ure omps 2010
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Analyzing Long-Term Summer Drought Effects Using Aqua-1 Satellite Data Integration with a Web Applicationt to Create Navigational Instructions for Locations on the Campus of ECSU


Abstract 2012-2013
Analyzing Long-Term Summer Drought Effects Using Aqua-1 Satellite Data

After observing the Palmer Drought Severity Index (PDSI) data sets for summer 2002-2011, provided by the State Climate Office of North Carolina NC CRONOS database, the team observed that there has been a long-term drought since 2007 in the Northern Coastal Plains of North Carolina. Summer is defined as the mounts of June, July, and August.The State Climate Office of North Carolina NC CRONOS defines that long-term drought as being cumilative and their data representative of weather patterns of the current months in compared with previous months. Therefore the PDSI attempts to measure the duration and intensity of the long-term drought-inducing circulation patterns without including man made changes. The PDSI denotes dry and wet spells on a scale between -6 to 6, respectively. After 2006 all DDSI values were negative, indicative of drought during this whole period.
Our team’s objective is to analyze how long-term drought in summer months effects vegetation and land surface temperature in the Pasquotank and Gates county areas. The team chose to focus on the summer months, June through August, so that data results will not be skewed due to fall, winter, and spring season conditions when vegetation will be in different stages.

The satellite that was chosen to analyze data from is Aqua-1, which is a polar orbiting satellite carrying the MODIS sensor. Polar Orbiters travel in twelve hour sequences, daytime and nighttime. Satellite data can have data degradation at the near and far edges. Therefore the team only downloaded daytime orbits with a minimum elevation of 55 degrees. Due to cloud coverage over our area of interest, monthly composites of images were generated by utilizing the “composite” command in TeraScan. The “composite” command takes a calculation that eliminates “bad-values” in multiples images, such as clouds, and creates one image. There are many versions of composite in TeraScan, and the team uses the method that averages the good values located in the same location in each image.

The team chose to use data from NASA’s LAADS website because of the ability to search for archived MODIS telemetry in our Lat/long parameters that is not limited to real time data. This was useful because the team needed to retrieve raw data from 2007 to 2011 with at least  thirty-five percent coverage for the “LocalECSU” master and seventy percent coverage for the “GlobalECSU” master. A master is defined as an area of interest in TeraScan from where the data will be exclusively extracted and processed. The team downloaded the files by using the “laad_fetch.sh” TeraScan function which executes the SeaSpace “configproc” script. During the “configproc” a series of conversions takes place to convert pds files into hdf files and hdf files into tdf files. A pds file is the original satellite data format, hdf is a common data format, and tdf is TeraScan data format. After the file is converted into a tdf file, a calculation with the different channels will be applied create both Land-Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) products. LST uses level-2 and level-3 in a algorithm designed specifically for the MODIS instruments. The NDVI equation is equal to the quantity of infrared channel minus the near-red channel divided by the quantity of the infrared plus the near-red channel. This equation will make the product greener to differentiate the differences in dense vegetation and low vegetation.
The channel resolution for the MODIS telemetry vary from 250-1000 m, dependent on channel. When the data is processed into LST and NDVI, TeraScan resamples it to 1 Km by 1 Km pixels over the AOI. Once the team has the LST and NDVI, the data will then be analysed in TeraVision using specified palettes to representing different values. Next is to identify subregions across the study area, then average values to use for comparisons between the years and against various locations.



Abstract 2011-2012
Integration with a Web Applicationt to Create Navigational Instructions for Locations on the Campus of ECSU

Mashups are an exciting genre of interactive Web applications that draw upon content retrieved from external data sources to create entirely new and innovative services. The purpose of the Mobile Applications Research Team was to create an interface mashup in which geographic information and meta data from buildings located on the campus of Elizabeth City State University (ECSU) can be presented to users via mobile platforms. The project includes HTML5 programming which referenced a database that housed information such as location, building establishment date, academic departments, and academic programs. The information was then compiled using a PHP Hypertext Processor (PHP) form to populate a MySQL database. HTML5 coupled with PHP programming was then used to render a mobile web page with both map and database information.

Using Google Map Maker paths, streets, and buildings were created in appropriate geographic locations on the ECSU campus. The Google Maps Application Programming Interface was then used to generate Uniform Resource Locator's to both retrieve user Global Positioning System coordinates and create walking directions to selected locations. The user then had the ability to generate walking directions to locations on the university's campus.