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.

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 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.

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.