objective and minimizing total slot displacements as the other objective. At the same time, the “Demand Smoothing Model” is introduced into the flight schedule optimization model to reduce the queuing delays for arrival and departure flights. We take into account all aircraft itineraries, the difficulty level of schedule coordination, and the maximum displacement of any single flight acceptable to airlines when optimizing flight schedule. Given an original schedule, the model produces a feasible modified schedule that obeys the slot limits specified for an airport without canceling any flights, increases transfer opportunities, and improves on-time performance for hub airports while reducing interference with airline scheduling preferences. The model was verified with the operating data of the Urumqi international airport, and the results show that minor adjustments to flight schedules can increase the transfer opportunities of the airport and significantly reduce flight queuing delays.

Concept of climate-charged airspaces: a potential policy instrument for internalizing aviation's climate impact of non-CO2 effects

ABSTRACT Approximately 50–75% of aviation's climate impact is caused by non-CO2 effects, like the production of ozone and the formation of contrail cirrus clouds, which can be effectively prevented by re-routing flights around highly climate-sensitive areas. Here, we discuss options how to incentivize re-routing approaches and apply multicriteria trajectory optimizations to demonstrate the feasibility of the concept of climate-charged airspaces (CCAs). We show that although climate-optimized re-routing results in slightly longer flight times, increased fuel consumption and higher operating costs, it is more climate-friendly compared to a cost-optimized routing. In accordance to other studies, we find that the averaged temperature response over 100 years (ATR) of a single flight can be reduced by up to 40%. However, if mitigation efforts are associated with a direct increase in costs, there is a need for climate policies. To address the lack of incentivizing airlines to internalize their climate costs, this study focuses on the CCA concept, which imposes a climate charge on airlines when operating in highly climate-sensitive areas. If CCAs are (partly) bypassed, both climate impact and operating costs of a flight can be reduced: a more climate-friendly routing becomes economically attractive. For an exemplary North-Atlantic network, CCAs create a financial incentive for climate mitigation, achieving on average more than 90% of the climate impact reduction potential of climate-optimized trajectories (theoretical maximum, benchmark). Key policy insights Existing climate policies for aviation do not address non- effects, which are very sensitive to the location and the timing of the emission. By imposing a temporary climate charge for airlines that operate in highly climate-sensitive regions, the trade-off between economic viability and environmental compatibility could be resolved: Climate impact mitigation of non- effects coincides with cutting costs. To ensure easy planning and verification, climate charges are calculated analogously to en-route and terminal charges. For climate mitigation it is therefore neither necessary to monitor emissions ( , etc.) nor to integrate complex non- effects into flight planning procedures of airlines. Its implementation is feasible and effective.

Monitoring of Emerging Water Stress Situations by Thermal and Vegetation Indices in Different Almond Cultivars

In recent years, the area dedicated to modern irrigated almond plantations has increased significantly in Spain. However, the legal irrigation allocations are lower than the maximum water requirements of the crop in most cases. Therefore, almond growers are forced to implement regulated deficit irrigation strategies on their farms, applying water stress in certain resistant phenological periods and avoiding it in sensitive periods. Given the need to monitor the water status of the crop, especially in the most sensitive periods to water stress, the objective of this work was to evaluate the sensitivity of two UAV-based crop water status indicators to detect early water stress conditions in four almond cultivars. The field trial was conducted during 2020 in an experimental almond orchard, where two irrigation strategies were established: full irrigation (FI), which received 100% of irrigation requirements (IR), and regulated deficit irrigation (RDI), which received 70% of IR during the whole irrigation period except during the kernel-filling stage when received 40% IR. The UAV flights were performed on four selected dates of the irrigation season. The Crop Water Status Index (CWSI) and the Normalized Difference Vegetation Index (NDVI) were derived from thermal and multispectral images, respectively, and compared to classical water status indicators, i.e., stem water potential (Ψstem), stomatal conductance (gs), and photosynthetic rate (AN). Of the four flights performed, three corresponded to mild water stress conditions and a single flight was performed under moderate water stress conditions. Under mild water stress, CWSI was not able to capture the differences between FI and RDI trees that were observed with Ψstem. Under moderate stress conditions, CWSI was sensitive to the water deficit reached in the trees and showed significant differences among both irrigation treatments. No differences were observed in the CWSI and NVDI response to water stress among cultivars. Although NDVI and CWSI were sensitive to water stress, the low signal intensity observed in NDVI makes this index less robust than CWSI to monitor crop water stress. It can be concluded that UAV-based CWSI measurements are reliable to monitor almond water status, although for early (mild) levels of water stress, Ψstem seems to be the preferred option.

Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448 and 720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2), respectively, make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g., single facilities) to regional-scale (e.g., agglomerations) sources in the context of the CoMet 1.0 campaign in early summer 2018. A quantum cascade–interband cascade laser (QCL–ICL)-based spectrometer adapted for airborne research was deployed aboard the German Aerospace Center (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility-level estimates. Emission rates from individual sources not only are needed for unambiguous comparisons between bottom-up and top-down inventories but also become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional-scale and facility-level emission estimates. We find instantaneous coal mine emission estimates of 451/423 ± 77/79 kt CH4 yr−1 for the morning/afternoon flight of 6 June 2018. The derived fuel-exploitation emission rates coincide (±6 %) with annual-average inventorial data from E-PRTR 2017 although they are distinctly lower (−28 %/−32 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

Harmonic radar tracking reveals that honeybee drones navigate between multiple aerial leks

SummaryMale honeybees (drones) are thought to congregate in large numbers in particular “drone congregation areas” to mate. We used harmonic radar to record the flight paths of individual drones and found that drones favored certain locations within the landscape which were stable over two years. Drones often visit multiple potential lekking sites within a single flight and take shared flight paths between them. Flights between such sites are relatively straight and begin as early as the drone's second flight, indicating familiarity with the sites acquired during initial learning flights. Arriving at congregation areas, drones display convoluted, looping flight patterns. We found a correlation between a drone's distance from the center of each area and its acceleration toward the center, a signature of collective behavior leading to congregation in these areas. Our study reveals the behavior of individual drones as they navigate between and within multiple aerial leks.

Assimilation of DAWN Doppler wind lidar data during the 2017 Convective Processes Experiment (CPEX): impact on precipitation and flow structure

Abstract. An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 µm Doppler Aerosol Wind (DAWN) lidar collected during the 2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Overall, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts.

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

Accuracy of Noise-Power-Distance Definition on Results of Single Aircraft Noise Event Calculation

Aircraft performance and noise database together with operational weights (depending on flight distances) and operational procedures (including low noise procedures) significantly influence results of noise exposure contour maps assessment in conditions of real atmosphere. Current recommendations of the Standard SAE-AIR1845A allow the definition of flight profiles via solutions of balanced motion equations. However, differences are still supervised between the measured sound level data and calculated ones, especially in assessing the single flight noise events. Some of them are well explained by differences between balanced flight parameters (thrust and velocity first of all) and monitored ones by the traffic control system. Statistical data were gathered to make more general view on these differences and some proposal to use them in calculations has been proven. Besides, the real meteorological parameters provide inhomogeneous atmosphere conditions always, which are quite different from the main assumptions of the SAE-AIR1845A, stipulating inaccuracies of sound level calculations.

The long-distance flight behavior of Drosophila supports an agent-based model for wind-assisted dispersal in insects

Despite the ecological importance of long-distance dispersal in insects, its mechanistic basis is poorly understood in genetic model species, in which advanced molecular tools are readily available. One critical question is how insects interact with the wind to detect attractive odor plumes and increase their travel distance as they disperse. To gain insight into dispersal, we conducted release-and-recapture experiments in the Mojave Desert using the fruit fly, Drosophila melanogaster We deployed chemically baited traps in a 1 km radius ring around the release site, equipped with cameras that captured the arrival times of flies as they landed. In each experiment, we released between 30,000 and 200,000 flies. By repeating the experiments under a variety of conditions, we were able to quantify the influence of wind on flies' dispersal behavior. Our results confirm that even tiny fruit flies could disperse ∼12 km in a single flight in still air and might travel many times that distance in a moderate wind. The dispersal behavior of the flies is well explained by an agent-based model in which animals maintain a fixed body orientation relative to celestial cues, actively regulate groundspeed along their body axis, and allow the wind to advect them sideways. The model accounts for the observation that flies actively fan out in all directions in still air but are increasingly advected downwind as winds intensify. Our results suggest that dispersing insects may strike a balance between the need to cover large distances while still maintaining the chance of intercepting odor plumes from upwind sources.

Dispersal of Neophilaenus campestris, a vector of Xylella fastidiosa, from olive groves to over‐summering hosts

AbstractNeophilaenus campestris is one of the spittlebugs (Hemiptera: Cercopoidea) able to transmit Xylella fastidiosa to olive trees. Considering its vector ability and the wide distribution of this species in Spain, N. campestris should be considered a serious threat to key crops such as olive, almonds and grapevines. Migration and dispersal abilities of insect vectors have profound implications in the spread of vector‐borne diseases. Thus, knowledge on the dispersal ability of N. campestris is essential to model, predict and limit the spread of the diseases caused by X. fastidiosa. A mass‐mark‐recapture technique was developed to track between‐field movements of N. campestris during its late spring migration from the ground cover grasses within olive groves to sheltered areas dominated by pine trees. The fluorescent dust used for marking did not affect the survival nor the flying ability of N. campestris. Spittlebug adults captured in olive groves during late spring were dusted with fluorescent colours and released in different locations. Six recapture samplings were performed 23–42 days after release in 12 different sites located within a maximum distance of 2.8 km from the release point. Results indicated that N. campestris was able to disperse a maximum distance of 2,47 m in 35 days. Furthermore, flight mill studies showed that N. campestris was able to fly long distances, reaching 1.4 km in an 82‐min single flight. Altogether, our findings suggest that eradication measures are of limited value because vectors are able to disperse rapidly over distances much longer than expected.

Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery

There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.

Improved optical-type measurement method of grain flow using array near-infrared photoelectric sensors

To improve the optical-type measurement accuracy of the grain flow in a flight elevator, it is important to analyze the effect of grain accumulation shape on the mass. In this paper, a method for measuring grain accumulation thickness using a near-infrared photoelectric sensor was developed. According to the attenuation characteristics of light intensity in the discontinuous medium, a Gaussian regression model was established to describe the relationship between the output voltage and the grain accumulation thickness. Calibration results indicated that the measurement error was less than 0.5 mm with the infrared wavelength of 940 nm. Then, three sensors were arranged in parallel as an array to measure the change process of grain accumulation thickness on the flight during the lifting process. A back propagation (BP) neural network which takes time series of the output voltage as inputs was developed to predict the grain mass. The measurement experiments were carried out on a single flight elevator test-rig. After training using the experimental data, the measurement error and the average error using the BP neural network were less than ± 18.2 g and 7.6 g with the grain mass in the range of 0 – 600 g. Finally, grain flow measurement tests were carried out on a grain flow test-rig, and the results indicated that the relative error reduced from 5.6 to 1.4% when the grain flow increased from 0.5 to 2.8 kg/s. Compared with the traditional method, the accuracy of the proposed method was significantly improved.

Accuracy and precision of citizen scientist animal counts from drone imagery.

Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Año Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was ‘Median without the lowest two values’, demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with imagery from drones, occupied aircraft, and/or remote cameras.

Monitoring winter wheat growth at different heights using aerial imagery

AbstractDrones (unmanned aerial vehicles) provide another system to mount sensors for measuring plant characteristics. For winter wheat (Triticum aestivum) this can include evaluating stands and making nitrogen (N) recommendations. Timing these flights and adequate camera resolution (based on flying height), must be known before applying tasks. This study observed six winter wheat planting populations (222, 297, 371, 445, 494, and 544 seeds m–2) at three different heights above ground level (30, 60, and 120 m) over three growing seasons. Plant populations could be separated at all growth stages and heights flown but were easier to separate right after emergence (GS11). In the spring, additional tillering caused the higher populations (371–544 seeds m–2) to have similar normalized difference vegetative index (NDVI), much like the final yields. Comparing changes in NDVI between flights was also successful in separating high and low planting populations, with inverse relationships in the fall and spring. A random forest classification of tiller counts by NDVI measurements ranked change in NDVI between stages as the most important compared to single flights. As separation and classification was successful at the lowest camera resolution (120 m), it can be possible for scouts to collect whole field imagery for analyses prior to deciding on split N applications.

Developing a Workflow for the Use of Unmanned Aerial Vehicles for Cadastral Mapping in Ghana

This paper proposes a generic workflow for using Unmanned Aerial Vehicles (UAV) to produce acceptable cadastral plans in Ghana. This was done by firstly verifying in the field UAV restricted zones specified by the Ghana Civil Aviation Authority (GCAA) and subsequently analyzing ground and aerial survey data from two sites within the Tema Municipality. The data analyzed consisted of one set of boundary coordinate data of the sites obtained from a static Global Navigation Satellite System (GNSS) survey and another set from on-screen digitization of site boundaries from aerials obtained from a Mavic Air UAV weighing 430 g with a camera resolution of 12 megapixels flying at altitudes of 40 and 60 m. A comparison of the two sets of boundary coordinates data showed differences under the limit of +/-3 ft specified by the Survey and Mapping Division (SMD) of the Lands Commission of Ghana. The paper thus outlines a generic workflow as follows: (1) Determining if selected site is within a flight restricted zone (2) Undertaking field reconnaissance to determine appropriate flight parameters and ground control point locations (3) Processing UAV imagery to obtain orthomosaics (4) Performing on-screen digitizing of site boundaries from orthomosaics and (5) Obtaining accurate boundary turning point coordinates from digitized boundary. The paper concludes that this approach if accepted may be used in obtaining multiple cadastral plans within built up areas from a single UAV flight and recommends that UAV and ground control data be submitted in Geotiff and Rinex formats respectively to facilitate checks by the Examinations Unit of the SMD. Keywords: unmanned aerial vehicles, cadastral plans, ground control points, global navigation satellite system, orthomosaic DOI: 10.7176/JEES/11-2-04 Publication date: February 28 th 2021

Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China

Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.