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  • 1
    Publication Date: 2019-08-03
    Description: Alabama is one of the most biodiverse states in the United States and has the greatest diversity of aquatic species. As urbanization continues to increase in Alabama, this biodiversity is at risk. This project partnered with the Land Trust of North Alabama to identify sensitive habitats that are at risk for urbanization within Madison and Limestone counties. The Land Trust of North Alabama works to preserve land, primarily in Madison and Limestone counties of North Alabama, and encourages stewardship through environmental education. The team conducted a supervised classification of land class types utilizing data from Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), and Shuttle Radar Topography Mission Version 4 (SRTM) to identify land cover changes and areas most vulnerable to future urbanization. Through incorporating land classification analysis and additional parameters indicative of urbanization, the team produced an urbanization prediction tool and a landscape fragmentation map. The urban prediction tool identified land highly suitable for development and found that, by 2045, 25% of highly suitable land will be urbanized using the measured 1% growth rate. Ecological impact was established using observation data of species of interest to the project partners. These tools will enable the Land Trust to target high risk areas of land for preservation.
    Keywords: Life Sciences (General)
    Type: NF1676L-29405 , Perpetua; 2; 2; 1-8
    Format: application/pdf
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  • 2
    Publication Date: 2019-08-13
    Description: The Mississippi River Basin is the fourth largest drainage basin in the world, and is susceptible to multi-level flood events caused by heavy precipitation, snow melt, and changes in water table levels. Conducting flood analysis during periods of disaster is a challenging endeavor for NASA's Short-term Prediction Research and Transition Center (SPoRT), Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey's Hazards Data Distribution Systems (USGS HDDS) due to heavily-involved research and lack of manpower. During this project, an automated script was generated that performs high-level flood analysis to relieve the workload for end-users. The script incorporated Landsat 8 Operational Land Imager (OLI) tiles and utilized computer-learning techniques to generate accurate water extent maps. The script referenced the Moderate Resolution Imaging Spectroradiometer (MODIS) land-water mask to isolate areas of flood induced waters. These areas were overlaid onto the National Land Cover Database's (NLCD) land cover data, the Oak Ridge National Laboratory's LandScan data, and Homeland Infrastructure Foundation-Level Data (HIFLD) to determine the classification of areas impacted and the population density affected by flooding. The automated algorithm was initially tested on the September 2016 flood event that occurred in Upper Mississippi River Basin, and was then further tested on multiple flood events within the Mississippi River Basin. This script allows end users to create their own flood probability and impact maps for disaster mitigation and recovery efforts.
    Keywords: Earth Resources and Remote Sensing
    Type: NF1676L-28668 , Pecora 20; Nov 13, 2017 - Nov 16, 2017; Sioux Falls, SD; United States
    Format: application/pdf
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