saeid shabani | Environmental Science | Best Researcher Award

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 🌳💨

Educational Background

Dr. Yongjie Ji holds a Master of Applied Science in Cartography and Geographic Information Systems and a Ph.D. in Forest Management, focusing on forestry remote sensing and information technology. His educational foundation equips him with a robust understanding of both geographical information systems and forest management.

Professional Experience

Currently, Dr. Ji serves as an Associate Professor at Southwest Forestry University. He is actively involved in academia as a guest editor for the journal Forests and contributes as a reviewer for multiple reputable journals, including GSIS, RS, IJDE, and JEM. His teaching portfolio includes courses such as Principles of Geographic Information System and Introduction to Remote Sensing, where he shares his expertise with students.

Research Focus

Dr. Ji’s research centers on the application of multispectral, hyperspectral, LiDAR, and SAR remote sensing data for the inversion of forestry parameters. This field is critical for understanding and managing forest resources effectively, making his work highly relevant to current environmental and ecological challenges.