Beijing Forestry University | Pakistan
Dr. Fahad Shahzad is a leading researcher in Remote Sensing and Geospatial Analysis, specializing in environmental monitoring, vegetation dynamics, forest fire prediction, and sustainable forest management. With an h-index of 11, 19 published documents, and 353 total citations, his work demonstrates significant impact in applying advanced machine learning and geospatial techniques to ecological and environmental challenges. He has developed ensemble machine learning models for forest fire prediction in Pakistan and China, spatio-temporal analyses of vegetation stress under climatic variability, and biomass and carbon stock modeling in Northern China forests. His research also explores urban heat island effects and vegetation dynamics in major Pakistani cities, along with long-term land-use and forest fragmentation analyses in Portugal. Dr. Shahzad has contributed extensively to high-impact journals including Fire Ecology, Earth Science Informatics, Scientific Reports, and Ecological Informatics, and actively serves as a reviewer for leading international SCI journals. Collaborating with multidisciplinary teams across China, Pakistan, and Europe, he integrates tools such as R, Google Earth Engine, and GIS to generate data-driven insights for climate resilience and environmental management. His work bridges fundamental research and applied solutions, advancing predictive modeling and geospatial approaches for global sustainability, while mentoring early-career researchers and contributing to collaborative, cross-border scientific initiatives.
Profiles : Scopus | Orcid | Google Scholar
Featured Publications
Shahzad, F., Mehmood, K., Anees, S. A., Adnan, M., Muhammad, S., Haidar, I., Ali, J., Hussain, K., Feng, Z., & Khan, W. R. (2025). Advancing forest fire prediction: A multi-layer stacking ensemble model approach. Earth Science Informatics.
Hussain, K., Badshah, T., Mehmood, K., Rahman, A. U., Shahzad, F., Anees, S. A., Khan, W. R., & Yujun, S. (2025). Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan. Earth Science Informatics.
Mehmood, K., Anees, S. A., Muhammad, S., Shahzad, F., Liu, Q., Khan, W. R., Shrahili, M., Ansari, M. J., & Dube, T. (2025). Machine learning and spatio temporal analysis for assessing ecological impacts of the Billion Tree Afforestation Project. Ecology and Evolution.
Ali, J., Haoran, W., Mehmood, K., Hussain, W., Iftikhar, F., Shahzad, F., Hussain, K., Qun, Y., & Zhongkui, J. (2025). Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: A case study using Sentinel-2 and Landsat-9 data in northern China. Frontiers in Environmental Science.
Hussain, K., Mehmood, K., Anees, S. A., Ding, Z., Muhammad, S., Badshah, T., Shahzad, F., Haidar, I., Wahab, A., Ali, J., et al. (2025). Retraction notice to “Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data” [Heliyon 10 (2024) e34710]. Heliyon.
Anees, S. A., Mehmood, K., Raza, S. I. H., Pfautsch, S., Shah, M., Jamjareegulgarn, P., Shahzad, F., Alarfaj, A. A., Alharbi, S. A., Khan, W. R., et al. (2025). Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities. Ecological Informatics.
Anees, S. A., Mehmood, K., Khan, W. R., Shahzad, F., Zhran, M., Ayub, R., Alarfaj, A. A., Alharbi, S. A., & Liu, Q. (2025). Spatiotemporal dynamics of vegetation cover: Integrative machine learning analysis of multispectral imagery and environmental predictors. Earth Science Informatics.
Al-Tameemi, N., Zhang, X., Shahzad, F., Mehmood, K., Xiao, L., & Zhou, J. (2025). From trends to drivers: Vegetation degradation and land-use change in Babil and Al-Qadisiyah, Iraq (2000–2023). Remote Sensing.
Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., Nooruddin, Ali, J., & Bilal, M. (2024). Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS.
Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., Ansari, M. J., Alharbi, S. A., & Khan, W. R. (2024). Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports.
Hussain, K., Mehmood, K., Anees, S. A., Ding, Z., Muhammad, S., Badshah, T., Shahzad, F., Haidar, I., Wahab, A., Ali, J., et al. (2024). Assessing forest fragmentation due to land use changes from 1992 to 2023: A spatio-temporal analysis using remote sensing data. Heliyon.