This project uses exploratory 3D geospatial analyses to assess the taphonomy of the Gray Fossil Site (GFS). During the Pliocene, the GFS was a forested, inundated sinkhole that accumulated biological materials between 4.9-4.5 mya. This deposit contains fossils exhibiting different preservation modes: from low energy lacustrine settings to high energy colluvial deposits. All macro-paleontological materials have been mapped in situ using survey-grade instrumentation. Vertebrate skeletal material from the site is well-preserved, but the degree of skeletal articulation varies spatially within the deposit. This analysis uses geographic information systems (GIS) to analyze the distribution of mapped specimens at different spatial scales. Factors underpinning spatial association, skeletal completeness, and positioning of specimens were examined. At the scale of the individual skeleton, analyses of the Mastodon Pit explore how element completeness and orientation/inclination of the mastodon reflect post-depositional processes.

This paper examines tactical and ethical considerations for the implementation of drones in the Swedish naval forces. Research concerning ethics and the use of drones differ between being an accepted technology in warfare and an unfair advantage for the user. Research of tactical aspects in using drones show clear advantages for naval tactics. While several nations worldwide have adopted drones in their navy Sweden has not. By examining what considerations can be made concerning ethics and tactics, the implementation of drones can be made easier.  By applying an ethical perspective aspect for drones and one theory concerning the tactical aspect for naval warfare and applying these to different drones, this study examines whether drones can fulfill naval duties simultaneously as well as being ethical in their usage.  The analysis shows that unarmed drones can with ease solve the majority of tactical tasks ethically. Drones which are produced with weapon capability can theoretically complete all tactical assignments however, cannot be considered as being ethical. The findings show that armed drones are not considered ethical; un- armed drones can be exploited in the Swedish naval forces while being ethical and that ethical theory con- cerning drones in the armed forces needs developing in both war and peacetime

Agriculture faces many challenges related to the increasing food demands of a growing global population and the sustainable use of resources in a changing environment. To address them, we need reliable information sources, like exploiting hyperspectral satellite, airborne, and ground-based remote sensing data to observe phenological traits through a crops growth cycle and gather information to precisely diagnose when, why, and where a crop is suffering negative impacts. By combining hyperspectral capabilities with unmanned aerial vehicles (UAVs), there is an increased capacity for providing time-critical monitoring and new insights into patterns of crop development. However, considerable effort is required to effectively utilize UAV-integrated hyperspectral systems in crop-modeling and crop-breeding tasks. Here, a UAV-based hyperspectral solution for mapping crop physiological parameters was explored within a machine learning framework. To do this, a range of complementary measurements were collected from a field-based phenotyping experiment, based on a diversity panel of wild tomato (Solanum pimpinellifolium) that were grown under fresh and saline conditions. From the UAV data, positionally accurate reflectance retrievals were produced using a computationally robust automated georectification and mosaicking methodology. The resulting multitemporal UAV data were then employed to retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework. Several approaches were evaluated to identify the best-performing regression supervised methods. An investigation of two learning strategies (i.e., sequential and retraining) and the value of using spectral bands and vegetation indices (VIs) as prediction features was also performed. Finally, the utility of UAVbased hyperspectral phenotyping was demonstrated by detecting the effects of salt-stress on the different tomato accessions by estimating the salt-induced senescence index from the retrieved Chl dynamics, facilitating the identification of salt-tolerant candidates for future investigations. This research illustrates the potential of UAV-based hyperspectral imaging for plant phenotyping and precision agriculture. In particular, a) developing systematic imaging calibration and pre-processing workflows; b) exploring machine learning-driven tools for retrieving plant phenological dynamics; c) establishing a plant stress detection approach from hyperspectral-derived metrics; and d) providing new insights into using computer vision, big-data analytics, and modeling strategies to deal effectively with the complexity of the UAV-based hyperspectral data in mapping plant physiological indicators.

To meet ambitious net-zero greenhouse gas emission targets by 2050, large-scale CO₂ reduction and removal are required. Nature-based solutions have been proposed as a potential aid to this process. Mangrove ecosystems, as well as their conservation and restoration, have the potential to make significant contributions in Saudi Arabia and other coastal regions. While field measurements of carbon assimilation rate and leaf area index (LAI) in mangroves provide important insights into carbon fluxes, they are time-consuming, labor-intensive, and limited when covering large areas. To address this issue, multispectral images captured by unmanned aerial vehicles (UAV) are used to generate spectral vegetation indices, which can then be used to build regression models for estimating mangrove LAI and carbon capture capabilities. The carbon assimilation rate measurements in the field for studying both diurnal and sub-seasonal fluxes revealed that Avicennia marina has a high carbon assimilation rate peak in the morning, which decreases thereafter, and a smaller peak in the afternoon. Furthermore, comparing all the studied sites, the KAM site (June) had the highest morning overall carbon assimilation rate values, ranging from 15- 20 μmol CO₂ m⁻² s⁻¹, followed by Island (October) ranging from 10- 17 μmol CO₂ m⁻² s⁻¹, and finally Rheem (February) ranging from 5- 15 μmol CO₂ m⁻² s⁻¹. Moreover, the acquired multispectral images were used to generate spectral vegetation indices, which were then used as input to build a random forest algorithm for estimating the LAI of mangroves. Following an evaluation of each mangrove site, the Rheem site dataset yielded the best Random forest algorithm (R²= 0.88 and RMSE= 0.39), so this model was used to create high resolution spatially distributed LAI-based maps for all of the mangrove sites studied. Knowing the carbon uptake per leaf area as well as the total leaf area (based on UAV-derived LAI estimates) within a mangrove site enabled us to create carbon capture maps (kg C yr⁻¹ per pixel) for all of the sites studied. To enable a more complete carbon accounting of mangrove ecosystems, future research should explore remote sensing approaches for inferring carbon assimilation in both belowground biomass and soils.

Autonomous Unmanned Aerial Vehicles (UAVs) in agriculture are increasingly in demand to reduce cost, labour and increase effectiveness and quality in farming. However, it is necessary to improve reliability for this technology to perform its full potential without harming humans, animals or the environment. The reliability increases by identifying the hazards and mitigating them. Therefore the risks are identified, analyzed and mitigated using analysis tools. Two different methods are used to analyze and reduce hazards, and each method utilizes various analysis tools. In addition, redundancy and preventive action are proposed to eliminate or minimize the danger. This thesis identifies risks by studying and reviewing a generic use-case from the AFarCloud project and compares the two hazard analysis methods to determine which method provides the most reliable result.

A non switching, non linear, quaternion based attitude P2 controller, together with a sensitivitynormalizing function for the control surfaces has been simulated and implemented on a flying fixedwing with non vectored engines. In simulations the controller worked well in all flight modes,hovering, transition and flying, and also rejected a simple wind disturbance in all modes. Thefirst implementation on hardware did not work due to programming errors causing crashes withunrepairable damages. The second aircraft was built out of a piece of plywood to further simplifythe testing and tolerate more crashes. A flat plate, a flying piece of plywood, is not a proper airfoiland so has no effects due to camber. It is therefore easier to both simulate and tune. The controllerworked acceptable in reality, but does need further tuning. Due to time constraints the weighingof airflow inside and outside the propeller wash could not be fully determined resulting in differentgain in the different flight modes, but initial estimation of the parameters were enough to achieverobust, stable hovering transitioning and flying even in winds stronger than 5m/s.The controllerwas not implemented on an EcoSoar due to time constraints, but the proof of concept flying pieceof plywood proved the controller feasible for future embedding in a modified EcoSoar. A VTOLcapable EcoSoar could be used for critical deliveries in for example the medical field in Malawiwhere suboptimal infrastructure is hindering progress. The need for medical supplies around therural parts of Malawi is great but roads and services are not capable yet. Since a VTOL flyingwing with delivery capabilities can be both cheap to build, and deliver supplies to areas withoutan airfield, it could accelerate development in Malawi and thus greatly increase quality of life forhumans.

Food security is a major concern of the 21st century, given climate change and population growth. In addition, high salt concentrations in soils affect ~20% of irrigated land and cause a substantial reduction in crop yield. Cultivating salt-tolerant crops could enable the use of salt-affected agricultural land, reduce the use of fresh water and alleviate yield losses. Innovative methods need to be developed to study traditional and novel traits that contribute to salinity tolerance and accurately quantify them. These studies would eventually serve for developing new salt tolerant crops, adapted to the harsh arid and semi-arid climate conditions. A study of 200 accessions of the wild tomatoes (Solanum pimpinellifolium) was conducted in field conditions with phenotyping using an unmanned aerial vehicle (UAV)-mounted hyperspectral camera. Six genotypes with different levels of salt tolerance were sampled for leaf pigment analyses, revealing a clear pattern for the high salt tolerant accession M007, where pigment content in the salt-treated plants significantly increased compared to their control counterparts only in harvesting campaigns 3 and 6, each performed two days after the first and second salt stress application events. Moreover, the light harvesting capacity was found to be better maintained under salt stress in the medium (M255) and highly salt tolerant (M007 and M061) accessions. Pigment quantitation data will contribute towards the groundtruthing of hyperspectral imaging for the development of remote sensing-based predictive pigment mapping methods. This work establishes a reliable quantification protocol for correlating pigment content with vegetation indices. Hence, pigment content captured by imaging techniques and validated using biochemical analysis would serve in developing a high-throughput method for pigment quantitation in the field using UAV-based hyperspectral imaging. This would serve as a tool for measuring pigment content in large number of genotypes in the field which would eventually lead to new salt-tolerant genes.

Begreppet drönare för de flesta utomstående förknippas oftast med hobbyverksamhet i form av foto- och videoskapande. Det senaste åren har allt fler byggföretag valt att satsa på arbetsmetoder där drönare tillåts ersätta befintlig utrustning. Detta examensarbete utfördes för att kartlägga potentialen hos drönarserien Phantom 4, samt de rådande för- och nackdelar med tekniken. Aktuellt är bland annat att studera hur planering-, utförande och bearbetningsskedet påverkas då drönare tillåts användas inom projekt. Syftet med denna studie är att planlägga vid vilka projekt dagens drönarteknik (Phantom 4) kan tillämpas samt de möjligheter som så kallad RTK-teknik kan ha för vidare betydelse för aktörer som vill uppgradera sin drönaranvändning och utrustning. Under arbetets gång har information granskats ifrån tidigare arbeten, litteraturstudier samt intervjuer med nyckelpersoner på Pontarius AB. Dessa personer arbetar dagligen med frågor och projekt som berör ämnesområdet, vilket varit användbart för att styrka det slutsatser som rapporten slutligen kunnat dra. Studien bekräftar att Phantom 4 serien har möjligheten att underlätta planeringen, utförandet och bearbetningen. Genom att göra arbetsmiljön säkrare uti fält, men också genom att minska mängden data som behöver korrigeras manuellt i efterhand. Samtidigt föreligger i dagsläget vissa begränsningar för drönartekniken. Däribland återfinns det faktum att drönaren inte når den precision i alla lägen som en traditionell inmätning gör och att den är begränsad av yttre faktorer, såsom vindkänslighet, väderberoende samt begränsad batteritid. Sammanfattningsvis kan studien konstatera att kunder till Pontarius AB fäller en avgörande roll inför vilken riktning som drönartekniken tillåts utvecklas inom företaget. Efterfrågan på den information som Phantom 4 kan tillhandahålla saknas i mångt och mycket ifrån kundens sida. Detta gör att tekniken inte kan nå sin fulla potential då den inte naturligt tillåts inkluderas i alla avseenden. Trots detta anser vi att en investering i en Phantom 4 RTK bör övervägas å Pontarius AB´s vägnar då denna drönartyp kan bidra med högre inmätningsnoggrannhet och enklare bearbetning av insamlat material.

This thesis developed a Rudder Augmented Trajectory Correction (RATC) method for small unmanned aerial vehicles. The goal of this type of controller is to minimize the lateral image errors of body-fixed non-gimbaled cameras. This is achieved through both aggressive trajectory following and elimination of the roll angle present in current aileron only trajectory correction autopilots. The analytical derivation of the rudder augmented trajectory correction controller is presented. Using estimated aerodynamic derivatives of the Aerosonde UAV, RATC, produced a stable and controllable system. This control algorithm was integrated into the AggieAir Minion-class UAV using the Paparazzi open source autopilot. Flight results are presented that show significant reduction in the roll angle present during trajectory correction. This is shown using both inertial measurement nit sensor data as well as payload imagery collected over a selected region of interest. The conclusion of this thesis is that the RATC algorithm is a viable solution to minimize lateral image errors for body-fixed cameras in realm of aerial surveying.

The unmanned aerial vehicles (UAVs) are also known as drones. Recently, UAVs have attracted the next generation researchers due to their flexible, dynamic, and cost-effective deployment, etc. Moreover, the UAVs have a wide range of application domains, such as rescue operation in the remote area, military surveillance, emergency application, etc. Given the UAVs are appropriately deployed, the UAVs provide continuous and reliable connectivity, on-demand, and cost-effective features to the desired destination in the wireless communication system. Thus, the UAVs can be a great choice to deploy as a mobile relay in co-existence with the base stations (BSs) on the ground to serve the 5G wireless users. In this thesis, the UAV-assisted mobile relay (UAV-MR) in the next generation wireless networks has been studied, which also considers the UAV-MR physical layer security. The proposed system also considers one ground user, one BS on the ground, and active presence of multiple eavesdroppers, situated nearby the ground user. The locations of these nodes (i.e., the ground user, the BS, and the eavesdroppers) are considered fixed on the ground. Moreover, the locations of the eavesdroppers are not precisely known to the UAV-MR. Thus, this thesis aims to maximize the achievable secrecy rate, while the BS sends the secure information to the ground user via the UAV-MR. However, the UAV-MR has some challenges to deploy in wireless networks, such as 3D deployment, robust resource allocation, secure UAV-MR to ground communication, the channel modeling, the UAV-MR flight duration, and the UAV-MR robust trajectory design, etc. Thus, this project investigates the UAV-MR assisted wireless networks, which addresses those technical challenges to guarantee efficient UAV-MR communication. Moreover, the mathematical frameworks are formulated to support the proposed model. An efficient algorithm is proposed to maximize the UAV-MR achievable secrecy rate. Finally, the simulation results show the improved performance for the UAV-MR assisted next-generation networks.