Physical Sciences and Mathematics Commons^™

Monitoring The Qosh Tepa Canal Project: A Geospatial Timeline Of Taliban Water Diversion, Enerel L. Crosslin

Honors Thesis

The World Food Program states that “acute malnutrition in Afghanistan is above emergency thresholds in 25 out of 34 provinces and is expected to worsen”. Aiming to support agriculture, the Taliban began to build the 285 km “Qosh Tepa Canal” to divert 17% of the Amu Darya, which supports the livelihoods of millions of people in downstream Uzbekistan and Turkmenistan. The World Bank estimated that roughly 2.4 million Central Asians could become climate refugees by 2050. Our objectives are to create a timeline of satellite imagery of the canal’s construction. Our research questions are (1) Can we characterize water diversion …

Don't Let Lead Lead On Environmental Justice: A Simulative Approach To Lead Remediation In The Big Data Era, Charles C. Knoble Ii

Theses, Dissertations and Culminating Projects

Environmental justice, as both a movement and a theoretical construct, continues to evolve in response to shifting societal, environmental, and technological conditions. This dissertation investigates the integration of big data, such as social media, remote sensing imagery, and internet search frequencies, into the identification, analysis, and remediation of environmental injustices. Framing environmental justice through the lenses of distributive and data justice, the project explores both the promises and pitfalls of using emergent data sources to enhance the spatial and temporal precision of environmental equity investigations. Through a combination of systematic literature review, spatial analysis, system dynamics simulation, and policy evaluation, …

Assessing Spatial And Temporal Variation In Photoprotective Responses Of Deciduous And Evergreen Tree Canopies With Leaf Spectroscopy, Alexander Piper

School of Natural Resources: Dissertations, Theses, and Student Research

Environmental conditions frequently prevent carbon fixation by plants, leading to the absorption of excess light that can damage photosynthetic machinery if not dissipated. To do so, plants utilize several photoprotective mechanisms, some detectable remotely using Photochemical Reflectance Index (PRI). Two components of PRI correspond to the facultative engagement of the xanthophyll cycle (ΔPRI) and constitutive changes in xanthophyll pigment pool sizes (PRI0), representing distinct mechanisms regulating shorter and longer-term photoprotection, respectively. Our understanding of interspecific and intraspecific variation in these mechanisms is limited, primarily because PRI components are often not clearly distinguished. This study aimed to assess the variation in …

Revealing Hidden Histories: A Multi-Sensor Aerial Survey For Detecting Unmarked Burials At Sinking Spring Cemetery, Abingdon, Va, Noah Hall

Electronic Theses and Dissertations

The Sinking Spring Cemetery, established in 1773 in Abingdon, Virginia, spans 11 acres and is divided by a road. The 9-acre southern section was reserved for white church members. Enslaved individuals and free people of color were buried in the smaller northern section, where few headstones remain today. This study aimed to map the unmarked graves using thermal, multispectral, and Light Detection and Ranging (LiDAR) sensors deployed on unmanned aerial systems. Graves were characterized by subtle topographic depressions mapped by LiDAR and cooler radiant temperature anomalies in thermal imagery. Thermal data collected at different times of the day and year …

Novel Methods For Assessing And Prioritizing Road-Stream Crossings For Aquatic Organism Passage, Lesley E. Twiner

All Theses

Anthropogenic barriers such as dams and culverts have led to riverine habitat fragmentation and decreased fish diversity worldwide. Low-head structures such as culverts are far more abundant than large dams and may have a larger cumulative impact on river connectivity. Many efforts to inventory road crossing barriers and prioritize removal and restoration efforts do not consider temporal variation in flow. We used cameras to gain continuous water level monitoring data to quantify the relationship between precipitation events and outlet drop height. In the spring of 2024, we selected 25 culvert sites in upstate South Carolina and set up cameras to …

A Comprehensive Review Of Carbon Sequestration And Its Assessment Techniques Using Remote Sensing And Geospatial Methods, Imen Ben Salem

All Works

Global warming has elevated carbon sequestration as a critical strategy for mitigating climate change, while enhancing sustainability in productivity. Agricultural land use systems contribute substantially to CO2 emissions due to crop residues, shifting cultivation practices, low-biomass crops, land degradation, and deforestation. The significant rise in CO2 emissions over the past thirty years is associated with burning fossil fuels, leading to substantial environmental changes, including global warming. Remote sensing (RS) and Geographic Information Systems (GIS) are advanced geospatial technologies that facilitate the rapid evaluation of terrestrial carbon stock over extensive regions. An integrated RS-GIS approach for carbon stock estimation and precision …

Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …

A Machine Learning/Deep Learning Investigation On Remote Manufacturing Machine State Classification, Ajeet S. Parmar

Theses and Dissertations

Determining the extent of manufacturing capabilities with respect to adversarial or hostile nations is a topic of significant importance to the Department of Defense. Manufacturing capabilities can serve as indications of a nation's industrial power and its economy of force in warfare. Remotely detecting machine operations via electromagnetic sensors may be possible via Deep Learning (DL) and Machine Learning (ML) algorithms. To predict machine states, sensor data is collected externally from a machine shop on a college campus to monitor the operating states of lathes and mills in individual and concurrent operation. Furthermore, several sensors are placed in various positions, …

A Machine Learning Model To Predict Wildfire Burn Severity For Pre-Fire Risk Assessments, Utah, Usa, Kipling B. Klimas, Larissa L. Yocom, Brendan P. Murphy, Scott R. David, Patrick Belmont, James A. Lutz, R. Justin Derose, Sara A. Wall

Wildland Resources Student Research

Background

High-severity burned areas can have lasting impacts on vegetation regeneration, carbon dynamics, hydrology, and erosion. While landscape models can predict erosion from burned areas using the differenced normalized burn ratio (dNBR), post-fire erosion modeling has predominantly focused on areas that have recently burned. Here, we developed and validated a predictive burn severity model that produces continuous dNBR predictions for recently unburned forest land in Utah.

Results

Vegetation productivity, elevation, and canopy fuels were the most important predictor variables in the model, highlighting the strong control of fuels and vegetation on burn severity in Utah. Final model out-of-bag R^{2 …}

Applications Of Uav In Landslide Research: A Review, Boneng Chen, Jeremy Maurer, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Uncrewed aerial vehicles (UAVs), commonly known as drones, have gained significant popularity in landslide research due to their operational flexibility, near real-time planning capabilities, cost-effectiveness, high-resolution data outputs, and enhanced safety. This review aims to provide an analysis of UAV-based landslide investigation methodologies and their applications. It begins with an examination of UAV platforms and UAV-equipped sensors employed in landslide research, evaluating their advantages and limitations to guide appropriate platform and sensor selection. The review then explores UAV applications across three primary landslide investigation domains: landslide mapping, landslide monitoring, and landslide hazard assessment, highlighting some representative achievements in each domain. …

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, Jiating Li, Yufeng Ge, Laila A. Puntel, Derek M. Heeren, Geng Bai, Guillermo R. Balboa, John A. Gamon, Timothy J. Arkebauer, Yeyin Shi

School of Natural Resources: Faculty Publications

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared …

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, J. Li, Y. Ge, J. Gamon, L. A. Puntel Et Al.

School of Natural Resources: Faculty Publications

No abstract provided.

Landslide At River's Edge: Alum Bluff, Apalachicola River, Florida, Joann Mossa, Yin-Hsuen Chen

Center for Geospatial Science, Education & Analytics Faculty Publications

When rivers impinge on the steep bluffs of valley walls, dynamic changes stem from a combination of fluvial and mass wasting processes. This study identifies the geomorphic changes, drivers, and timing of a landslide adjacent to the Apalachicola River at Alum Bluff, the tallest natural geological exposure in Florida at similar to 40 m, comprising horizontal sediments of mixed lithology. We used hydrographic surveys from 1960 and 2010, two sets of LiDAR from 2007 and 2018, historical aerial, drone, and ground photography, and satellite imagery to interpret changes at this bluff and river bottom. Evidence of slope failure includes a …

Evaluating The Causes Of Land Subsidence On The U.S. Mid-Atlantic Coast Measured With Insar, Jordan Leigh Diprima

Honors Theses and Capstones

Land subsidence is a frequently overlooked geologic hazard that is caused by natural processes and anthropogenic stressors. The goal of this study is to quantify vertical land motion (VLM) on Long Island, New York and Virginia’s Eastern Shore and evaluate the potential causes of subsidence. The causes considered in this work are glacial isostatic adjustment, groundwater extraction, infrastructure loading, and land cover. This study utilizes interferometric synthetic aperture radar (InSAR) satellite data from Sentinel-1 to calculate linear VLM trends from 2017 to 2023. Datasets for each hypothesis were qualitatively compared to VLM data. Subsidence rates in both regions were found …

Spatiotemporal Economic Impact Analysis Of The Taal Volcano Eruption Using Electricity Consumption And Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Analyzing the spatiotemporal dimension of the economic impacts of disasters is critical for providing timely and proportionate support. However, traditional economic impact measures often lack spatiotemporal details. Hence, we estimated the daily electricity consumption (EC) loss using high-frequency EC data and analyzed temporal dimensions of the immediate impacts of Taal Volcano’s eruption on January 12, 2020. Subsequently, we computed the nighttime light (NTL) change using high-resolution NTL data to analyze the spatial distribution of these impacts. The temporal analysis revealed two EC loss peaks. The first peak coincided with the power outage and decreased energy demand, followed by a brief …

Applications Of Remote Sensing Data To Understanding Geohazards In Wyoming And Kentucky, Meredith Swallom

Theses and Dissertations--Earth and Environmental Sciences

Shallow geohazards such as earthquakes, landslides, and floods profoundly impact communities globally. Constraining susceptibility to these hazards is critical for effective hazard mitigation and building more resilient communities. This dissertation includes multiple studies that leverage remotely sensed datasets to address hazard susceptibility, including: (1) characterizing a potential northern extension of the Teton fault system, (2) deriving a vegetation-based metric to improve landslide susceptibility maps in eastern Kentucky, with potential global implications, and (3) combining numerical modeling, landscape models, and field constraints to understand how anthropogenic factors affect flood severity in southern Appalachian catchments.

In the first study, analysis of new …

High-Altitude Balloon-Launched Uncrewed Aircraft System Measurements Of Atmospheric Turbulence And Qualitative Comparison With Infrasound Microphone Response, Anisa Haghighi

Theses and Dissertations--Mechanical and Aerospace Engineering

This study explores the use of a balloon-launched uncrewed aircraft system (UAS) to measure atmospheric turbulence in the troposphere and lower stratosphere using both wind velocity measurements and infrasonic acoustic energy. The UAS, a glider configured for autonomous descent along a predefined trajectory, had on board, in situ sensors to capture thermodynamic and kinematic atmospheric parameters. Additionally, it carried an infrasonic microphone to evaluate its potential for remotely detecting clear-air turbulence by capturing infrasonic waves. The system’s performance was assessed over the course of three test flights conducted in New Mexico, USA, in 2021. The descent enabled high-resolution profiling, with …

High Antarctic Coastal Productivity In Polynyas Revealed By Considering Remote Sensing Ice-Adjacency Effects, Hilde Oliver, Jessica S. Turner, Alexandre Castagna, Henry Houskeeper, Heidi Dierssen

OES Faculty Publications

Ocean color-based estimates of Antarctic net primary productivity (NPP) have indicated low nearshore productivity in ice-adjacent waters, contrasting with coupled physical–biogeochemical models. To understand this discrepancy, we assessed satellite records of polynya NPP by comparing field data with two satellite imagery datasets derived using different processing schemes. Our results indicate historical underestimation of chlorophyll a for imagery obtained using default atmospheric correction processing within approximately 100 km of ice-covered coastlines due to adjacency effects. Using radiative transfer modeling, we find that biases in ocean color polynya observations due to adjacency effects correspond to the high albedo of ice and snow. …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Impacts Of Southern Pine Beetle (Dendroctonus Frontalis Zimmerman) On Loblolly Pine (Pinus Taeda L.) Canopy And Water Use In The Homochitto National Forest, Mississippi, Usa, Sasha Goodnow, Yun Yang, Hui Liu, Ashley Schulz

Endeavors: Mississippi State Undergraduate Research Journal

Abiotic and biotic forest disturbances can have many impacts to forest ecosystem services, including to forest water use. Studies on impacts to forest evapotranspiration have been conducted on the mountain pine beetle (Dendroctonus ponderosae) in western North America, but not on the southern pine beetle (Dendroctonus frontalis), which is a native pest of loblolly pine (Pinus taeda) and shortleaf pine (Pinus echinata), in the southeastern United States. Stressed pine trees produce pheromones that attract southern pine beetles and, with enough stressed trees, beetle populations can quickly grow to epidemic levels and attack healthy trees, which results in widespread tree mortality. …

Analysis Of Aerosols In The Asian Monsoon Anticyclone As Observed By The Atmospheric Chemistry Experiment, M. Lecours, R. Dodangodage, C. D. Boone, P. F. Bernath

Chemistry & Biochemistry Faculty Publications

During the Asian summer monsoon season, pollutants from the lower troposphere are transported through deep convection to the upper troposphere and lower stratosphere. Surface pollutants such as CO are transported upward and trapped in the anticyclone during this unique atmospheric phenomenon. Associated with the anticyclone is a layer of enhanced aerosols located near the tropopause often referred to as the Asian tropopause aerosol layer (ATAL). The chemical and physical properties of aerosols in the ATAL are not yet fully understood as direct observations of the aerosols are limited. The Atmospheric Chemistry Experiment (ACE) is a satellite mission that provides high-resolution …

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Small Area Estimation Of Forest Biomass Via A Two-Stage Model For Continuous Zero-Inflated Data, Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad, Julian F. Schmitt, Niels H. Korsgaard, Jie Hu, George C. Gaines Iii, Tracey S. Frescino, Kelly S. Mcconville

Faculty Journal Articles

Nationwide Forest Inventories (NFIs) collect data on and monitor the trends of forests across the globe. Users of NFI data are increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, resulting in a need for assessment and development of small area estimation techniques in forest inventory. We implement a small area estimator and parametric bootstrap estimator that account for zero-inflation in biomass data via a two-stage model-based approach and compare the performance to a Horvitz–Thompson estimator, a post-stratified estimator, and to the unit- and area-level empirical best linear unbiased prediction (EBLUP) estimators. We conduct …

Bridging The Gap Between Plot-Level And Landscape-Scale Analysis For Wildfire Risk Assessment, Vanessa Leigh Niemczyk

Graduate Student Theses, Dissertations, & Professional Papers

Remote sensing technology has advanced greatly over the past couple of decades proving its ability to aid in wildfire risk assessment and improve our understanding of forest structure and fuel inventory across the landscape. While some aerial and satellite sensors perform better than others, they all have a common weakness, their reduced ability to capture understory fuels with high detail. Terrestrial laser scanning is an emerging solution due to its understory perspective. This research leverages the beneficial aspects of both terrestrial laser scanning and various aerial- or satellite-based remote sensing platforms (aerial laser scanning, digital aerial photogrammetry, and Sentinel-2) to …

Atmospheric Chemistry Experiment (Ace) V.5.3 Winds: Validation And Model Comparisons, Matthew Wyatt, Peter F. Bernath, Chris Boone, Leo Lavy, Ryan Johnson

Physics Faculty Publications

The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) uses limb geometry to measure transmittance spectra of Earth's atmosphere by solar occultation. Line-of-sight wind speeds can be derived via Doppler shifts of molecular lines in infrared spectra. The wind look direction angles relative to geodetic north are derived from geometry. We validate the new ACE version 5.3 (v.5.3) line-of-sight winds with MIGHTI and meteor radar vector wind observations and find a -15 m s^-¹ (+15 m s^-¹) sunrise (sunset) shift above 80 km. We also compare line-of-sight winds from ACE-FTS v.5.2 and v.5.3 with vector winds from …

Development Of An Quadcopter Unmanned Aerial Vehicle For Atmospheric Remote Sensing, Omar J. Addasi

Dissertations and Theses

This paper explores the development of a quadcopter unmanned aerial vehicle (UAV, a.k.a. drone) for atmospheric remote sensing of temperature, pressure, humidity, and PM2.5 particulate matter quantities. A 3-D printed drone body is designed and flight tuning is performed. An optimal length for an upward extending mast for the drone body is determined. Low cost, lightweight sensors are compared against higher precision sensors in both static and dynamic conditions. In addition, an initial investigation into the design of a pressure sensor based anemometer is performed.

Mapping Kirtland’S Warbler (Setophaga Kirtlandii) Stationary Non-Breeding Habitat: Characterizing Land Cover Within The South-Central Bahamas And Evaluating The Impacts Of Sea Level Rise, Cole Anthony Scrivner

Antioch University Dissertations & Theses

Understanding the spatial distribution of current and future habitat for the Kirtland’s Warbler (Setophaga kirtlandii) and other threatened species is critical for guiding conservation planning in The Bahamas. Our study mapped land cover and impacts of sea level rise across the south-central Bahamian islands, as an initial step to determine suitable areas for Kirtland’s Warblers in the region. We used a Random Forest (RF) classification of multispectral satellite image bands and indices from Landsat and Sentinel-2 image composites to predict land cover. The classification identified tropical dry forest communities (broadleaved, semi-evergreen trees and shrubs known as coppice) as …

An Integrated Ecosystem Monitoring Technology For Coal Mining Subsidence Areas And Its Application In The Shendong Mining Area, Cheng Yang, Liu Wei, Zhang Na, Li Guanjie, Liu Kai, Zhang Chengye, Li Jun

Coal Geology & Exploration

Background The ecosystem monitoring of arid and semi-arid coal mining subsidence areas acts as a significant prerequisite for regional ecosystem conservation and management, holding critical significance for accelerating green mine construction. Methods Based on the Chinese government's regulatory requirements for mine ecosystems, this study analyzed the difficulties in ecosystem monitoring in the coal mining subsidence area of the Shendong mining area (also referred to as the Shendong coal mining subsidence area). By detailing the integrated coal-rock-water-soil-air-vegetation-carbon monitoring technology system, this study determined the factors, principal methods, and technology roadmap for the integrated ecosystem monitoring in the Shendong coal mining subsidence …

Estimating Medium-Term Regional Monthly Economic Activity Reductions During The Covid-19 Pandemic Using Nighttime Light Data, Ma Flordeliza P. Del Castillo, Toshio Fujimi, Hirokazu Tatano

SOSE Affiliate: Manila Observatory

Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study used NTL data to determine the medium-term regional monthly economic impacts of the pandemic in the Philippines in terms of the Economic Activity Reduction (EAR) index. We generated a spatial error model, regressing pre-pandemic NTL on mean temperature, maximum rainfall, and built-up area. This model explained …