Prof. Guofan Shao_ICRSTA 2023

Biography
Prof. Guofan Shao Department of Forestry and Natural Resources, Purdue University, USA
Biography: Prof. Guofan Shao is affiliated with the Department of Forestry and Natural Resources, Purdue University. He serves as Editor-in-Chief for the International Journal of Sustainable Development and World Ecology, published by Taylor & Francis. He started research on geospatial applications in ecosystem modeling and monitoring in 1991 when he became a post-doc at the Department of Environmental Sciences, University of Virginia. Since he joined Purdue University in 1997, he has been teaching and studying remote sensing applications in land use and land cover mapping and its accuracy assessment. He has contributed to research on the mixed forests in eastern Eurasia and central hardwood forests in the US. His current research interest focuses on machine learning classification of remotely sensed imagery acquired from different platforms. He created image classification efficacy as a transformed metric to consistently evaluate the performance of classifiers that deal with different datasets. He has authored or co-authored 6 books, 23 book chapters, and 172 peer-reviewed journal papers. The book chapters include Satellite Data and Remote Sensing in Encyclopedia of Environmetrics in 2012 and 2016, and Optical Remote Sensing in International Encyclopedia of Geography in 2014 and 2019.

Biography

Prof. Guofan Shao
Department of Forestry and Natural Resources, Purdue University, USA

Biography:
Prof. Guofan Shao is affiliated with the Department of Forestry and Natural Resources, Purdue University. He serves as Editor-in-Chief for the International Journal of Sustainable Development and World Ecology, published by Taylor & Francis. He started research on geospatial applications in ecosystem modeling and monitoring in 1991 when he became a post-doc at the Department of Environmental Sciences, University of Virginia. Since he joined Purdue University in 1997, he has been teaching and studying remote sensing applications in land use and land cover mapping and its accuracy assessment. He has contributed to research on the mixed forests in eastern Eurasia and central hardwood forests in the US. His current research interest focuses on machine learning classification of remotely sensed imagery acquired from different platforms. He created image classification efficacy as a transformed metric to consistently evaluate the performance of classifiers that deal with different datasets. He has authored or co-authored 6 books, 23 book chapters, and 172 peer-reviewed journal papers. The book chapters include Satellite Data and Remote Sensing in Encyclopedia of Environmetrics in 2012 and 2016, and Optical Remote Sensing in International Encyclopedia of Geography in 2014 and 2019.