Item talk:Q318137

{

 "id": "10.5066/p13fjck5",
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   "creators": [
     {
       "name": "Chak Wa Cheang",
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         "Contractor to the United States Geological Survey"
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       "name": "Kristin B Byrd",
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         "United States Geological Survey"
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     {
       "name": "Nicholas M Enwright",
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       "affiliation": [
         "United States Geological Survey"
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           "nameIdentifier": "https://orcid.org/0000-0002-7887-3261",
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     {
       "name": "Daniel D Buscombe",
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       "affiliation": [
         "Contractor to the United States Geological Survey"
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     {
       "name": "Dean B Gesch",
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   "titles": [
     {
       "title": "A Tool for Rapid-Repeat High-Resolution Coastal Vegetation Maps to Improve Forecasting of Hurricane Impacts and Coastal Resilience (Version 1.0.0)"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2024,
   "subjects": [],
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       "rights": "Creative Commons Zero v1.0 Universal",
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       "schemeUri": "https://spdx.org/licenses/",
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   "descriptions": [
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       "description": "Natural coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, provide nature-based solutions to climate-related impacts of hurricanes. Extensive research has shown that vegetation cover in coastal settings significantly controls coastal flooding, erosion, and barrier island breaching during extreme storms through spatially variable wave energy dissipation. Coastal modelers require simple and fast ways to obtain up-to-date high resolution coastal vegetation cover for modeling coastal impacts. This project addresses this critical need by developing a Jupyter Notebook Application and a Graphical User Interface that use Planet Labs Super Dove 8-band, 3-meter multispectral imagery provided through the NASA Commercial Smallsat Data Acquisition (CSDA) program and a machine learning classification model to deliver high-resolution maps of coastal vegetation showing near real-time conditions. The remote sensing modeling approach employs a reliable random forest machine learning technique for image classification that is transferable across a large geographic region. The pre-trained model has 93% overall accuracy and is trained on project areas within three states (North Carolina, Louisiana, and Florida). Planet Data API and Orders API were also integrated into the application. Users only need to provide their Area of Interest, Planet API keys, and desired sensing date range. The application consists of two modules: the satellite imagery download module automatically downloads Planet imagery and the remote sensing module generates coastal vegetation raster data products using the pre-trained machine learning algorithm. This application can also be used for a wide array of ecosystem research purposes, including coastal zone and habitat management and climate adaptation studies.",
       "descriptionType": "Abstract"
     }
   ],
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   "url": "https://code.usgs.gov/western-geographic-science-center-public/a-tool-for-rapid-repeat-high-resolution-coastal-vegetation-maps-to-improve-forecasting-of-hurricane-impacts-and-coastal-resilience",
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   "created": "2024-08-06T18:35:20Z",
   "registered": "2024-08-06T18:35:20Z",
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   "updated": "2024-08-06T18:35:20Z"
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