Seagrass Loss in Belize: Studies of Turtlegrass (Thalassia testudinum) Habitat Using Remote Sensing and Ground-Truth Data

Spatial and temporal change in turtlegrass (Thalassia testudinum) habitat of the South Water Caye Marine Reserve (SWCMR) in Belize were analyzed using satellite images backed up with ground-truth data. We had two pri-mary objectives. First, we wanted to determine areal expanse of seagrass across a large area (~12 km by 3 km) of the SWCMR, and address its change over time. We used paired satellite images taken during 2001 and 2005 to determine coverage by seagrass and measure temporal variables. These analyses recorded an overall seagrass loss of 1.8% (52.3 ha) during the 4 yr period. Secondly, we wanted to determine whether seagrass gains or losses were consistent across the study area. Replicate sampling was used as a statistical basis and confirmed a significant loss of seagrass across the region. It also helped identify two regions of significant seagrass loss; one 600 ha area lost 12.4% of its seagrass; another 240 ha area lost nearly 40%. These components helped us assess seagrass habitat in an area perceived as critical to Belize fisheries, and provided the scale and statistical rigor necessary to adequately assess a broad region of study. The salient results from our study were not the magnitude of seagrass loss per se, but the loss in seagrass habitat from an area that is thought to be relatively pristine. Seagrass-habitat loss in this region of the Caribbean Sea may be evidence that even near-pristine areas can be impacted by anthropogenic factors. Determining the causes of habitat loss may help prevent loss of productivity, habitat, and livelihood for the associated human and nonhuman communities

Chemostratigraphy of the upper jurassic (oxfordian) smackover formation for little cedar creek and brooklyn fields, alabama

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The Upper Jurassic(Oxfordian Age)Smackover Formation is a significant source for hydrocarbon production in southwest Alabama. Brooklyn Field is in southeast Conecuh County, Alabama, and has been a major producer of oil and natural gas for the state. The Smackover is a carbonate formation that has been divided into seven distinct lithofacies in the Brooklyn and Little Cedar Creek fields. In southwest Alabama, the facies distribution in the Smackover Formation was influenced by paleotopography of the underlying Paleozoic rocks of the Appalachian system. The goal of this study is to determine elemental ratios in rock core within the Smackover Formation using an X-ray fluorescence(XRF)handheld scanner and to correlate these elemental characteristics to the lithofacies of the Smackover Formation in the Brooklyn and Little Cedar Creek fields. Eight wells were used for the study within Brooklyn Field and Little Cedar Creek fields. Cores from the eight wells were scanned at six-inch intervals. Chemical logs were produced to show elemental weights in relation to depth and lithofacies. The chemical signatures within producing zones were correlated to reservoir lithofacies and porosity. Aluminum, silicon, calcium, titanium, and iron were the most significant(\u3e95% confidence level)predictors of porosity and may be related to the depositional environment and subsequent diageneses of the producing facies. The XRF data suggests relative enrichments in iron, titanium, and potassium. These elements may be related to deposition in relatively restricted marine waters

Seagrass Health Modeling and Prediction with NASA Science Data

Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time

Application of High Resolution Multispectral Imagery for Levee Slide Detection and Monitoring

The objective is to develop methods to detect and monitor levee slides using commercially available high resolution multispectral imagery. High resolution multispectral imagery like IKONOS and QuickBird are suitable for detecting and monitoring levee slides. IKONOS is suitable for visual inspection, image classification and Tasseled Cap transform based slide detection. Tasseled Cap based model was found to be the best method for slide detection. QuickBird was suitable for visual inspection and image classification

Soil Moisture Estimation in South-Eastern New Mexico Using High Resolution Synthetic Aperture Radar (SAR) Data

Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site

Microwave Remote Sensing of Soil Moisture in Semi-arid Environment

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Commencement Ceremony 2023. School of Engineering

Interim Dean of the School of Engineering: Dr. Greg Easso

Estimating Speed and Direction of Small Dynamic Targets through Optical Satellite Imaging

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI reveals vehicles and their velocities using commercial imagery from a passive optical satellite-mounted sensor. With simple process of image differencing, the MTI can automatically recognize conveyances in motion (speed and direction) represented by polygons formed by a group of pixels from successive images. Micro-change detection with an existing commercial satellite requires special considerations of differences in spatial and spectral resolution between images. Complications involving the movement detection system such as vehicle overlap, vehicle clusters, and zones of low confidence are refined by adding error-reducing modules. This process is tested on a variety of vehicles, their concentrations, and environments, confirming the feasibility of utilizing an MTI with commercial optical satellite imagery for movement recognition and velocity estimation

Chemostratigraphy of the Upper Jurassic (Oxfordian) Smackover Formation for Little Cedar Creek and Brooklyn Fields, Alabama

The Upper Jurassic (Oxfordian Age) Smackover Formation is a significant source for hydrocarbon production in southwest Alabama. Brooklyn Field is in southeast Conecuh County, Alabama, and has been a major producer of oil and natural gas for the state. The Smackover is a carbonate formation that has been divided into seven distinct lithofacies in the Brooklyn and Little Cedar Creek fields. In southwest Alabama, the facies distribution in the Smackover Formation was influenced by paleotopography of the underlying Paleozoic rocks of the Appalachian system. The goal of this study is to determine elemental ratios in rock core within the Smackover Formation using an X-ray fluorescence (XRF) handheld scanner and to correlate these elemental characteristics to the lithofacies of the Smackover Formation in the Brooklyn and Little Cedar Creek fields. Eight wells were used for the study within Brooklyn Field and Little Cedar Creek fields. Cores from the eight wells were scanned at six-inch intervals. Chemical logs were produced to show elemental weights in relation to depth and lithofacies. The chemical signatures within producing zones were correlated to reservoir lithofacies and porosity. Aluminum, silicon, calcium, titanium, and iron were the most significant (>95% confidence level) predictors of porosity and may be related to the depositional environment and subsequent diageneses of the producing facies. The XRF data suggests relative enrichments in iron, titanium, and potassium. These elements may be related to deposition in relatively restricted marine waters