Michael Sunde, PhD

Assistant Research Professor

School of Natural Resources

Contact Information

Email sundem@missouri.edu
Address 333 Anheuser-Busch Natural Resources Building


  • PhD, University of Missouri, Natural Resources

Research Summary

  • Sunde’s research focuses on using quantitative modeling, geospatial and remote sensing approaches to evaluate the impacts of stressors such as urbanization, deforestation and climatic changes on the environment and to map ecosystems at large scales, with an emphasis on watershed hydrology. His work uses an array of geographic, climatic and remotely sensed data, along with machine learning and physically based models to produce information that can be of utility to planners and decision makers.

Selected Publications

  • Ma, T., Liang, Y., Sunde, M. G., Lau, M. K., Liu, B., Wu, M. M., & He, H. S. (2021). Assessing the effects of climate variable and timescale selection on uncertainties in dryness/wetness trends in conterminous China. International Journal of Climatology, 41(5), 3058–3070. https://doi.org/10.1002/joc.7005
  • Sunde, M. G., Diamond, D. D., Elliott, L. F., Hanberry, P., & True, D. (2020). Mapping high-resolution percentage canopy cover using a multi-sensor approach. Remote Sensing of Environment, 242, 111748. https://doi.org/10.1016/j.rse.2020.111748
  • Dang, Y., He, H., Zhao, D., Sunde, M., & Du, H. (2020). Quantifying the Relative Importance of Climate Change and Human Activities on Selected Wetland Ecosystems in China. Sustainability, 12(3), 912. https://doi.org/10.3390/su12030912
  • Sunde, M. G., He, H. S., Hubbart, J. A., & Urban, M. A. (2018). An integrated modeling approach for estimating hydrologic responses to future urbanization and climate changes in a mixed-use midwestern watershed. Journal of Environmental Management, 220. https://doi.org/10.1016/j.jenvman.2018.05.025
  • Fu, Y., He, H. S., Zhao, J., Larsen, D. R., Zhang, H., Sunde, M. G., & Duan, S. (2018). Climate and spring phenology effects on autumn phenology in the Greater Khingan Mountains, northeastern China. Remote Sensing, 10(3). https://doi.org/10.3390/rs10030449
  • Sunde, M. G., He, H. S., Hubbart, J. A., & Urban, M. A. (2017). Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed-use watershed. Hydrological Processes, 31(9), 1790–1803. https://doi.org/10.1002/hyp.11150
  • Sunde, M., He, H. S., Hubbart, J. A., & Scroggins, C. (2016). Forecasting streamflow response to increased imperviousness in an urbanizing Midwestern watershed using a coupled modeling approach. Applied Geography. https://doi.org/10.1016/j.apgeog.2016.05.002
  • Chinnasamy, P., & Sunde, M. G. (2016). Improving spatiotemporal groundwater estimates after natural disasters using remotely sensed data – a case study of the Indian Ocean Tsunami. Earth Science Informatics, 9(1), 101–111. https://doi.org/10.1007/s12145-015-0238-y
  • Sunde, M. G., He, H. S., Zhou, B., Hubbart, J. A., & Spicci, A. (2014). Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2014.01.007