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.





A novel hyperspectral-based approach for identification of maize kernels infected with diverse Aspergillus flavus fungi
Near infrared hyperspectral imaging over the spectral range of 900–2500 nm was investigated for its potential to identify maize kernels inoculated with aflatoxigenic fungus (AF13) from healthy kernels and kernels inoculated with non-aflatoxigenic fungus (AF36). A total of 900 kernels were used with 3 treatments, namely, each 300 kernels inoculated with AF13, AF36 and sterile distilled water as control, separately. One hundred kernels from each treatment of 300 kernels were incubated for 3, 5 and 8 days, to obtain diverse samples. Based on the full mean spectra extracted from the same kernel side(s), the best mean overall prediction accuracies achieved were 96.3% for the 3-class (control, non-aflatoxigenic and aflatoxigenic) classification and 97.8% for the 2-class (aflatoxigenic-negative and -positive) classification using the partial least-squares discriminant analysis (PLS-DA) method. The 3-class and 2-class models using the full mean spectra extracted from different kernel sides had the best mean overall prediction accuracies of 91.5% and 95.1%. Using the most important 30, 55 and 100 variables determined by the random frog (RF) algorithm, the simplified type I-RF-PLSDA models achieved the mean overall prediction accuracies of 87.7%, 93.8% and 95.2% for the 2-class discrimination using different kernel sides’ information. Among the most important 55 and 100 variables, a total of 25 and 67 variables were consistently selected in the 100 random runs and were therefore used further for establishing the type II-RF-PLSDA models. Using these 25 and 67 variables, the type II-RF-PLSDA models obtained the mean overall prediction accuracies of 82.3% and 94.9% separately.
Research Document





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





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





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





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.





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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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.
DRIADS Email Contact





Dissolved organic matter and trace element variability in a blackwater-fed bay following precipitation
Highlights Dissolved organic matter compositions were used for source-tracking trace metals. Flux of As, Cu, U, PO4, and NO3 correlated with protein-like and soil-derived DOM. The trace element and DOM mobility was controlled by precipitation and discharge. Multivariate statistics revealed anthropogenic sources for the trace metals. Abstract 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.
Science Direct





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





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.
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 (
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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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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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 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.
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





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.
Abstract





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





Sequential Applications of Diquat to Control Flowering Rush (Butomus Umbellatus L.) in Mesocosms. Journal of Aquatic Plant Management
Flowering rush (Butomus umbellatus L.) is an aggressive, invasive, aquatic plant spreading throughout water bodies in the northern United States and southern Canada, displacing many native aquatic/wetland plants. This can disrupt ecosystem processes and affect human uses of water bodies. Operational management in Detroit Lakes, MN, reduced flowering rush biomass and propagules by >80% using two sequential, submersed applications of diquat (0.37 mg L-1 ) per growing season (4 wk apart). However, in dense colonies, long-term control has taken years to achieve, suggesting a more aggressive treatment regime may be necessary. A mesocosm study was initiated in 2015 and repeated in 2016 to further investigate diquat (0.37 mg L-1 ; 12 h exposure time) efficacy using one to four biweekly (every other week) sequential herbicide applications to improve flowering rush control.
Abstract Journal of Plant and Aquatic Management,





Simulated mechanical control of flowering rush (Butomus umbellatus) under mesocosm conditions
lowering rush (Butomus umbellatus L.) is an invasive aquatic and wetland plant capable of developing monotypic stands in emergent and submersed sites. This plant can rapidly outcompete native vegetation and impede human practices by reducing recreation (boating, fishing, and skiing) and disrupting agricultural use of water resources (irrigation canals). Mechanical removal practices occurring biweekly, monthly, bimonthly, and once per growing season were compared with chemical control with diquat applied sequentially at 0.19 ppmv ai for two consecutive months over 2 yr (2016 and 2017). Biweekly removal gave the most consistent control of B. umbellatus biomass and propagules. Diquat application along with monthly and bimonthly clippings gave varying degrees of B. umbellatus control. Clipping once per growing season did not control B. umbellatus when compared with reference plants, while clipping B. umbellatus every 2 wk (biweekly) controlled rush propagules most effectively. However, it is unlikely this method will be sufficient as a stand-alone control option due to the slow speed of harvester boats, the potential these boats have to spread B. umbellatus propagules to more sites, and the expense of mechanical operations. However, clipping could be used as part of an integrated strategy for B. umbellatus control.
Abstract Cambridge: Invasive Plant Science Management





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





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.
Photo of Cover Document





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





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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Torpedograss control via submersed applications of systemic and contact herbicides in mesocosms
Torpedograss control via submersed applications of systemic and contact herbicides in mesocosms
Research Gate





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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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 Spatial Variability in the Rate of Change of Chlorophyll a to Improve Water Quality Management in a Subtropical Oligotrophic Estuary
Abstract Anthropogenic eutrophication threatens numerous aquatic ecosystems across the globe. Proactive management that prevents a system from becoming eutrophied is more effective and cheaper than restoring a eutrophic system, but detecting early warning signs and problematic nutrient sources in a relatively healthy system can be difficult. The goal of this study was to investigate if rates of change in chlorophyll a and nutrient concentrations at individual stations can be used to identify specific areas that need to be targeted for management. Biscayne Bay is a coastal embayment in southeast Florida with primarily adequate water quality that has experienced rapid human population growth over the last century. Water quality data collected at 48 stations throughout Biscayne Bay over a 20-year period (1995�2014) were examined to identify any water quality trends associated with eutrophication. Chlorophyll a and phosphate concentrations have increased throughout Biscayne Bay, which is a primary indicator of eutrophication. Moreover, chlorophyll a concentrations throughout the northern area, where circulation is restricted, and in nearshore areas of central Biscayne Bay are increasing at a higher rate compared to the rest of the Bay. This suggests increases in chlorophyll a are due to local nutrient sources from the watershed. These areas are also where recent seagrass die-offs have occurred, suggesting an urgent need for management intervention. This is in contrast with the state of Florida listing of Biscayne Bay as a medium priority impaired body of water.
Abstract Research Document





Variation in Dissolved Organic Matter, Trace Metals, and Acidification Parameters in the Western Mississippi Sound
The total amount of oysters harvested in the Mississippi Sound has declined by 85.71% over the last 13 years. The objective of this study was to assess the influence of water quality on the oysters, specifically the temporal changes in dissolved organic matter (DOM), trace elements, and ocean acidification parameters over the largest oyster bed in the western Mississippi Sound.
Abstract





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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