GRI RESEARCH

Emerging Research
The Emerging Research category encompasses cutting-edge research and technologies that GRI scientists are developing in real-time, and spans many current topics that are facing our environment at present.





Adaptive Management of Flowering Rush Using the Contact Herbicide Diquat in Detroit Lakes, Minnesota 2016 - Final Report
Flowering rush (Butomus umbellatus L.) is an emergent invasive plant that has invaded the Detroit Lakes area, specifically, Detroit Lake (Big Detroit, Little Detroit, and Curfman Lakes), Lake Sallie, Lake Melissa and Mill Pond (Becker County) since the 1960s. It is native to Europe and Asia and first entered the United States in 1928. Flowering rush has continued to be a problem in the Detroit Lakes system for the past three decades. However, applications of the contact herbicide diquat over the last six years have helped to control the spread and density of the plant. Although flowering rush has been in North America for over forty years, very little information is known about its biology, ecology, and management. Bellaud (2009) reports that it was first observed in North America in St. Lawrence River (Quebec) in 1897. Flowering rush is currently found in all of the southern Canadian provinces, and all of the states bordering Canada and the Great Lakes (NRCS 2013)
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Coastal Ocean Color Trade Study
GRI scientists have created a system of unique data sets to enable a better understanding of environmental processes that occur in coastal environments. Coastal and inland waters and their environments were targeted for the initial mission due to their importance to various aspects of human activity and the inability of current systems to accurately sense these unique environments. This mission works in support of the planned GEO-CAPE satellite mission that monitors these environments and is critical for evaluating and understanding the spatial variations and dynamics associated with coastal environments.
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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.
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Developing, Deploying, and Strategically Evolving the NASA Earth Science Research Knowledge Database, Enterprise Architecture, and Future Solutions Network
NASA's Applied Sciences Program tasked GRI to develop information technology which facilitates searches for potential applications of NASA assets. This technology can help generate ideas for new ways to use NASA missions, research, and/or models in conjunction with operational decision-making processes to achieve a particular benefit to society. The resulting system is called the Earth Science Knowledge Base (ESKB).
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Development of a Northern Gulf of Mexico Operational Forecast System
The NOAA National Ocean Service's Physical Oceanographic Real-Time Systems (PORTS) along the northern coast of the Gulf of Mexico will provide real-time oceanographic data to promote safe and efficient navigation. The Northern Gulf Institute, through Mississippi State University, will manage and coordinate this Operational Forecast System (OFS) University of Massachusetts-Dartmouth project activity in the development of a model to support the PORTS. A global or basin-scale model will provide boundary conditions to a proposed northern Gulf of Mexico Shelf domain model.
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Disruptions to Rail-Impacts Analysis and Decision Support (DRIADS)
This research seeks to explore the positive effects of combining Homeland Security issues with regional transportation infrastructure decision-making and economic development potential within the State of Mississippi and southeast region. This combined approach provides a geographically specific, but highly transferable demonstration of a solution relevant to the Department of Homeland Security (DHS) which integrates currently disparate geospatial and transportation analysis and modeling systems with policy and decision-making.This new generation of modeling capabilities can significantly improve regional transportation system resiliency.
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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.
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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





Forecasting Episodic Changes in Hurricane Intensity and Structure over the Gulf of Mexico
The primary goal of this proposed initial one-year project is to provide greater insight into forecasting time-sensitive trends of rapid formation, changing intensity, and changing wind field area (or size) of hurricanes over the Gulf Mexico in the interest of reducing the uncertainty in the risk posed to Gulf Coast residents and infrastructure. The focus would be to identify key features or processes present in the ambient atmosphere and in the Gulf of Mexico that led to critical episodic changes in the intensity and structure of recent hurricanes: Humberto, Gustav, and Ike.
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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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Health and Productivity of Louisiana Salt Marshes
This study will allow the identification of hotspots of marsh degradation in Louisiana by evaluating marsh biophysical characteristics including distribution of chlorophyll content, green leaf area and green marsh canopy cover. This assessment of marsh health and productivity is due to the Deepwater Horizon oil spill. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images will be used to retrieve and map these characteristics across the coastal Louisiana salt marshes before and after the spill. The maps and tools produced from the study will be helpful to coastal managers across Louisiana as they evaluate and prioritize the marsh restoration effort which will take place due to the oil spill. Tangible map products will be generated for the first time that can quantitatively assess the effect of the restoration activities and speed of marsh ecosystem recovery.
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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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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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Levee Evaluation through Remote Sensing
GRI researchers are developing a means to use remote sensing to determine the strength of river levees through the utilization of airborne synthetic aperture radar for levee condition assessment and develop classification software. The team has set out to develop new methods and software to improve knowledge of levee conditions and help levee managers prioritize their efforts to inspect, test and repair levees.
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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





New Data Compression Process
GRI is investigating the use of a new type of dimensionality reduction and data compression for principal component analysis. GRI researchers have developed a process to shift the computational burden to a base-station decoder. This process is called compressive-projection PCA or CPPCA. CPPCA dramatically departs from traditional PCA because it allows its dimensionality-reduction and compression performance to be realized with a system that puts computational burden on the decoder. Continued development of the process could help the conservation, protection, utilization and enhancement of natural resources in the rural South.
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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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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.
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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





Tools for Enhanced Mapping and Managing Post-Disaster Debris
The overall objective of this research effort is to enhance recovery from and resilience to large scale disasters by providing Mississippi state agency personnel, as well as Mississippi local governments with tools to enhance their ability to manage disaster related debris. The research in this proposal will be carried out in four general thrust areas: 1) Use of Remote Sensing Data to Enhance Effectiveness of Debris Management, 2) Evaluation of an Alternative Treatment Technology for Selected Waste Streams, 3) Development of a Preliminary Debris Disposal Cost Projection Model and 4) Filling in Technical Data Gaps for Debris Management.
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Toward an Understanding of Gulf Coast Resident Preferences on Risk and Restoration
The results of this work will provide useful insights into whether seemingly anomalous coastal risk taking behavior can be explained by more robust behavioral models. Policy makers and scientists concerned with coastal management will obtain clarification of whether coastal resident behavior is driven by a lack of information, misguided perceptions, or simply personal preferences. Additionally, this work will allow for identification of perceived benefits from restoration and how individuals prioritize them. Finally, it may allow for identification of incentives that can be used to induce socially-optimal behavior.
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Weather Research and Forecasting Modeling System
This research includes assimilation of NEXRAD radial winds in a regional mesoscale model and the use of Lagrangian models to estimate the transport and dispersion of gasses/particles over the Southeastern United States. It is our plan to provide daily plume (smoke) forecast information, as well as atmospheric wind and other conditions over the Gulf coast. Therefore, the information can be used to assess how the smoke due to burning oil over the Gulf of Mexico propagates in time.
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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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Modified: May 17, 2016  •  WebMaster  •  Intranet