John A Young
John Young is a Research Biologist (Biogeography/GIS) at the USGS Eastern Ecological Science Center and Lead of the EESC Remote Sensing and GIS Team.
John Young is a Research Biologist (Biogeography/GIS) whose research interests include developing innovative applications of GIS and remote sensing technologies to assess the impact of landscape structure on the distribution of aquatic and terrestrial species and their habitats. His past research activities have included assessing watershed land use effects on aquatic communities, modeling the distribution of endangered, rare, and at risk plants and animals, remote sensing monitoring and assessment of forest vegetation communities, and characterizing forest structure and change using aerial lidar. His work has also included development of risk and vulnerability models using multi-criteria decision support tools and geospatial modeling, and development of spatial sampling designs for field data collection.
Professional Experience
2000 to present USGS Eastern Ecological Science Center (formerly USGS Leetown Science Center), Kearneysville, WV, Research Biologist (Biogeography/GIS).
1994-2000 USGS Leetown Science Center, Kearneysville, WV, Biologist (GIS Coordinator).
1991-1994 U.S. Forest Service, Pacific Northwest Research Station, Olympia, WA, Geographer/GIS Coordinator.
Education and Certifications
M.S. Geography, 1992, Virginia Tech
B.A. Geography, 1987, Virginia Tech
Affiliations and Memberships*
International Association of Landscape Ecologists, International Biogeography Society
Science and Products
Using Multiple Indicators to Assess Stream Condition in the Chesapeake Bay
Brook trout vulnerability to drought: eastern component of USGS national integrated ecohydrological research
Mapping riverine habitats of the Delaware River using bathymetric LiDAR
Assessing stream health and fish habitat in streams of the Chesapeake Bay Watershed
Evaluation and testing of standardized forest vegetation metrics derived from lidar data
“DelRiv 24k – LU": Land Use/Land Cover Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments “DelRiv 24k – LU": Land Use/Land Cover Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments
“DelRiv 24k – NE": Natural Environment Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments “DelRiv 24k – NE": Natural Environment Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments
2021 Potomac River Phase II Topobathymetric Lidar Validation - USGS Field Survey Data 2021 Potomac River Phase II Topobathymetric Lidar Validation - USGS Field Survey Data
Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed
Potomac River ADCP Bathymetric Survey, October 4-7, 2021 Potomac River ADCP Bathymetric Survey, October 4-7, 2021
“ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments (ver. 4.0, August 2025) “ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments (ver. 4.0, August 2025)
Joint pilot fish habitat framework Joint pilot fish habitat framework
Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities
Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin
Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition
Assessing tradeoffs between current and desired vegetation condition in a National Park using historical maps and high resolution lidar data Assessing tradeoffs between current and desired vegetation condition in a National Park using historical maps and high resolution lidar data
Multispecies approaches to status assessments in support of endangered species classifications Multispecies approaches to status assessments in support of endangered species classifications
Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Fishway Structure Data in the Eastern United States Fishway Structure Data in the Eastern United States
Science and Products
Using Multiple Indicators to Assess Stream Condition in the Chesapeake Bay
Brook trout vulnerability to drought: eastern component of USGS national integrated ecohydrological research
Mapping riverine habitats of the Delaware River using bathymetric LiDAR
Assessing stream health and fish habitat in streams of the Chesapeake Bay Watershed
Evaluation and testing of standardized forest vegetation metrics derived from lidar data
“DelRiv 24k – LU": Land Use/Land Cover Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments “DelRiv 24k – LU": Land Use/Land Cover Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments
“DelRiv 24k – NE": Natural Environment Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments “DelRiv 24k – NE": Natural Environment Related Data Summaries for the Delaware River Watershed Within NHD Plus HR catchments
2021 Potomac River Phase II Topobathymetric Lidar Validation - USGS Field Survey Data 2021 Potomac River Phase II Topobathymetric Lidar Validation - USGS Field Survey Data
Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed Attribution of fish sampling data to NHDPlus HR catchments within the Chesapeake Bay Watershed
Potomac River ADCP Bathymetric Survey, October 4-7, 2021 Potomac River ADCP Bathymetric Survey, October 4-7, 2021
“ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments (ver. 4.0, August 2025) “ChesBay 24k – NE": Natural Environment Related Data Summaries for the Chesapeake Bay Watershed Within NHD Plus HR catchments (ver. 4.0, August 2025)
Joint pilot fish habitat framework Joint pilot fish habitat framework
Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities
Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin
Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition
Assessing tradeoffs between current and desired vegetation condition in a National Park using historical maps and high resolution lidar data Assessing tradeoffs between current and desired vegetation condition in a National Park using historical maps and high resolution lidar data
Multispecies approaches to status assessments in support of endangered species classifications Multispecies approaches to status assessments in support of endangered species classifications
Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Fishway Structure Data in the Eastern United States Fishway Structure Data in the Eastern United States
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government