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This application provides SSC civil servants, contractors, and tenants the ability to view the Center's weather radar and current site status bulletin from a mobile device. The application also alerts users via push notification when a new site status is posted. Available at the Apple App Store.

An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within pre-selected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the modulation transfer function (MTF) and the relative edge response (RER). Developed using the MATLAB programming language and environment, this automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data set, enabling the appropriate use of those images in a number of applications. The SRVT has been validated against traditional techniques using IKONOS and QuickBird satellite imagery.

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and parametric mathematical models for simulating remote sensing systems, developed in MATLAB. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities. ART is designed to run on a standard Windows NT/2000 workstation and MATLAB version 6.5. A Programmers Reference is included to provide additional detail on setting up the operational environment.

Methodology and software were implemented to automatically detect Sargassum spp., a floating aquatic seaweed, in 30-meter LANDSAT-8 Operational Land Imager (OLI) imagery. This Sargassum spp. detection is an extended form of Hus approach to derive a floating algae index (FAI), which is defined as the difference between the reflectance at the near infrared band (NIR, 859 nm) (vegetation red edge) and the linear baseline between the red band (645 nm) and short-wave infrared band (SWIR, 1240 or 1640 nm). The Hus algorithm was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) data, but we extended it for use with the OLI bands (655, 865, and 1609 nm), which are comparable to MODIS bands (645, 859, and 1640 nm). We also developed a high resolution true color product to mask cloud pixels in the OLI scene by applying a threshold to top of the atmosphere (TOA) radiances in the red (655 nm), green (561 nm), and blue (443 nm) wavelengths, and software to remove false positive identifications of Sargassum spp in the imagery. Hus algorithm derives a FAI for each Sargassum spp identified pixel. Our algorithm only flags the presence of Sargassum spp in an OLI pixel by indicating that any pixel with a FAI > 0.0 is a Sargassum spp pixel. Additionally, our software geo-locates the flagged Sargassum spp pixels identified in the OLI imagery into the U.S. Navy Global HYCOM model grid. One element of the model grid used, covers an area 0.125degree of latitude by 0.125 degree of longitude. An OLI pixel is only 30m by 30 m. To resolve the differences in spatial coverage, a scheme was developed in which the total number of Landsat OLI pixels within a grid element of the model are counted, and if more than 1% of those pixels are pixels flagged as containing Sargassum spp, then the geographical location within the model is annotated. The end product is an ASCII file that contains latitude, longitude pairs that are used as input for the next stage in the processing stream, which is a clustering algorithm or a model initialization parameter. The numerical model grid must be in p lace before the geolocation takes place.The focus of this application is the automatic identification of Sargassum spp. in visible data of the Operational Land Imager (OLI) on board LANDSAT-8 in the Gulf of Mexico. The identification is followed up by a routine to map their location inside the Hybrid Coordinate Ocean Model MODEL (HYCOM) surface grid. This methodology, developed by the Naval Research Laboratory for the NASA/Applied Science and Technology Project (ASTPO), is applicable worldwide requiring only minor changes in the geolocation of the area of interest.

NOSS is a web based central configuration interface for NASA Data Acquisition System(NDAS) or can additionally be used as a frontend for other types of data acquisition systems. NOSS is accessed using a web browser and allows for concurrent multi-user access. After configuration is complete, a user can export a xml that can be used in the configuration of a data acquisition system. NOSS is organized around two main building blocks: nodes and measurements. Nodes represent physical and logical components of a test facility. Examples of physical components could be signal conditioner cards, receptacle box connection points, filters, attenuators, digitizer cards, transducers, valves, etc. Logical components could be acquisition system channels, facility redline system channels, or any other software-type endpoint systems. As such, NOSS can be used to track and manage the current state of all data acquisition components on a test facility. Measurements are defined by linking together specific nodes in the system. Measurements represent the full signal path from a signal source (commonly a transducer) to acquisition system endpoint. Additional measurement information can also be defined such as a unique measurement name, description, etc.

The Coastal Salinity and Temperature Monitoring (CSalT) web application Version 1.0, objective is to provide daily access to salinity and temperature data on a continuous and unrestricted basis to authorized users. Numerical model data from multiple sources is collected and integrated with NASA remotely sensed satellite data to provide up-to-date and historical information on water temperature, salinity and oyster lease locations for coastal environments along the Gulf of Mexico. This information is provided in a Google Maps based interface with custom controls for navigation and analysis enabling users to review trends and statistics for their areas of interest. The data can be used to complement in-situ data to help assess oyster health and has the potential to provide information that can aid in the decision making process for identifying suitable habitat for future oyster reef locations.

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3-D spatio-temporal arrays) generated by the Time Series Product Tool (TSPT) and outputs spatial grids (2-D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally filters the time series to produce a continuous, smoothed vegetation index product in which data voids are eliminated. Both the TSPT and PPET use Moderate Resolution Imaging Spectroradiometer (MODIS) satellite multi-spectral data as a default, but each software package is easily modifiable and could be used with any high-temporal-rate remote sensing system that is capable of producing vegetation indices. Unlike other known plant phenological parameter estimation software, the PPET produces not only common phenological parameters but also real-time and custom parameters without a priori assumptions about the shape of the phenological cycle. Common phenological parameters, like those produced in PPET, are associated with the annual vegetation growth cycle. They quantitatively describe vegetative states related to annual cyclical growing seasons, such as green-up, maturity, senescence, and dormancy, by analyzing the temporal shape of given vegetation index time series. The real-time phenological and custom parameters are formed from a cumulative sum (integral) produced at a fixed temporal interval. In addition, a cumulative vegetation index and time-specific/pest-specific phenological parameters can be designed to optimize the detection of vegetation damage from specific pests and diseases. These problem-specific phenological parameters have the potential to be integrated into near real-time, predictive surveillance systems (i.e. early warning systems) and, with improved vegetative state information, could assist decision makers in making intelligent vegetation and associated land resource management choices. MATLAB, MATLAB Runtime Library and ERDAS IMAGINE are required to run the software.

Enhancements and modifications to a code developed for plume spectral data analysis in 1994 have made the original computer program applicable to the Space Shuttle Maine Engine and the Diagnostic Test-bed Facility Thruster (DTFT). The new code can now handle the non-uniform wavelength intervals at which spectral computations are made.

EUGEN converts digitized sensor output voltage data to engineering units. The tool creates individual processed data files (one file per transducer per test run), converting raw voltage to meaningful measurements such as pressure or temperature. Engineering Units are generated by the Low Speed Data Processing System (LSDPS) in addition to performing calculated values or parameters at different levels in order to provide the customer with the final data product. LSDPS utilizes the NIST REFPROPS program to calculate thermo-physical properties over a wide range of temperatures and pressures. Use of these programs requires proficiency in Linux, C and PV-Wave (separate license required) programming languages and bash scripts. A GCC compiler is invoked with the make file in each C code program directory. Winplot is the general-purpose plotting utility. A Developers Guide provides specifics on the hardware and software requirements needed to execute the tool.