Welcome to my webpage. My research interests focus 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. Some key research questions include:

  • What are the causes of land cover change?
  • How does land cover change affect ecosystem services?
  • How do drivers and consequences vary across different spatial and temporal scales?

I integrate land change simulation model and ecohydrological models to address these broad questions.

  • Faculty:
  • Yang Shao
  • Associate Professor
  • College of Natural Resources and Environment (CNRE), Department of Geography
  • Education
    • Ph.D. Geography,UNC-Chapel Hill
    • MS studies Geography, Nanjing University
    • B.S. Geography, Nanjing University
  • Current Students:
  • Clay Wise (PhD student)
  • Adam Campos (PhD student)
  • Alexander Miele(MS student)
  • Former Students:
  • Heng Wan (PhD)
  • Alexander Rosenman (MS)
  • Ruoyu Zhang (MS)
  • Callie Lambert (MS) (co-chair with Lynn Resler)
  • Andy Skeen (MS)
  • Austin Cooner (MS)
  • Leyang Feng (MS)
  • Brandon Wheeler (MS)
  • Gina Li (Undergraduate Research)
  • Suwen Zhao (MS)
  • Haitao Wang (MS) (co-chair with Lisa M. Kennedy)
  • Justin White (MS) (co-chair with Lisa M. Kennedy)
  • Image processing and high performance geocomputation(move over image to enlarge)
  • Agricultural intensification

Research

  • Remote sensing digital image processing
  • Land change study (e.g., tropical deforestation, urban expansion, and agricultural extensification).
  • Ecohydrological modelling
  • High Performance Geocomputation
  • Webgis, Open Source GIS and RS:
    • Urban Forest Management - Virginia Beach Project
      • A New Tool to Inform Urban Forest Conservation and Restoration: Conservation and restoration (i.e., afforestation) of urban forests are potentially significant tools for local and landscape-scale planning aimed at reducing flood risk. Forests provide flood risk reduction in two primary ways: water storage and water removal. This study quantifies water storage (Soil Water Storage and Depressional Surface Water Storage ) and Evapotranspiration (ET) at high spatial resolution by integrating available spatial data (e.g., soil classes, land cover classes, LiDAR topography, and remotely sensed ET) and then compare these services among land cover types and scenarios of land use change. Using our integrated dataset, we developed a new web-based tool that can easily quantify flood reduction services for user-defined locations and, in doing so, inform decisions regarding future forest conservation and restoration efforts (Daniel McLaughlin, Yang Shao, Heng Wan, Brian van Eerden).
    • Norway grassland project
    • Chesapeake Bay watershed
  • Tropical deforestation
  • Urban expansion

Publications

(*denotes student collaborator)
Google Scholar
Year Article
2021 Wan, H.*, McLaughlin, D., Shao, Y., Eerden, B.V., Ranganathan, S., Deng, X. 2021. Remotely-sensed Evapotranspiration for Informed Urban Forest Management, Landscape and Urban Planning, https://doi.org/10.1016/j.landurbplan.2021.104069.
2021 Ren,J.*, Shao, Y.,Wan, H., Xie, Y., Campos, A., 2021. A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data, ISPRS Journal of Photogrammetry and Remote Sensing , 176, pp.69-82.
2021 Shao, Y.,Cooner, A.J., Walsh, S.J., 2021. Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping, Remote Sensing, 13(8), p.1523.
2020 Bhattarai, S.*, Kolivras, K. N., Ghimire, K., & Shao, Y, . 2020. Understanding the relationship between land use and land cover and malaria in Nepal, Geospatial Health, 15(2), https://doi.org/10.4081/gh.2020.855
2020 Iiames, J.S., Cooter, E., Pilant, A., Shao, Y., 2020.Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales, Remote Sensing, 12(17), p.2764
2020 Weiss, D. J., Nelson, A., Vargas-Ruiz, C. A., Gligori?, K., Bavadekar, S., Gabrilovich, E.,Bertozzi-Villa, A., Rozier, J., Gibson, H. S.,Shekel, T., Kamath, C., Lieber, A.,Schulman, K., Shao, Y., Qarkaxhija, V., Nandi, A. K., Keddie, S. H., Rumisha, S., Cameron, E., Battle, K. E., Bhatt, S., Gething, P. W. , 2020.Global maps of travel time to healthcare facilities, Nature Medicine, https://doi.org/10.1038/s41591-020-1059-1.
2020 Yan,X., Li, J., Shao, Y., Hu, Z., Yang, Z., Yin, S., Cui, L., 2020.Driving forces of grassland vegetation changes in Chen Barag Banner, Inner Mongolia GIScience and Remote Sensing, DOI: 10.1080/15481603.2020.1794395. .
2020 Lambert, C.B.*, Resler, L.M., Shao, Y., Butler, D.R., 2020.Vegetation change as related to terrain factors at two glacier forefronts, Glacier National Park, Montana, U.S.A. Journal of Mountain Science, 85(10),715-724.
2019 Wan, H.*, Shao, Y., Campbell, J.B., Deng, X.W. 2019. Mapping annual urban change using time-series Landsat data and NLCD. Photogrammetric Engineering and Remote Sensing, 85(10),715-724.
2019 Poor,E.E*, Shao, Y., and Kelly, M.J. 2019. Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050. Journal of Environmental Management, Volume 231, Pages 397-404.
2019 Tran, H.T.*, Campbell, J.B., Wynne, R.H., Shao, Y. , and Phan, S.V., 2019. Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area Remote Sensing, 11(3), p.333.
2018 Taff, G.N., Shao, Y., Ren, J.*, and Zhang, R.*, 2018. Image Classification by Integrating Reject Option and Prior Information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI:10.1109/JSTARS.2018.2870978.
2018 Jensen, C. K.*, McGuire, K. J., Shao, Y., and Andrew Dolloff, C. 2018. Modeling wet headwater stream networks across multiple flow conditions in the Appalachian Highlands. Earth Surf. Process. Landforms, https://doi.org/10.1002/esp.4431.
2017 Ren, J.*, Campbell, J.B., Shao, Y.,2017. Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops. Remote Sensing, 2017, 9(7), 722; doi:10.3390/rs9070722
2017 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 Reports, 7: 2792|DOI:10.1038/s41598-017-03026-y
2017 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.
2016 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.
2016 Shao, Y. , Ross S. Lunetta, Brandon Wheeler*, John S. Iiames and James B. Campbell. 2016. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sensing of Environment, 174, pages 258-265.
2016 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.
2016 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.
2016 Chen, G., Glasmeier, A.K., Zhang, Min., 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

2016 Gonzalo Ferreira, Eleonor L Cayford, Leyang Feng*, Shao, Y. , Marcos Isla Casares. 2016. Use of satellite remote-sensing techniques to predict the variation of the nutritional composition of corn (Zea mays L) for silage. Maydica , 61:M7.
2016 Shao, Y. , Ren, J.*, Campbell, J.B. 2016. Multi-temporal remote sensing data analysis for agricultural application. In Remote Sensing Applications for Societal Benefits edited by Stephen J. Walsh and published by Elsevier

2015 Shao, Y. , Campbell, J.B., Taff, G.N. and Zheng, B. 2015. An Analysis of Cropland Mask choice and Ancillary Data for Annual Corn Yield Forecasting using MODIS data. International Journal of Applied Earth Observation and Geoinformation, Volume 38, Pages 78-87

2015 Shao, Y. , Gina L. Li*, Eric Guenther*, Campbell, J.B. 2015. Evaluation of topographic correction on sub-pixel impervious cover mapping with CBERS-2B data. IEEE Geoscience and Remote Sensing Letters, DOI10.1109/LGRS.2015.2419135
2014 Wang, H.*, Shao, Y. , Kennedy, L. 2014. Temporal generalization of sub-pixel vegetation mapping with multiple machine learning and atmospheric correction algorithms. International Journal of Remote Sensing, 35(20), pages 7118-7135.
2014 Resler, Lynn M., Shao, Y., Tomback, Diana F., and Malanson, George P. 2014. Predicting functional role and occurrence of Whitebark Pine (Pinus albicaulis)at alpine treelines: Model accuracy and variable importance. Annals of the Association of American Geographers, vol. 104(4),703-722
2013 Zheng, B.*, Campbell, J.B., Shao, Y., Wynne, R.H., 2013. Broad-scale monitoring of tillage practices using sequential Landsat imagery, Soil Science Society of America Journal, Vol. 77 No. 5, p. 1755-1764.
2013 White, J.*, Shao, Y., Kennedy, L., Campbell, J.B., 2013. Landscape Dynamics on the Island of La Gonave, Haiti, 1990-2010. Land 2(3):493-507.
2013 Macpherson, A.J., Principe, P.P., Shao, Y., 2013. Controlling for exogenous environmental variables when using data envelopment analysis for regional environmental assessments, Journal of Environmental Management, 119, 220-229.
2013 Shao,Y., Lunetta, R., Macpherson, A., Chen, G., Luo, J., 2013. Assessing sediment yield for selected watersheds in the Great Lakes Basin for future agricultural land use change scenarios, Environmental Management, 51:59-69.
2012 Shao,Y., Lunetta, R., 2012. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78-87.
2011 Shao,Y., Taff, G.N., Walsh, S.J., 2011. Shadow detection and building height estimation using IKONOS data. International Journal of Remote Sensing, , 32(22), 6929-6944.
2011 Shao, Y. , Taff, G.N., Lunetta, R, 2011. A review of selected MODIS algorithms, data products, and applications, in Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, edited by Qihao, Weng and published by CRC/Taylor and Francis, pages 31-55.

2011 Yu, Shaocai, Rohit Mathur, Jonathan Pleim, David Wong, Annmarie G. Carlton, Shawn Roselle, S.T. Rao, and Shao, Y. , 2011.Simulation of the Indirect Radiative Forcing of Climate Due to Aerosols by the Two-Way Coupled WRF-CMAQ over the Eastern United States, NATO Science for Peace and Security Series C: Environmental Security, 2012, 4, Part 5, 579-583. .

2010 Shao,Y., Lunetta, R., 2010. Sub-pixel mapping of tree canopy, impervious surfaces and cropland in the Laurentian Great Lakes Basin using MODIS time-series data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2), 336-347.
2010 Shao,Y., Taff, G.N., Walsh, S.J., 2010. Comparison of early stop criteria for neural network-based sub-pixel classification. IEEE Geoscience and Remote Sensing letters, 8(1), 113-117.
2010 Shao, Y., Lunetta, R., Ediriwickrema, J., Iiames, J., 2010. Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogrammetric Engineering and Remote Sensing, 75(1), 73-84.
2010 Lunetta,R., Shao, Y., Ediriwickrema, J., Lyon, J.G., 2010. Monitoring agricultural cropping patterns in the Great Lakes Basin using MODIS-NDVI data. International Journal of Applied Earth Observation and Geoinformation, 12, 81-88.
2008 Shao,Y., , Walsh, S.J., Entwisle, B., Rindfuss, R.R., 2008. Spatial clustering and urban settings of rural migrants in Bangkok, Thailand. GeoCarto International, 23, 1, 32-52
2008 Walsh, S.J., Shao, Y., Mena, C.F., McCleary, A.L., 2008. Integration of Hyperion satellite data and a household social survey to characterize the causes and consequences of reforestation patterns in the Northern Ecuadorian Amazon. Photogrammetric Engineering and Remote Sensing, 74(6), 725-735.
2008 Walsh, S.J., McCleary, A.L., Mena, C.F., Shao, Y., Tuttle,J.P., Gonzalez, A., and Atkinson,R.,2008. QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management, Remote Sensing of Environment, 112, 1927-1941.

Teaching

  • GEOG4084/5084 Modelling with GIS (Fall 2018)
    • Classes taught:
  • GEOG4984/5984 Programming for Geospatial Research (R or Python)
  • GEOG4334/5334 Land Change Modelling
  • NR6104 Advanced Topics in Remote Sensing
  • GEOG4374/5374 Remote Sensing and Phenology

Prospective Students

If you are interested in graduate school and research in my group, please contact me through email (yshao@vt.edu) or phone (540-231-1867).

Contact us

Email: yshao@vt.edu
Phone: 540-231-1867
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