Assoc. Prof. Dr. Saeid Pourmanafi is a leading environmental and geospatial researcher at Isfahan University of Technology, focusing on arid region landscapes and sustainable planning. He integrates remote sensing, spatial modeling, and machine learning to address ecological, agricultural, and urban challenges in central Iran. His work spans wetland conservation, habitat suitability, soil erosion, and urban sustainability. Through interdisciplinary collaboration, he contributes to environmental monitoring, landscape planning, and decision support systems. His research leverages satellite imagery analysis (Landsat, Sentinel‑2, WorldView‑3) and spatial classification tools to inform evidence‑based ecological restoration, urban land use policy, and biodiversity protection in arid ecosystems.
Education
Dr. Pourmanafi holds advanced training in environmental science, geoinformatics, and remote sensing with emphasis on ecological applications in arid landscapes. His education equipped him with proficiency in GIS, multi-source satellite image classification, and time-series analysis for ecosystem monitoring. He developed expertise in machine learning approaches such as regression neural networks, classification algorithms, and connectivity modeling methods (GARP, DOMAIN, NPMR). He also learned to apply ecological decision frameworks—MEDALUS for desertification, RUSLE for erosion, MCDA/NSGA‑II/MOLA for conservation zoning, and InVEST for habitat quality modeling. His academic foundation bridges environmental modeling, spatial analytics, and sustainable planning tailored to semi-arid ecosystems.
Professional Experience
With a robust track record of applied research, Dr. Pourmanafi has led projects on habitat mapping for wetlands, desertification modeling, and spatial evaluation of urban land use. He has implemented soil erosion assessments using RUSLE, salinity monitoring via OLI sensor data, and thermal power plant suitability modeling using GIS-based multi-criteria analysis. He has contributed to wetland restoration prioritization using MC‑SDSS, crop type mapping via Landsat and Sentinel‑2, and connectivity modeling for endangered ungulates. His experience includes assessing ecotourism potential with NSGA‑II, evaluating urban sustainability through neighborhood-scale spatial modeling, and integrating ecosystem services hotspots into land-use planning in central Iran.
Awards and Recognition
While specific awards are not reported, Dr. Pourmanafi’s consistent publication record in high-impact journals—Scientific Reports, Global Change Biology, Ecological Indicators, Sustainable Cities and Society, International Journal of Applied Earth Observation, and Journal of Arid Environments—demonstrates academic esteem. His application of innovative methodologies in areas such as machine learning, classification algorithms, and multi-criteria spatial planning underscores his scholarly recognition. The breadth and quality of multi-author and international collaborations reflect trust in his expertise. Frequent citations of his studies on habitat connectivity, climate‑driven land cover changes, and ecological modeling further highlight his standing in environmental geoinformatics and integrated landscape management.
Research Skills
Dr. Pourmanafi excels in remote sensing, ecosystem modeling, and spatial decision support. His core competencies include satellite data analysis (Landsat, Sentinel‑2, WorldView‑3), hybrid and hierarchical image classification, and regression neural networks. He is skilled in RUSLE-based erosion analysis, MEDALUS desertification assessment, connectivity modeling (GARP, DOMAIN, NPMR), and habitat quality evaluation through InVEST. Advanced proficiency in multi-criteria decision analysis (MCDA), MOLA, NSGA‑II, and ecosystem services hotspot mapping equips him to design conservation zoning and ecotourism suitability models. He also employs GWR modeling for carbon sequestration mapping and integrates land cover change detection techniques to inform climate adaptability and sustainable land management.
Publications
Pourmanafi, S., et al. (2025). “Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models” in Scientific Reports.
Pourmanafi, S., et al. (2025). “Employing Sentinel‑2 time‑series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods” in International Journal of Applied Earth Observation and Geoinformation.
Pourmanafi, S., et al. (2025). “Multivariate analysis of dynamic correlations between urban form and air pollution: Implications for sustainable urban planning” in Sustainable Cities and Society.
Pourmanafi, S., et al. (2025). “Impacts of Climate‑Land Dynamics on Global Population and Sub‑Populations of a Desert Equid” in Global Change Biology (Cited by 1).
Conclusion
Assoc. Prof. Dr. Pourmanafi’s body of work exemplifies excellence at the intersection of environmental geoinformatics, ecological modeling, and spatial planning. His interdisciplinary approach blends remote sensing, machine learning, and multi-criteria frameworks to address challenges in arid landscapes—from wetland dynamics to habitat connectivity and urban sustainability. His consistent scholarly output across leading journals demonstrates a strong impact and methodological innovation. With technical skillsets transferable to policy and management, he is a key contributor to evidence-based planning and conservation. His research trajectory promises continued contributions to sustainable land use, biodiversity protection, and integrated environmental decision support across semi‑arid and urban ecosystems.