This research assesses the relationship between income per capita and the amount of maintenance received for the major roads across the State of Texas. Relevant datasets and analysis techniques such as demographic (census data), population density (distribution), road network, maintenance records, etc. will be carried out using ArcGIS Pro software. A series of maps highlighting analysis results derived based on the various parameters will be produced to provide a comprehensive overview of the relationship between the variables, if any, that would be useful for future decision-making.

Trees provide an array of benefits to urban communities, such as oxygen production, flood mitigation, air pollution reduction, increased property value, cooling effects, and cultural services. Tree distribution, however, varies across cities, impacting the delivery of these services from neighborhood to neighborhood. Research has shown that low-income neighborhoods are more likely to have less trees than wealthier neighborhoods, leaving these neighborhoods more vulnerable to health impacts, such as heat-related and respiratory illnesses. The purpose of this study is to analyze tree canopy cover in relation to a set of demographic variables at the US Census block group level to understand if inequities exist in Fort Worth, Texas. We used a geographic information system (GIS) to assess the extent to which age, race, income, and housing characteristics are related to tree canopy. We conducted a correlation analysis between percent canopy cover and the demographic variables. Preliminary results suggest that majority minority and low-income block groups possess less canopy cover than predominantly white, higher-income areas in Fort Worth.

As urbanization continues to fragment landscapes, wildlife become more isolated and find it harder to access the necessary resources to survive. Finding ways to connect fragmented landscapes is necessary for wildlife, but it is important to know what areas wildlife currently. Trees provide valuable habitat for many species, but it is important to know how much wildlife depend on trees. This study will be using a NAIP landcover dataset as well as point locations obtained from citizen science sources. The point locations will be used to create habitat range maps and analyze how much habitat ranges of different species overlap with tree canopy cover. The habitat range of the study species should have a high overlap with canopy cover, so we will identify what areas of the habitat range are disconnected and could be targeted for restoration.

The expansion of urban areas is a threat to wildlife because it fragments habitat and reduces the access to resources. Consequently, there is a need to improve the quality of urban habitats by increasing connectivity between habitats and resources. For volant species like bats, birds, and flying invertebrates, linear features such as tree-lines and/or connected canopies can create corridors that allow these wildlife to move along. In an urban environment, the structure of the urban forest (essentially all the trees in an urban area) can provide connectivity, if appropriate, thereby increasing access to resources and landscape permeability. Thus, in this study we used behavioral observation and acoustics surveys to monitor the commuting activity of bats in Fort Worth, Texas along 15 potential commuting routes. At each route, we measured tree height, percent canopy cover, gap distance, number of gaps, and rugosity or ruggedness of the canopy edge to identify what tree canopy features aided bat movement. Using GLM, we found that routes surrounded with more linear canopy cover and less gap distance resulted in more bats commuting. Moreover, we found that an increase in rugosity negatively influenced route use, as undulating tree canopies increased obstacles that created an inefficient commuting route (i.e., straight lines save energy). Our study demonstrates that the urban environment can be managed to increase connectivity and we provide recommendations on how to better manage the urban forest to increase commuting corridors for bats in this landscape.

Currently over 1,400 households use the Roubidoux Aquifer in Northeastern Oklahoma as their main source of drinking water. Additionally, the total water demand is projected to increase 56% from 2010 to 2060. This increase in water demand is concerning due to the Boone and Roubidoux aquifers being highly susceptible to surface contamination, containing elements such as lead and zinc, from the Tar Creek Superfund site located (TCSS) in Picher, OK. This study seeks to determine, using spatial analysis tools in GIS, the contamination susceptibility of the Boone and Roubidoux aquifer recharge zones as a result of direct surface contaminants and processes that facilitate their propagation.

Flood is a major threat to many communities worldwide, despite many areas lacking flood hazard mapping due to data scarcity. Under such a scenario, remote sensing and GIS-based approaches could be a promising solution for assessing and characterizing flood hazard risk. Therefore, the objectives of this research project are to develop a flood hazard risk map for Rowlett Creek Watershed using remote sensing data and GIS (Geographic Information Systems) techniques to identify and evaluate flood risk areas over the study site. The research will involve development of complied flood hazard index (FHI) using GIS software based on flood causative factors such as slope, flow accumulation, drainage network density, distance from drainage channel, geology, land use/cover, soil moisture and rainfall intensity. Filed data of geology will be obtained from SSURGO and other data will be extracted from remote sensing product such as SRTM, NLCD, CROPCASMA and PERSIAN. The expected outcome of the research is the development of flood hazard risk thematic map and further verify it with the inundation area of a historical flood events in the study area, which will help to purpose proper mitigation and management strategies in flood-prone area. This research looks over a remote sensing and GIS-based approach for characterizing flood hazard risk, which will provide valuable information for policymakers, disaster management agencies, and other stakeholders working towards reducing the impact of floods even in data-scarce areas.

The present study is aimed at comparing the annual income per household to amenities in districts within the Dallas Fort Worth metroplex. Several datasets and analysis results including the spatial distribution of public and private schools, school ratings, proximity to health facilities, parks, and other government provided services will be combined to investigate the research question. Some of the data analysis techniques that will be implemented using ArcGIS Pro include creating buffer zones which act as visual guides to better demonstrate comparisons and communicate the findings in an interactive way.

Each year, as much as 40 percent of food produced in the United States is wasted by producers or consumers. When food waste is sent to landfills, it competes for limited space and generates the greenhouse gas methane. Globally, food loss and waste represent 8 percent of anthropogenic greenhouse gas emissions. Restaurants are known to be a contributor to food waste, both in the kitchen and from consumers. The purpose of this research is to examine food recovery efforts of restaurants in Fort Worth. We sent a survey to 371 restaurants in Fort Worth asking about their food waste management practices. The survey was designed to document restaurants’ current food waste practices, interest in food donation and composting programs, and business characteristics. We analyzed the responses through descriptive statistics and other statistical methods to evaluate how practices varied by business demographics and type of establishment. From this survey, we discovered local barriers to food recovery and from these, we suggest forms of outreach or programming that would help restaurants to reduce food waste. We also used responses to identify and map ideal food waste drop-off locations for restaurants that the city could use to plan recovery efforts.

Urban forests are a common way to integrate nature into heavily populated areas. Urban forests provide a range of benefits to urban communities. Trees provide economic, social, and cultural benefits. For instance, trees provide opportunities for individuals to engage with the environment, reduce stress, and increase property values. Trees also contribute to ecosystem services as well by filtering air pollution, providing habitat for wildlife, and mitigating storm water runoff. The purpose of this research is to assess the biodiversity and the climate resiliency of trees in an urban forest in Arlington, Texas that was part of a program for environmental mitigation of a flood plain. To assess the biodiversity and climate resiliency of the area’s trees, we collected the following data: tree diameter measured at 4.5 feet above the ground (DBH), GPS coordinates of trees, species, and tree condition. We analyzed the data using the Simpson’s Biodiversity Index and the Shannon Diversity Index values to assess the biodiversity of present tree species and identify their climate resiliency. We compared these results to two Representative Concentration Pathways (RCPs) to understand the potential impact of climate change on the urban forest. Finally, we offer suggestions to increase the resiliency of this urban forest and the potential for incorporating these findings in future urban forest management plans.

At the Water and Society Lab at TCU, we are studying the presence of Escherichia coli (E. coli). The Village Creek tributary of the Trinity River is the subject of this testing, and it is one of the many water bodies monitored by the US Geological Service. Using information provided by the monitoring location alongside the data gathered from our research, we can assess and monitor the concentration of E. coli in the Village Creek.

Through weekly sample collection and analysis, we can determine the prevalence of E. coli in the Village Creek. Our method is to collect water samples, add EPA-approved Colilert testing chemicals, and incubate the samples at 35℃ for 24 hours. During analysis, the presence of E. coli is quantified by colony forming units (CFU). This research indicates whether or not the sample contains unsafe levels of E. coli. According to the Texas Commission on Environmental Quality and the US EPA, 126 CFU per 100 mL of water is deemed unsafe. In addition to recording CFU, we also document certain hydro-climatological variables such as ambient and water temperature, rainfall, and turbidity. Through these findings, we can be applied to water management and quality decisions throughout the Dallas-Fort Worth and north-central Texas regions.

Wildfires are a global concern as they are unpredicted fires that cause harm to their surrounding environment, local wildlife, and humans. The negatives of these wildfires outweigh the positives as their occurrence is natural but also caused by human negligence. This past year there were about 69,000 wildfires reported nationally; of these fires, 835 were from Colorado. This study assesses the risk of wildfires in Colorado by using GIS and spatial data to map fire risk and determine possible mitigation techniques through utilizing livestock.
For the past 20 years, Colorado has experienced their largest wildfires, and with no decrease in the number of wildfires each year, mitigation techniques are crucial. In determining what method to use, it is valuable to focus on all the factors that add to these fires, such as the amount of rainfall, elevation, humidity, human activities, and more. Multiple past studies have used livestock as a form of wildfire mitigation. Using grazers to eat the fuel these fires thrive on can decrease the spread of future fires. Grazers are an environmentally stable form of fire mitigation as they eat the vegetation and then process it to become nutrients for the soil. We can determine the areas of high risk in Colorado by assessing how the stated factors contribute to Colorado wildfires and see if grazers are a possible mitigation method.

Water represents one of the required resources for wildlife to live and thrive in an area. Due to urbanization, we have seen an increase in the transformation of natural water sources (i.e. lakes, streams, and rivers) to semi-natural (i.e. retention ponds, reservoirs, and drainage ditches), for which we create for the urban infrastructure and for animals. The objective of the following study was to assess whether water quality influences the direct use of water sources by terrestrial wildlife in an urban environment utilizing bats as our indicator species. We, therefore, hypothesize that water sources with higher water quality will have an abundant and diverse community of bats using them (i.e., foraging and drinking), while lower quality water sources will have little to no bat activity and lower species diversity. We conducted this study using thermal cameras and acoustic monitoring to determine whether water quality has discernible influences for water resource use by bats at water sources across six urban parks and greenspaces in Fort Worth, Texas. We observed increased bat activity at water sources that were listed as areas with higher water quality standards with very slow moving water, and little activity in areas that have been known to have lower water quality. Understanding how the water quality of urban sources impacts bats, may not only be used as an indicator of water availability for other wildlife species in urban areas, but also provide insights into the environmental health of local parks and surrounding neighborhoods.

Sinkhole hazards pose a major threat to key infrastructure and human lives in Taylor and Jones counties in West Central Texas. These counties are underlain by soluble evaporite and carbonate rocks. In this study, a data fusion approach was adopted in which multi-source datasets and techniques were combined to detect and map the spatial distribution of sinkholes, quantify their displacement rates, and identify the processes and factors controlling their occurrence. Preliminary results indicate: (a) there is a spatial correspondence between depressions (area: 625 m2 - 2500 m2) identified using Light Detection and Ranging (LIDAR) datasets and previously- mapped sinkholes; (b) deformation rates over the mapped depressions derived using Persistent Scatterer Interferometry technique applied on 53 level-1 Sentinel-1 images (2016 – 2021) and calibrated using long-term (2006 – 2021) GNSS data indicate an average and peak subsidence rates of -6 mm/yr and +5 mm/yr, respectively; (c) clusters of high subsidence rates were noted over areas underlain by evaporites belonging to the Clear Fork Group; (d) efforts to validate the accuracy of the sinkhole detection techniques are currently underway using 2D Electrical Resistivity Tomography (ERT) surveys carried out on the identified subsiding depressions. In addition, groundwater level and discharge time series and other relevant datasets are being integrated to assess the processes and factors that induce the formation of these features. Results of this study could be used to develop an early warning system to implement mitigation strategies to curtail the impacts of the sinkhole hazards in Texas and other parts of the globe.

The Mendocino National Forest was affected by fire in August 2020. It devastated a substantial area of land over the period of three months, resulting in hundreds of millions of dollars in damage and the evacuation of thousands of people. Moreover, many of the local plantations were destroyed. To evaluate the severity of the impacted area for rehabilitation and restoration, severity data and maps are crucial. This study will combine several geospatial data including multitemporal remote sensing data to identify changes in forest structure and moisture content affected by the fires through burn severity maps. This study will use the Normalized Burn Ratio (NBR) technique to identify burned areas and provide a measure of burn severity. The NBR is calculated as a ratio between the NIR and SWIR values bands 5 and 7 obtain from pre-fire and post-fire Landsat 8 imageries. This will be followed by generating the Differenced Normalized Burn Ratio (ΔNBR) for pre and after-imageries to map the fire severity. The result of the NBR analysis will be integrated with the Normalized Difference Vegetation Index (NDVI) to map vegetation greenness over the study area that will be helpful to validate the accuracy of the NBR analysis. Moreover, elevation dataset (Digital Elevation Model (DEM)) will be used to assess factors that exacerbate emerging wildfires such as topography and slope.

The objective of this research is to conduct wind farm suitability analysis (for energy generation) with a focus on areas that either heavily rely non-renewable sources of energy (parts of Australia) or areas that have limited access to energy. The study will combine several spatial datasets (road networks, population distribution, high mean windspeed, etc.) and analysis products (proximity to roads, national grids, etc.) to determine, through the suitability analysis, whether the wind energy is ideal and economical source of energy for the investigated areas.

As we move further into the 21st century, Earth's functional processes are experiencing a steady shift, particularly in terms of climate and sea levels. Anthropogenic warming has accelerated the rise of sea levels and increased the frequency, intensity, and rainfall of cyclones and hurricanes. To investigate the impact of rising sea levels on storm surges in vulnerable areas, we utilized remote sensing and GIS technology to come up with an understanding of the influence land cover type has on flood intensity and assess the vulnerability of the Houston area based on storm surges from 2015 - 2022. Our findings underscore the critical need for urgent adaptation and mitigation measures to mitigate the risks associated with changing weather patterns and rising sea levels.

The project combines demographic (median income, marital status, etc.) and other relevant datasets (e.g., pet ownership, marketing data) to identify locations across the Dallas Fort Worth metroplex that are suitable for a successful launch of an app that is designed for pet owners. Selecting an suitable site is important for the successful growth and mass use of the app. A series of maps and other analysis products that illustrate the different variables will be generated using ArcGIS Pro which will ultimately be integrated for identifying the most suitable locations for the app launch.

For our research project, we plan to use GIS remote sensing technology to locate and identify potential land plots for urban farming. The purpose of this project is to recognize and assist in the issue of food deserts in urban areas such as the DFW (Dallas Fort Worth) metroplex, NYC, and Los Angeles. A food desert refers to any area with limited or no access to affordable, nutritious food. This could include a lack of access to farmers’ markets, vegetable shops, or fresh produce. This project aims to recognize and assist in the issue of food deserts in urban areas with a particular focus on the East Fort Worth/Arlington areas in Tarrant County. Several relevant datasets including high spatial resolution commercial remote sensing and other relevant spatial (such as property appraisal datasets, soil data) and non-spatial datasets, and data analysis products (such as the proximity of the areas to fresh produce/major grocery stores) will be combined in a GIS environment to identify empty plots of lands that could be used for the purposes of urban agriculture and assess their potential for food growth.

The greater East Texas Basin represents the portion of the Cretaceous Texas Shelf north of the San Marcos Arch, proximal to the Woodbine siliciclastics sourced from the Ouachita and Sabine uplifts. During the Early to Middle Cenomanian the basin underwent a time-transgressive transition from an oxygenated carbonate platform to an anoxic shelf. The Cenomanian-Turonian aged Woodbine and Eagle Ford Groups have been studied since the late 1800’s; a confusing nomenclature system has been developed for them due to outdated biostratigraphic studies and inaccurate age interpretations, obscuring the age relationships of the various lithostratigraphic units. To study this time-transgressive transition and better understand and define the Woodbine-Eagle Ford contact in north Texas, stratigraphic and X-ray Fluorescence (XRF) geochemical data will be collected from USGS near-surface cores drilled in Dallas and Grayson counties, and paired with X-ray diffraction (XRD), inductively coupled plasma-mass spectrometry (ICP-MS), and core spectral gamma ray data provided by the USGS, and biostratigraphic data provided by Denne. Field work will also be conducted on several outcrop locations in the Dallas-Fort Worth (DFW) Metroplex for detailed descriptions and measured sections to be made as well as sample collection for thin section, detrital zircon, and further XRF analysis. The data collected for this study will be used to lithostratigraphically and geochemically define the Woodbine-Eagle Ford transition zone in north Texas with the intent of determining the paleoceanographic conditions during deposition, and determine if this transition is time-transgressive across the DFW Metroplex and North Texas region.

Current in-situ assessments of water quality in lakes can be significantly improved by leveraging recent advances in remote sensing and algorithm development for a faster and more cost-effective approach. This study leveraged satellite- (Landsat 7/8 and Sentinel-2) and UAV-based remote sensing datasets to detect and monitor changes in key water quality parameters (Chlorophyll-a (Chl-a) and turbidity) within the epilimnion of Lake Arlington (Texas) during the past 20 years. In addition, remote sensing algorithms were developed to capture the spatial variability of the water quality parameters across the entire extent of the water body. The investigation period was divided into two segments: before and after the EPA-established Watershed Protection Plan program (WPP) in 2012 to mitigate the lake's water quality deterioration. A regression model, using satellite-based and historical in-situ observations (2002 – 2020), was developed to predict the targeted water quality parameters across the extent of the lake. Our preliminary results indicate: (1) Chl-a levels at the lake's inlet decreased significantly after 2012 (before: 32.1ug/L; after: 9.2ug/l); also turbidity (via Secchi Disk Depth) across the lake decreased after 2012 (before: 0.6 m; after: 0.5 m); and the spring season had the highest levels of Chl-a followed by the summer season for both before and after 2012 while high turbidity values also coincided with high Chl-a values in the summer, (2) regression analysis revealed a high correlation between the in-situ Chl-a and Landsat (before 2012: spring R2 = 0.62, summer R2=0.66; p-value < 0.01; after 2012: spring R2 = 0.54, summer R2=0.73; p-value < 0.01) and Sentinel-2 bands (2015-2020: spring R2 = 0.99, summer R2=0.82; p-value >0.05). Similarly, the regression analysis revealed a high correlation (2015-2020: spring R2 = 0.98, summer R2=0.57; p-value >0.05) between reflectance from Sentinel-2 bands and in-situ turbidity levels; (3) The optimum spectral band to detect Chl-a was found to be between 590-880nm for Landsat and 665-940 nm for Sentinel-2 while for turbidity it was between 450-670nm for Landsat and 560-705nm for Sentinel-2. Therefore, Sentinel-2 bandwidth was better at detecting Chl-a and turbidity levels in the lake because of its wider bandwidth; (4) Water quality controlling factors in lake Arlington include landcover change, precipitation rates, and the EPA WPP measures. Landcover change between 2001 and 2019 shows an overall 25% increase in urban areas, a 9.5% increase in wetlands, and a 10.7% decrease in grassland which may have contributed to the decline in Chl-a and turbidity values. Finally, efforts to calibrate and improve the accuracy of the satellite-based observations are underway with UAV-acquired multispectral imagery obtained at the time of the Sentinel-2 overpass over the lake.

Given the drastic rise in renewable energy investment across the US and globally, it is important to develop techniques for resource assessment. The study aims to identify the most suitable areas for renewable energy development in Texas by analyzing various geospatial factors that influence renewable energy production, such as terrain and land use. Likewise, resource-specific data such as surface direct normal irradiance (DNI) and wind speed were used to ensure resource availability. Products generated use an integration of remote sensing data, geospatial analysis, and machine learning algorithms to develop a A spatially-explicit multi-criteria decision analysis (MCDA) with GIS.

This study will develop (livable) suitability index for areas within Fort Worth with respect to the availability of various amenities (walkability, parks, etc.), public transport, proximity to fresh produce and entertainment (restaurants, etc.), and other relevant services. This is important as the City of Fort Worth has some of the lowest transit scores compared to major cities across the US especially with those having similar population as Fort Worth. Several spatial analysis techniques including proximity and overlay analysis will be undertaken using tools in ArcGIS Pro and ArcGIS Online to attain the objectives of the study.

As the nature and quantity of new/novel nanomaterials continue to expand to meet industrial, medical, and domestic demands, their accidental or intentional release becomes inevitable. To this end, an evolving understanding of the interaction dynamics between nanomaterials and naturally occurring geomaterials is central to supporting continued sustainable development and use of nanomaterials. The current study explores the chemodynamics of the organic nanomaterial, polyamidoamine (PAMAM) dendrimers, binding to (and debinding from) ferrihydrite. Specific focus is placed on how PAMAM size and pH affects the reaction between three carboxyl-terminated PAMAMs (Gx.5-COOH) sorbing/desorbing to/from the variably-charged ferrihydrite (FFH). Since both ferrihydrite and PAMAM exhibit pH-dependent variation of speciation, it is expected that binding/debinding dynamics of differing sizes of PAMAM will vary. Investigating the quantity, rate, and dynamics of these reactions provides insight into the type of bonding occurring (physiosorption, electrostatic bonding, or hydrogen bonding) and the location of bonding (surface versus micropore spaces). The information gained from this study will help to develop a more holistic picture of the environmental fate of synthetic nanomaterials.

In Tarrant County, Texas, food deserts affect approximately 275,000 residents. Chronic health conditions affect households living in food-insecure communities, leading the government to spend billions of dollars treating preventable diseases. Implementing sustainable urban agriculture in areas of high need to produce food using geospatial technology to aid in soil management can play an important role in helping farmers. The objective is to create an urban soil analysis map from the data collected on the soil properties, distribution, and variability of how these properties affect landscapes.

The aim for this project is centered around understanding carbon sequestration and the potential for carbon capture, utilization, and storage (CCUS) in the United States of America. An in depth look at the CO2 emissions for given areas of the U.S. will be looked at to gain an idea of where localized hotspots for emissions are located and how the impact of these emissions can be reduced using CCUS. By coupling emission data with existing infrastructure data (such as active and abandoned wells, pipelines, storage facilities, etc.) an outlook on the possibility of CCUS and reduction of emissions can be achieved. Geologic formations also play a specific role in how CCUS works. Understanding the various rock formations below and how the injected CO2 will be sealed away deep in the ground is a vital piece for any CCUS project. Combining the geological data with the emissions and infrastructure data will piece together a variety of information to better understand the possibility of reducing carbon emissions in various areas around the United States.