Sensing the Sounds

EarthzineDEVELOP 2015 Fall VPS, DEVELOP Virtual Poster Session, Monitoring Change for Resource Management, Original

This is a part of the 2015 Fall VPS. For more VPS articles, click here

A series of data examples used throughout the project. Image includes a Landsat 8 composite (bottom left), layers from various USGS and USDA datasets (upper left), our training sites projected in Google Earth (upper right), a sample of the finished product (bottom right) and the study area (bottom center). Image Credit: North Carolina Ecological Forecasting Team

A series of data examples used throughout the project. Image includes a Landsat 8 composite (bottom left), layers from various USGS and USDA datasets (upper left), our training sites projected in Google Earth (upper right), a sample of the finished product (bottom right) and the study area (bottom center). Image Credit: North Carolina Ecological Forecasting Team

Category:åÊForecasting Wetland Cover and Species Habitat

Project Team: North Carolina Ecological Forecasting

Team Location: NASA Langley Research Center ‰ÛÒ Hampton, Virginia

Authors:

Ben Roberts-Pierel

Brett Buzzanga

Michelle Pasco

Jake Patrick

Benjamin Charlem

Taylor Sage

Mentors/Advisors:

Dr. Kenton Ross (NASA DEVELOP National Program)

Abstract:

This project focused on ecological forecasting for wetlands in the Albemarle-Pamlico watershed in Northeastern North Carolina and Southeastern Virginia. The Albemarle-Pamlico watershed encompasses the second largest estuary system in the United States. Understanding land cover types and uses is incredibly important in managing the myriad of uses for, and stressors on, this valuable resource. In partnership with the Albemarle-Pamlico National Estuary Partnership (APNEP), this project aimed to provide an updated version of NOAA‰Ûªs Coastal Change Analysis Program (C-CAP) land-use classification, with a specific focus on delineation of wetland types within this watershed. The project also further disaggregated land cover types such as crop varieties and the invasive species, Phragmites australis. The team utilized a supervised land classification methodology and cross-referenced Landsat 8 imagery with ground truth, LiDAR or Digital Elevation Models (DEM), the National Hydrological Dataset (NHD) and soil datasets to create inputs for the R classifying model. The end goal of the project was to produce maps and a methodology by which APNEP can continually update wetland types and to establish if there is a correlation between wetland type and wetland health within the watershed. This was all with the aim of helping APNEP to make informed policy and management decisions.

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