Mohammad Safaei

Focus Areas:

Country of Origin: Iran

Degree Program: PhD

Entered Program: Fall 2022

Expected Graduation: Fall 2026

Dissertation Topic: Data-Driven Conservation: Integrating AI and Remote Sensing for Monitoring Ecosystem Changes

Research Statement: My research focuses on integrating remote sensing, geospatial analysis, and AI techniques within the field of Land Change Science. I work on remote sensing of vegetation dynamics, land change modeling, and time-series analysis to detect and model changes in the landscape. My goal is to contribute to better environmental management and conservation strategies.

Adviser: Dr. Jane Southworth

Educational Background

  • M.S. in Remote Sensing and GIS, Kharazmi University, 2019
  • B.S. in Natural Resources Engineering, Yazd University, 2016

Recent Publications

Safaei, M., Southworth, J., Gibbes, C., Herrero, H. V., Rahaman, M., Tefera, B. B., & Blackburn, J. K. (2025). Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach. Ecological Informatics, 90, 103279.

Tefera, B. B., Southworth, J., Mossa, J., Rahaman, M., Safaei, M., Yang, D., & Karuppannan, S. (2025). Predictive groundwater quality responses to land cover and lithology in the upper Awash River basin (Ethiopia) with stacking ensembles. Journal of Environmental Management, 394, 127572.

Rahaman, M., Southworth, J., Amanambu, A. C., Tefera, B. B., Alruzuq, A. R., Safaei, M., ... & Smith, A. C. (2025). Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data. Journal of Environmental Management, 376, 124323.

Southworth, J., Smith, A. C., Safaei, M., Rahaman, M., Alruzuq, A., Tefera, B. B., ... & Herrero, H. V. (2024). Machine learning versus deep learning in land system science: A decision-making framework for effective land classification. Frontiers in Remote Sensing, 5, 1374862.