Yang Shao

Associate Professor

Specialities:

·     Remote sensing

·     GIS

·     Land use and land cover change

·     Watershed assessment and modeling

My research and teaching is focused on remote sensing digital image processing, GIS, and statistical modelling. I develop advanced image classification algorithms to improve land cover mapping accuracy and monitor land cover change. My image processing algorithms rely heavily on machine learning and target near real-time monitoring. In addition to remote sensing image processing, I study land change patterns and processes using spatial statistical models. I am particularly interested in understanding the drivers and consequences of land cover change. I integrate land change simulation model and ecohydrological models to address these broad questions.

Courses Recently Taught:

·     Modeling in GIS/Advanced Modeling in GIS (GEOG 4084/5084)

·     Programming for Geospatial Research (R or Python) (GEOG4984/5984)

·     Land Change Modelling (GEOG4334/5334)

·     Advanced Topics in Remote Sensing (NR6104)

·     Remote Sensing and Phenology (GEOG4374/5374)

Recent Publications:

*student collaborator

Taff, G.N., Shao, Y., Ren, J.*, and Zhang, R.*, 2018. Remote Sensing Image Classification by Integrating Reject Option and Prior Information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, in press.

Jensen, C.*, McGuire, K., Shao, Y. 2018. Modeling headwater stream networks across multiple flow conditions in the Appalachian Highlands, Earth Surface Processes and Landforms, doi: 10.1002/esp.4431.

Ren, J.*, Campbell, J.B. and Shao, Y., 2017. Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops. Remote Sensing, 9(7), p.722.

Jin, X., Shao, Y., Zhang, Z., Resler, L.M., Campbell, J.B., Chen, G., Zhou, Y., 2017. The evaluation of land consolidation policy in improving agricultural productivity in China. Scientific Report, 7: 2792, DOI:10.1038/s41598-017-03026-y.

Cooper, B.*, Dymond, R., Shao, Y., 2017. Impervious Comparison of NLCD versus a Detailed Dataset over Time. Photogrammetric Engineering and Remote Sensing, 83(6), 429-437.,

Shao, Y., R. Lunetta, J.B. Wheeler*, Iiames, J., Campbell, B. 2016. Evaluating time-series smoothing algorithms for multi-temporal land cover classification. Remote Sensing of Environment, 174, 258–265.

Shao, Y., Taff, G.N., Ren, J.*, Campbell, J.B. 2016. Characterizing major agricultural land change trends in the Western Corn Belt. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 116-125.

Cooner, A.*, Shao, Y, Campbell, J.B. 2016. Automatic Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: The 2010 Haiti Earthquake. Remote Sensing, 8(10), 868; doi:10.3390/rs8100868.

Ren, J.*, Campbell, J.B., Shao, Y. 2016. Spatial and Temporal Dimensions of Agricultural Land Use Changes, 2001-2012, East-Central Iowa. Agricultural Systems, 148, Pages 149-158.

Chen, G., Glasmeier, A.k., Zhang, M., Shao, Y. 2016. Urbanization and Income Inequality in Post-Reform China: A Causal Analysis Based on Time Series Data. PLoS ONE. 11(7): e0158826. doi:10.1371/journal.pone.0158826.

Yang Shao