Fraser_Benjamin

Benjamin Fraser

Visiting Assistant Professor
Visiting Assistant Professor
Office: James Hall, Room G62, Durham, NH 03824
Websites:
Curriculum Vitae

For over 10 years, I have worked at the intersection of technical tools such as GIS (geomatics) and remote sensing, and applied fields such as forestry, wildlife conservation, and natural resource management. My research and technical consulting span scales ranging from individual tree surveys to multi-million-acre protected landscapes. As a Visiting Assistant Professor at UNH and Director of the Basic and Applied Spatial Analysis Lab, I focus on cutting-edge technologies such as Unpiloted Aerial Systems (UAS or UAV) and deep learning algorithms to evaluate their effectiveness in meeting the needs of local land managers. Most recently, my research has been in collaboration with the NH Division of Forests and Lands, to investigate the ability of new UAS methods for classifying oak tree recovery following the outbreak of spongy moth (Lymantria dispar).

 

As a technical consultant for GIS, Image Analysis, and Machine Learning applications, my collaborators have included Tetra Tech, Wildlife Conservation Society, the US Agency for International Development (USAID), the German Agency for International Cooperation (giz GmbH), SERVIR-SEA (NASA), Birdlife International, SIG-NAL, and various state and governmental agencies. These projects, at some points, simultaneously span nine countries across three continents. My core area of expertise and interests include spatial data science, geovisualizaiton, spatial data accuracy, and natural resource conservation. Through these consultancies, I have become adept at project management and leading multi-organization training sessions for interdisciplinary teams, even those connected through language barriers. As I continue to grow my skills and connections, I look forward to enhancing the potential of the stakeholders that I come to collaborate with.

Courses Taught

  • NR 658: Intro Geographic Info Systems
  • NR 757/857: Remote Sensing of the Environ
  • NR 759/859: Digital Image Process Nat Res

Research Interests

  • Geographic Information Systems (GIS)
  • Remote Sensing
  • Remote Sensing Technology
  • Photogrammetry
  • Forest Ecology
  • Environmental Conservation
  • Sustainable Development

Selected Publications

  • Fraser, B. T., Congalton, R. G., & Ducey, M. J. (2025). Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests. Remote Sensing, 17(6), 1010. doi:10.3390/rs17061010

  • Chea, M., Fraser, B. T., Nay, S., Sok, L., Strasser, H., & Tizard, R. (2024). A Survey of Changes in Grasslands within the Tonle Sap Lake Landscape from 2004 to 2023. Diversity, 16(8), 448. doi:10.3390/d16080448

  • Kanaskie, C. R., Routhier, M. R., Fraser, B. T., Congalton, R. G., Ayres, M. P., & Garnas, J. R. (2024). Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery. Remote Sensing, 16(14), 2608. doi:10.3390/rs16142608

  • Fraser, B. T., Robinov, L., Davidson, W., O’Connor, S., & Congalton, R. G. (2024). A Comparison of Unpiloted Aerial System Hardware and Software for Surveying Fine-Scale Oak Health in Oak–Pine Forests. Forests, 15(4), 706. doi:10.3390/f15040706

  • Bunyon, C. L., Fraser, B. T., McQuaid, A., & Congalton, R. G. (2023). Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes. Remote Sensing, 15(11), 2839. doi:10.3390/rs15112839

  • Fraser, B. T., & Congalton, R. G. (2021). Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models. REMOTE SENSING, 13(23). doi:10.3390/rs13234873

  • Fraser, B. T., & Congalton, R. G. (2021). A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery. FORESTS, 12(9). doi:10.3390/f12091290

  • Fraser, B. T., & Congalton, R. G. (2021). Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories. REMOTE SENSING, 13(15). doi:10.3390/rs13152971

  • Fraser, B. T., & Congalton, R. G. (2019). Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests. FORESTS, 10(1). doi:10.3390/f10010024

  • Fraser, B. T., & Congalton, R. G. (2018). Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. REMOTE SENSING, 10(6). doi:10.3390/rs10060908

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  • Most Cited Publications