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Ford Groups have been studied since the late 1800’s; a confusing nomenclature system has been developed for them due to outdated biostratigraphic studies and inaccurate age interpretations, obscuring the age relationships of the various lithostratigraphic units.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.