GRI RESEARCH

Visualization
GRI scientists are striving to advance the technology and utilization of scientific visualization, information visualization, visual analysis, and image processing for the computational sciences. The fundamental goal of visualization is to enable and enhance human comprehension of complex phenomena.





Advection of Karenia Brevis Blooms from the FL Panhandle towards the MS Bight and Sound
Harmful Algal Blooms (HABs) of Karenia brevis have been documented along coastal waters of every state bordering the Gulf of Mexico (GoM). Some Gulf Coast locations, such as Florida and Texas, suffer from recurrent intense and spatially large blooms, while others such as Mississippi seem to rarely observe them. The main objective of this work is to understand the dynamics that led to the K. brevis bloom in Mississippi coastal waters in fall 2015. Blooms of K. brevis from the Florida Panhandle region are often advected westward towards the Mississippi-Alabama coast; however there is interannual variability in their presence and intensity in Mississippi coastal waters. The 2015 K. brevis bloom was compared to the 2007 Florida Panhandle K. brevis bloom, which showed a westward advection pattern, but did not intensify along the Mississippi coast. Cell counts and flow cytometry were obtained from the Mississippi Department of Marine Resources, Alabama Department of Public Health, Florida Fish and Wildlife Conservation Commission and The University of Southern Mississippi. Ocean color satellite imagery from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite was used to detect and delineate the blooms in 2007 and 2015.
Abstract and Document Site





Aquatic Invasive Species- Habitat Suitability Modeling
GRI researchers are using Habitat Suitability Modeling which uses computer algorithms to manipulate data that create models to predict, control and narrow the expansive search area required for detection of new non-native species of likely avenues for the spread of existing plant populations. Researchers have found four ways to control aquatic invasive species. They include chemical, biological, physical and mechanical methods. These methods can control or eradicate invasive plant species in an area.
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Compression of Virtual-Machine Memory in Dynamic Malware Analysis
Lossless compression of memory dumps from virtual machines that run malware samples is considered with the goal of significantly reducing archival costs in dynamic-malware-analysis applications. Given that, in such dynamic-analysis scenarios, malware samples are typically run in virtual machines just long enough to activate any self-decryption or other detection- avoidance maneuvers, the virtual-machine memory typically changes little from that of the baseline state, with the difference being attributable in large degree to the loading of additional executables and libraries. Consequently, delta coding is proposed to compress the current virtual-machine memory dump by coding its differences with respect to a predicted memory image formed by loading the same executables and libraries into the baseline memory. Experimental results reveal a significant improvement in compression efficiency as compared to straightforward delta encoding without such predictive executable / library loading.
Abstract and Document





Corn crop density assessment using texture analysis on visible imagery collected using unmanned aerial vehicles
Determining corn crop density on a large field is of tremendous value to monitor plant health and damages caused by hogs and deer. Texture modelling techniques are investigated to map three different densities (Low, Medium and High) on a corn field by using visible imagery collected using an Unmanned Aerial Vehicle (UAV).
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Detecting Aflatoxin using Hyperspectral Imaging
Hyperspectral imaging is a way of seeing what is invisible to the human eye. GRI researchers are using this to detect biological and chemical toxins that contaminate crops. This is done by splitting the electromagnetic spectrum into many spectral bands, which expose hidden information invisible to the natural eye. The specific contaminant being studied is a fungal metabolite called aflatoxin. This lethal toxin is produced by a fungus called Aspergillus. It is a known carcinogen associated with liver and lung cancers in humans. Many external stresses cause the fungi to react but hot and humid weather conditions increase its production of aflatoxin that invades corn and other commodities. The goal is to help improve detection and accuracy.
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Effects of Rainfall, Geometrical and Geomorphological Variables on Vulnerability of the Lower Mississippi River Levee System to Slump Slides
This study investigated the importance of rainfall and various geomorphological and geometrical factors to the vulnerability of earthen levees to slump slides. The study was performed using a database including 34 slump slides that occurred in the lower Mississippi River levee system from 2008 to 2009. The impact of rainfall within the six months prior to slide occurrence was studied for 23 slides for which an accurate occurrence date was available. Several variables were used to develop a logistic regression model to predict the probability of slump slide occurrence. The proposed model was verified for both slide and non-slide cases. The regression analysis depicts the impact of channel width, river sinuosity index, riverbank erosion, channel shape condition and distance to river. Excluding the sinuosity index, the impact of the other independent variables examined was found to be significant. Occurrence of riverbank erosion around the slide locations was the most significant predictor factor. A channel width of less than 1000 m was ranked as the second most significant variable. The proposed model can aid in locating high-risk areas on levees in order to take prompt protective measures, increase monitoring efforts and enable early response under emergency conditions.
Abstract





Enhanced Soils Mapping For Productive Capacity Assessments
This research uses geospatial technologies to create methodology used in defining soil management zones that address soil variability in distinct areas and identify the soil properties that limit crop production while increasing soil conservation. Determining appropriate soil management zones can lead to an increased profit by either increasing yield in areas of fields that are being underutilized or decreasing fertilization in areas of fields where maximum economic yield has already been attained. Moreover, robust and repeatable methodology for construction of management zones will provide an empirical basis for developing variable rate fertilizer prescriptions that optimize profitability and minimize off-site nutrient transport, thereby benefiting the producer, the public, and the environment.
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Estuarine Influence on Biogeochemical Properties of the Alabama Shelf during the Fall Season
Estuarine-shelf exchange can drive strong gradients in physical and biogeochemical properties in the coastal zone and exert a significant influence on biological processes and patterns. Physical, biogeochemical, and plankton data from an across-shelf transect extending south of Mobile Bay, Alabama, in conjunction with regional time series data, were used to determine the relative importance of estuarine-shelf interactions on the physical-biological structuring of the shelf environment during fall conditions (i.e., well-mixed, low discharge).This period was also characterized by a relatively unique weather event associated with the remnants of Hurricane Patricia, which drove a meteorological flushing of estuarine water onto the shelf. Survey data indicated generally low N:P ratios across the shelf, with slightly elevated dissolved inorganic nitrogen in the Region of Freshwater Influence (ROFI) that extended approximately 30 km offshore. The ROFI had higher values of chlorophyll-a, diatom-specific production, marine snow, and primary productivity, with notable contributions from the larger size cells ( > 5
Abstract Document





FloodViz: Visual Analytics for Assessment and Interpretation of Simulated River Flooding
The FloodViz project involves the development and testing of visual analytics software to enable scientists and forecasters to better interpret and distribute hydrologic information. This software will be useful in the research community as an interpretation tool for river level and flood data. The tools developed serve as a useful platform for hydrologic forecasters within the National Weather Service to more quickly and accurately determine areas at risk for flooding and allow NOAA river forecasters to better visualize the extent of flooding. Additionally, these tools allow forecasters to relay more information to the emergency management community while issuing forecasts to help protect lives, property and the nation.
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GeoVol-Geospatial Volume Rendering
This project created software using direct volume rendering techniques that achieved real-time performance and high image quality. A user study was conducted to compare the implemented volume rendering technique with state-of-the-art isosurface rendering to examine hurricane data. The results of the study established that both volume rendering and isosurface visualizations were effective in examining data from computer simulations of hurricanes. Because of the higher image quality and the interactive frame rates, direct volume rendering was preferred. Future studies will be conducted to quantify performance differences between using the traditional 2D methods and the 4D methods.
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GIS for Aquatic Plant Management
Geographic Information Systems (GIS) have become the new tool for information management, planning and presentation for invasive aquatic plant management programs and is critical in every component of the program.
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Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC)
GRI has partnered with the GCPO LCC to provide critical LCC research and computing capacity for LCC activities. As a research hub for the GCPO LCC, GRI has established over $4 million in cooperative agreements with the U.S. Fish and Wildlife Service to fund more than 20 different LCC research projects. This diverse research program includes exploration of ecosystem health, resilience to climate change and urbanization and interrelationships among species and their habitats.
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Hurricane Landfall Estimation and Storm Surge
The storm surge of Hurricane Katrina, which made landfall in Mississippi and Louisiana in 2005, was unprecedented for its elevation, area coverage, and levee breaches. Due to the storm surge, areas along the Gulf Coast were severely flooded and destroyed. GRI is addressing recent Mississippi and Louisiana storm surge issues using the finite element model ADCIRC. The research will facilitate answers to the sensitivity of the storm surge in Mississippi to wind profiles of major hurricanes, as well as to hurricane eye size and landfall estimation.
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Inflow of Shelf Waters into the Mississippi Sound and Mobile Bay Estuaries in October 2015
The exchange of coastal waters between the Mississippi Sound (MSS), Mobile Bay and Mississippi Bight is an important pathway for oil and pollutants into coastal ecosystems. This study investigated an event of strong and persistent inflow of shelf waters into MSS and Mobile Bay during October 2015 by combining in-situ measurements, satellite ocean color data and ocean model predictions. Navy Coastal Ocean Model (NCOM) predicted high salinity shelf waters continuously flowing into the estuarine system, and forecasted low salinity waters trapped inside the estuaries which did not flush out until the passage of tropical cyclone Patricia
Abstract and Document





Integrated Ecosystem Assessment (IEA) Tool
This research was implemented as part of an overall Ecosystem Approach to Management (EAM). It looks at all indicators, such as tourism and recreation, climate change, fish populations and conservation and energy demands to evaluate ocean health. In the past, scientists, because of the limits of scientific knowledge and technology could only concentrate on individual segments and species of the ocean. The EAM approach using IEA management assessment tool allows them to combine data and look at the ocean as a whole. Research is being carried out at four sites in the Northern Gulf of Mexico: Perdido Bay, Florida; Mississippi Sound, Mississippi; Barataria Basin, Louisiana; and Galveston Bay, Texas.
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Integrated Pest Management Systems and Resistance Management Using Geospatial Technologies
This research has evaluated the use of remote sensing technologies to detect and predict spatial distribution of weed populations for the purpose of designing site-specific herbicide prescriptions and monitoring the spread of herbicide resistant weed species. Associated spatial technologies have been used to generate guidelines for creation of site-specific harvest-aid, plant growth regulator, and insecticide prescriptions. A unique contribution of this research has been the development of novel statistical models that more fully characterize geographic, topographic, hydrological, edaphic, and producer-induced sources of variation in yield than previously understood. The research also highlights the immense complexity of spatial data collection, management, geoprocessing, and integration for decision support in site-specific agriculture. Outcomes of this study may increase efficiency and profitability, reduce the threat of off-target movement of residual herbicides in runoff to surface and groundwater, and reduce herbicide usage through precision applications.
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Invasive Species Program
GRI researchers actively study invasive plants that take over agricultural and natural areas, with expertise for studies ranging from regional impacts through use of remote sensing and GIS, to cellular and molecular studies of plant uptake, and genetic composition. GRI brings together multidisciplinary research teams comprised of university and government researchers to address diverse questions on the management of invasive species.
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Investigating the Correlation between Radar Backscatter and In Situ Soil Property Measurements
Utilizing remote sensing techniques to extract soil properties can facilitate several engineering applications for large-scale monitoring and modeling purposes such as earthen levees monitoring, landslide mapping, and off-road mobility modeling. This study presents results of statistical analyses to investigate potential correlations between multiple polarization radar backscatter and various physical soil properties. The study was conducted on an approximately 3 km long section of earthen levees along the lower Mississippi river as part of the development of remote levee monitoring methods. Polarimetric synthetic aperture radar imagery from UAVSAR was used along with an extensive set of in situ soil properties. The following properties were analyzed from the top 30
Abstract and Document





IPAMS - Invasive Plant Atlas of the MidSouth
The Invasive Plant Atlas of the Mid-South (IPAMS) is an integrated research and extension project to develop an invasive plant program for the Mid-South states of Alabama, Arkansas, Louisiana, Mississippi, and Tennessee. Research activities include conducting systematic regional vegetation surveys to assess the distribution of key invasive plants, developing models for predicting the occurrence of target species based on land use and cover, and evaluating the relative effectiveness of professional versus volunteer surveys.
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Keeping up with technology: Teaching Parallel, Distributed and High-Performance Computing
This special issue is devoted to progress in addressing one of the most important challenges facing education pertinent to computing technologies. The work published here is of relevance to those who teach computing technology at all levels, with greatest implications for undergraduate education. Parallel and Distributed Computing (PDC) along with High Performance Computing (HPC) has become pervasive. Common users now depend on parallel processing technology, as it is integral to the digital ecosystem comprising infrastructures ranging from clouds, data centers and supercomputers to personal computers, laptops, and mobile devices, with even smartphones containing multicore Central Processing Units and many core Graphics Processing Units. This necessitates that every programmer understands how parallel processing affects problem solving. Thus, teaching only traditional, sequential programming is no longer adequate.
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Mapping of Invasive Phragmites (Common Reed) in Gulf of Mexico Coastal Wetlands Using Multispectral Imagery and Unmanned Aerial Systems
In coastal wetlands of the Gulf of Mexico, the invasive plant species Phragmites australis (common reed) rapidly alters the ecology of a site by shifting plant communities from heterogeneous mixtures of plant species to homogenous stands of Phragmites. Phragmites grows in very dense stands at an average height of 4.6 m and outcompetes native plants for resources. To restore affected wetlands, resource managers require an accurate map of Phragmites locations. Previous studies have used satellite and manned aircraft-based remote-sensing images to map Phragmites in relatively large areas at a coarse scale; however, low-altitude high-spatial-resolution pixel-based classification approaches would improve the mapping accuracy. This study explores the supervised classification methods to accurately map Phragmites in the coastal wetlands at the delta of the Pearl River in Louisiana and Mississippi, USA, using high-resolution (8 cm ground sample distance; GSD) multispectral imagery collected from a small unmanned aerial system platform at an altitude of 120 m. We create a map through pixel-based Support Vector Machines (SVM) classification using blue, green, red, red edge, and near-infrared spectral bands along with a digital surface model (DSM), vegetation indices, and morphological attribute profiles (MAPs) as features. This study also demonstrates the effects of different features and their usefulness in generating an accurate map of Phragmites locations. Accuracy assessment based on a) a subset of training/testing samples (to show classifier performance) and b) the entire ground reference (GR) map (to show the quality of mapping) is demonstrated. Kappa, overall accuracy (OA), class accuracies, and their confidence intervals (CIs) are reported. An OA of 91% and kappa of 63 is achieved. The results of this study indicate that features such as MAPs are very useful in accurately mapping invasive Phragmites compared with existing region-based approaches.
Abstract and Document





Micromechanics of Undrained Response of Dilative Granular Media Using a Coupled DEM-LBM Model: A Case of Biaxial Test
In this paper, the Discrete Element Method (DEM) is coupled with the Lattice-Boltzmann Method (LBM) to model the undrained condition of dense granular media that display significant dilations under highly confined loading. DEM-only models are commonly used to simulate the micromechanics of an undrained specimen by applying displacements at the domain boundaries so that the specimen volume remains constant. While this approach works well for uniform strain conditions found in laboratory tests, it does not realistically represent non-uniform strain conditions that exist in the majority of real geotechnical problems. The LBM offers a more realistic approach to simulate the undrained condition since the fluid can locally conserve the system volume. To investigate the ability of the DEM-LBM model to effectively represent the undrained constraint while conserving volume and accurately calculating the stress path of the system, a two-dimensional biaxial test is simulated using the coupled DEM-LBM model, and the results are compared with those attained from a DEM-only constant volume simulation
Abstract Document





Post-Logging Estimation of Loblolly Pine (Pinus Taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System
This study describes an unmanned aerial system (UAS) method for accurately estimating the number and diameters of harvested Loblolly Pine (Pinus taeda) stumps in a final harvest (often referred to as clear-cut) situation. The study methods are potentially useful in initial detection, quantification of area and volume estimation of legal or illegal logging events to help estimate the volumes and value of removed pine timber. The study sites used included three adjacent pine stands in East-Central Mississippi. Using image pattern recognition algorithms, results show a counting accuracy of 77.3% and RMSE of 4.3 cm for stump diameter estimation. The study also shows that the area can be accurately estimated from the UAS collected data. Our experimental study shows that the proposed UAS survey method has the potential for wide use as a monitoring or investigation tool in the forestry and land management industries.
Post-Logging Estimation of Loblolly Using UAS Document





Providing Accurate Data for Field Monitoring of Peanut Production
Reliable yield monitors have been developed for a variety of crops including corn, soybeans, wheat, and cotton. Due to the nature of harvesting and threshing peanuts, however, the ability to provide accurate yield data has been rudimentary, at best. The objective of this research is to use a system for yield measurement previously developed at Mississippi State University and commercialized through MSTX Agricultural Sensor Technologies (MAST), LLC to compare management zones, buy-point and field weights from adjusted and raw yield data in peanuts. The results of this study will potentially allow peanut producers to evaluate inputs, manage pests, make better land-use decisions and perform economic analysis in peanut production.
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Quantifying Damage from Wild pigs with Small Unmanned Aerial Systems
Wild pig (Sus scrofa) population expansion and associated damage to crops, wildlife, and the environment is a growing concern in the United States. The destructive rooting behavior of wild pigs indicates where they have foraged and their general presence on the landscape. We used aerial imagery with a small unmanned aerial system to assess damage of corn (Zea mays) fields by wild pigs in the Mississippi Alluvial Valley of Mississippi, USA, during the 2016 growing season. Images were automatically classified using segmentation
Abstract and Document





Recent development of optical methods in rapid and non-destructive detection of aflatoxin and fungal contamination in agricultural products
The demand for developing rapid and non-destructive techniques that is suitable to real-time and on-line detection of aflatoxin and fungal contamination has received significant attentions. Measurement techniques based on fluorescence spectroscopy (FS), near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) have provided interesting and promising results for detecting aflatoxin and/or fungal contamination in a variety of foods. As such, the main goal of this article is to give an overview of the current research progress of FS, NIRS and HSI techniques in rapid detection of aflatoxin and fungal contamination in different varieties of agricultural products.
Abstract and Document Site





Recent Developments and Applications of Hyperspectral Imaging for Rapid Detection of Mycotoxins and Mycotoxigenic Fungi in Food Products
Mycotoxins are the foremost naturally occurring contaminants of food products such as corn, peanuts, tree nuts, and wheat. As the secondary metabolites, mycotoxins are mainly synthesized by many species of the genera Aspergillus, Fusarium and Penicillium, and are considered highly toxic and carcinogenic to humans and animals. Most mycotoxins are detected and quantified by analytical chemistry-based methods. While mycotoxigenic fungi are usually identified and quantified by biological methods. However, these methods are time-consuming, laborious, costly, and inconsistent because of the variability of the grain-sampling process. It is desirable to develop rapid, non-destructive and efficient methods that objectively measure and evaluate mycotoxins and mycotoxigenic fungi in food. In recent years, some spectroscopy-based technologies such as hyperspectral imaging (HSI), Raman spectroscopy, and Fourier transform infrared spectroscopy have been extensively investigated for their potential use as tools for the detection, classification, and sorting of mycotoxins and toxigenic fungal contaminants in food. HSI integrates both spatial and spectral information for every pixel in an image, making it suitable for rapid detection of large quantities of samples and more heterogeneous samples and for in-line sorting in the food industry. In order to track the latest research developments in HSI, this paper gives a brief overview of the theories and fundamentals behind the technology and discusses its applications in the field of rapid detection and sorting of mycotoxins and toxigenic fungi in food products. Additionally, advantages and disadvantages of HSI are compared, and its potential use in commercial applications is reported.
Abstract





Satellite Rainfall Applications for Surface Hydrology
GRI has evaluated results which examine how soil moisture states simulated by land surface models are impacted when forced with various precipitation datasets. These datasets are from a collection of Global Precipitation Mission satellite constellation configurations gathered over the continental United States.
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Screening Mississippi River Levees Using Texture-based and Polarimetric-based Features from Synthetic Aperture Radar Data
This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming, therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the NASA JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi river in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT) features and gray level co-occurrence matrix (GLCM) features provided higher overall classification accuracies compared to the pixel-based polarimetric features.
Abstract Document





Spatial and spectral Hyperspectral Classification Using Local Binary Patterns and Markov Random Fields
Local binary patterns (LBPs) have been extensively used to yield spatial features for the classification of general imagery, and a few recent works have applied these patterns to the classification of hyperspectral imagery. Although the conventional LBP formulation employs only the signs of differences between a central pixel and its surrounding neighbors, it has been recently demonstrated that the difference magnitudes also possess discriminative information. Consequently, a sign-and-magnitude LBP is proposed to provide a spatial
Abstract Document Site





Spatial Detection of Agri-terrorism
This GRI project develops and deploys an automated target recognition system that utilizes hyperspectral imagery to detect biological or chemical contamination of vegetation. The Automated Target Recognition - ATR - system is applied to the problem of BioSecurity, i.e. the detection of crop contamination via biological or chemical agents.
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Spatial Logistic Regression for Support-Vector Classification of Hyperspectral Imagery
The traditional use of support-vector machines for hyperspectral imagery exploits spectral information alone; however, classifiers that incorporate spatial context have witnessed increasing interest due to their potential for significant improvement over spectral-only approaches. A new paradigm for spatial-spectral support-vector classification is introduced in which spatial context is included into the logistic regression commonly used with support-vector classifiers to provide a probabilistic output. In experimental results, the proposed approach is compared to methods representative of two prominent families of spatial-spectral support-vector classifiers—composite kernels and post-processing regularization—and it is observed that the proposed approach provides superior classification accuracy.
Abstract Document





Temporal Effects on Internal Fluorescence Emissions Associated with Aflatoxin Contamination from Corn Kernel Cross-sections Inoculated with Toxigenic and Atoxigenic Aspergillus Flavus
Non-invasive, easy to use and cost-effective technology offers a valuable alternative for rapid detection of carcinogenic fungal metabolites, namely aflatoxins, in commodities. One relatively recent development in this area is the use of spectral technology. Fluorescence hyperspectral imaging, in particular, offers a potential rapid and non-invasive method for detecting the presence of aflatoxins in maize infected with the toxigenic fungus Aspergillus flavus. Earlier studies have shown that whole maize kernels contaminated with aflatoxins exhibit different spectral signatures from uncontaminated kernels based on the external fluorescence emission of the whole kernels. Here, the effect of time on the internal fluorescence spectral emissions from cross-sections of kernels infected with toxigenic and atoxigenic A. flavus, were examined in order to elucidate the interaction between the fluorescence signals emitted by some aflatoxin contaminated maize kernels and the fungal invasion resulting in the production of aflatoxins.
Document





Using Unmanned Aerial Vehicles for High-Resolution Remote Sensing to Map Invasive Phragmites Australis in Coastal Wetlands
The wetland plant species, Phragmites australis, is present on every continent except Antarctica. Both native and non-native subspecies thrive in the USA with the non-natives quickly displacing native wetland plants. Along the Gulf Coast, Phragmites grows in very dense stands, and at heights of greater than 4.6 m, is usually the tallest grass species in a wetland, estuary, and marsh ecosystems. Phragmites is known to alter the ecology of these wetland systems making them less suitable as habitat for many species of flora and fauna. Furthermore, Phragmites presents a navigation hazard to smaller boats by impairing visibility along shorelines and around bends of canals and rivers. Management efforts targeting non-native Phragmites rely heavily on accurately mapping invaded areas. Historically, mapping has been done through walking the perimeter of a stand with a Global Positioning System (GPS) unit, using satellite imagery, or through the use of aerial photography from manned aircraft. These methods are time consuming, are expensive, can have an inadequate resolution, and in some cases are prone to human error. In this work, an Unmanned Aerial System (UAS) was used to capture visible imagery to create a basin-wide distribution map of a large wetland along the US Pearl River delta in southeastern Louisiana. The imagery was collected in the summer and individual images were mosaicked to create a larger map. We then evaluated the use of texture analysis on the mosaics to automatically map the invasive. Specifically, Gabor filters, grey level co-occurrence matrices, segmentation-based fractal texture analysis, and wavelet-based texture analysis were compared for mapping the Phragmites. Our experimental results, conducted using the imagery we collected over four study areas (approximately 2250 ha) along the US Pearl River delta, indicate the proposed texture-based approach yields an average accuracy of 85%, an average kappa accuracy of 70%. These maps have shown to be very useful for resource managers to hasten the eradication efforts of Phragmites.
Abstract and Document





Visualization Techniques for Improving Understanding of Severe Storms
This project advances the visual analysis tools to increase a modeler or analyst's ability to understand hurricane structure, intensity and dynamics. The project focuses on developing new 2D and 3D visualization tools which produce visualization products that can be made publicly available, easily interpreted and can be viewed on personal computers or used in television coverage. The goal is to create a hurricane visualization system that accepts both simulated and measured data as input and put all the data into a single geographic context.
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WISDOM - Weather In-Situ Deployment Optimization Method
GRI scientists and students are participating in WISDOM, the Weather In-Situ Deployment Optimization Method research program that seeks to improve hurricane forecasting time by three to seven days before a storm's landfall by providing wind and atmospheric data in areas of the Atlantic basin that are poorly observed. The WISDOM program launches small super-pressure balloons with payloads that include GPS and satellite radio communications capabilities.
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