The North Florence Dunal Aquifer is the only feasible source for drinking water for the coastal city of Florence, Oregon and Florence's Urban Growth Boundary. High infiltration rates and a shallow groundwater table leave the aquifer highly susceptible to contamination from septic tank effluent, storm runoff, chemical fertilizers and recreational ATV use throughout the dunes. Public interest in the quality and quantity of the aquifer water has been sparked since the City of Florence received a grant from the Environmental Protection Agency for a watershed protection and restoration project. Delineation of the shallow groundwater surface and its relationship to the surface water bodies within the dunes is crucial in protecting this drinking water source from contamination. This thesis project created a GIS representation of the shallow groundwater elevation and associated prediction error map. Surface water bodies were confirmed as window lakes into the dunal aquifer and no signs of perched aquifer conditions were observed between Holocene and Pleistocene dunes. Ground Penetrating Radar, well data provided by the city of Florence and LiDAR were the primary sources for data collection.

<p>Accurate 3D landscape models of cities or mountains have wide applications in mission</p> <p>planning, navigation, geological studies, etc. Lidar scanning using drones can provide high</p> <p>accuracy 3D landscape models, but the data is more expensive to collect as the area of</p> <p>each scan is limited. Thanks to recent maturation of Very-High-Resolution (VHR) optical</p> <p>imaging on satellites, people nowadays have access to stereo images that are collected on a</p> <p>much larger area than Lidar scanning. My research addresses unique challenges in satellite</p> <p>stereo, including stereo rectification with pushbroom sensors, dense stereo matching using</p> <p>image pairs with varied appearance, e.g. sun angles and surface plantation, and rasterized</p> <p>digital surface model (DSM) generation. The key contributions include the Continuous 3D-</p> <p>Label Semi-Global Matching (CoSGM) and a large scale dataset for satellite stereo processing</p> <p>and DSM evaluation.</p>

<p dir="ltr">Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, Lidar technology has provided a straightforward approach to measuring canopy height using various platforms such as terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite sensors. However, satellite Lidar data, even with its global coverage, has a sparse sampling pattern that doesn’t provide continuous coverage over the globe. In contrast, satellites like LANDSAT offer seamless and widespread coverage of the Earth's surface through spectral data. Can we exploit the abundant spectral information from satellites like LANDSAT and ECOSTRESS to infer structural information obtained from Lidar satellites like Global Ecosystem Dynamic Investigation (GEDI)? This study aims to develop a deep learning model that can infer canopy height derived from sparsely observed Lidar waveforms using multi-sensor spectral data from spaceborne platforms. Specifically designed for localized site, the model focuses on county-level canopy height estimation, taking advantage of the relationship between canopy height and spectral reflectance that can be established in a local setting – something which might not exist universally. The study hopes to achieve a framework that can be easily replicable as height is a dynamic metric which changes with time and thus requires repeated computation for different time periods.</p><p dir="ltr">The thesis presents a series of experiments designed to comprehensively understand the influence of different spectral datasets on the model’s performance and its effectiveness in different types of test sites. Experiment 1 and 2 utilize Landsat spectral band values to extrapolate canopy height, while Experiment 3 and 4 incorporate ECOSTRESS land surface temperature and emissivity band values in addition to Landsat data. Tippecanoe County, predominantly composed of cropland, serves as the test site for Experiment 1 and 3, while Monroe County, primarily covered by forests, serves as the test site for Experiment 2 and 4. When compared to the Airborne Lidar dataset from the United States Geological Survey (USGS) – 3D Elevation Program (3DEP), the model achieves a Root Mean Square Error (RMSE) of 4.604m for Tippecanoe County using Landsat features while 5.479m for Monroe County. After integrating Landsat and ECOSTRESS features, the RMSE improves to 4.582m for Tippecanoe County but deteriorates to 5.860m for Monroe County. Overall, the study demonstrates comparable results to previous research without requiring feature engineering or extensive pre-processing. Furthermore, it successfully introduces a novel methodology for integrating multiple sources of satellite data to address this problem.</p>

In 2019, 770 million people worldwide lived without electricity. As many as 490 million people could be electrified with 210,000 mini-grids by 2030. Obtaining information for decision-making is crucial to determine the viability of such a project. Currently, it is a major challenge for mini-grid developers to gather this information at the speed and scale necessary to make effective investment choices. Village Data Analytics (VIDA) is a decision-making tool used for mini-grid project planning and site selection. This paper presents a method to estimate the cost of a mini-grid distribution network on a site-by-site basis. This method can estimate the total demand, potential connections, distribution infrastructure components and corresponding costs for each site. The model can make predictions for 50 sites within two hours so the tool is especially useful for preliminary estimates in the planning phase. A more detailed study of the individual sites is recommended. Comparison with a benchmark has shown that on-site conditions often reveal activities that can only be captured by a survey. However, collecting on-site data is time-consuming and costly. Therefore, GIS and modelling tools can serve as a good approximation of the on-ground reality and are relevant to accelerate planning and support timely decision-making. / 2019 levde 770 miljoner människor världen över utan elektricitet. Så många som 490 miljoner människor skulle kunna elektrifieras med 210 000 mininät till 2030. Att få information för beslutsfattande är avgörande för att avgöra om ett sådant projekt är lönsamt. För närvarande är det en stor utmaning för utvecklare av mininät att samla in denna information i den hastighet och skala som krävs för att göra effektiva investeringsval. Village Data Analytics (VIDA) är ett beslutsfattande verktyg som används för projektering av mininät och platsval. Det här dokumentet presenterar en metod för att uppskatta kostnaden för ett distributionsnät för mininät på plats för plats. Denna metod kan uppskatta den totala efterfrågan, potentiella anslutningar, komponenter för distribution sinfrastruktur och motsvarande kostnader för varje plats. Modellen kan göra förutsägelser för 50 platser inom två timmar, så verktyget är särskilt användbart för preliminära uppskattningar i planeringsfasen. En mer detaljerad studie av de enskilda platserna rekommenderas. Jämförelse med ett riktmärke har visat att förhållanden på plats ofta avslöjar aktiviteter som bara kan fångas genom en undersökning. Men att samla in data på plats är tidskrävande och kostsamt. Därför kan GIS- och modelleringsverktyg fungera som en bra approximation av verkligheten på marken och är relevanta för att påskynda planering och stödja beslutsfattande i rätt tid.

<p dir="ltr">We explore the efficacy of sparsity and ensemble model in the classification of hyperspectral images, a pivotal task in remote sensing applications. While Convolutional Neural Networks (CNNs) and Transformer models have shown promise in this domain, each exhibits distinct limitations; CNNs excel in capturing the spatial/local features but falter to capture spectral features, whereas Transformers captures the spectral features at the expense of spatial features. Furthermore, the computational cost associated with training several independent CNN and Transformer networks becomes expensive. To address these limitations, we propose a novel ensemble framework comprising pruned CNNs and Transformers, optimizing both spatial and spectral feature utilization while curbing computational costs. By integrating sparsity through model pruning, our approach effectively reduces redundancy and computational complexity without compromising accuracy. Through extensive experimentation, we find that our method achieves comparable accuracy to its non-sparse counterparts while decreasing the computational cost. Our contribution enhances remote sensing analytics by demonstrating the potential of sparse and ensemble models in improving the precision and computational efficiency of hyperspectral image classification.</p>

<p>Integrating unoccupied aerial systems (UAS) into agricultural remote sensing has revolutionized several domains, including crop yield estimation. This research arises from the need to combat citrus greening disease, a major threat to citrus production. Accurately estimating crop yields is crucial for evaluating the effectiveness of treatments and controls for this disease. In response, our study examined the efficacy of phenotypic data extracted from multi-temporal RGB and multispectral UAS images in estimating individual citrus tree yields before harvest and then using this as an indicator to analyze the effectiveness of the treatments and control choice.</p> <p>This study presents machine learning-based regression models for estimating individual citrus tree yields, utilizing the diverse features extracted to provide comprehensive insights into the citrus trees under investigation. Four machine learning algorithms, random forest regression, extreme gradient boosting regression, adaptive boosting, and support vector regression, were employed to build the yield estimation models. The experiment was designed in two phases: single-temporal and multi-temporal modeling.</p>

Tillståndsprocessen för att bedriva miljöfarlig verksamhet är manuell och ineffektiv vilket hämmar svenska företag i deras klimatarbete. Geospatial information har till följd av lokaliseringsprincipen i miljöbalken en central roll inom samhällsbyggnad och dess planering för placering av investeringar. Det finns i dagsläget inget geospatialt beslutsstödsystem (SDSS) som ämnar att underlätta för verksamhetsutövare i tillståndsprocessen vid beslut som rör placering av nya investeringar i industri. Syftet med studien var att utveckla en IT-artefakt med intentionen att stödja processen samt beslutsfattande för industriföretag i skapandet av en tillståndsansökan för miljöfarlig verksamhet. Detta genom att ta fram en webbapplikation som ska fungera som ett processtöd för användaren genom att redogöra de nödvändiga stegen som ingår i en miljötillståndsansökan med fokus på de aspekter som inkluderar geospatial data och information. Målet är att artefakten i dessa steg ska fungera som ett hjälpmedel för verksamhetsutövaren att fatta strategiska beslut kring geografisk plats för nya investeringar i industri. Studien använder sig av Design Science Research Methodology (DSRM) och har hämtat in empiri genom fokusgruppsintervjuer. Arbetet resulterade i en IT-artefakt som visar att det är möjligt att implementera denna typ av lösning på problemet samt de identifierade designprinciperna som implementerades. / The permit process for conducting environmentally hazardous activities is manual and inefficient, which impedes Swedish companies in their climate action. As a result of the “location principle” in the Swedish Environmental Code, spatial information has a central role in community building and its planning for location of investments. There is currently no spatial decision support system (SDSS) that aims to make it easier for operators to make decisions regarding the location of new investments in industry during the permit process. The purpose of the study was to develop an IT artefact with the intention to support the process and decision making for industrial companies in the creation of permit applications for environmentally hazardous activities. This by developing a web application that will function as a process support for the user by describing the necessary steps that are included in an environmental permit application with a focus on the aspects that include spatial data and information. The aim is that the artifact in these steps should function as an aid for the operator to make strategic decisions about the geographical location for new investments in industry. This research uses Design Science Research Methodology (DSRM) and has obtained empirical data through focus group interviews. The work resulted in an IT artifact that proves that it is possible to implement this kind of solution to the problem and the identified design principles that were implemented.

<p> The hydrological cycle is affected by land use variability. Land use spatial and temporal variability has the power to alter watershed runoff, water resource quantity and quality, ecosystems, and environmental sustainability. In recent decades, agriculture lands, pastures, plantations, and urban areas have increased, resulting in significant increases in energy, water, and fertilizer usage, as well as significant biodiversity losses. </p>

<p dir="ltr">As a new spaceborne laser remote sensing system, the Global Ecosystem Dynamics Investigation, or GEDI, is being widely used for monitoring forest ecosystems. However, its measurements are subject to uncertainties that will affect the calculation of ground elevation and vegetation height. This research intends to investigate the quality of the GEDI elevation data and its relevance to topography and land cover.</p><p dir="ltr">In this study, the elevation of the GEDI data is compared to 3DEP DEM, which has a higher resolution and accuracy. All the experiments in this study are conducted for two locations with vastly different terrain and land cover conditions, namely Tippecanoe County in Indiana and Mendocino County in California. Through this investigation we expect to gain a comprehensive understanding of GEDI’s elevation quality in various terrain and land cover conditions.</p><p dir="ltr">The results show that GEDI data in Tippecanoe County has better elevation accuracy than the GEDI data in Mendocino County. GEDI in Tippecanoe County is almost four times more accurate than in Mendocino County. Regarding land cover, GEDI have better accuracy in low vegetation areas than in forest areas. The ratio can be around three times better in Tippecanoe County and around one and half times better in Mendocino County. In terms of slope, GEDI data shows a clear positive correlation between RMSE and slope. The trend indicates as slope increases, the RMSE increases concurrently. In other words, slope and GEDI elevation accuracy are inversely related. In the experiment involving slope and land cover, the results show that slope is the most influential factor to GEDI elevation accuracy.</p><p dir="ltr">This study informs GEDI users of the factors they must consider for forest biomass calculation and topographic mapping applications. When high terrain slope and/or high vegetation is present, the GEDI data should be checked with other data sources like 3DEP DEM or any ground truth measurements to assure its quality. We expect these findings can help worldwide users understand that the quality of GEDI data is variable and dependent on terrain relief and land cover.</p>

This research study aims to enhance the effectiveness of the architectural design process regarding the exploration and framing of the best visual connections to the outside environment within built environments. Specifically, it aims to develop a framework for developing and integrating an inside-out design approach augmented and informed by digital 3D geospatial data as a way to enhance the explorative ability and decision-making process for designers regarding the visual connection to the outside environment. To do so, the strategy of logical argumentation is used to analyze and study the phenomenon of making visual connections to a surrounding context. The initial recommendation of this stage is to integrate an inside-out design approach that operates within the digital immersion within 3D digital representations of the surrounding context. This strategy will help to identify the basic logical steps of the proposed inside-out design process. Then, the method of immersive case study is used to test and further develop a proposed process by designing a specific building, specifically, an Art Museum building on the campus of Virginia Tech. Finally, the Delphi method is used in order to evaluate the necessity and importance of the proposed approach to the design process and its ability to achieve this goal. A multi-round survey was distributed to measure the consensus among a number of experts regarding the proposed design approach and its developed design tool. Overall, findings refer to a total agreement among the participating experts regarding the proposed design approach with some different concerns regarding the proposed design tool. / Doctor of Philosophy / Achieving a well-designed visual connection to one's surroundings is considered by many philosophers and theorists to be an essential aspect of our spatial experience within built environments. The goal of this research is to help designers to achieve better visual connections to the outside environment and therefore create more meaningful spatial experiences within the built environment. This research aims to enhance the ability of designers to explore the best possible views and make the right design decisions to frame these views of the outdoors from the inside of their buildings. Of course, the physical presence of designers at a building site has been the traditional method of determining the best views; however, this is not always possible during the design process for many reasons. Thus, this research aims to find a more effective alternative to visiting a building site in order to inform each design decision regarding the quality of its visual connection to the outdoors. To do so, this research developed a proposed inside-out design approach to be integrated into the design process. Specifically, it outlines a process that allows the designers to be digitally immersed within an accurate 3D representation of the surrounding context, which will help designers to explore views from multiple angles both inside the space and in response make the most suitable design decision. For further developing the proposed process, it was used during conducting this research to design an Art Museum on Virginia Tech Campus.