Physical Sciences and Mathematics Commons^™

An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of …

In The Margins: Reconsidering The Range And Contribution Of Diazotrophs In Nearshore Environments, Corday R. Selden

OES Theses and Dissertations

Dinitrogen (N₂) fixation enables primary production and, consequently, carbon dioxide drawdown in nitrogen (N) limited marine systems, exerting a powerful influence over the coupled carbon and N cycles. Our understanding of the environmental factors regulating its distribution and magnitude are largely based on the range and sensitivity of one genus, Trichodesmium. However, recent work suggests that the niche preferences of distinct diazotrophic (N₂ fixing) clades differ due to their metabolic and ecological diversity, hampering efforts to close the N budget and model N₂ fixation accurately. Here, I explore the range of N₂ fixation …

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets And Machine Learning, S. Majumdar, Ryan G. Smith, J. J. Butler, V. Lakshmi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests -- …

Volcano Video Data Characterized And Classified Using Computer Vision And Machine Learning Algorithms, Alex J. C. Witsil, Jeffrey B. Johnson

Geosciences Faculty Publications and Presentations

Video cameras are common at volcano observatories, but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual analysis. For cameras to serve as effective monitoring tools, video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable activity. In this study, we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations. Data were collected at Villarrica Volcano, Chile from two visible band cameras located ~17 km from …

A 30-Year Agroclimatic Analysis Of The Snake River Valley American Viticultural Area - Descriptive And Predictive Methods, Charles L. Becker

Boise State University Theses and Dissertations

Climate change poses serious threats to global agriculture, however some localities and crops may benefit from increasing temperatures. Grape production in southern Idaho may be a beneficial example as vineyard acreage has increased over 300% since the designation of the Snake River American Viticultural Area (SRVAVA) in 2007. We perform a statistical characterization of agroclimate within the SRVAVA that centers around four primary objectives: utilization of a novel, 30-year high resolution climate dataset to provide insight and agrometrics unavailable at coarser resolutions, climatic implications of the unique topography within the SRVAVA, identification of statistical trends, and correlation of SRVAVA climate …

A Sense Of Scale: Mapping Exotic Annual Grasses With Satellite Imagery Across A Landscape And Quantifying Their Biomass At A Plot Level With Structure-From-Motion In A Semi-Arid Ecosystem, Monica Vermillion

Boise State University Theses and Dissertations

The native vegetation communities in the sagebrush steppe, a semi-arid ecosystem type, are under threat from exotic annual grasses. Exotic annual grasses increase fire severity and frequency, decrease biodiversity, and reduce soil carbon storage amongst other ecosystem services. The invasion of exotic annual grasses is causing detrimental impacts to land use by eliminating forage for livestock and creating a huge economic cost from fire control and post-fire restoration. To combat invasion, land managers need to know what exotic annual grasses are present, where they are invading, and estimates of their biomass. Mapping exotic annual grasses is challenging because many areas …

Estimating Abiotic Thresholds For Sagebrush Condition Class In The Western United States, Stephen Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. Major

United States Geological Survey: Staff Publications

Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping …

A Machine Learning And Data-Driven Prediction And Inversion Of Reservoir Brittleness From Geophysical Logs And Seismic Signals: A Case Study In Southwest Pennsylvania, Central Appalachian Basin, Tobi Micheal Ore

Graduate Theses, Dissertations, and Problem Reports

In unconventional reservoir sweet-spot identification, brittleness is an important parameter that is used as an easiness measure of production from low permeability reservoirs. In shaly reservoirs, production is realized from hydraulic fracturing, which depends on how brittle the rock is–as it opens natural fractures and also creates new fractures. A measure of brittleness, brittleness index, is obtained through elastic properties of the rock. In practice, problems arise using this method to predict brittleness because of the limited availability of elastic logs.

To address this issue, machine learning techniques are adopted to predict brittleness at well locations from readily available geophysical …

Uncovering Host-Microbiome Interactions In Global Systems With Collaborative Programming: A Novel Approach Integrating Social And Data Sciences [Version 1; Peer Review: Awaiting Peer Review], Jenna Oberstaller, Swamy Rakesh Adapa, Guy Dayhoff Ii, Justin Gibbons, Gregory S. Herbert

School of Geosciences Faculty and Staff Publications

Microbiome data are undergoing exponential growth powered by rapid technological advancement. As the scope and depth of microbiome research increases, cross-disciplinary research is urgently needed for interpreting and harnessing the unprecedented data output. However, conventional research settings pose challenges to much-needed interdisciplinary research efforts due to barriers in scientific terminologies, methodology and research-culture. To breach these barriers, our University of South Florida OneHealth Codeathon was designed to be an interactive, hands-on event that solves real-world data problems. The format brought together students, postdocs, faculty, researchers, and clinicians in a uniquely cross-disciplinary, team-focused setting. Teams were formed to encourage equitable distribution …