CyberGIS Projects

Mapping the Rice Kingdom

The Clemson Center for Geospatial Technologies has supported many research projects which have utilized the advanced computing resources available to Clemson. This includes developing processing workflows and scripts which utilize the GalaxyGIS and Palmetto clusters as well as training and instruction in how to modify our scripts or create your own for your project.

 

Below are a few examples which illustrate the diversity of projects utilizing advanced computing. If you currently have a GIS analysis that takes more than 30 minutes to run on your PC, contact us to see how to reduce that processing time!

 

 

Rice agriculture was abandoned in South Carolina after the Civil War; while it has enjoyed slight resurgence recently and at points in history it never has returned to its apogee and consequently the landscape today is littered with abandoned rice fields. Many of these function as valuable freshwater wetlands. Instead of rice, these wetlands today are home to thousands of migratory waterfowl. Ducks are a valuable commodity for hunting as well as birdwatching revenues and landowners are very interested in restoring and maintaining historical rice fields - for cultural and wildlife reasons. Other wildlife use the wetlands as well, including myriads of reptiles and amphibians, birds, fish, and invertebrates. We suspect the wetlands may provide a range of other ecosystem services including flood and nutrient retention, protecting downstream communities and ecosystems. To better understand the magnitude of these impacts, Clemson University is partnered with Low Country stakeholders to use remote sensing and mapping technologies to discover the true extent of rice fields. To date the partnership has mapped over 200,000 acres of inland and coastal rice fields. The maps will be field-validated and completed by August of 2018.

 -Dr. Robert Baldwin

The Antebellum era gave rise to rice culture in North America, which in turn provided such rich benefits that the slave-driven economy increased ever more rapidly. Growing rice was brutal work involving clearing of ancient forests by hand, re-channeling of surface and tidal water over huge areas and moving mountains of earth in building dikes. Africans with special knowledge of rice culture from their home mangrove estuaries were prized and it is argued that without them, the rice kingdom of the Carolinas may never have evolved. Historian Edda Fields Black of Carnegie Mellon University - and Clemson collaborator - studies rice culture in Africa and its historical connections to South Carolina. Her book, "Deep Roots" illuminates the profound legacy of West African rice farmers and their thousand years of technology on the history, economy, and culture of the American South - and world. Now, geospatial scientists are uncovering the extent of their impact on the land itself.

The remnant drainage features such as dykes and canals are difficult to identify in imagery data (left). With the help of CCGT and the GalaxyGIS cluster, the researchers created high-resolution hillshaded DEM's from LiDAR. The elevation data have abundant drainage features (right) and were used to map the extent of rice fields boundaries with unprecedented detail .

This project has the following major collaborators from Clemson University: Dr. Rob Baldwin, Dr. Daniel Hanks, Mr. Richard Coen, Mr. Michael Gouin, and the Clemson Center for Geospatial Technologies.

 

Stakeholders and Funding Agencies: The Nemours Wildlife Foundation, The Nature Conservancy, ACE Basin Project, Folk Land Management, Inc., Margaret H. Lloyd-SmartState Endowment; James C. Kennedy Waterfowl and Wetlands Conservation Center

Tree Cover Analysis using LiDAR-based DEM's

The advent of the availability of LiDAR data covering large land areas in high levels of detail provides opportunities for new analysis of both topography and land cover.  Because LiDAR returns can be classified by the surface from which they are reflected (ground, trees, water, buildings, etc.)  models of ground surface as well as anything above ground surface can be made.  Using this data, opportunities to measure tree heights and map forest canopy structure abound.

Our research seeks to link existing stream quality data with land cover and stream conditions for the watersheds that contribute to the sampling points from which this data was taken.  Past research suggests that one of the important metrics that may be associated with the water quality at these points is the percent of the watershed which is covered in trees.  Above ground heights greater than 1.5 m were identified to classify tree cover. This height cutoff was chosen based on literature suggestions as well as to include the large number of stands of young trees in the study areas.  Values for percent forest cover will be adjusted for building presence based on detailed imager-based land classification.  Further measurements for this study regarding tree cover may include the percent of the floodplain in tree cover, the percent of 30m stream buffer in tree cover, as well average tree height within these buffers and/or watersheds.

- Dan Callaghan and Dr. Chris Post

The LiDAR processing resources at CCGT were used to create high-resolution canopy height maps from terrain and surface models derived from LiDAR to characterize water quality in streams.

Calculating Intersections

A graduate research project in civil engineering involved calculating all possible intersects between analyzed traffic routes (1.9 million observations) and all the traffic data collection sites that are spread throughout the city of Greenville. In order to solve this problem, we can used Condor and took a three-step approach:

1. Break up the large 1.9 million entry data set of roadways into smaller chunks of 5,000 roads each. This added up to 395 individual road data sets.


2. Submit each separate data set through Condor to be processed separately and concurrently to calculate the intersection of each road with each collection site.


3. Merge all those observations back into one large data set.

Using the GalaxyGIS cluster, the processing time was cut from over 4 days to about 3 hours!

Summarizing Rasters Using Boundary Zones

Parallel processing enables a raster to be summarized within the boundary of hundreds or thousands of polygons very efficiently. A PhD. student needed to quantify the area of each land use category within every watershed in the eastern US. The source data were from the National Land Cover Database (NLCD) and the National Hydrographic Dataset (NHD). We utilized our GalaxyGIS cluster and the following workflow to perform this operation:

 

1. Clip the NLCD dataset to each watershed polygon, resulting in 459 individual pieces.


2. Submit each clipped raster as a concurrent job. The raster was reclassified and summarized, then it's resulting value attribute table exported.


3. Append the land cover tables to a single table which is related to the watershed data using the Hydrologic Unit Code (HUC). Merge the reclassified land cover rasters to a single file.

The student was unable to perform the analysis on their PC, but using the GalaxyGIS cluster was able to get the output in 45 minutes.

 

Solar Suitability Analysis

A PhD. student wanted to assess PV solar suitability zones in southeast Asia at the country-wide level. The analysis required generating multi-ring buffers from 500 to 1500 meters around 380,000 protected areas, national parks, residential zones, and other unsuitable areas which were used in a suitability model. CCGT helped develop a workflow to discretize the analysis and generate the multi-ring buffers, including customizing the ArcGIS function so it was aware of neighboring data subsets and preventing overlapping output. Our workflow consisted of:

 

1. Split the area of interest using a grid of 75 km square cells and use these to clip the input data. The clipped data is packaged as an individual job submitted to the GalaxyGIS cluster.


2. Each input feature is sequentially buffered and each buffer level is dissolved into a single feature. The grid cell extent is used to clip any buffered features which extend beyond the analysis area.


3. Each resulting dataset is merged using the buffer radius attribute to provide a total area in each zone.

 

Each time the student attempted the analysis on a single PC, it locked up entirely, but our computing resources enabled the analysis to be completed in 5 time for the dissertation defense!

 

Upcoming Projects

We are currently starting on several new collaborative projects in summer of 2018 with Dr. Michael Carbajales-Dale and Jacob Arnold in the E3 Systems Group within the Department of Environmental Engineering and Earth Sciences. These projects will utilize parallel distributed computing and our GalaxyGIS cluster.

 

Potential for Rooftop PV in South Carolina

This project explores the potential for rooftop solar photovoltaic (PV) deployment in South Carolina by using remote sensing (e.g. satellite imagery)
and machine learning coupled with CyberGIS to identify suitable locations for rooftop solar across the state. An initial assessment of Clemson's main campus has already been conducted, which identified a number of high-value locations. This project will expand on that analysis by using known building footprints on campus to conduct supervised image classification to identify and calculate suitable rooftop area across the state. This project is being undertaken with help from Clemson Center for Geospatial Technologies. Results from this project will be
coupled with the Center's Solar Radiation Analysis for South Carolina, to determine the performance characteristics of solar deployed across the state.

 

Bottom-Up Regional Energy Model

The goal of this project is to develop an hourly electricity demand model for South Carolina. This model will be built 'bottom-up' using load profile models of residential, commercial, and industrial customers available from the Department of Energy. The first step in this process is to determine scaling factors for different types of businesses and residences as a function of population (or population density) across the US by querying Esri's ArcGIS Business Analyst data by census tract. This query will be run with help from Clemson Center for Geospatial Technologies through the GalaxyGIS cluster to reduce the time requirements.

 

Once the scaling laws are understood, the model will be able to predict patterns in peak and average load based on regional population and economic development projections. This effort feeds into the Energize! project and will also assist in integrated resource planning by electric utilities, as well as the development of future South Carolina State Energy Plans. Future work will expand to other energy resources (such as transportation fuels), and look to couple the energy model with food and water systems to explore the food-energy-water nexus for the state.

 

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