Environmental Indicators and Impact Assessment Commons^™

Solar Powered Conservation: The Effects Of Solar Photovoltaic Facilities On Avian Community Composition, Michael Christopher Ferrara

Graduate Theses and Dissertations

The expansion of solar photovoltaic energy infrastructure is transforming landscapes worldwide. Increasingly, facilities are planted with native vegetation, yet how vegetation characteristics and landscape variables shape wildlife communities in solar facilities remains poorly understood. Because avian populations have been in decline and are sensitive to habitat changes, understanding how solar facilities influence avian communities is important. In Chapter 1, we used autonomous recording units, local vegetation measurements, and land use and land cover data to quantify how avian community occupancy is influenced by solar cover in solar facilities and comparable reference sites. We used a Bayesian multi-species occupancy model to …

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …

Seagrass Persistence In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly Kibler, Melinda Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_seagrasspersistence_IRL_LWL.shp was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon (IRL) and Lake Worth Lagoon (LWL). The dataset presented herein represents persistence of seagrass throughout waters of the IRL and LWL.

Elucidating The Relationship Between Water Quality And Antibiotic Resistance Of Rainwater Microbes Across Western Humboldt County, Theo P. Murphy, Eve Wendley, Justice Laskowski, Tyler Paredes

Humboldt Journal of Microbiology

Limited studies have been conducted regarding the fecal contamination and microbial dispersal through rainwater, with no known studies investigating the potential connection between antibiotic resistant microbes and water quality in rainwater. This study seeks to identify the presence of a correlation between water quality and incidence of antibiotic resistance in rainwater microbes. We hypothesize that there will be a correlation between these two variables. We additionally anticipate a high incidence of fecal contamination across rural and urban collection sites alike, with fecal contamination being more prevalent in rural areas than urban locals. To determine water quality, we measured the concentration …

Living Shoreline Prioritization And Hydrodynamic Habitat Suitability Models For Restoration In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly M. Kibler, Melinda J. Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_shorelinemodel_IRL_LWL.shp, was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon and Lake Worth Lagoon.

The dataset presented herein includes:

1. shoreline data from >15,000 transects collected in the field,
2. shoreline wave climate data created through hydrodynamic modeling and frequency analysis of historic data,
3. categorization of potential risk for shoreline boat wake impact,
4. persistence of seagrass within 210 m of the shoreline, …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

Quantifying Single, Compound And Cascading Climate Extremes: Implications For Agricultural Resilience In California, Shahryar Fazli, Wenzhao Li, Rejoice Thomas, Surendra Maharjan, Mohammad Sina Jahangiri, Andre Daccache, Hesham Morgan, Mohamed Allali, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

As climate change intensifies, extreme weather increasingly threatens California’s Central Valley (CV), a vital agricultural region exposed to rising risks from heatwaves (HW), droughts (DR), and compound extremes. These events disrupt crop productivity and broader processes like water demand, pest dynamics, and soil stability, posing systemic risks. This study examines the spatiotemporal dynamics of HW, coldwaves (CW), DR, excessive rainfall (ER), and their compound (e.g., HWDR) and cascading forms from 1951 to 2025, using NOAA nClimGrid-Daily data. We assessed trends in frequency, intensity, and duration over long-term (1951–2025) and mid-term (1981–2025) periods. Results show increasing HW and DR in the …

Carbon Footprint Of Carrots In Western Australia, Christophe D'Abbadie, Saloomeh Akbari, Sang Ravindran

Horticulture research reports

This report presents the findings of a comprehensive carbon accounting study conducted on carrot production in the Myalup area of Western Australia.

The study reveals that carrot emissions are approximately 81 kg CO2e per tonne of carrot sold from the April harvest and 76 kg CO2e per tonne from the October harvest, when using rejected carrots for other economic uses. These figures represent competitive emissions intensities compared to international benchmarks.

Key findings include:

• Yield improvement is the primary driver for emissions reduction: Increasing productivity, without proportional input increases, offers the most effective pathway to lower emissions’ intensity while improving economic …

A Feature Engineering Technique For Enhancing The Generalization Of Machine Learning Models In Estimating Crop Evapotranspiration, Gaku Yokoyama, Sohta Harigai, Shigehiro Kubota, Koichi Nomura, Gregory R. Goldsmith, Daisuke Yasutake, Tomoyoshi Hirota, Masaharu Kitano

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and precise estimation of evapotranspiration (ET) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of ET, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (e.g., stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate ET by capturing relationships between ET and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic …

Western Australian And South Australian Grain Enterprise Emission Intensities And Gross Margins: A Review, Christophe D'Abbadie, Ross Kingwell

Farm Systems and Economics research articles

Drawing on various datasets, crop enterprise emission intensities and gross margins are reported for the main broadacre crops grown in Western Australia and South Australia. Values are reported for various rainfall regions and across time, and for different farm performance groupings. As a generalisation, in each region, wheat and canola production display marked increases in emissions intensity across the study period whilst legume crops’ emission intensities increase far less. Wheat crops display upward emission intensity trajectories across time whilst displaying different rates of increase in emissions intensity according to regional rainfall. Canola demonstrates higher emissions intensities per tonne compared to …

Early-Life Exposure To Organic Chemical Pollutants As Assessed In Primary Teeth And Cardiometabolic Risk In Mexican American Children: A Pilot Study, Vidya S. Farook, Feroz Akhtar, Rector Arya, Alice Yau, Srinivas Mummidi, Juan Lopez Alvarenga, Alvaro Diaz-Badillo, Roy G. Resendez, John Blangero

Human Genetics Publications

Early-life exposure to organic chemicals (OCs) may influence childhood obesity and associated cardiometabolic risk. These conditions have been shown to disproportionately affect minority populations such as Mexican Americans (MAs). However, information on the impact of organic chemicals on cardiometabolic risk in MA children is limited. Therefore, we conducted a pilot study to assess the extent to which exposure to organic chemicals influences cardiometabolic traits (CMTs) in MA children. We recalled 25 children from a previous study and collected 25 primary teeth from them. Chemical analyses of the teeth were performed using established protocols. Target analytes included acetaminophen (APAP); 3,5,6-trichloro-2-pyridinol (TCPy), …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

A Case Study Of Long-Term Disease Burden In A Rural Community Near An Open Burn Facility, Arundhati Bakshi, Liana Baconguis, Md Abdullah Al-Mamun, Qingzhao Yu, Jennifer Richmond-Bryant, Stephania A. Cormier

School of Public Health Faculty Publications

Open burning and open detonation (OB/OD) of explosive and hazardous wastes creates various toxic waste products, including particulate matter, that is released into the atmosphere and capable of generating significant health impacts upon exposure. The last commercially run OB/OD thermal treatment facility in operation in the United States is located near the rural community of Colfax in central Louisiana. To evaluate the community’s concerns about the potential health impacts from air pollution due to the facility’s regular open burning of explosive and hazardous wastes, we examined the disease burden in Colfax compared to the surrounding parish and state. In a …

Warming Induces Unexpectedly High Soil Respiration In A Wet Tropical Forest, Tana E. Wood, Colin Tucker, Aura M. Alonso-Rodríguez, M. Isabel Loza, Iana F. Grullón-Penkova, Molly A. Cavaleri, Christine S. O'Connell, Sasha C. Reed

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Tropical forests are a dominant regulator of the global carbon cycle, exchanging more carbon dioxide with the atmosphere than any other terrestrial biome. Climate models predict unprecedented climatic warming in tropical regions in the coming decades; however, in situ field warming studies are severely lacking in tropical forests. Here we present results from an in situ warming experiment in Puerto Rico, where soil respiration responses to ⁺4 ^oC warming were assessed half-hourly for a year. Soil respiration rates were 42-204% higher in warmed relative to ambient plots, representing some of the highest soil respiration rates reported for any …

Resource Assessment Report No. 5: West Coast Deep Sea Crustacean Resource 2024 Assessment, Emma-Jade Tuffley, Simon De Lestang

Resource Assessment Reports

The West Coast Deep Sea Crustacean resource consists of crystal crab (snow) (Chaceon albus), champagne crab (spiny) (Hypothalassia acerba) and giant crab (Pseudocarcinus gigas). The resource is mainly accessed by the commercial West Coast Deep Sea Crustacean Managed Fishery (WCDSCMF), which primarily targets crystal crabs. The West Coast Rock Lobster Managed Fishery (WCRLMF) also retains a small amount of champagne crabs as byproduct of deep-water rock lobster fishing. The WCDSCMF is a pot-based fishery using baited pots operated in a long-line formation in shelf edge waters ( > 150 m in depth) off the West …

Resource Assessment Report No. 3: Gascoyne Demersal Scalefish Resource 2024 Assessment, Emily A. Fisher, Rachel Marks, Sybrand A. Hesp, Ainslie Denham, Gary Jackson, David V. Fairclough

Resource Assessment Reports

The Gascoyne Demersal Scalefish Resource (GDSR) comprises over 80 species inhabiting inshore (20-250 m deep) and offshore ( > 250 m deep) waters in the Gascoyne Coast Bioregion (GCB; south of Onslow to north of Kalbarri). The GDSR is primarily targeted by commercial, charter and recreational boat-based line fishers. Snapper and Goldband Snapper are the indicator species selected for monitoring and assessing the status of the inshore suite of the GDSR, while Ruby Snapper and Greybanded Grouper are the indicators for the offshore suite.

Periodic assessments of GDSR indicator species demonstrated that historical fishing of Snapper led to stocks being below …

Resource Assessment Report No. 2: West Coast Demersal Scalefish Resource 2025 Assessment, Emily A. Fisher, David V. Fairclough, Elaine Lek, Jeremy Briggs, Sybrand Alex Hesp, Ainslie Denham

Resource Assessment Reports

The West Coast Demersal Scalefish Resource (WCDSR) comprises over 100 species inhabiting inshore (20-250 m deep) and offshore ( > 250 m deep) waters of the West Coast Bioregion (WCB; north of Kalbarri to east of Augusta). The WCDSR is primarily targeted by commercial, charter and recreational boat-based line fishers, including the commercial West Coast Demersal Scalefish (Interim) Managed Fishery (WCDSIMF). Indicator species selected for monitoring and assessing the status of the inshore suite of the WCDSR include Snapper, WA Dhufish and Baldchin Groper, while indicators for the offshore suite include Hapuku, Bass Groper and Blue-eye Trevalla.

The WCDSR is more …

Resource Assessment Report No. 4: North Coast Demersal Scalefish Resource - Kimberley 2025 Assessment, Fabian Trinnie, Rubie Evans-Powell, Ainslie Denham, Sybrand A. Hesp, Corey Wakefield, Craig L. Skepper, Brett Crisafulli, Stephen J. Newman

Resource Assessment Reports

The North Coast Demersal Scalefish Resource (NCDSR) comprises ecological suites of tropical demersal fish species that occur predominantly in inshore waters (20–250 m deep) and offshore waters ( > 250 m deep) of the North Coast Bioregion (NCB). More than 60 demersal species are landed by fisheries operating in the Kimberley and Pilbara regions of the NCB each year, including high-value snappers (Lutjanidae), groupers (Epinephelidae), and emperors (Lethrinidae). As outlined in the NCDSR Harvest Strategy (DPIRD, 2017), the Kimberley resource is currently monitored through annual reviews of total removals and catch rate trends of indicator species, as well as periodic (every …

Assessment Of Bat Species (Mammalia: Chiroptera) In Canubas Cave, Kibawe, Bukidnon, Philippines, Jurriel Mari D. Alce, Jasmine Rouida M. Abenes, Leo Gabriel Cervantes, Matthew Eli K. Llacer, Von Zhyrus P. Oche, Ian Jay P. Saldo, Jhovel Roy D. Calo

Sinaya: A Philippine Journal for Senior High School Teachers and Students

Bats, flying mammals, are essential to ecosystems, playing vital roles in pollination, seed dispersal, and insect control. Despite their ecological importance, no prior bat research exists for Canubas Cave, Kibawe, Bukidnon. This study aimed to assess the bat species at the site, documenting their morphometric data, diversity and richness indices, and conservation statuses. A purposive sampling design was implemented by setting up mist nets inside and outside the cave. A total of 13 individuals were identified, comprising three species from two families: Rousettus amplexicaudatus and Eonycteris spelaea (family Pteropodidae, fruit bats), and Miniopterus schreibersii (family Miniopteridae, bent-winged bats). Based on …

Fisheries Research Report No. 353: Ecological Risk Assessment For The North Coast Demersal Scalefish Resource, Kimberley A. Smith, Brett Crisafulli, Gabby E. Mitsopoulos, Fabian I. Trinnie, Taylor Grosse, T Thompson, Stephen J. Newman

Fisheries Research Reports

On the 26 March 2025, the Department of Primary Industries and Regional Development (DPIRD) convened an ecological risk assessment (ERA) of the fisheries that access the North Coast Demersal Scalefish Resource (Resource). This Resource comprises a large number of tropical demersal scalefish species that occur in waters of the North Coast Bioregion, including tropical snappers, emperors and cods.

This document contains the results of the ERA along with the background information used to support the risk scoring process. This includes an overview of the Western Australian commercial fisheries that access the Resource, namely the Pilbara Fish Trawl Interim Managed Fishery, …

Fisheries Research Report No. 361: 2024 Assessment Of The Status Of The Elasmobranch Resource Of Western Australia, Matias Braccini, Alex Hesp

Fisheries Research Reports

This document provides the 2024 risk-based weight of evidence stock assessment for the elasmobranch (sharks and rays) resource of Western Australia (WA). Gummy (Mustelus antarcticus), dusky (Carcharhinus obscurus), whiskery (Furgaleus macki), and sandbar (C. plumbeus) sharks are the most commonly captured species (~80% of the elasmobranch catch) and have been selected as indicator species for the status of the temperate elasmobranch ‘suite’. However, over 100 elasmobranch species have been caught in commercial and/or recreational fisheries in WA and, with the increasing number of elasmobranch species being listed in national and international protection lists, species-specific scientific advice on stock status at …

Sustainability: Buzz Word Or Future Of Fashion? Measuring The Feasibility Of Outright Sustainability Among Fast Fashion’S Biggest Agents, Ryan Miller

Apparel Merchandising and Product Development Undergraduate Honors Theses

Abstract

The fashion industry is currently experiencing unsustainable rates of pollution within its supply chains. The rapid increase in demand for short lead times perpetrated by large-scale retailers has led to hazardous practices negatively affecting both the environmental conditions and working conditions of producing countries. With this increased pressure, relationships between brands and suppliers have become untenable. Limited transparency and imbalanced power dynamics at the hand of the industry’s leading retailers require restructuring in order to build more sustainable and equitable supply-chain practices. Further, governmental regulation is currently limited in its capacity to enforce sustainable business practices on a global …

Groundwater-Dependent Ecosystems In Southeastern Utah: An Integrated Analysis Of Hydrology, Ecology, And Landscape Change, Colten Lay

All Graduate Reports and Creative Projects, Fall 2023 to Present

In Southeastern Utah, springs play a critical role in sustaining groundwater-dependent ecosystems (GDEs) in a semi-arid environment. However, they remain understudied and face growing threats from climate change, land use modifications, and hydrologic alterations. This study addresses knowledge gaps in spring occurrence, hydrologic function, and ecological condition by integrating field surveys, remote sensing, and geospatial analysis across multiple spatial scales. It provides a comprehensive assessment of spring hydrology, water quality, and ecological conditions using field surveys, remote sensing, and GIS analysis to establish a foundational baseline of GDE resources in the region. I applied a multi-scale monitoring approach across the …

Multi-Crop Systems And Crop-Switching Strategies To Enhance Water Use Efficiency And Climate Resilience In Arid Agricultural Regions, Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture in the Lower Colorado River (LCR) region faces mounting challenges from climate change, arid conditions, and water scarcity. This study evaluates water use efficiency (WUEc) and crop-switching strategies under SSP2-4.5 and SSP5-8.5 scenarios for 2025–2049, 2050–2074, and 2075–2099. Using historical data, climatic drivers such as temperature and precipitation were analyzed for their influence on key crops, including durum wheat, winter wheat, and corn. Results show SSP2-4.5 supports water use reductions up to 16%, stable profits (80–90%), and modest calorie increases (up to 15%), while SSP5-8.5 poses severe challenges, with water use reductions of 2–5%, profits dropping to around 20%, …

Adaptive Crop Switching For Irrigated Agriculture In Response To Climate Change In The Western U.S., Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Aqil Tariq, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Adaptive irrigation strategies are crucial for balancing water use, economic viability, and food security in the arid Western United States. However, as a key indicator vital in regulating agricultural productivity and crop irrigation, water use efficiency (WUEc) is becoming increasingly complex to estimate due to climate change. This study explores the critical role of key meteorological drivers, such as maximum temperature (tmax) and vapor pressure deficit (vpd), and their impacts on crop-specific WUEc. Future impacts are also assessed through integrating machine learning models with climate projections from the CMIP6 framework under SSP2–4.5 and SSP5–8.5 scenarios to forecast WUEc trends from …

High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le

Mathematics, Physics, and Computer Science Faculty Articles and Research

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.

Factors Motivating Clothing Choice: Environmental Impact Of T-Shirts, Lauren Hunt

DePaul Discoveries

Second only to oil, the fashion industry is one of the most pollutive industries globally. Due to the rise of fast fashion, this environmental issue is continually growing. The level of production has dramatically increased while quality of apparel has decreased, causing shorter garment lifespans. This research utilizes life cycle assessment to identify the environmental impacts of various textiles used in the production of t-shirts, including cotton, polyester, viscose, and elastane. The research scope focuses from material extraction through garment disposal. The life cycle assessment research is paired with a community survey to determine consumer behavioral patterns surrounding clothing consumption …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and …

State And Transition Models For Mulga Rangelands Of Western Australia, Alison O'Donnell, Anna E. Richards, Suzanne Prober, Peter-Jon A. Waddell, Sarah Luxton, Ian Watson, Brett Abbott, Philip Thomas, Joshua E. Foster

Natural resources published reports

This report details a collaborative project between the Western Australian Department of Primary Industries and Regional Development and CSIRO that focused on developing State and Transition Models (STMs) for mulga rangelands in Western Australia. The overarching aim of the project was to improve the common understanding of the characteristics and dynamics of mulga rangeland ecosystems and the expected impacts of management. Specifically, the project aimed to collate expert knowledge and monitoring information using a nationally consistent framework to develop quantitative and dynamic STMs. The geographic scope of the project covers the extensive mulga rangelands of Western Australia, particularly the Gascoyne …