Sensors and Modeling
Satellite technology has revolutionized our spatial perspective of global systems over the years. Under the Sensors and Modeling category, GRI researchers are developing programs to better understand how spatial and environmental processes affect the earth's systems.

A Rapid and Nondestructive Method for Simultaneous Determination of Aflatoxigenic Fungus and Aflatoxin Contamination on Corn Kernels
Conventional methods for detecting aflatoxigenic fungus and aflatoxin contamination are generally time-consuming, sample-destructive, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening detection, real-time, and on-site analysis. Therefore, the potential of visible
Abstract Publication in A Journal of Food and Chemistr

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.
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An Updated Synoptic Climatology of Lake Erie and Lake Ontario Heavy Lake-Effect Snow Events
Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.
Abstract Research Document

Application of Structure from Motion Techniques in Late-season Corn Damage (UAS)
Green snap occurs when corn stalks are broken in high wind. Late in the season, green snap produces yield losses that are immediate and irreversible. Losses are proportional to the percentage of plants lost, thus estimation of damage extent is important for calculating economic loss. Unmanned aerial vehicles are capable of creating a 3D-model, called a digital surface model, of the crop canopy using structure from motion techniques. The objective of this study was to compare image- versus structure-based calculations of damaged area. The structure-based calculations were more reflective of the field assessment. While the latter approach showed promise, more development of protocols is needed for reliability and operational success.
Abstract Precision Agriculture

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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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.
Abstract and Document

CONCORDE Meteorological Analysis (CMA) Data Guide
CONCORDE is the CONsortium for oil spill exposure pathways in COastal River-Dominated Ecosystems (CONCORDE), and is an interdisciplinary research program funded by the Gulf of Mexico Research Initiative (GoMRI) to conduct scientific studies of the impacts of oil, dispersed oil and dispersant on the Gulf
Abstract Document Oceanography Article Discuses CMA

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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Cost Effective BMPs for Resilient Communities
This project developed a tool that will enable the development industry to design and build more resilient and sustainable communities through the inclusion of BMPs (Best Management Practices) in new commercial and residential construction.
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Data Software Development
GRI has developed software, a NetCDF-Java Toolbox for MATLAB, along with NOAA which would allow MATLAB users to standardize access to IOOS (Integrated Oceans Observing Systems)-compliant gridded data.

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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Detection of Aflatoxin B1 on Corn Kernel Surfaces Using Visible-near Infrared spectroscopy: Journal of Near Infrared Spectroscopy. 1-11.
In this study, visible-near infrared spectroscopy over the spectral range of 400
Journal of Near Infrared Spectroscopy. 1-11.

Development of High Speed Dual Camera System for Batch Screening of Aflatoxin Contamination of Corn Using Multispectral Fluorescence Imaging
Aflatoxins are fungal toxins produced by . Food and feed crops contaminated with carcinogenic aflatoxins result in economic losses as well as potentially serious health issues. Grain elevators need to unload, on average, one 2.27 metric ton (MT) truckload every 2 min. Current sampling-based analytical chemistry methods for aflatoxin detection cannot meet these large throughput screening requirements. Therefore, a high-speed, batch screening system with reliable accuracy is needed. To develop a high-speed multispectral screening system, two high-performance cameras in conjunction with dual UV excitation sources and novel image processing software were used to collect fluorescence images of corn samples.
Abstract and Research

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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Dissolved Organic Matter and Trace Element Variability in a Blackwater-fed Bay Following Precipitation
Dissolved organic matter (DOM) often forms complexes with trace metals. The main objective of this study was to identify the sources of trace elements to a coastal bay that is fed by two blackwater rivers using DOM compositions. Surface water samples from twelve sites in Weeks Bay, Alabama were collected during four field trips and a bottom sediment sample was collected during one of the trips. Spectroscopic measurements in tandem with parallel factor analysis and multivariate statistics were used to derive DOM compositions and inductively coupled plasma mass spectrometry was used for determining trace metal concentrations. DOM chemistry and trace element concentrations together with precipitation and discharge, watershed land use and land cover data, and physicochemical parameters were used to determine the source of trace elements in the adjoining areas of the watershed to the bay and finally settling into the bay sediments.
Abstract Document Site

Earth Dams and Levees (EDLs) Sustainability
Research in using remote sensing to improve earth dam and levee sustainability has the potential to affect tens of millions of people who live or work behind levee-protected areas. MSU/GRI researchers in an international partnership are conducting research on multi-scale monitoring science to enable a sustainable future for the vast worldwide array of earth dam and levees. The research examines EDL critical infrastructure that provides flood protection, fresh water storage and renewable energy to developed and underdeveloped nations. This project is currently in year two and uses polarimetric and interferometric synthetic aperture radar (SAR) to examine earth deformations at a very small scale.

Earth Science Knowledge Base
Under this project led by the Geosystems Research Institute, the NASA Applied Sciences Program has funded the Mississippi Research Consortium (MRC) to develop information technology that will facilitate searches for potential applications of NASA assets to various needs in the earth sciences community. In particular, it will help generate ideas for new ways to use NASA missions, research, and/or models in conjunction with operational decision-making processes (or decision support systems) to achieve a particular benefit to society. The main output of this work is the development of information technology that will facilitate that ability. The resulting system is called the Earth Science Knowledge Base (ESKB).

Effect of Photo-biodegradation and Biodegradation on the Biogeochemical Cycling of Dissolved Organic Matter across Diverse Surface Water Bodies
The objective of this research was to quantify the temporal variation of dissolved organic matter (DOM) in five distinct waterbodies in watersheds with diverse types of land use and land cover in the presence and absence of sunlight. The water bodies were an agricultural pond, a lake in a forested watershed, a man-made reservoir, an estuary, and a bay. Two sets of samples were prepared by dispensing unfiltered samples into filtered samples in 1:10 ratio (V/V). The first set was exposed to sunlight (10
Abstract and Research

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.

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 (
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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Geospatial and Education Outreach Project
The Geospatial and Education Outreach Project is training Mississippi's workforce to become more organized and efficient. GEO's value is realized in various applications by different business entities and government organizations. For instance logistic employees use geographical information systems to plan optimal delivery routes; insurance assessors use GIS to measure risk and vulnerability; emergency personal use it to share street name/location and building floor plans with first responders; farmers use it to improve their yield per bushel of grain; and the business industry uses it to offer consumers optimal service. Since 2006 more than 3,000 Mississippians have participated in over 300 workshops across the state.
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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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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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Hurricane Debris Model
Based on GRI's researcher's experiences in dealing with Hurricane Katrina and other natural and man-made disasters, researchers have developed a plan for applications of geospatial technologies to disaster response and recovery, dealing primarily with data flow to and from emergency management personnel, especially for hurricane response.

Hydrological and Biogeochemical Controls of Seasonality in Dissolved Organic Matter Delivery to a Blackwater Estuary
Changes in riverine discharge of dissolved organic matter (DOM) serves as an indicator of linkages between terrestrial ecosystem and receiving aquatic environments. In this study, we test the hypothesis that the seasonal variability of DOM in an estuary fed by a blackwater river is primarily controlled by water discharge and also modified by photochemical and biological processes. We collected surface water samples during 4-week-long field campaigns to the lower Pearl River estuary located in southeastern Louisiana, two during high discharge in spring and two during low discharge in winter and summer, respectively. DOM composition was determined using spectrofluorometric indices and a site-specific parallel factor model, and dissolved organic carbon (DOC) concentrations. Spring samples with low salinity showed higher abundance of terrestrial, humic-like DOM and higher DOC concentrations, indicating the export of flood plain-derived DOM during high discharge. In contrast, summer and winter samples with high salinity had greater proportions of labile DOM and higher biological and fluorescence indices, which may reflect enhanced photochemical and biological degradation during summer and better preservation of labile DOM in winter. Spring DOM displayed highly variable source and quality character, relative to winter and summer samples.
Abstract and Research

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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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

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.

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.

Linking Cultural, Biological and Economic Values into Wetland Programs: Mississippi Band of Choctaw Indians' Pearl River Wetland Demonstration Project
This project aims to develop procedures for more reliably regenerating rivercane and for planting potential restoration sites in the Coastal Plain, and for maintenance of stands for cultural use by native peoples. We are assessing ecological factors associated with the establishment and maintenance of rivercane stands, developing methods for vegetative propagation of rivercane from rhizome segments, and attempting to transfer our findings directly to the Choctaw and other American Indian groups through local and regional symposia, workshops, and field days.
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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

Naval Coastal Ocean Model (NCOM)/Hybrid Coordinate Ocean Model (HYCOM)
NOAA's Office of Oceanic and Atmospheric Research entrusted GRI to study two versions for each of these models- their global and Gulf of Mexico adaptations. Data was analyzed from instruments tethered to floating and moored buoys, as well as unmanned gliders that look like miniature submarines. The goal was to determine the accuracy of the four model forecasts, as well as their ranking with respect to each other.
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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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Nutrient and Sediment Runoff from Agricultural Landscapes with Varying Suites of Conservation Practices in the Mississippi Alluvial Valley
Increasing concern regarding environmental degradation in coastal areas that experience annual hypoxic zones has led to the need for mitigation of nutrient laden runoff from inland landscapes. An annual occurrence of a hypoxic zone in the Gulf of Mexico has led to the development and implementation of nutrient reduction strategies throughout the Mississippi River Basin (MRB). With federal, state, and private financial and technical assistance, landowners have implemented best management practices (BMPs) to combat nutrient and sediment nonpoint source pollution; however, the effectiveness of these BMPs alone or utilized together has not been quantified. This study uses a field-scale, paired watershed approach in two watersheds in the Mississippi Alluvial Valley to test for differences in sediment and nutrient runoff concentrations between four management systems.
Abstract and publication site

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.

Remote Sensing of Wildfire Using SUAS: Post-fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay National Estuarine Research Reserve, Mississippi. Drones
Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.
Abstract Drones Open Access Journal

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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Southern P Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison
Phosphorus (P) Indices in the southern United States frequently produce different recommendations for similar conditions. We compared risk ratings from 12 southern states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas) using data collected from benchmark sites in the South (Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas). Phosphorus Index ratings were developed using both measured erosion losses from each benchmark site and Revised Universal Soil Loss Equation 2 predictions; mostly, there was no difference in P Index outcome. The derived loss ratings were then compared with measured P loads at the benchmark sites by using equivalent USDA–NRCS P Index ratings and three water quality models (Annual P Loss Estimator [APLE], Agricultural Policy Environmental eXtender [APEX], and Texas Best Management Practice Evaluation Tool [TBET]). Phosphorus indices were finally compared against each other using USDA–NRCS loss ratings model estimate correspondence with USDA–NRCS loss ratings. Correspondence was 61% for APEX, 48% for APLE, and 52% for TBET, with overall P index correspondence at 55%. Additive P Indices (Alabama and Texas) had the lowest USDA–NRCS loss rating correspondence (31%), while the multiplicative (Arkansas, Florida, Louisiana, Mississippi, South Carolina, and Tennessee) and component (Georgia, Kentucky, and North Carolina) indices had similar USDA–NRCS loss rating correspondence—60 and 64%, respectively. Analysis using Kendall’s modified Tau suggested that correlations between measured and calculated P-loss ratings were similar or better for most P Indices than the models.
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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 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

Spatial Variation and Temporal Trend of Water Quality
NGI conducted ground truth observations and standardize algorithms to produce and evaluate the spatial and temporal variations of water quality parameters in the Northern Gulf of Mexico (NGOM). The effort is aimed at improving the monitoring of the NGOM ecosystem based on remote sensing and understanding the dynamics of harmful algae blooms in the region.
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Spectral-Based Screening Approach Evaluating Two Specific Maize Lines With Divergent Resistance to Invasion by Aflatoxigenic Fungi
In an effort to control aflatoxin contamination in food and/or feed grains, a segment of research has focused on host resistance to eliminate aflatoxin from susceptible crops, including maize. To this end, screening tools are key to identifying resistant maize genotypes. The traditional field screening techniques, the kernel screening laboratory assay (KSA), and analytical methods (e.g., ELISA) used for evaluating corn lines for resistance to fungal invasion, all ultimately require sample destruction. A technological advancement on the basic BGYF presumptive screening test, fluorescence hyperspectral imaging offers an option for non-destructive and rapid image-based screening. The present study aimed to differentiate fluorescence spectral signatures of representative resistant and susceptible corn hybrids infected by a toxigenic (SRRC-AF13) and an atoxigenic (SRRC-AF36) strain of Aspergillus flavus, at several time points (5, 7, 10, and 14 days), in order to evaluate fluorescence hyperspectral imaging as a viable technique for early, non-invasive aflatoxin screening in resistant and susceptible corn lines. The study utilized the KSA to promote fungal growth and aflatoxin production in corn kernels inoculated under laboratory conditions and to provide actual aflatoxin values to relate with the imaging data. Each time point consisted of 78 kernels divided into four groups (30-susceptible, 30-resistant, 9-susceptible control, and 9-resistant control), per inoculum. On specified days, kernels were removed from the incubator and dried at 60
Research Document

Structure from Motion with UAVs: A Best Practice Guide for Users
Tips and tricks to help you get started using low-cost unmanned aerial vehicles to generate accurate three-dimensional models for stream channels and drainage ditches.
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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.

The Application of Structure from Motion Techniques in Late-season Corn Damage
Green snap occurs when corn stalks are broken in high wind. Late in the season, green snap produces yield losses that are immediate and irreversible. Losses are proportional to the percentage of plants lost, thus estimation of damage extent is important for calculating economic loss. Unmanned aerial vehicles are capable of creating a 3D-model, called a digital surface model, of the crop canopy using structure from motion techniques. The objective of this study was to compare image- versus structure-based calculations of damaged area. The structure-based calculations were more reflective of the field assessment. While the latter approach showed promise, more development of protocols is needed for reliability and operational success.
Abstract Precision Agriculture

The Mississippi Digital Earth Model
The Mississippi Digital Earth Model (MDEM) is composed of seven framework layers as defined by the Federal Geographic Data Community's National Spatial Data Infrastructure. Data for the MDEM is acquired and managed through joint operations between the Mississippi Department of Environmental Quality and the Mississippi Department of Information Technology Services.
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The Science of William M. Gray: His Contributions to the Knowledge of Tropical Meteorology and Tropical Cyclones
Advances in knowledge in tropical meteorological research are discussed in the context of contributions made by Professor William M. Gray. Gray pioneered the compositing approach to observational tropical meteorology through assembling of global radiosonde data sets and tropical cyclone research flight data. In the 1970s he made fundamental contributions to knowledge of convective-larger scale interactions. Throughout his career he wrote seminal papers on tropical cyclone structure, cyclogenesis, motion and seasonal forecasts. His conceptual development of a seasonal genesis parameter also laid an important framework for both seasonal forecasting as well climate change studies on tropical cyclones. His work was a blend of both observationally-based studies as well as the development of theoretical concepts. This paper reviews the progress in knowledge in the areas where Dr. Gray provided his largest contributions and describes the scientific legacy of Gray
Abstract and Document Site

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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UAS with MicroSense RedEdge Payload Help Monitor and Manage Forest Resortation
Unmanned Aerial Systems (UAS) carry a multispectral senor that produce images that provides biologist and geographic information system specialists with changes in the woodlands and vegetation in almost real time. These systems also show the density and regrowth of woodland, marsh, and coastal areas. Mississippi State Universityâs Geosystems Research Institute is assisting the Grand Bay National Estuarine Research Reserve (GBNERR) and the U.S. Forestry Service with surveying the 4,200 acres that were burned during a large wildfire that reaches from southeast Jackson County into Alabama. These experts utilized the Altavian Nova Block III to inspect the area from 1,000 feet, and they deployed the MicaSense RedEdge. The MicaSense RedEdge has the ability to sense energy at five different wavelengths. Two of the five wavelengths exceed our vision in the Near Infrared Region (NIR) of the electromagnetic spectrum. The senor provides researchers with accurate data that projects the status of vegetation and stress of areas within the ecosystem. With this knowledge, researchers and specialists can aid in the revegetation of the burned woodlands and marsh of the GBNERR, the Grand Bay National Wildlife Refuge and neighboring lands.

Unmanned Aerial Systems
GRI scientists are researching the effective use of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USV's) commonly referred to as drones, in how they can be used safely to assess crops, evaluate woodlands, conduct wildlife surveys, gauge river flow and monitor the Gulf of Mexico environmental health and watershed, as well as helping NOAA increase the accuracy of severe weather forecasts. These unmanned aerial and surface/water systems use remote sensing, global positioning--and geographic information systems to collect and analyze sites specific data that farmers, foresters, wildlife rangers, oceanographers and marine scientists can use to create and apply effective prescriptions for every inch of an agricultural field, ocean, river, forest and wildlife management areas.

Use of Visible–Near-Infrared (Vis-NIR) Spectroscopy to Detect Aflatoxin B1 on Peanut Kernels
Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels.
Abstract and Reearch

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

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.

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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