This thesis investigates the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, per- forms in predicting seasonal variations across different months. Through compre- hensive analysis and visualization, the study examines the model’s ability to cap- ture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture represen- tation in different landscapes. The research introduces modifications to the Sat- NeRF network. The implemented versions of the network include geometrically rendered shadows, a signed distance function, and a month embedding vector, where the last version mentioned resulted in Planet-NeRF. Comparative evalua- tions reveal that Planet-NeRF outperforms prior models, particularly in refining seasonal predictions. This advancement contributes to the field by presenting a more effective approach for seasonal representation in satellite imagery analysis, offering promising avenues for future research in this domain.

Forage quality is a principal factor in managing both herbivores and the landscapes they use. Nutrition varies across the landscape, and in turn, so do the distributions of these populations. With the rise of remote sensing technologies (i.e. satellites, unmanned aerial vehicles, and multi/hyperspectral sensors), comes the ability to index forage health and nutrition swiftly. However, no methodology has been developed which allows managers to use unmanned aerial systems to the fullest capacity. The following methodologies produce compelling evidence for predicting forage quality metrics (such as fiber, carbohydrates, and digestibility) using 5 measured bands of reflectance (Blue, Green, Red, Red Edge, and NIR), 3 derived vegetation indices (NDVI, EVI and VARI), and a variety of environmental factors (i.e. time and sun angles) in a LASSO framework. Fiber content, carbohydrates, and digestibility showed promising model performance in terms of goodness-of-fit (R2= 0.624, 0.637, and 0.639 respectively).

In a world where gamification is increasingly prevalent, Dungeons & Dragons is a vast, seemingly untapped arena into which students in an EFL classroom could potentially venture. By exploring the various applications of the hugely popular tabletop role-playing game, there are several aspects in which students and teachers can explore in order to find practical as well as theoretical uses for a role-playing game of this size and scope. This study has discussed and analyzed the benefits and drawbacks of implementing Dungeons & Dragons in the EFL classroom through mental imagery and immersion as an effective teaching tool. The implementation of D&D in an EFL classroom is achievable through scaffolding and preparation. This study has also discussed the gamification aspects of the EFL classroom and the game specific areas of game-based pedagogy.

This Master’s thesis investigates the application of dynamically generated procedural terrain textures for texturing 3D representations of the Earth’s surface. The study explores techniques to overcome limitations of the currently most common method – projecting satellite imagery onto the mesh – such as insufficient resolution for close-up views and challenges in accommodating external lighting models. Textures for sand, rock and grass were generated procedurally on the GPU. Aliasing was prevented using a clamping technique, dynamically changing the level of detail when freely navigating across diverse landscapes. The general color of each terrain type was extracted from the satellite images, guided by land cover rasters, in a process where shadows were eliminated using HSV color space conversion and filtering. The procedurally generated textures provide significantly more details than the satellite images in close-up views, while missing some information in medium- to far-distance views, due to the satellite images containing information lacking in the 3D mesh. A qualitative analysis spanning six data sets from diverse global locations demonstrates that the proposed methods are applicable across a range of landscapes and climates.

The purpose of the study was to determine if there is an effect on retention of vocabulary when music and imagery are used, to accelerate learning. As background for the study four brain functioning theories as developed from recent neuroscientific research were explored. These were the theory of right/left cerebral dominance (Sperry & others), triune brain theory (MacLean), taxon and locale long term memory (O'Keefe & Nadel), and holographic memory (Pribram). These four theories all suggest multiple channels of input will increase information retention. The research utilized three conditions: an experimental condition of music plus imagery, an experimental condition of music only, and a no treatment condition.

This study aims to conduct an assessment of the land cover change of the Mississippi and Alabama coastal region, an integral part of the Gulf Coast ecological makeup. Landsat satellite data were used to perform a supervised classification using the imagery captured by Landsat sensors including Landsat 1-2 Multispectral Scanner (MSS), Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI) from 1973 to 2015. The objective of this study is to build a long-term assessment of urban development and land cover change over the past four decades for the Alabama and Mississippi Gulf Coast and to characterize these changes using Landscape Metrics (LM). The findings of this study indicate that the urban land cover doubled in size between 1973 and 2015. This expansion was accompanied by a high degree of urban fragmentation during the first half of the study period and then a gradual leveling off. Local, state, and federal authorities can use the results of this study to build mitigation plans, coastal development planning, and serve as the primary evaluation of the current urban development for city planners, environmental advocates, and community leaders to reduce degradation for this environmentally sensitive coastal region.

When he is not paying artistic homage to Diego Rivera and Balthus, or inventing the myth of how masculine and feminine relationships are held together by butter, or creating a “Gospel of Two Sisters” which chronicles the loss and reclamation of language, or exercising the limits of his Anagram poetic form; Terrance Hayes—in Hip Logic—employs the African-American rhetorical trope of signifying in order to examine the historic and contemporary role of the African-American male as victim, as heroic-icon, and as father by using real and imaginary Black-masculine figures. My collection, I Mean to Signify, employs signifying to engage with topics of Black male victimization and Northern elitism. Additionally, my collection depends heavily on the Gospel tradition of African-American domesticity, and engages with the universal topics of fear, death, and romantic relationships.

<p> </p> <p>Modern remote sensing platforms such as unmanned aerial vehicles (UAVs) that can carry a variety of sensors including RGB frame cameras, hyperspectral (HS) line cameras, and LiDAR sensors are commonly used in several application domains. In order to derive accurate products such as point clouds and orthophotos, sensors’ interior and exterior orientation parameters (IOP and EOP) must be established. These parameters are derived/refined in a triangulation framework through minimizing the discrepancy between conjugate features extracted from involved datasets. Existing triangulation approaches are not general enough to deal with varying nature of data from different sensors/platforms acquired in diverse environmental conditions. This research develops a generic triangulation framework that can handle different types of primitives (e.g., point, linear, and/or planar features), and sensing modalities (e.g., RGB cameras, HS cameras, and/or LiDAR sensors) for delivering accurate products under challenging conditions with a primary focus on digital agriculture and stockpile monitoring application domains. </p>

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