Prof. Dr. Shaban Shataee | Forestry | Best Researcher Award

Academic member at Gorgan University of Agricultural Sciences and Natural Resources, Iran

Prof. Dr. Shaban Shataee is an esteemed expert in forestry and remote sensing, with a comprehensive educational background and a wealth of professional experience. 🌳📡 His research has significantly advanced the field, particularly in forest type mapping, forest stand volume estimation, and flood prediction using remote sensing technologies. He is passionate about leveraging cutting-edge methods to improve forest management and conservation. 🌍✨

Profile:

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🎓 Educational Background:

Prof. Dr. Shaban Shataee is a distinguished scholar in forestry and remote sensing, with an extensive educational background that highlights his dedication to the field. He completed a Sabbatical in 2010 at the Forest Biometry Department of Freiburg University, Germany, where he specialized in forest structure information estimation using Lidar and optical remote sensing data. He earned his Ph.D. in Forestry-Remote Sensing from Tehran University, Iran, in 2003 with a dissertation focused on the possibility of forest type mapping using imagery, under the supervision of Dr. Ali Darvishsefat. In 2002, he pursued a Post-Diploma at Zurich University, Switzerland, in the Geography Department’s Remote Sensing Lab, concentrating on object-oriented classification and forest type classification techniques under Dr. Tobias Kellenberger. Prof. Shataee obtained his M.Sc. in Forestry-Remote Sensing from Tehran University in 1997, where he focused on forest extent mapping using satellite data, also under Dr. Ali Darvishsefat. He completed his B.S. in Forestry from the same university in 1995.

Professional Experience:

Prof. Shaban Shataee has a prolific career in the academic and research domains of forestry and remote sensing. His professional journey includes numerous publications in renowned international scientific journals, demonstrating his expertise in integrating remote sensing technologies for forest management and conservation. His notable works encompass forest type mapping, flood prediction, forest stand volume estimation, and soil surface salinity prediction. His research often employs advanced remote sensing data, such as Landsat ETM+, ASTER, and airborne laser scanning, and he has made significant contributions to the field by comparing various statistical and machine learning models for forest attribute estimation.

🔬 Research Interests:

Prof. Shataee’s research interests are diverse and centered on remote sensing applications in forestry. He specializes in optical, Lidar, and radar remote sensing, spatial modeling in forestry, vegetation mapping, GIS, and GPS. His work aims to enhance the accuracy and efficiency of forest monitoring, management, and conservation practices through innovative remote sensing techniques.

Publication Top Notes:

• Shataee, Darvishsefat, Kellenberger. (2007). Forest Type Mapping Using Incorporation of Spatial Models and ETM+ Data. Pakistanian Journal of Biological Sciences, 10(14), 2292-2299.
• Shataee, Najjarlou. (2007). Up-to-date mapping of the reforested area using multi-date ETM+ data. Journal of Applied Sciences.
• Shataee, Malek. (2008). Flood prediction and assessment of vulnerability risk in the Southern Coasts of the Caspian Sea. International Journal of Digital Earth, 1(03), 291-303.
• Mohammadi, Shataee, Yaghmaee, Mahiny. (2010). Modeling Forest Stand Volume and Tree Density Using Landsat ETM+ Data. International Journal of Remote Sensing, 31(11), 2959-2975.
• Mohammadi, Shataee. (2010). Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran. Journal of Remote Sensing of Environment, 114, 1504-1512.
• Tajgardan, Ayoubi, Shataee, Sahrawat. (2010). Soil Surface Salinity Prediction Using ASTER Data: Comparing Statistical and Geostatistical Models. Australian Journal of Basic and Applied Sciences, 4(3), 457-467.
• Shataee, Wienacker, Babanejad. (2011). Plot-level Forest Volume Estimation Using Airborne Laser Scanner and TM Data. Procedia Environmental Science.
• Mohammadi, Shataee, Babanezhad. (2011). Estimation of forest stand volume, tree density, and biodiversity using Landsat ETM+ Data. Procedia Environmental Science.
• Shataee, Kalbi, Fallah, Pelz. (2012). Forest Attributes Imputation Using Machine Learning methods and ASTER Data. International Journal of Remote Sensing, 33(19), 6254-6280.
• Kazemi, Thmasebi, Kamkar, Shataee, Sadeghi. (2012). Comparison of interpolation methods for estimating pH and EC in agricultural fields of Golestan province (north of Iran). International Journal of Agriculture and Crop Science, 4(4), 157-167.
• Shataee. (2013). Forest Attributes Estimation Using Aerial Laser Scanner and TM Data. Forest Systems Journal, 22(3), 484-496.
• Porma, Shataee. (2013). Estimation of species diversity of trees and shrubs using ETM+ sensor data. International Journal of Advanced Biological and Biomedical Research, 1, 71-78.
• Kalbi, Fallah, Shataee. (2014). Forest-type classification using tree-based algorithms and SPOT-HRG Data. International Journal of Environmental Resources Research, 1(2), 263-274.
• Naghavi, Fallah, Shataee, Latifi, Soosani, Ramezani, Conrad. (2014). Canopy cover estimation across semi-Mediterranean woodlands. Journal of Applied Remote Sensing.
• Kalbi, Fallah, Shataee. (2014). Estimation of forest attributes in the Hyrcanian forests. Journal of Applied Remote Sensing.