Att använda obemannade flygfarkoster, även kallat UAS (unmanned aerial systems), i karterings- och modelleringssyften har blivit en välanvänd metod de senaste åren. Mycket på grund av den tekniska utvecklingen som till stor del automatiserat processen med att framställa höjdmodeller och ortofoton. Inom ramen för denna studie kommer vi att titta närmare på hur olika faktorer påverkar höjdosäkerheten hos en höjdmodell framställd med data insamlat med en Real-Time Kinetic-UAS (RTK-UAS). Studien kommer dels att undersöka hur stor osäkerheten blir om endast den integrerade nätverks-RTK:n (NRTK) används vid georeferering av flygbilderna, dels att se hur stor påverkan adderade markstödpunkter har på osäkerheten. Studien kommer även undersöka hur stor påverkan flyghöjden har på osäkerheten genom att jämföra data från två flyghöjder: 100 m och 50 m. Det sista studien som undersöks är vilken inverkan snedbilder har på osäkerheten. Detta genom att jämföra en flygning där lodbilder tagits med en flygning där kameran har haft en vinkling på 60° från lod. Studien genomfördes med hjälp av Falun kommuns mättekniker som manövrerade UAS:en. För att kunna testa markstödpunkternas inverkan på osäkerheten mättes nio punkter in. Även kontrollprofiler mättes för att kunna kontrollera höjdmodellerna som producerades. Totalt genomfördes 3 olika flygningar: 100 m med lodbilder, 50 m med lodbilder samt 50 m med snedbilder. De insamlade fotografierna importerades till programvaran Agisoft Metashape där de georefererades med olika metoder. För att undersöka hur markstödpunkter påverkar osäkerheten genomfördes fem olika georefereringsmetoder av fotografierna tagna på 100 m flyghöjd med olika antal markstödpunkter i varje. RMS-värdet varierade från 0,060 m för NRTK + 1 GCP till 0,068 m för NRTK+2 GCP som fick den högsta osäkerheten.Undersökningen av flyghöjder visade att en lägre flyghöjd har en tydlig effekt på mätosäkerheten. En minskning av RMS-värdet sågs när 50 m flyghöjd användes jämfört med när 100 m flyghöjd användes. Användningen av snedbilder gav ingen tydlig effekt på mätosäkerheten. RMS-värdet blev 0,014 m då lodbilder användes och 0,017 m då snedbilder användes. Snedbildernas resultat försämrades något på grund av den adderade höjden från gräset, så på endast hårdgjorda ytor blir RMS-värdet från snedbildsflygningen noterbart lägre än RMS-värdet från lodbildsflygningen. / The technology of Unmanned Aerial Systems (UAS) has gained popularity as atool for mapping and modeling applications in recent years. This is mainly dueto the technological developments that have largely automated the process ofproducing digital elevation models (DEMs) and orthophotos. This study investigates the factors that effect the height uncertainty in anelevation model that is produced with data collected with a NRTK-UAS(Network Real-Time Kinematic UAS). We also evaluate two differentscenarios i.e. how the uncertainty is affected by using only NRTK-UAS andthe effect of adding ground control points (GCPs) to NRTK-UAS. It is alsoinvestigated how the flying height and using oblique images affect the DEMuncertainty. This will be assessed by comparing two flights i.e. by capturingnadiral and oblique images. The oblique images were captured at a 60° angle. The study was realised with help from the surveying engineer of Falunmunicipality, who maneuvered the UAS. The study area was around three anda half ha and consisted mainly of park. To be able to test differentgeoreferencing methods GCP:s were surveyed, as well as control profiles thatserved as a reference for investigating the uncertainty of the elevation model.There were totally 3 different flying methods tested: 100 m with nadiralorientation, 50 m with nadiral orientation and 50 m with oblige orientation. The acquired data was processed in the software Agisoft Metashape, where itwas georeferenced with different above-mentioned methods. To be able toexamine which impact GCP has on the uncertainty, five different sets withdifferent number of GCP were made with the photos captured from 100 mflying height. The RMS value varied from 0,060 m for NRTK+1 GCP whichhad the lowest RMS value to 0,068 m for NRTK+2 GCP which had the highest RMS value. We used the combination of NRTK-UAS and GCPs for testing the impact offlying height on the uncertainty. The flying heights 100 m and 50 m wascompared. A decrease of the uncertainty was observed when the flying heightwas 50 m instead of 100 m. Our results show that the RMS-value increased from 0,014 m to 0,017 musing nadiral and oblique images, respectively. The difference is too small tobe able to draw a conclusion. The results for the oblique images improvedwhen only hard surfaces such as asphalt, concrete etc. were observed.

The drone industry is growing and the need for increased autonomy will be required if large fleetof drones will be able to fly without a single pilot per drone. A useful part of automating the flighten-route can be achieved with the upcoming standard of Direct Remote Id (DRI), which signalspositional data for drones and can be used as the perceptive part in a collision avoidance systembetween drones with the advantage of limited weight penalties and minimal financial cost.Simulations were carried out to understand different kinds of evasive maneuvers and develop asimple yet effective algorithm for avoiding obstacles and continue towards the next waypoint ona mission. Positional data can be retrieved with an ESP-32 board from a flight computer withMavlink protocol, which can then be broadcasted and received to an ESP-32 board using DirectRemote Id. The distances between the nearest drones can be computed, along with the shortest al-lowable distance and closest positions of the drones, if they were to continue on a straight course. Ifthe closest passing distance turned out closer than a set safety distance, an evasive maneuver is cal-culated and executed, with preliminary work focusing on evasion maneuvers on an horizontal plane.Flight tests showed that an evasive position could be calculated, and the drone successfully di-verted to it, while continuing with the mission after the evasion was completed. These resultsshowed the potential of using Direct Remote Id as a simple close proximity detection for use withcollision avoidance / Drönarindustrin växer allt snabbare och det kommer att krävas en större grad av autonomitet för att kunna få drönare att flyga av sig själva utan att ha en pilot per drönare. En användbar del av att kunna autonomisera flygrutten vid flykt kan vara den nya standarden Direct Remote Id (DRI), som sänder ut positionsdata för den individuella drönaren och kan användas för att kunna upptäcka och bli upptäckt av andra drönare med minimal vikt- och priskostnad.Simuleringar gjordes för att undersöka samt förstå olika typer av undanmanövrar och för att utveckla en simpel och effektiv algoritm för att kunna undvika objekt och fortsätta med en planerad rutt. Positionsdata kan skickas till ett ESP-32 kretskort fån en flygdator med hjälp av Mavlink protokoll, denna data kan sedan sändas och bli mottaget av ett annat ESP-32 kretskort med Direct Remote Id standarden. Avståndet till den närmsta drönaren kan beräknas samt den minsta passagedistansen mellan drönarna om de skulle fortsätta i rak riktning. Om den minsta passagedistansen mellan drönarna är mindre än ett satt säkerhetsavstånd, beräknas en undanmanöver samt utförs. Flygtester visade att en undanmanöver kunde beräknas samt att drönaren omdirigerades till sidan om objektet och därefter fortsatte på sin planerade flygrutt. Dessa resultat visade potentialen i att använda Direct Remote Id som ett enkelt sätt att upptäcka andra drönare för att användas i ett kollisionsundvikande system.

This study aims to compare advantages and disadvantages between detail surveying done with a drone (UAS) and GNSS equipment. Thus, in order to examine if detail surveying with UAS can be applicable for creating base maps in a more time efficient way. Aspects such as accuracy, environment, surroundings and ethics are shown consideration for. This was carried out by comparing data sampled from UAS flying and detailed surveying with GNSS. The flight altitude was 65 meters and the aerial photos were processed in Agisoft Metashape and ArcMap. GNSS was used to measure objects with high frequency, roughly 16 points per second and was later processed in GEO and ArcMap. Additional surveying was done with a total station in areas where the accuracy didn’t meet the requirements set by HMK. Establishment of free station was used when measuring with total station and the objects were then surveyed with a prism and direct measuring. Results that are relevant to this study are mainly RMS (Root Mean Square) and standard uncertainty. The results show that the time required for detailed surveying with UAS is 6 hours and 45 minutes, whilst for GNSS the time required is 8 hours and 30 minutes. Considering the RMS value and the standard uncertainty, the differences are marginally different. RMS value for UAS is 0.088 meters and standard uncertainty is 0.062 meters whilst for GNSS the RMS value is 0.084 meters and standard uncertainty is 0.058 meters. All measurements and results are within the 2nd standard level which are the requirements for a base map within an urban area. The results can be seen as reliable as the requirements set by HMK when practicing detailed surveying with UAS, GNSS and total station was followed. The time efficiency achieved when doing detailed surveying with UAS can make up for the increase in standard uncertainty as long as the results are within the recommendations set by HMK. Further studies could be applied to see how similar surveying results could look in areas with different environments and different sizes.

Sammanfattning Undersökningens syfte är att förstå hur noggrann UAS-fotogrammetrin i dagsläget (2013) är.  Frågeställningarna som undersökningen utgick ifrån var: kan UAS-fotogrammetri i dagsläget ge precisa punkter med hjälp av att mäta in centrum av 1x1 meter utlagda plattor som kan ses i ortofoto?;  Kan det ge snarlik noggrannhet med pixelstorleken? samt Kan UAS-tekniken idag användas för att producera pålitliga höjdmodeller? För att uppnå syftet har en undersökning utförts med jämförelse på koordinater insamlade med totalstation och insamlade med UAS-fotogrammetriska metoder. Resultatet visade att medelfelet var drygt 1 pixel på plana koordinater samt på koordinater i höjd. Pixlarnas storlek var mellan 4.7-9.3 cm. Största felkällan ser ut att vara upplösningen på bilderna, men tekniken utvecklas fort. UAS-fotogrammetrin lever väl upp till frågeställningarnas förväntningar. / Abstract The purpose of the study is to understand what the accuracy of UAS photogrammetry today (2013) is. The study was based on the following questions: Can UAS photogrammetry today give precise points, measuring the centre of 1x1 meter plywood boards viewed from an orthophoto?; Can it give similar accuracy as the size of the pixels? And can UAS technology today be used to produce elevation models of good quality? To investigate these questions, a study has been made to compare coordinates collected from a total station and UAS photogrammetric methods. The results show that the standard error is approximately 1 pixel on flat coordinates and 1 pixel on elevated coordinates. The pixel size was between 4.7 and 9.3 cm. The biggest source of error seems to be the resolution on the pictures, but the technology develops quickly. The UAS photogrammetry method definitely meets the expectations of the questions.

The popularity of using UAV in image-taking for the production of 3D models and orthophotos has increased over time. Karlskoga Municipality has recently acquired an UAV to produce their own 3D models and orthophotos. This project paper aims to study the geospatial accuracy of the orthophotos and DEM files produced using the images taken with their UAV. The flight takes only a few minutes but a considerable time is spent in the production processes. Difficulty is experienced in determining the right center point for most GCPs. Produced orthophotos in the software Photoscan have a resolution from 1.7 to 2.4 centimeters while DEM files have a resolution from 3.4 to 4.8 centimeters. Four orthophotos and four DEM files are produced where GCPs are used and not used and at two different flight heights, 76 and 105 meters. The spatial data of the ten GCPs are identified on the orthophotos and DEM files in ArcMap and compared with GNSS NRTK measurements and Lantmäteriet's data. A visual control in terms of completeness of data, alignment, residual tilt and scale is also done. Standard deviations in plane for orthophotos there GCPs are not used are greater than 2 meters, while there GCPs are used are around 0.7 meters. Standard deviations for DEM files are observed at 0.8 meters.

The purpose of this study is comparing elevation models from Terrestrial laser scanner (TLS) and Unmanned aerial system (UAS) photogrammetry focusing on detecting microtopography and the relationship between elevation differences and image textures. The soils on agricultural lands are permanently modified by intensive farming activities almost every year. The microtopography of the soil, that plays an important role in the surface runoff and infiltration, depends on cultivation practices and the field environment. By way of example: crop residues, furrows, tillage direction, and slope may impact the soil nutrient and erosion. To better understand and prevent soil degradation via erosion, 3-D reconstructions of high-resolution soil monitoring are required. In this study, we try to circumnavigate the soil roughness associated with sustainable practices and physical characteristics of fields by collecting soil datasets from non-contacted remote sensing platforms. The amount of soil roughness was observed environmental conditions derived from the Terrestrial Laser Scanner (TLS) and the Unmanned Aerial System (UAS) photogrammetry within harvested fields in Eastern Central Iowa. Additionally, by focusing on local relief detections and the relationship between outlier distributions and image textures, the two datasets were compared. Both TLS and UAS derived point clouds successfully reconstructed digital elevation models ~ 5cm RMSE after the registration and merge process, and these models showed local reliefs of study areas with fine details. However, several outlier cluster points were detected in the comparisons between TLS and UAS derived DEMs. To discover the outlier distributions, image texture was addressed with global and local block analysis. Since there were no significant correlations, most of the study sites show that poor texture of ground may trigger high elevation errors. To enhance the texture of images, several possible solutions are described, such as local contrast enhancement using the Wallis filter.

Sammanfattning: Detta självständiga arbete har utifrån ett taktiskt perspektiv studerat på vilket sätt obemannade system i kategorin UAS, Unmanned Aircraft Systems, kan tänkas bidra till taktisk uppgiftslösning inom marinen om dessa infördes idag. Arbetet har genom kvalitativ litteraturanalys studerat utvecklingen av dessa system och vad som är rimligt att förmågemässigt förvänta sig av dem idag. En analys har gjorts av den för arbetet aktuella operationsmiljön, den kvalificerade sjöstriden. Vidare har en analys av hur utvecklingen av UAS i USA och Ryssland gjorts för att sätta ett svenskt system i en kontext. Arbetet har haft sin grund i teorin om sjökrigets principer då dessa ger en god teoretisk grund för hur sjöofficeraren bör agera. Slutsatser som redovisas i arbetet är att UAS kan komma att spela en stor roll i att ge ökad förmåga till god lägesuppfattning för marinens taktiska chefer. Vidare redovisas hur UAS kan bidra till att lösa vissa av de marina taktiska uppgifterna och stridsuppgifter som finns i Försvarsmaktens Taktikreglemente för marinstridskrafterna. Vidare redovisas att en UAS-förmåga även kan bidra till att sjöofficerare har större möjlighet att agera utefter de i teorin redovisade sjökrigets principer. Ytterligare slutsatser som redovisas är att UAS kan lösa marina uppgifter av både offensiv och defensiv karaktär. Även uppgifter inom marin logistik kan lösas av UAS.

Unmanned Aerial Vehicles (UAVs) är obemannade flygfarkoster som främst använts och utvecklats inom det militära. Under senare år har användandet även tagit fart inom den civila sektorn, däribland mätningsbranschen. För att samla in geodata används Unmanned Aircraft Systems (UAS), vilka är system som består av fler komponenter än endast luftfarkosten t.ex. även kamera och kontrollstation. UAS är ett bra alternativ till traditionell flygfotografering då högupplösta bilder kan genereras till en låg kostnad. Eftersom UAS är en relativt ny metod måste osäkerheten utvärderas. Syftet med detta examensarbete är att utvärdera hur stödpunkter påverkar osäkerheten vid georeferering av UAS-bilder. Data erhölls från en flygning utförd av Sweco i november 2012. För att kunna utvärdera stödpunkternas inverkan översignalerades det 5 ha stora området med 35 stödpunkter. Nio olika konfigurationer av stödpunkter georefererades i programvaran Agisoft PhotoScan 0.9.0 och resultatet analyserades i Microsoft Excel, Geo Professional och Surfer 10L. Resultaten visar att osäkerheten för georefereringen minskar när antalet stödpunkter ökar, förutsatt att en jämn placering tillämpas. Bra georeferering uppnåddes när fyra stödpunkter användes. Vi rekommenderar ändå att minst fem stödpunkter används, fem stycken ger bra möjligheter till en god geometri – en i varje hörn och en i mitten. Det lägsta RMS-värdet i 3D (72 mm) erhölls med 17 stödpunkter jämnt fördelade över området. Det högsta RMS-värdet i 3D (190 mm) fick konfigurationen med sex stödpunkter placerade i ett av områdets hörn, något som tydligt visar hur stödpunkters placering (geometrin) påverkar osäkerheten av georefereringen. Även om fyra stödpunkter (en i varsitt hörn) bara får marginellt större RMS-värde än om en extra stödpunkt placeras i mitten, rekommenderas den sistnämnda för den bättre geometrin. För att kontrollera georefereringen rekommenderas några extra inmätta kontrollpunkter i området. / The main use and development of Unmanned Aerial Vehicles (UAVs) havethrough history been driven for military purposes, but in recent years the usehas increased also in the civilian sector, including the surveying industry. Inorder to collect geodata Unmanned Aircraft Systems (UAS) are used. UAS aresystems that consist not only of the unmanned vehicle, but also of componentslike a camera and a control station. UAS is a good alternative to traditionalaerial survey due to the high resolution images and the low operational cost.The uncertainty of UAS must be evaluated further since it is a relatively newsurveying method. The purpose of this study is to analyze the number of groundcontrol point’s (GCP’s) impact on the uncertainty of georeferencing UAS images.Data was collected from a flight conducted by Sweco in November 2012. The areawhich was flown (5 ha) was “over-signalized” by 35 GCPs in order to evaluate theirimpact on the georeferencing uncertainty. Nine different configurations of GCPswere georeferenced in the software Agisoft PhotoScan 0.9.0 and the result wasanalyzed in Microsoft Excel, Geo Professional and Surfer 10L. The result showsthat the uncertainty of the georeferencing decreases when the number of GCPsincreases, provided their distribution is even in the area. A goodgeoreferencing was obtained when four GCPs were used. Regardless, we recommendthe use of five, five provide a good geometry – one in each corner and one inthe middle. The least RMS value in 3D (72 mm) was found with 17 GCPs evenlydistributed in the area. The highest RMS value in 3D (190 mm) was found whenall six GCPs were placed in one of the corners of the area. This shows that thedistribution of GCPs has a great impact on the uncertainty of thegeoreferencing. Even if four GCPs (one in each corner) just get a little higherRMS value than if one extra GCP is placed in the middle, the latter isrecommended because of the favourable geometry. To be able to control thegeoreferencing it is recommended to survey some extra GCPs in the area.

Digitala ytmodeller (Digital Surface Model – DSM) används ofta i geodetiskt sammanhang. DSM har länge skapats bland annat med hjälp av fotogrammetri där flygbilder har tagits med traditionella flygningar. Intresset tilltar nu för att framställa DSM med hjälp av obemannade flygfarkoster, så kallade UAS (Unmanned Aircraft System). Den största fördelen med UAS är att det går snabbt och enkelt att få den lilla flygfarkosten upp i luften för att ta flygbilder och framställa DSM kostnadseffektivt.Syftet med detta examensarbete var att undersöka vilken höjdosäkerhet som kan uppnås i DSM som framställts genom fotogrammetri med UAS. För att åstadkomma detta har två flygningar gjorts den 25 april 2013 med en Gatewing X100 över ett område i Grillby där cirka 350 flygbilder togs sammanlagt. Efter flygningarna mättes med en totalstation 16 kontrollprofiler in på olika terrängtyper över flygområdet enligt rekommendationer i SIS-TS 21145:2007 ”Statistisk provning av digital terrängmodell”.Från de två flygningarna som gjordes i Grillby framställdes två olika DSM i programvaran AgiSoft Photoscan. DSM importerades därefter till SBG Geo där höjdskillnaderna mellan kontrollprofilerna och DSM beräknades. Medelavvikelsen i höjd varierade mellan -0,112 m och 0,050 m för de olika provytorna. De provytor som systematiskt avvek från DSM var asfaltprofilerna, dessa låg konstant (ca 0,1 m) under DSM. Anledningen tros ligga i bildmatchningen i programvaran AgiSoft Photoscan.De DSM som framställdes i detta examensarbete uppfyllde kraven för klass 4 enligt SIS-TS 21144:2007 vilket innebär att max medelavvikelse i höjd får vara 0,15 m. Det innebär, enligt samma SIS-TS, att framställda DSM är lämpade som projekteringsunderlag för arbetsplan väg och systemhandling järnväg (i jämn terräng). / Digital Surface Models (DSM) is common used for geodetic measurement today. Digital surface models have been created for a long time using photogrammetry where aerial photographs have been taken with traditional flights. The interest to produce DSM using unmanned air vehicles (UAS) has increased lately. The main advantage of a UAS system is that it is quick and easy to get the little aircraft up in the air to take aerial photographs and produce DSM cost-effective.The aim of this thesis was to investigate the height of uncertainty that can be achieved in DSM created by photogrammetry using UAS. To achieve this two flights have been made the 25th of April 2013 with a Gatewing X100. The flights were made over an area in Grillby where approximately 350 aerial photographs in total were taken. After the flights 16 control profiles were measured with a total station on different terrain types over the flight area as recommended by the document SIS-TS 21145:2007 “Statistical testing of Digital Terrain Models”.From the two flights that were made in Grillby, two different DSM was produced in the software AgiSoft Photoscan. The DSM was imported to SBG Geo and height differences between the control profiles and the DSM were calculated. This resulted in height differences which ranged between -0.112 m and 0,050 m in the various sample surfaces. The sample surface that deviated most from the DSM was the asphalt profiles that deviated about -0.1 m. It was considered to be a systematic error, but the source of the systematic error has not been located among the measurements. The error is believed to instead be in the image matching done by AgiSoft Photoscan. The DSM created in this thesis is classified as class 4 in a table from SIS-TS 21144:2007 which means that the max mean difference in height inside the DSM is ±0,15 m. This shows us that the DSM created with photogrammetry using UAS is suited for both as material for planning in railway and road constructions and for visualization of the ground.

<div> <p>Calibration of crop models is an expensive and time intensive procedure, which is essential to accurately predict the possible crop yields given changing climate conditions. One solution is the utilization of unmanned aircraft systems (UAS) deployed with Red Green Blue Composite (RGB), and multispectral sensors, which has the potential to measure and collect in field biomass and yield in a cost and time effective manner. The objective of this project was to develop a relationship between remotely sensed data and crop indices, similar to biomass, to improve the ability to parametrize crop models for local conditions, which in turn could potentially improve the quantification of the effect of hydrological extremes on predicted yield. An experiment consisting of 750 plots (350 varieties) was planted in 2018, and a subset of 18 plots (9 varieties) were planted in 2019. The in-situ above ground biomass along with multispectral and RGB imagery was collected for both experiments throughout the growing season. The imagery was processed through a custom software pipeline to produce spectrally corrected imagery of individual plots. A model was fit between spectral data and sampled biomass resulting in an R-square of 0.68 and RMSE of 160 g when the model was used to estimate biomass for multiple flight dates flights. The VIC-CropSyst model, a coupled hydrological and agricultural system model, was used to simulate crop biomass and yield for multiple years at the experiment location. Soybean growth was parametrized for the location using CropSyst’s Crop Calibrator tool. Biomass values generated from UAS imagery, along with the in-situ collected biomass values were used separately to parametrize soybean simulations in CropSyst resulting in very similar parameter sets that were distinct from the default parameter values. The parametrized crop files along with the default files were used separately to run the VIC-CropSyst model and results were evaluated by comparing simulated and observed values of yield and biomass values. Both parametrized crop files (using in-situ samples and UAS imagery) produced approximately identical results with a max difference of 0.03 T/Ha for any one year, compared to a base value of 3.6 T/Ha, over a 12-year period in which the simulation was ran. The parametrized runs produced yield estimates that were closer to in-situ measured yield, as compared to unparametrized runs, for both bulk varieties and the run experiments, with the exception of 2011, which was a flooding year. The parametrized simulations consistently produced simulated yield results that were higher than the measured bulk variety yields, whereas the default parameters produced consistently lower yields. Biomass was only assessed for 2019, and the results indicate that the biomass after parametrization is lower than the default, which is attributed to the radiation use efficiency parameter being lower in the parametrized files, 2.5 g/MJ versus 2.25 g/MJ. The improved accuracy of predicting yield is evidence that the UAS based methodology is a suitable substitute for the more labor intensive in-situ sampling of biomass for soybean studies under similar environmental conditions.</p> </div><p> <br></p>