A Ground Penetrating Radar (GPR) is a non-invasive measurement tool to locate objects in the subsurface. The GPR transmits electromagnetic waves into the ground and records the waves reflected from surface interfaces of different materials. To accurately find these surfaces after measuring, it is important to record the precise location of the GPR and minimize reflected noise. Since a GPR cannot distinguish the direction from which the waves were reflected, this can result in a misinterpretation of the data if waves are reflected from surrounding objects. This problem can be reduced by also mapping objects in the surroundings. The work of this thesis is aimed at implementing a system that uses a Real-Time Kinematics (RTK) GNSS (Global Navigation Satellite System) receiver for precise positioning together with a 2D-LiDAR (Light Detection And Ranging) to record a 3D map of the surroundings. We used the 3D-LiDAR system to record vertical planes (cross-sections) that were processed into a 3D volume map. We found that the RTK GNSS receiver performed well and delivered the position within centimeters when provided with corrections, while it was about 2.5 m off without corrections. The performance was compared with a professional-grade Leica RTK receiver and the difference in latitude and longitude ranged from 0.001-0.002 m and 0.002-0.004 m, respectively. By fusing the RTK position with the LiDAR data using the software Robot Operating System (ROS), we created 3D maps that represented the surroundings along the traveled path. Our developed system, consisting of an RTK GNSS receiver and the 2D LiDAR, gave promising results and we are optimistic that combining the system with a GPR can improve the interpretation of the subsurface. Thus, the proposed method seems promising to be used during GPR mapping.

Short rotation woody crops (SRWC) eastern cottonwood (Populus deltoides) and hybrid poplar plantations were established in 2021 in Pontotoc and Oktibbeha counties of Mississippi to study the biomass potential of SRWC for biofuel production. We used a novel backpack LiDAR system to measure forest metrics and harvested sample trees to build aboveground biomass (AGB) and leaf area index (LAI) equations. The results showed that LiDAR-derived variables accurately estimated aboveground biomass (R2 =0.81 and 29.22 % RMSE). However, the LAI estimation results showed that the LiDAR metrics moderately explained field measurements of LAI (R2 =0.31 and 18.05% RMSE) for individual-trees and poorly explained plot-level LAI measured with the LAI-2200C (R2 =0.11 and 66% RMSE). The backpack LiDAR system can be valuable for forest managers and researchers, enabling non-destructive AGB and LAI estimation. However, further research is required to overcome its limitations and achieve precise measurements of AGB and LAI.

Forests cover a large portion of the global land area and provide critical resources such as timber, food, and medicine in addition to playing a significant role in the global carbon cycle. As such, sustainable forest management practices are required to balance forest economies and climate change mitigation with other non-timber objectives. A key aspect of many sustainable forest management programs is forest monitoring, for which technological and methodological development has led to enhanced forest inventory (EFI) methods, many of which rely on remote sensing data from high-resolution light detection and ranging (LiDAR) and optical imagery. However, to date, current applications of EFI methods have mostly focused on timber attributes with limited research on non-timber attributes or analyses regarding multi-temporal monitoring, method scaling, or method transferability. The objective of this thesis is to expand applications of EFIs in monitoring and analysis through two distinct studies, first evaluating the utility of LiDAR-based EFI methods in multi-temporal silvicultural treatment assessment and secondly in the pre-harvest estimation of merchantable wood and non-merchantable wood left as logging residues. The first study evaluates a process that expands the sampling of fertilization treatment effects on forest stands to the wider treatment area by utilizing paired LiDAR blocks made up of raster cell estimates from a multi-temporal area-based model. Results showed promise for detecting treatment impacts on stand volume, biomass, and height and highlights the potential for the methods to be used as a means to rapidly expand analysis from sample plots to the entire treatment area. The second study focuses on the use of a hybrid area-based and individual tree EFI approach to model merchantable and non-merchantable forest wood volumes while exploring the scalability of these models to harvest blocks and the transferability to additional blocks without prior training. Results from this study indicated that models for both volume attributes are successfully scalable and transferable to harvest blocks. Overall, the research results presented in this thesis demonstrate the potential of enhanced forest inventory methods for the monitoring and assessment of timber attributes, such as wood volume or biomass, as well as alternative attributes, such as stand height, or non-merchantable wood volume, over multiple years. This work further demonstrates the potential for these methods to expand areas of assessment and increase prediction accuracies. / Graduate / 2022-08-17

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Enorme Mengen an Waldbrandrauch von außerordentlich starken und unkontrollierbaren Waldbränden in Kanada (2017), Sibirien (2019) und Australien (2019-2020) gelangten in den letzten Jahren in die Stratosphäre. Die Auswirkungen des Rauchs auf das Klimasystem der Erde wurden von vielen Forschungsgruppen untersucht. Mithilfe von Polarisations-Raman-Lidarmessungen wurden im Rahmen dieser Arbeit die optischen Eigenschaften des Rauchs vermessen und einige Aspekte der Auswirkungen auf das Klimasystem untersucht. Der sibirische Rauch verteilte sich über der gesamten Arktis und wurde fast ein Jahr lang während der MOSAiC-Kampagne mit einem Lidar auf dem deutschen Eisbrecher Polarstern in der Nordpolregion beobachtet. Dabei wurde eine 10km dicke Aerosolschicht (etwa zwischen 8 und 18km Höhe) in der oberen Troposphäre und unteren Stratosphäre (UTLS) detektiert. Die Schicht zeigte eindeutige Eigenschaften von Waldbrandrauch. Die Lidarverhältnisse lagen bei 55 sr (355nm Wellenlänge) und 85 sr (532nm Wellenlänge) und die optische Dicke des Aerosols (AOT) erreichte Werte bis zu 0,1 im Herbst 2019. Waldbrandrauch erreicht die Stratosphäre normalerweise nur im Zusammenhang mit Pyrokumulonimbus-Konvektion (pyroCb). Der sibirische Waldbrandrauch gelangte allerdings ohne die Hilfe von pyroCbs in die Stratosphäre, vermutlich durch sogenannte Selbsthebung, wie in der Arbeit ausgeführt wird. Demnach steigt der Rauch innerhalb von 3-7 Tagen bis zur Tropopause und in die untere Stratosphäre auf. Die Hypothese wird unterstützt durch CALIOP-Beobachtungen und ECRAD-Modellsimulationen. Während der Hauptphase der großen australischen Feuer (zwischen dem 29. Dezember 2019 und dem 4. Januar 2020) entstanden etwa 40 pyroCbs über den ausgedehnten Feuerflächen. Nie zuvor war eine derart hohe Anzahl von pyroCbs beobachtet worden. Sie verursachten eine stratosphärische Verschmutzung mit Rauch, wie sie nie zuvor beobachtet worden war. Der australische Rauch verteilte sich über der gesamten Südhemisphäre und wurde während der DACAPO-PESO-Kampagne in Punta Arenas (Chile) mit einem Lidar vermessen. Zwischen Januar 2020 und November 2021 befand sich der Rauch typischerweise zwischen 9 und 24km Höhe. Im Januar 2020 war die maximale optische Dicke des Rauchs über Punta Arenas etwa 1,0. Die Kombination von Lidar- und Photometermessungen ergab, dass das Lidarverhältnis des Rauchaerosols bei 69±19 sr (355 nm), 91±17 sr (532 nm) und 120±22 sr (1064 nm) lag. Der Rauch in der Stratosphäre hatte einen Einfluss auf die Strahlung, auf die chemische Zusammensetzung der Stratosphäre und auf die Entwicklung von Zirruswolken an der Tropopause. Herausragend starke Ozonlöcher bildeten sich teilweise (Arktis, 2020) und vollständig (Antarktis 2020 und 2021) in rauchverschmutzter Luft beider polarer Gebiete. Dabei wurden erstmals klare Hinweise auf einen Einfluss des Rauchs auf die Ozonlöcher gefunden (besonders ausgeprägt in der Antarktis 2020), wie in der Arbeit dargelegt wird. Zusammengefasst präsentiert diese Dissertation die Messungen der geometrischen, optischen und mikrophysikalischen Eigenschaften der stratosphärischen Rauchschichten über der Arktis und über Punta Arenas, sowie das Abklingverhalten dieser großen stratosphärischen Störungen in vier Publikationen. Darüber hinaus wird der Einfluss von Rauchpartikeln auf den Ozonabbau diskutiert und die Hypothese, dass Rauch von der freien Troposphäre bis zur Tropopause selbst aufsteigt wird aus der Prespektive von Beobachtungen und Modellergebnissen gezeigt.:1 Introduction 2 Measurement campaigns 2.1 DACAPO-PESO 2.2 MOSAiC 3 Stratospheric aerosol perturbations 3.1 Historical observations of aerosol in the stratosphere - an overview 3.2 Siberian wildfires 3.3 Australian wildfires 4 Instrumentation and data analysis 4.1 PollyXT 4.2 Lidar-derived optical properties 4.3 Data analysis 5 Results 5.1 First Publication: Ohneiser et al., Optical properties of Australian smoke over Punta Arenas, Chile, ACP, 2020 5.2 Second Publication: Ohneiser et al., Decay Phase of Australian wildfire smoke in the stratosphere, ACP, 2020 5.3 Third Publication: Ohneiser et al., Unexpected smoke layer in the High Arctic winter stratosphere 2019–2020, ACP, 2021 5.4 Fourth Publication: Ohneiser et al., Self-lofting of wildfire smoke in the troposphere and stratosphere, ACPD (preprint), in review, 2022 6 Summarizing discussion, conclusion, and outlook A Author’s contribution to the four publications B Lists B.1 List of Abbreviations B.2 List of Symbols B.3 List of Figures B.4 List of Tables References Acknowledgements

3D immersive visualization systems, or CAVEs™, have found wide adoption for use in geosciences, planetary science, medical research, and computer science. However, much of the potential for such systems in practical civil and environmental engineering settings has been severely limited due to 1) extreme costs in both hardware and software; 2) immobility due to calibration and darkroom requirements; and 3) extensive and expensive manpower requirements for both operation and maintenance. This thesis presents the development and testing of a new mobile low-cost immersive stereo visualization system -- the "VuePod" -- that attempts to address these challenges through the use of commercial-off-the-shelf technologies, open source software, consumer grade passive 3-D television monitors, an active tracking system, and a modular construction approach. The VuePod capitalizes on recent functional advancements and cost decreases in both hardware and software and is demonstrated herein as a viable alternative to projector-based walk-in CAVEs and their limitations. Additionally, I have selected twelve representative 3D immersive systems and performed a side-by-side analysis of each in terms of cost, viewing capabilities, computing and user experience. The purpose of performing this analysis is to classify the variety of systems available and simplify the system procurement and configuration processes. The availability of this comparative system information should facilitate the increased utilization of immersive 3D interface technologies in science and engineering.

The prospect of sea level rise due to melting ice sheets affirms the urgency of gaining knowledge on ice sheet dynamics during deglaciation. The Fennoscandian Ice Sheet serves as an analogue, whose retreat can be reconstructed from the geomorphological record. The recent development of a high-resolution LiDAR-derived elevation model can reveal new relationships between landforms, even for well-studied areas such as the Torneträsk region in northwestern Sweden. Therefore, this study aims to refine the reconstruction of the deglaciation in this region based on an updated glacial geomorphological map. A range of glacial landforms were mapped, which by means of an inversion model were utilized to form swarms representing spatially and temporally coherent ice sheet flow systems. Additionally, glacial lake traces allowed for the identification of ice margins that dammed lakes in Torneträsk, Rautasjaure, and other (former) lake basins. Eight glacial lake stages were identified for the Torneträsk basin, where final drainage occurred through Tornedalen. Over 20 glacial lake stages were identified for the Rautasjaure basin, where drainage occurred along the margins of a thinning ice lobe. The disparity between the glacial lake systems results from different damming mechanisms in relation to the contrasting topography of the basins. A strong topographic control on the retreat pattern is evident, as the ice sheet retreated southward in an orderly fashion in the premontane region, but disintegrated into ice lobes in the montane region. The temporal resolution of current dating techniques is insufficient to constrain the timing of ice retreat at the spatial scale of this study. Precise dating of the Pärvie fault would pinpoint the age of the ice margin which at the time of rupture was located between two glacial lake stages of Torneträsk. Collectively, this study provides data for better understanding the final retreat of the ice sheet and associated processes, such as interactions between glacial lakes and ice dynamics.

Environment perception within autonomous driving aims to provide a comprehensive and accurate model of the surrounding environment based on information from sensors. For the model to be comprehensive it must provide the kinematic state of surrounding objects. The existing approaches of object detection and tracking (estimation of kinematic state) are developed for dense 3D LiDAR data from a sensor mounted on a car. However, it is a challenge to design a robust detection and tracking algorithm for sparse 3D LiDAR data. Therefore, in this thesis we propose a framework for detection and tracking of unknown objects using sparse VLP-16 LiDAR data which is mounted on a heavy duty vehicle. Experiments reveal that the proposed framework performs well detecting trucks, buses, cars, pedestrians and even smaller objects of a size bigger than 61x41x40 cm. The detection distance range depends on the size of an object such that large objects (trucks and buses) are detected within 25 m while cars and pedestrians within 18 m and 15 m correspondingly. The overall multiple objecttracking accuracy of the framework is 79%. / Miljöperception inom autonom körning syftar till att ge en heltäckande och korrekt modell av den omgivande miljön baserat på information från sensorer. För att modellen ska vara heltäckande måste den ge information om tillstånden hos omgivande objekt. Den befintliga metoden för objektidentifiering och spårning (uppskattning av kinematiskt tillstånd) utvecklas för täta 3D-LIDAR-data från en sensor monterad på en bil. Det är dock en utmaning att designa en robust detektions och spårningsalgoritm för glesa 3D-LIDAR-data. Därför föreslår vi ett ramverk för upptäckt och spårning av okända objekt med hjälp av gles VLP-16-LIDAR-data som är monterat på ett tungt fordon. Experiment visar att det föreslagna ramverket upptäcker lastbilar, bussar, bilar, fotgängare och även mindre objekt om de är större än 61x41x40 cm. Detekteringsavståndet varierar beroende på storleken på ett objekt så att stora objekt (lastbilar och bussar) detekteras inom 25 m medan bilar och fotgängare detekteras inom 18 m respektive 15 m på motsvarande sätt. Ramverkets totala precision för objektspårning är 79%.

A method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not purposely aligned. These features are commonly raised roadbeds but may occur due to other manmade alterations to the terrain or natural terrain. The implementation uses the TauDEM watershed delineation software included in the MapWindow open source Geographic Information System to initially extract watershed boundaries. The watershed boundaries are then examined computationally to determine which sections warrant inclusion in the storm surge mesh. Introductory work towards applying image analysis techniques as an alternate means of vertical feature extraction is presented as well. Vertical feature lines extracted from a LiDAR dataset for Manatee County, Florida are included in a limited storm surge finite element mesh for the county and Tampa Bay. Storm surge simulations using the ADCIRC-2DDI model with two meshes, one which includes linear raised features as element edges and one which does not, verify the usefulness of the method.

The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. / Master of Science