Title: Design and Installation of a Video Conference Solution at the Center of Excellence in Remote Sensing Education and Research at ECSU Spring 2016 Elizabeth City

Keywords—Networking, video conferencing, CERSER, ECSU

Mentor: Mr. Calton Lamb
Abstract:
During the 2016 Spring Semester, the Research Experience Undergraduates Networking team project identified, evaluated, and implemented a video conference solution. The main objective was to establish a fully functioning video conferencing solution in four locations: Dixon-Patterson Hall, Rooms 226, 232 and Lane Hall, Rooms 111 and 119. To understand and create the scope of work for the project, the team had to research/analyze the rigorous standards which are set in place by the International Telecommunications Union. This agency works directly under the authority of the United Nations and is charged with issues relating to information and communication technologies. The team examined the H.323 standard for Telemedicine, how Telemedicine has evolved, and how the H.323 standard has progressively changed the way we conduct our lives. After replicating the layout of the four spaces, the next objective was to identify and evaluate a software solution. After identifying and evaluating multiple video conferencing applications, the team selected a specific application. An example of an issue which eliminated one application was when an application indicated that a user would only have to open a link in the browser to be able to connect; but it did not indicate that the link would only work from within a certain browser. As for the hardware, the technical specifications of components were used to identify the hardware components. This method of selection, immediately gave preference to specific devices. The team also analyzed the history of video conferencing and how it has evolved. This research project enables the Center of Excellence in Remote Sensing Education and Research (CERSER) participants and invited guests to engage with others through video conferencing services.

Research Experience for Undergraduates at Elizabeth City State University in Elizabeth City, North Carolina Center for Remote Sensing of Ice Sheets in Ocean, Marine, and Polar Science

Title: Analyzing Long-Term Drought Effects on Land Surface Temperature and Vegetation Using Aqua-1 Satellite Data

Keywords: Land Surface Temperature; Vegetation; Aqua- 1; TeraScan©; SeaSpace Corp.; Drought’ MODIS; Pasquotank county; Gates county; Perquimans county; Camden county; MODIS; AVHRR

Abstract

After observing the Palmer Drought Severity Index (PDSI) data sets for summer months of June - July, 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 has been defined as the months of June, July, and August. The State Climate Office of North Carolina NC CRONOS defines that long-term drought as being cumulative 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 was to analyze how long-term drought in summer month’s affects vegetation and land surface temperature in the Pasquotank,Gates, Perquiman and Camden county areas. The team focused on the summer months, June through August, so that data results would not be skewed due to fall, winter, and spring season conditions when vegetation would have been in different stages.

The satellite that was chosen to analyze data was from NOAA series polar orbiting satellites, carrying the MODIS sensor. Polar Orbiters travel in twelve-hour sequences, daytime and nighttime. The team only downloaded daytime orbits with a minimum elevation of 55 degrees. Seaspace TeraVision application survey tool was used to analyze and compare LST and NDVI values located in the Northern coastal plains of North Carolina during different times of the day.

The team used data from  the Terascan pass disk as it comes in live from the June and July months. This was useful because the team needed to retrieve raw data from June and July 2007 to 2011 with amt least  thirty-five percent coverage for the “LocalECSU” master and seventy percent coverage for the “GlobalECSU” master. A master has been defined as an area of interest in TeraScan®  from where the data has 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 tdf files into hdf files and hdf files into tdf files. A pds file was the original satellite data format, hdf was a common data format, and tdf was TeraScan® data format. After the file was converted into a tdf file, a calculation with the different channels have been applied create both Land-Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) products. LST uses level-2 and level-3 in an algorithm designed specifically for the MODIS instruments. The NDVI equation would 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 made the product greener to differentiate the differences in dense vegetation and low vegetation.

The channel resolution for the MODIS telemetry varied from 250-1000 m, dependent on channel. The data was processed into LST and NDVI and TeraScan® resamples it to 1 Km by 1 Km pixels over the AOI. Once the LST and NDVI products have been composited, the data would then be analyzed in TeraVision using specified palettes to representing different values. From the Pasquotank and Gates County’s townships, data points were taken across the study area. The averaged Values at the same position were compared to one another according to the month in each year between the years and against various locations. The data points resulted in a little to no correlation of 11% Between LST and NDVI.