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Guofan Shao

Forestry and Natural Resources 

  • Professor of Remote Sensing
PFEN Room 221B
715 W State St.
West Lafayette, IN 47907

Dr. Guofan Shao's website

Dr. Guofan Shao is a professor of the Department of Forestry and Natural Resources (FNR) at Purdue University. Dr. Shao received his PhD in ecology from Chinese Academy of Sciences in 1989, received his post-doctoral education from the Department of Environmental Sciences, University of Virginia between 1991 and 1994, and received professional trainings in remote sensing and GIS technologies from world-geospatial leaders, such as ESRI and ERDAS, in the 1980s-2000s. Dr. Shao regularly teaches Remote Sensing Analysis and Applications (FNR 55800), Fundamental Remote Sensing (FNR 35700), and Forest Resources Practicum (FNR 37010). He participates in teaching Purdue ECE undergraduate courses as part of the vertical integrated projects. He is among Purdue CoA faculty members who have initiated AGR 33300: Data Science for Agriculture.

Dr. Shao’s overall research interest is digital forestry, which involves remote sensing, geographic information systems (GIS), forest modeling, and forestry decision-support systems (DSS). His remote sensing research is to advance the use of satellite remote sensing, photogrammetry, Lidar, and drone remote sensing for sustainable forest management. Dr. Shao is especially interested in applying state-of-the-art algorithms for accurate image classification in landscape mapping and analysis. His research group has conducted a variety of remote sensing experiments in the agriculture-dominated landscapes in Indiana. Dr. Shao has identified uncertainties commonly hidden in classification accuracy assessment of remotely sensed imagery, and proposed a stricter standard of accuracy assessment that is badly needed in the era of artificial intelligence and popular remote sensing. Dr. Shao has published nearly two hundred scholarly works, including 160 journal papers, six books, and 24 book chapters.

Research Group - Digital Natural Resources

Facilities - John S. Wright Center, Forest Informatics and Landscape Monitoring Lab (FILM)

Related Centers - Center for the Environment, Purdue Climate Change Research Center, Purdue Interdisciplinary Center for Ecological Sustainability, Laboratory for Applications of Remote Sensing, Purdue Terrestrial Observation

Awards & Honors

(2017) Excellent Associate Editor. Springer’s Journal of Forestry Research.


Shao, G. Rapid Tree Diameter Computation with Stereoscopic Photogrammetry . U.S. Patent No. D2020-0039. Washington, D.C.: U.S. Patent and Trademark Office.

Selected Publications

Sun, L., Tang, L. N., Shao, G., Qiu, Q. Y., Lan, T., & Shao, J. Y. (2020). A machine learning-based classification system for urban built-ip areas using multiple classifiers and data sources. Remote Sensing, (12), 91. doi:10.3390/rs12010091

Shao, G., Tang, L., & Liao, J. (2019). Overselling overall map accuracy misinforms about research reliability. Landscape Ecology, 34(11), 2487-2492. doi:10.1007/s10980-019-00916-6

Shao, G. (2019). Optical remote sensing. In International Encyclopedia of Geography: People, the Earth, Environment, and Technology (2390–2395). Wiley & Sons, Inc.

Reynolds, K. M., Twery, M., Lexer, M. J., Vacik, H., Ray, D., Shao, G., & Borges, J. (2018). Decision support systems in forest management. In Handbook on Decision Support Systems 2,Part of the International Handbooks Information System book series (INFOSYS) (499-533). Springer.

Shao, G., Shao, G., Gallion, J., Saunders, M., Frankenberger, J. R., & Fei, S. (2018). Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity. REMOTE SENSING OF ENVIRONMENT, 204, 872-882. doi:10.1016/j.rse.2017.09.011

Chen, X., Zhou, G., Chen, Y., Shao, G., & Gu, Y. (2017). Supervised multiview feature selection exploring homogeneity and heterogeneity with ℓ1,2-norm and automatic view generation. IEEE Transactions on Geoscience and Remote Sensing, 55(4), 2074--2088.

Wen, Z., Shao, G., Mirza, Z. A., Chen, J., Lu, M., & Wu, S. (2015). Restoration of shadows in multispectral imagery using surface reflectance relationships with nearby similar areas. international Journal of Remote Sensing, 36(16), 4195-4212. doi:10.1080/01431161.2015.1079343

Tang, L., & Shao, G. (2015). Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26(4), 791-797. doi:10.1007/s11676-015-0088-y

Wang, X., Shao, G., Chen, H., Lewis, B. J., Qi, G., Yu, D., . . . Dai, L. (2013). An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China. ENVIRONMENTAL MANAGEMENT, 52(3), 612-620. doi:10.1007/s00267-013-0089-6

Su, X., Shao, G., Vause, J., & Tang, L. (2013). An integrated system for urban environmental monitoring and management based on the Environmental Internet of Things. INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY, 20(3), 205-209. doi:10.1080/13504509.2013.782580

Forestry and Natural Resources, 715 West State Street, West Lafayette, IN 47907-2061 USA, (765) 494-3590

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