Assist. Prof. Dr saeid shabani, AREEO, Iran
Assist. Prof. Dr. Saeid Shabani is a distinguished forestry researcher specializing in forest monitoring, natural hazards, and sustainable ecosystem development 🌳🌍. He holds a Ph.D. in Forestry from Tarbiat Modares University, Iran (2015), where he developed models for predicting soil and forest stand disturbances caused by logging 🏗️🌲. His research integrates GIS, machine learning, and statistical modeling to assess forest fragmentation, carbon stock monitoring, and climate change impacts 📊🛰️. Dr. Shabani has led numerous projects on afforestation, ecosystem assessments, and genetic variations in tree species 🌱🔬. His publications in high-impact journals, along with his role as a reviewer for esteemed scientific outlets, solidify his reputation as a leading expert in forestry research 📖🌿. With expertise in ArcGIS, R, and SPSS, he bridges the gap between technology and environmental conservation 💻🍃. His dedication to sustainable forest management makes him an outstanding candidate for the Best Researcher Award 🏅👏.
Publication Profile
Google Scholar
Education
Dr. Saeid Shabani earned his Bachelor of Science in Forestry from the University of Guilan, Iran (2005) 🌲, where he developed a strong foundation in forest management and conservation. He pursued his Master of Science in Forestry at Tarbiat Modares University (2009) 📊, focusing on the relationship between forest gaps, physiographic factors, and vegetation in Lalis Forest, Nowshahr 🌳🗺️. He further advanced his expertise with a Ph.D. in Forestry from the same institution (2015) 🏗️, specializing in modeling soil and forest stand disturbance caused by logging operations 🏞️🔍. Dr. Shabani’s academic journey emphasizes sustainable forest ecosystem development, leveraging GIS, machine learning, and data-driven modeling 📈🌍. His interdisciplinary research bridges ecological conservation and technological advancements to enhance forestry management strategies 💡🌱.
Experience
Dr. Saeid Shabani has extensive experience in forestry research, specializing in forest monitoring, sustainable development, and climate impact assessments 🌍🌿. He has led and collaborated on multiple projects across the Hyrcanian and Arasbaran forests, focusing on afforestation, forest road monitoring, and carbon stock assessment 🏞️📊. His expertise in GIS, R, and machine learning has enabled him to develop predictive models for forest stand disturbances and susceptibility to environmental threats like snowstorms and windthrow 🌪️🛰️. As a scientific reviewer, he contributes to journals such as Scientific Reports, Turkish Journal of Forestry, and Ecology of Iranian Forests 📖📝. He has also been involved in standardizing forestry job competencies and ecosystem differentiation. His impactful work in forest conservation and ecosystem modeling positions him as a leading researcher in environmental sustainability and forestry science
Awards & Honors
Dr. Saeid Shabani has received multiple recognitions for his groundbreaking contributions to forestry research 🌿🏆. His work on forest sustainability, ecosystem monitoring, and climate resilience has earned him prestigious awards and funding. He was a recipient of the Chinese Government Scholarship 🎓🇨🇳 and has won Best Paper Awards for high-impact forestry research in journals like the European Journal of Forest Research 📜🏅. His expertise as a reviewer has been acknowledged with Reviewer Recognition Awards from Scientific Reports, South African Geographical Journal, and Journal of Forest Research & Development 🔍📖. He has secured project grants from environmental organizations for studies on afforestation, soil health, and carbon stock modeling 🌲💰. His Excellence in Forestry Research Award highlights his innovative use of GIS and machine learning in forest monitoring 🏗️🛰️. Through his dedication to sustainable forestry and advanced modeling techniques, he has cemented his reputation as an award-winning researcher in environmental science.
Research Focus
Dr. Saeid Shabani is a distinguished researcher specializing in forest monitoring, ecosystem sustainability, and climate impact assessment 🌲🌎. His expertise lies in applying GIS, machine learning, and statistical modeling to predict forest disturbances caused by natural hazards and human activities 🛰️📊. His research focuses on forest fragmentation and logging impact modeling 🏗️🌳, assessing the effects of snowstorms, windthrow, and climate change on forest ecosystems ❄️🌪️, and evaluating carbon stock in Hyrcanian and Arasbaran forests 🌱📈. Additionally, he contributes to afforestation efforts and sustainable forest management strategies 🌿♻️ while analyzing soil health and biodiversity conservation in forest stands 🔬🍂. Through cutting-edge methodologies, he develops innovative solutions to preserve global forest ecosystems and mitigate environmental risks 🌍💡. His work plays a vital role in policy-making and sustainable forestry development, ensuring the long-term health of natural resources.
Publication Top Notes
-
📜 Spatial Prediction of Soil Disturbance Caused by Forest Logging Using Generalized Additive Models and GIS – European Journal of Forest Research 🏗️🌲
-
🌍 Forest Stand Susceptibility Mapping During Harvesting Using Logistic Regression and Boosted Regression Tree Models – Global Ecology and Conservation 📊🛰️
-
📏 Spatial Modeling of Forest Stand Susceptibility to Logging Operations – Environmental Impact Assessment Review 🏞️📉
-
❄️ Modeling the Susceptibility of Uneven‑Aged Broad‑Leaved Forests to Snowstorm Damage – Environmental Science and Pollution Research 🌨️🌲
-
🌡️ How Do Different Land Uses/Covers Contribute to Land Surface Temperature and Albedo? – Sustainability 🏡🌞
-
🌱 Soil Health Reduction Following Conversion of Primary Vegetation Covers in Semi-Arid Environments – Science of the Total Environment 🏜️🌾
-
🗺️ Modeling and Mapping of Soil Damage Caused by Harvesting in Caspian Forests Using CART and RF Techniques – Journal of Forest Science 📈📊
-
🌪️ Prediction of Windthrow Phenomenon in Deciduous Temperate Forests Using Logistic Regression & Random Forest – Cerne Journal 🌳💨