Yang Shao

• Modeling in GIS/Advanced Modeling in GIS (GEOG  4084/5084)
• Programming for Geospatial Research (R or Python) (GEOG 4984/5984G)
• Land Change Modelling (GEOG 4334/5334)
• Advanced Topics in Remote Sensing (NR 6104)
• Remote Sensing and Phenology (GEOG 4374/5374)

• Jack Tolar. 2024. M.S. Modeling urban growth in Hampton Roads, Va: A statistical and machine learning approach.
  Professor(s): Yang Shao
• Alexander Miele. 2023. M.S. Linking streamflow trends with land cover change in a Southern U.S. water tower.
  Professor(s): Yang Shao
• Heng Wan. 2020. Ph.D. Assessing Annual Urban Change and its Impacts on Evapotranspiration.
  Professor(s): Yang Shao
• Alexander Rosenman. 2020. M.S. Multiscale Remote Sensing Analysis of Vegetation Patterns in an Alpine Environment, Glacier National Park, Montana.
  Professor(s): Yang Shao
• Callie Lambert. 2018. M.S. patio-Temporal Vegetation Change as Related to Terrain Factors at Two Glacier Forefronts, Glacier National Park, Montana.
  Professor(s): Lynn M. Resler and Yang Shao
• Ruoyu Zhang. 2018. M.S. An Evaluation of a Data-Driven Approach to Regional Scale Surface Runoff Modelling.
  Professor(s): Yang Shao
• Andy Skeen. 2017. M.S. Estimating Impervious Surface Cover in Flathead County, Montana.
  Professor(s): Yang Shao
• Austin Cooner. 2016. M.S. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake.
  Professor(s): Yang Shao
• Leyang Feng. 2015. M.S. Sensitivity Analysis of Hot/Cold Pixel Selection in SEBAL Model for ET Estimation.
  Professor(s): Yang Shao
• Brandon Wheeler. 2015. M.S. Evaluating Time-Series Smoothing Algorithms for Multi-Temporal Land Cover Classification.
  Professor(s): Yang Shao
• Suwen Zhao. 2015. M.S. Simulating Urban Growth for Baltimore-Washington Metropolitan Area by Coupling SLEUTH Model and Population Projection.
  Professor(s): Yang Shao
• Haitao Wang. 2014. M.S. Monitoring Vegetation Dynamics in Zhongwei, an Arid City of Northwest China.
  Professor(s): Lisa Kennedy and Yang Shao

Physically Informed, Equitable, & Efficient Hurricane Surge Characterization.
Sponsor: US Army Corps of Engineers 
Investigator(s): Irish (PI), Bensi (Co-PI) and Shao (Co-PI). 
Amount: $500,000
2024-2026

Harmful Algal Bloom (HAB) Study for Smith Mountain Lake. 
Sponsor: Virginia Department of Environmental Quality
Investigator(s): McLaughlin (PI), Shao (Co-PI), Walker (Co-PI), and McGuire (Co-PI).
Amount: $150,000
2024-2025

Mapping school bus depots of the U.S. using machine learning and google earth engine.
Sponsor: World Resources Institute
Investigator(s): Shao (PI).
$40,000
2023

Flood Reduction Potential of Urban Forests in Virginia Beach. Phase II: Stormwater Modeling to Quantify Runoff Reduction. Sposor: City of Virginia Beach 
Investigator(s): Sample (PI), McLaughlin (Co-PI), Shao (Co-PI).
Amount: $60,000
2020-2023

Conserving Urban Forests to Reduce Flooding.
Sponsor: The Nature Conservancy
Investigator(s): McLaughlin (PI), Shao (Co-PI).
Amount: $70,500
2020-2023

• Ren, J., Shao, Y., & Wang, Y. (2024). Annual Cropping Intensity Dynamics in China from 2001 to 2023. Remote Sensing, 16(24), 4801.
  https://doi.org/10.3390/rs16244801
• Ma, B., Shao, Y., Yang, H., Lu, Y., Gao, Y., Wang, X., ... & Wang, X. (2024). Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models. Remote Sensing, 16(20), 3759. 
  https://doi.org/10.3390/rs16203759
• Yan, X., Li, J., Shao, Y., Ma, T., Zhang, R., & Su, Y. (2024). Assessing open-pit mining impacts on semi-arid grassland: A framework for boundary and intensity quantification. Journal of Cleaner Production, 475, 143464. 
  https://doi.org/10.1016/j.jclepro.2024.143464
• Nourali, Z., Shortridge, J. E., Bukvic, A., Shao, Y., & Irish, J. L. (2024). Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding. Water, 16(2), 263. 
  https://doi.org/10.3390/w16020263
• Schneider, M., Winchester, C., Goldman, E., & Shao, Y. (2023). Mapping cocoa and assessing deforestation risk for the cocoa sector in Côte d’Ivoire and Ghana. Technical Note. Washington, DC: World Resources Institute. https://www.wri.org/re search/mapping-cocoa-assessing-deforestation-risk-cocoa-cote-divoire-ghana.
• Bukvic, A., Mitchell, A., Shao, Y., & Irish, J. L. (2023). Spatiotemporal implications of flooding on relocation risk in rural and urban coastal municipalities. Land Use Policy, 132, 106754. 
  https://doi.org/10.1016/j.landusepol.2023.106754
• Islam, M. S., Crawford, T. W., & Shao, Y. (2023). Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh. Frontiers in Environmental Science, 11, 1144686. 
  https://doi.org/10.3389/fenvs.2023.1144686
• Mitchell, A., Bukvic, A., Shao, Y., Irish, J. L., & McLaughlin, D. L. (2023). Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale. Environmental Management, 71(4), 741-754. 
  https://doi.org/10.1007/s00267-022-01759-9
• Naurath, B., Irish, J. L., & Shao, Y. (2023). Using an Urban Growth Model Framework to Project the Impacts of Climate Change on Coastal Populations. In World Environmental and Water Resources Congress 2023 (pp. 405-412). 
  https://doi.org/10.1061/9780784484852.0
  
• Shao, Y., Lunetta, R., Macpherson, A. J., Luo, J., & Chen, G. (2022). SWAT Modeling of Sediment Yields for Selected Watersheds in the Laurentian Great Lakes Basin. In Geospatial Information Handbook for Water Resources and Watershed Management, Volume I (pp. 177-195). CRC Press.
• Lunetta, R., Lyon, J. G., Shao, Y., & Ediriwickrema, J. (2022). Monitoring Common Agricultural Cropping Across the US and Canadian Laurentian Great Lakes Basin Watershed Using MODIS-NDVI Data. In Geospatial Information Handbook for Water Resources and Watershed Management, Volume I (pp. 155-175). CRC Press.
• Hu, Z., Ranganathan, S., Shao, Y., & Deng, X. (2022). Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information. arXiv preprint arXiv:2209.08421. https://doi.org/10.48550/arXiv.2209.08421
• 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, 69-82. 
  https://doi.org/10.1016/j.isprsjprs.2021.04.007
• Wan, H., McLaughlin, D., Shao, Y., van Eerden, B., Ranganathan, S., & Deng, X. (2021). Remotely-sensed evapotranspiration for informed urban forest management. Landscape and Urban Planning, 210, 104069. 
  https://doi.org/10.1016/j.landurbplan.2021.104069
• 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), 1523. 
  https://doi.org/10.1016/j.landurbplan.2021.104069
• Iiames, J. S., Cooter, E., Pilant, A. N., & Shao, Y. (2020). Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales. Remote sensing, 12(17), 2764. 
  https://doi.org/10.3390/rs12172764
• 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 & Remote Sensing, 57(6), 753-769. 
  https://doi.org/10.1080/15481603.2020.1794395
• Resler, L. M., Shao, Y., Campbell, J. B., & Michaels, A. (2020). Land cover and land use change in an emerging national park gateway region: Implications for mountain sustainability. In The Elgar companion to geography, transdisciplinarity and sustainability (pp. 270-292). Edward Elgar Publishing. 
  https://doi.org/10.4337/9781786430106.00026
• Walsh, S. J., Brewington, L., Laso, F., Shao, Y., Bilsborrow, R. E., Arce Nazario, J., ... & Pizzitutti, F. (2020). Social-ecological drivers of land cover/land use change on islands: A synthesis of the patterns and processes of change. Land Cover and Land Use Change on Islands: Social & Ecological Threats to Sustainability, 63-88. 
  https://doi.org/10.1007/978-3-030-43973-6_3
• Shao, Y., Wan, H., Rosenman, A., Laso, F. J., & Kennedy, L. M. (2020). Evaluating land cover change on the Island of Santa Cruz, Galapagos Archipelago of Ecuador through cloud-gap filling and multi-sensor analysis. Land Cover and Land Use Change on Islands: Social & Ecological Threats to Sustainability, 167-182.
  https://doi.org/10.1007/978-3-030-43973-6_7
• 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, USA. Journal of Mountain Science, 17(1), 1-15.
  https://doi.org/10.1007/s11629-019-5603-8
• Wan, H., Shao, Y., Campbell, J. B., & Deng, X. (2019). Mapping annual urban change using time series Landsat and NLCD. Photogrammetric Engineering & Remote Sensing, 85(10), 715-724. 
  https://doi.org/10.14358/PERS.85.10.715
• Tran, H. T., Campbell, J. B., Wynne, R. H., Shao, Y., & Phan, S. V. (2019). Drought and human impacts on land use and land cover change in a Vietnamese coastal area. Remote Sensing, 11(3), 333. 
  https://doi.org/10.3390/rs11030333
• Poor, E. E., Shao, Y., & Kelly, M. J. (2019). Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050. Journal of environmental management, 231, 397-404. 
  https://doi.org/10.1016/j.jenvman.2018.10.065
• 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. 
  https://doi.org/10.1109/JSTARS.2018.2870978
• Jensen, C.*, McGuire, K., Shao, Y. 2018. Modeling headwater stream networks across multiple flow conditions in the Appalachian Highlands. Earth Surface Processes and Landforms. 
  https://doi.org/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. 
  https://doi.org/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. 
  https://doi.org/10.14358/PERS.83.6.429
• Shao, Y., Lunetta, R. S., Wheeler, B., Iiames, J. S., & Campbell, J. B. (2016). An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sensing of Environment, 174, 258-265. 
  https://doi.org/10.1016/j.rse.2015.12.023
• 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. 
  https://doi.org/10.1016/j.isprsjprs.2016.10.009
• 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. 
  https://doi.org/10.1016/j.agsy.2016.07.007
• 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. 
  https://doi.org/10.1371/journal.pone.0158826

*student collaborator