Aircraft modeling and simulation has become increasingly important in the aviation world. Simulations allow for safer and more economical training prior to flight testing. In this project, a three degree-of-freedom control simulation coded in a MATLAB environment is used to assess and simulate the dynamic stability of group three unmanned aircraft system. By calculating, evaluating, and simulating the static and dynamic stability derivatives of the aircraft, this project will allow for the estimation of the handling qualities of an unmanned aircraft system. This can help mitigate risks that come along with altering the mass and aerodynamic properties of an aircraft, therefore creating a safer environment for flight testing.

Unmanned Aerial Systems (UAS) have become increasingly popular recently for surveying and mapping because of their efficiency in acquiring remotely sensed data in a short amount of time and the low cost associated with them. They are used to generate digital elevation models (DEM) derived from aerial photography for various purposes such as the documentation of cultural heritage sites, archaeological surveying or earthwork volume calculations. This thesis investigates the possible effects different file formats may have on the quality of elevation models. In this thesis, an UAS survey was simulated using a digital camera to produce six DEMs based on JPEG, TIFF and RAW format in Agisoft Photoscan by taking two sets of images of a city model, in different light conditions. Furthermore, a reference DEM was produced in Geomagic Studio using data from a Leica Nova MS50 Multistation. The DEMs were then compared in Geomagic Control. The results from the 3D comparison in Geomagic Control show that the standard deviation of all elevation models is 4 mm with the exception of the elevation model derived from raw-edited images taken with lighting, which has a standard deviation of nearly 6 mm. Also, all of the models have an average deviation of 0.4 mm or less. The significant deviations in all DEMs occur in areas where the multistation lacked vision of certain objects of the city model such as walls, or on the edges of the analysed area. Additionally, the georeferencing results from Photoscan show that the DEMs based on normal light condition images have slightly lower georeferencing errors than the DEMs with lighting. It has been concluded that it is difficult to say whether file formats have any noticeably effect on the uncertainty of digital elevation models.

Detta projekt undersöker vilken mätavvikelse man kan få av punktmoln från Unmanned Aircraft System (UAS)-insamlade bilder i jämförelse med terrester laserskanning. Skillnaden i noggrannhet mellan manuell identifiering och automatisk identifiering av markstödpunkter undersöks också. Arbetet undersöker även vad som krävs för att framställa 3D-modeller lämpliga för 3D-utskrift utifrån UAS-fotogrammetri. Projektet är en förstudie för dokumentation av Hälsingegårdar. UAS är en teknik som har blivit mer och mer populär under senaste år då det har blivit tillgänglig för allmänheten efter att tidigare enbart har används för militärt bruk. UAS består av en mindre flygfarkost, en sensor, ett tröghetsnavigationssystem (Inertial Navigation System, INS), en Global Positioning System (GPS)-mottagare, en radiolänk och en styrdator. 3D-modeller skapade från UAS insamling kan i sin tur användas för deformationsundersökningar, ombyggnadsprojekt eller friforms-framställning, även kallat 3D-utskrift. Studieområdet för detta projekt består av en byggnad vid Högskolan i Gävle. UAS som användes var en AscTec Falcon 8 oktokoper utrustad med Global Navigation Satellite System (GNSS) och en digitalkamera. Två flygningar utfördes, första flygningen tog lodbilder från höjden 75 m, andra flygningen tog snedbilder i höjdintervallet 10-15 m. Bilderna processades i programmet Agisoft PhotoScan inför skapande av ett tätt punktmoln. Jämförelsen mellan automatisk och manuell identifiering av markstödpunkter gjordes i PhotoScan med två olika sorters markstödpunkter, kodade A4 papper och traditionella 40 x 40 cm pappskivor. Byggnaden skannades också in med en LeicaScanStation C10 och punktmolnen fördes samman till ett gemensamt punktmoln i programmet Leica Cyclone. Punktmolnen, från UAS-bilderna och från laserskanningen, jämnfördes i form av stickprov i programmet CloudCompare. Fyra digitala 3D-modeller skapades, två utifrån sned- och lodbildernas punktmoln och två utifrån en kobination av lodbildernas och laserskanningens punktmoln. Första modellen skapades i PhotoScan som en Modelling Enviroment for Software and Hardware (MESH). Andra modellen var även den en MESH skapad i CloudCompare. Tredje modellen skapades av en kombination av lodbildernas och laserskanningens punktmoln i Cyclone genom att använda polyface-MESH. Fjärde modellen skapades i AutoCAD som solida objekt genom att använda modellen från Cyclone som referens. Utifrån stickproven i CloudCompare kan det konstateras att matta ytor med mörka färger eller ytor som ligger i skugga, avviker mer i avstånd från laserskanningspunktmolnet. Vid automatisk identifiering av kodade markstödpunkter från PhotoScan kunde programmet inte hitta några punkter automatiskt. Programmet hade inga problem med att hitta de traditionella 40 x 40 cm markstödpunkterna vid en automatisk identifiering. Utifrån resultaten från beräkningen av mätosäkerhet för 40 x 40 cm markstödpunkterna kan det konstateras att automatisk identifiering är noggrannare än manuell, vilket också har påståtts i manualen för PhotoScan. Däremot är skillnaden obetydlig och vid val av metod kommer automatisk identifiering vara fördelaktigt tidsmässigt. För att få en modell så skalenlig och detaljrik som möjlig så är det att föredra att använda en kombination av laserskanning och punktmoln från lodbilder, tills tekniken för att utvinna punkmoln från snedbilder har utvecklats mer.

Syftet med studien är att uppdatera Gävle kommuns baskarta för området Furuviks camping. Området har genomgått stora förändringar, bland annat har campingen flyttats på grund av ombyggnation av väg och järnväg. Genom användning av UAS och flygfotografering samt framställning av ortofotomosaik i programvaran PhotoScan samt kontrollmätningar i fält har baskartan uppdaterats. UAS är förkortningen för “unmanned aerial system” eller “obemannat flygsystem”. Idag används UAS-teknik i större utsträckning än tidigare för att ta fram underlag till kartframställning av olika kartprodukter. Användningen av UAS har visat sig vara både effektivt och billigt. Innan flygning kunde genomföras placerades flygsignaler ut med god spridning i terrängen. Flygsignalerna mättes in med Global Navigation Satellite System (GNSS) nätverks-real time kinematic (N-RTK). Detta gjordes i två omgångar för att undvika tidskorrelation hos satelliterna. När flygfotograferingen var färdig bearbetades bilderna i PhotoScan där blockutjämning samt georeferering genomfördes. Vid bearbetningen i PhotoScan upptäcktes att en del bilder blivit överexponerade pga. skiftande väderlek under dagen för flygningen. Dessa bilder gick därför inte att använda vid georeferering. Därefter användes ortofotomosaiken till kartering av de nytillkomna objekten i baskartan. Detta gjordes i ArcMap med hjälp av Gävle kommuns kartmanér. Slutligen genomfördes en kartkontroll på befintliga objekt samt digitaliserade objekt utifrån ortofotot för att säkerställa kvalitén av den uppdaterade baskartan. Jämförelser av Gävle kommuns objekt (kontrollpunkter 1–5) visade att alla punkter låg inom toleransen på 0,050 m i lägesosäkerhet. Jämförelsen av de objekt som digitaliserats med hjälp av ortofotmosaiken (kontrollpunkter 6–15) visade att två av punkterna marginellt överskred toleransen på 0,050 m. / The purpose of the study is to update the Gävle municipality's base map for the Furuvik campsite. The area has undergone major changes, among other things, the campsite has been moved due to road and rail rebuilding. Through the use of UAS and aerial photography as well as the production of orthophotomosaics in the PhotoScan software and control by field measurements the base map has been updated. UAS is the abbreviation for "unmanned aerial system". Today, UAS technology is used to a greater extent than before to provide basis for production of various map products. The use of UAS has proven to be both efficient and inexpensive. Before the flight was carried out, ground control targets were spread out well in the terrain. The ground control targets were measured with Global Navigation Satellite System (GNSS) network real-time kinematic (N-RTK). This was done in two rounds to avoid time correlation with the satellites. When aerial photography was completed the pictures were processed in PhotoScan where block smoothing and georeferencing were performed. During the processing of PhotoScan some images were overexposed due to the fact that changing weather conditions during the day of the flight. These pictures therefore failed to be used for georeferencing. Thereafter the orthophotomosaic was used to mapping the newly added objects in the base map. This was done in ArcMap using the base map of Gävle municipality. Finally a map control was made on existing objects and digitized items from the orthophotomosaic to ensure the quality of the updated base map. Comparisons of Gävle municipality objects (checkpoints 1-5) showed that all points were within the tolerance of 0.050 m in uncertainty. The comparison of the objects digitized by the orthophotomosaic (control points 6-15) showed that two of the points marginally exceeded the tolerance of 0.050 m.

Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided. / Doctor of Philosophy / Informed forest management requires accurate, up-to-date information. Groundbased sampling (inventory) is commonly used to generate estimates of forest characteristics such as total wood volume, stem density per unit area, heights, and regeneration survival. As the importance of assessing forest resources has increased, resources are often not available to conduct proper assessments. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study investigated the effects of two forest inventory data aggregation methods on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after tree planting. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting. The imagery was found to be unreliable for use in accurately detecting seedlings in the conditions evaluated. Following establishment, forest conditions are assessed at additional points in forest development. Using a class of statistical estimators known as small-area estimation, a combination of ground and light detection and ranging data generated more confident estimates of forest conditions. Further investigation found that more coarse ancillary information can be used with similar confidence in the conditions evaluated. Forest inventory data are used to generate estimates of future conditions needed for management decisions. The final component of this research found that there are significant differences between two inventory data aggregation strategies when forest conditions are highly spatially variable. The results of this research are of interest to forest managers who regularly assess forest resources with inventories and models. The incorporation of ancillary information has potential to enhance forest resource assessments. Further, managers have guidance on strategies for using this information for estimating future conditions.

Current radio direction finding techniques are limited in flexibility and focus on specific applications. Commercial off the shelf systems exist for a wide range of applications from navigation to search and rescue and wildlife tracking. However these systems rely on commercially available VHF receivers and are limited in transmission modulation techniques and frequency ranges. The majority of these systems are expensive which places them outside the reach of most individuals while the current open source designs require specialized skills and knowledge to build. The goal of this work was to design a low cost system capable of determining the approximate location of a non-cooperative VHF transmitter that could easily be implemented on a variety of unmanned systems. One unmanned aerial system was designed, built, and evaluated. Existing open source hardware and software systems were utilized for the development of the pseudo Doppler direction finding system, and work was conducted utilizing recursive Bayesian techniques to estimate the VHF transmitter's location. Results and explanations of system behaviors are presented along with limitations and possible modifications to improve performance and reliability. / Master of Science / Radio direction finding uses specialized radio equipment to determine the direction that a radio signal is coming from. Commercial systems are often expense, and existing hobbyist designs require specialized skills, and both are not flexible in application or frequency. The same is true for commercially available drones, which tend to be expensive or face other limitations. In this work a low cost radio direction finding system that uses easily found open source hardware and software was built and evaluated, along with a low cost unmanned aerial system. Then using the data collected, a computer algorithm was tested that could estimate the transmitting radio’s location. After testing it was determined that all systems did work, but still had room for improvement. Future steps and system modifications are presented that could improve the system’s performance.

Railroad tracks require consistent and periodic monitoring to ensure safety and reliability. Unmanned Aerial Vehicles (UAVs) have great potential because they are not constrained to the track, allowing trains to continue running while the UAV is inspecting. Also, they can be quickly deployed without human intervention. For these reasons, the first steps towards creating a track-monitoring UAV system have been completed. This thesis focuses on the design of algorithms to be deployed on a UAV for the purpose of monitoring the health of railroad tracks. Before designing the algorithms, the first steps were to design a rough physical structure of the final product. A small multirotor or fixed-wing UAV will be used with a gimbaled camera mounted on the belly. The camera will take images of the tracks while the onboard computer processes the images. The computer will locate the tracks in the image as well as perform defect detection on those tracks. Algorithms were implemented once a rough physical structure of the product was completed. These algorithms detect and follow rails through a video feed and detect defects in the rails. The rail following algorithm is based on a custom-designed masking technique that locates rails in images. A defect detection algorithm was also created. This algorithm detect defects by analyzing gradient data on the rail surface. / Master of Science

<p dir="ltr">The increasing age and number of bridges in the United States have led to the development and integration of new technologies to reduce the workload and cost of bridge inspectors. Unmanned Aerial Systems (UAS) have exemplified a technology that can reduce labor hours and equipment costs while improving safety and increasing the quality of bridge inspections. Despite the benefits of using UAS during bridge inspections, UAS face operational challenges such as turbulent wind flows, wind shear, and limited GPS reception. These challenges can discourage bridge inspectors from using the vehicle adequately during a bridge inspection. Therefore, providing UAS operators with information typically gained through extensive flight experience can expedite the learning curve.</p><p dir="ltr">The following research was conducted to develop and validate a process capable of predicting hazardous wind locations around a bridge structure. The methodology focused on utilizing a multi-step process across existing technologies. First, the bridge model was created using Computer Assisted Models (CAD) or photogrammetry modeling. Second, the resulting model is processed in a Computational Fluid Dynamics (CFD) application with a Reynolds Averaged Navier Stokes (RANS) solver to simulate wind flows around the bridge structure. Three inlet conditions were processed using a weather station at the bridge, the closest Aviation Weather Observation Station (AWOS), and height-adjusted velocities depending on the bridge height. Finally, the resulting wind models are compared against the wind velocity values collected by a sonic anemometer mounted to a UAS flown around the bridge.</p><p dir="ltr">The validation process used three real-world bridges across multiple weather conditions to validate the CFD model predictions. The three bridges used were a pony truss, a simulated girder, and a truss bridge. The pony truss bridge was extensively used as a validation structure where multiple flights occurred around the same structure with variations in wind heading and velocity. The simulated girder and truss bridge were treated as a simulated bridge inspection to validate the wind velocities around the structure. The resulting Root Mean Square Error (RMSE) of the validation measurements compared to the simulations ranged between 0.5 and 1.0 meters per second (15% - 30% error). The results demonstrated that CFD simulation is a valid prediction tool for estimating wind flows around a bridge structure.</p>

Denna studie undersöker reflektorlös mätning av vägmitt med totalstation samt skapande av markmodeller av vägar genom terrester laserskanning (TLS) och unmanned aerial system (UAS). Målet är att utvärdera metoder som undviker traditionell mätning på väg med handhållen prisma eftersom detta innebär att den som mäter måste befinna sig på vägen, vilket medför risker för trafikrelaterade olyckor samt kostnader för skyddsåtgärder. Studieområdet är en asfaltsbelagd parkeringsyta med vita markeringar liknande väglinjer. Genom TLS och fotogrammetrisk bearbetning av UAS-bilder skapades punktmoln och sedan markmodeller över en 60x10 m yta. Toleransen för markmodeller av asfaltsytor beskrivs i SIS-TS 21144:2016, som anger att den maximalt tillåtna medelavvikelsen i höjd är 0,02 m. Markmodellerna från både TLS och UAS-fotogrammetri uppfyllde kraven och erhöll medelavvikelser på -0,001 m respektive 0,005 m. Reflektorlös mätning som utförs från marknivå och ned mot terrängen med totalstation eller TLS resulterar i stora infallsvinklar, vilket i sin tur orsakar en förlängd träffbild. Pga. stråldivergens ökar dessutom träffbildens storlek med avstånd. En ytterligare faktor som påverkar reflektorlös mätning är mätobjektets reflektans. Asfalt har en reflektans på knappt 20 %, medan reflektansen hos de vitmålade markeringarna är tre gånger så hög. När det gäller TLS minskar dessutom punkttätheten, som påverkar vilka detaljer som kan identifieras, i samband med ökande avstånd. Toleranserna för inmätning av kontrollpunkter har i denna studie satts till 0,05 m i plan och 0,02 m i höjd. Resultaten visar att reflektorlös totalstationsmätning mot väglinjer uppnår RMS under toleransgränsen på upp till 55 m från instrumentet. I plan syns en trend som visar att avvikelserna ökar i samband med avstånd/infallsvinkel. I höjd syns inte en lika tydlig trend men vid ett avstånd på 57,5 m ökar avvikelserna drastiskt i både plan och höjd, vilket får betraktas som ett överskridande av den maximala räckvidden. Med dessa mätmetoder behöver hänsyn tas till bl.a. fri sikt, vägens utformning och skick, faktorer i omgivningen, trafikbelastning samt det rådande vädret. För både punktmoln och bilder behöver dessutom georeferering göras och lämplig upplösning bestämmas. Slutsatsen är dock att samtliga metoder kompletterar varandra och kan användas för insamling, kontroll, inpassning och komplettering av markmodeller och undviker samtidigt riskerna som medföljer handhållen prismamätning på väg. / This study evaluates the potential of reflectorless total station (TS) measurements when surveying road centerlines and the creation of digital terrain models (DTM) of road surfaces based on terrestrial laser scanning (TLS) and unmanned aerial system (UAS). The aim is to evaluate methods that avoid traditional surveying using a handheld prism since this means that the surveyor must be on the road, which creates risks of traffic related accidents as well as costs for protective measures. The study area is an asphalt-covered surface with white markings resembling roadlines. Through TLS and photogrammetric processing of UAS-images, pointclouds and subsequently DTM:s were created over a 60x10 m surface. The tolerance for DTM:s of asphalt-covered surfaces is specified in SIS-TS 21144:2016, which states a maximum average vertical deviation of 0,02 m. The DTM:s from the TLS and UAS-photogrammetry both fulfilled the requirements and obtained a quality of -0,001 m and 0,005 m respectively. Reflectorless measurements performed from ground level and down toward the terrain with TS or TLS result in large angles of inclination, which in turn causes an extended footprint of the laser beam. Because of beam divergence the footprint expands further with longer distances. Another factor that affects reflectorless measurements is the reflectance of the surface. Asphalt has a reflectance of barely 20 % while the reflectance of the white painted markings is three times as high. Additionaly when it comes to TLS, the point cloud density, which affects what details can be identified, diminishes with increasing distance. The tolerances for surveying of control points has in this study been set to 0,05 m horizontally and 0,02 m vertically. The results show that the reflectorless measurements of roadlines with TS achieves RMS below the tolerance limit up to 55 m from the instrument. The horizontal coordinates show a trend that the deviations increase with distance/angle of inclination. Vertically such a trend is not quite as clear, but at a distance of 57,5 m both the horizontal and vertical deviations increase drastically, which may be considered as exceeding the maximum range. With these methods adherences to some limitations need to be taken into consideration, such as line of sight, the road’s shape and condition, environmental factors, traffic and the weather. Furthermore, for both point clouds and images georeferencing needs to be done and a suitable resolution determined. The conclusion however, is that all methods complement each other and can be used for collecting, checking, fitting and completing DTM:s while avoiding the risks that traditional surveying methods using handheld prism creates.

Företaget Team Exact levererar mätningstekniska tjänster, där den främsta verksamheten är riktad mot byggnads- och markindustrin. Företaget använder UAS och levererar tjänster till kunder med ortofoto och DEM som kan användas till kartläggning, volymberäkningar och planering. Team Exact använder konsultföretagets SkyMap’s webbaserade plattform i fotogrammetrisk bearbetning av UAV genererade flygbilder. DEM behöver uppnå HMK-standardnivå 3 för att användas som underlag till bygghandlingar. För att uppnå HMK-standardnivå 3 så krävs det en lägesosäkerhet på 0,02–0,05 m/ 0,03–0,07 m (plan/höjd). Team Exact uppnår god lägesosäkerhet i plan men har varierande resultat i höjdåtergivningen. Studien har således en målsättning att hitta metoder för att säkerställa höjden inom ett studieområde med varierande topografi, terräng och markytor. Faktorer som ska undersökas är markstödspunkter, RTK-data, flygstråk, kamerainställningar och tänkvärda åtgärder i skiftande topografi samt att se tendenser hur höjdåtergivningen varierar på olika markytor.  Ett stomnät etablerades över studieområdet med tre fastställda koordinatsatta stompunkter, punkterna var inmätta med statisk NRTK mätning under 1 minut. Nätet jämnades ut med totalstation och därefter blev kontrollpunkter, profiler, ytor och markstödspunkter inmätta. Studien utredde lägesosäkerheten med 0, 5, 9 och 12 markstödspunkter. Den UAV som användes i studien är försedd med en RTK-modul och förväntades därav tillhandahålla positioneringsdata som var av värde att utreda. Markstödspunkternas utplacering planerades med fyra konstanta i studieområdets yttrehörn och en femte konstant på studieområdets högsta höjd. Resterande punkter placerades ut i en jämnfördelning över områdets toppar och dalar.  Flygmetoderna som utvärderades var förankrade i tidigare studier. Gemensamma inställningar över samtliga metoder var studieområdets avgränsning, en flyghöjd på 40 m samt flyghastigheten på 3 m/s. Resterande var flytande parametrar som var av värde att utreda. Studien justerade parametrarna gällande flygstråk, övertäckning, kameravinkel och kamerainställningar. Totalt blev det tre flygmetoder där de fyra olika markstödskombinationerna undersöktes vilket gav 12 processer att utvärdera. Utvärderingen utfördes mot 77 kontrollpunkter där RMSE-värde för höjd och plan undersöktes. Kontrollpunkterna var jämnt fördelade över ytan och marktyperna. En ytterligare analys utfördes med volymberäkningar mellan referens terrängmodeller och de genererade terrängmodellerna.  Flygmetod 3 gav bästa resultat där fotogrammetriinställningen Double Grid användes och överlappningen var 80/60 % samt att kameran tiltades till -70°. Sensorkänsligheten var inställd på ISO100, bländaren ett öppningsvärde f/5 och slutartiden var inställd på 1/500s. Studiens resultat visar att flygmetod 3 som blockutjämnats med 12 markstödspunkter genererade bästa resultat på en lägesosäkerhet i plan på 0,015 m samt 0,035 m i höjd. / The company Team Exact delivers measurement technical services, and the main business is aimed at the construction and land industry. The company uses UAS and offers services to customers and delivers products such as orthophotos and DEMs that can be used for mapping, volume calculations and planning. Team Exact uses the consulting company SkyMap’s web-based platform for photogrammetric processing of UAV-generated aerial images. DEM needs to achieve good positional uncertainty, to achieve HMK standard level 3, it is required that the basis for construction documents has a positional uncertainty of 0.02–0.05 m / 0.03–0.07 m (level / height). Team Exact achieves good positional uncertainty in horizontal coordinates but has varying results in height reproduction. The study thus aims to find methods to ensure the height within a study area with varying topography, terrain and ground surfaces. Factors to be investigated are ground control points, RTK data, flight paths, camera settings and conceivable measures in varying topography, as well as seeing trends in how the height representation differs on different ground surfaces. A coordinate network was established over the study area with three established coordinate reference points, the points were measured with static NRTK measurement 1 minute. The network was levelled with the total station and then control points, profiles, surfaces, and ground control points were measured. The study investigated the location uncertainty with 0, 5, 9 and 12 ground control points. The UAV used in the study is equipped with an RTK module and was therefore expected to provide positioning data that was worth investigating. The placement of the ground support points was planned with four constants in the outer corner of the study area and a fifth constant at the highest level of the study area. The remaining points were placed in an even distribution over the area’s peaks and valleys. The evaluated flight methods were rooted in previous studies. Common settings across all methods were the study area delimitation, 40 m flight altitude and the flight speed of 3 m/s. Remaining were floating parameters that were of value to investigate. The study adjusted the parameters regarding flight path, coverage, camera angle and camera settings. In total, there were three flight methods where the four different ground support combinations were examined, which gave 12 processes to evaluate. The evaluation was performed against 77 control points where the RMSE value for height and plane was examined. The control points were evenly distributed over the surface and soil types. A further analysis was performed with volume calculations between the reference terrain models and the generated terrain models. Flight method 3 gave the best results where the photogrammetry setting Double Grid was used and the overlap was 80/60 % and the camera was tilted to -70 °. The sensor sensitivity was set to ISO100, the shutter had an aperture value of f/5 and the shutter speed was set to 1/500s. The results of the study indicate that flight method 3, which was levelled with 12 ground support points, generated the best results on a positional uncertainty in horizontal coordinates of 0,015 m and 0,035 m in height.