Parinaz Rahimzadeh-Bajgiran

Expertise

? Climate change, ?Data science, ? Environmental sciences, ? Forests

Rahimzadeh employs remote sensing and geospatial analysis to assess and forecast forest health and productivity under varying conditions (e.g. disturbance, drought, spruce budworm defoliation). Her current projects aim to address sustainable forest management issues and forest site productivity modeling. She is also involved with projects focused on multi-temporal remote sensing and geo-spatial analysis of landscape dynamics and it’s consequences on people’s livelihoods and ecosystem services. Visit Rahimzadeh’s biography to learn more.

A bubble chart demonstrating a 50% research 50% teaching appointment split.
Rahimzadeh balances time between teaching (50%) courses about Remote Sensing and Applied Geographic Information Systems, and research (50%) on spruce budworm defoliation detection and quantification and forest site productivity modeling in Northeastern forests of Canada and U.S. using Landsat-8 and Sentinel-2 imagery.

Appointment details

Rahimzadeh’s work is supported by:

  • School of Forest Resources at the College of Natural Sciences, Forestry and Agriculture
  • Maine Agricultural and Forest Experiment Station

Experiment Station contributions

  • Current project: Remote Sensing and Geo-spatial Approaches for Forest Health Assessment and Mapping. McIntire-Stennis project number ME042119.