Remote Sensing and Mapping of Urban Environments
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Opportunity Title: |
Remote Sensing and Mapping of Urban Environments |
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Research Area: |
Health |
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EPA Lab/Center/Office: |
National Exposure Research Laboratory (NERL) |
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Location: |
Research Triangle Park, NC |
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Duration: |
12 months |
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Brief Summary: |
This project is part of a larger remote sensing effort for high resolution land cover mapping and subsequent analysis of communities for sustainability and human and ecosystem health. It builds on EPA EnviroAtlas Meter-scale Urban Land Cover (MULC) data. The emphasis will be on developing efficient and repeatable workflows for mapping large areas at high spatial resolution. Knowledge of remote sensing, image classification and GIS is required |
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Opportunity Description: |
The Subject of this research is using remote sensing and earth observation techniques to map land cover and analyze landscapes at high spatial resolution (1 m pixels). Land cover data can provide an invaluable tool for sound environmental management, urban planning, habitat conservation, natural hazard mitigation, tree planting, urban heat island mitigation and many other applications. EPA EnviroAtlas (epa.gov/enviroatlas) is an online geospatial data and information tool for aggregating, screening, analysis and decision-making to support sustainable and healthy communities. To address the fine spatial scale of many urban phenomena, the EnviroAtlas team has developed high resolution land cover data at 1 m pixel size for 26 US communities, called Meter-scale Urban Land Cover (MULC). Land cover is defined here as the characteristic material present on the landscape, assigned to the following classes: Impervious Surface, Tree, Grass - Herbaceous, Shrub, Soil - Barren, Water, Wetland (Woody and Emergent) and Agriculture (the last two mapped using ancillary data). MULC is produced using aerial photo or satellite imagery, and lidar (Light Detection and Ranging) data sets at ~1 m pixel size using object-based image analysis methods. The fellow will be performing image classification, geospatial data integration, quality assurance and analysis to support a topic at the intersection of human health and the environment. The research Scope spans GIS, digital image processing and classification, scripting with R, Python, ArcGIS and QGIS/GRASS, cloud processing in Google Earth Engine, integration with EnviroAtlas data products and preferably, a use case demonstration and analysis. The Goals of this project include developing new approaches and methods for large-area high-resolution urban image classification, geospatial data integration, production of meter-scale urban land cover (MULC) maps and application of such data to solving human and ecosystem health challenges in urban environments. |
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Opportunities for Professional Development: |
The fellowship provides the fellow an opportunity to apply integrated and multidisciplinary approaches and urban ecosystem and public health knowledge to address real-world critical issues. The fellowship will also allow the fellow to gain substantive knowledge in research, development and science policy. |
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Point of Contact or Mentor: |
Drew Pilant (pilant.drew@epa.gov) |
For more information about EPA Research Fellowship opportunities, visit: https://19january2021snapshot.epa.gov/research-fellowships/graduate-research-internship-program-grip-opportunities-epa