Song, Xiaopeng

Bio

Dr. Xiaopeng Song is an Assistant Professor in the Department of Geographical Sciences. His research combines satellite observations, machine learning/AI, spatial analysis, and interdisciplinary approaches to examine human-induced land system changes. His work has broad implications for issues such as food and energy security, and climate change. Dr. Song has published 60 peer-reviewed articles in journals such as Nature, Nature Climate Change, Nature Food, Nature Sustainability, Proceedings of the National Academy of Sciences, Remote Sensing of Environment and Science Advances. He was recognized by Clarivate as a Highly Cited Researcher in Cross-Field. His current research is supported by NASA, USGS, World Resources Institute and Google. He was a Co-Investigator of the Landsat Science Team (2018-2023). He was a chapter lead for the CEOS (Committee on Earth Observation Satellites) Land Cover and Change Map Accuracy Assessment and Area Estimation Good Practices Protocol. He served as the area editor for the Land Surface Type volume of the book Comprehensive Remote Sensing (2nd edition). He co-edited a special issue on Land Cover and Land Use Change in Conflicted Societies in Science of Remote Sensing. Dr. Song has delivered invited lectures to organizations and professional societies including USDA, INPE, Conab, Bloomberg, and IEEE GRASS. He has contributed materials to public media including BBC, NPR and Reuters. At the University of Maryland, he is the instructor for several courses ranging from large General Education courses (240 enrollments) to upper-level technical courses in remote sensing (30-40 enrollments). 

Dr. Song publishes under the name Xiao-Peng Song.

Degrees

  • Geographical Sciences, University of Maryland, 2015 - PhD

  • Geographical Information Science and Economics (double major), Peking University, 2008 - BS

Areas of Interest

  • Land cover and land use change
  • Tropical deforestation
  • Crop mapping and area estimation
  • Land use in sustainable energy transition

Research Topics

GEOG201/211 Geography of Environmental Systems

A systematic introduction to the processes and associated forms of the atmosphere and earth's surfaces emphasizing the interaction between climatology, hydrology and geomorphology. This is a GenEd course with 240 enrollments. GEOG201 fulfills a Distributive Studies - Natural Sciences with Lab (DSNL) requirement if taken with GEOG211

GEOG371 Programming for Image Analysis 

The course will provide an introduction to application programing interface (API) functions and image processing techniques for efficient processing of satellite images. The main programing language of the course is Python. The course will use a Geospatial Data Abstraction Library (GDAL), which provides a unified way of manipulating images incorporating geospatial information. For image processing, the course will use Python-based libraries such as scikit-image and OpenCV.

GEOG417/617 Land Cover Characterization Using Multi-Spectral Remotely Sensed Data Set 

Students will be introduced to the image processing steps required for characterizing land cover extent and change. Key components of land cover characterization, including image interpretation, algorithm implementation, feature space selection, thematic output definition, and scripting will be discussed and implemented.

  • Li, H., Song, X.-P., Hansen, M.C. et al. (2023). Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation. Remote Sensing of Environment, 294, p.113623.
    • Meng, Z. et al. Post-2020 biodiversity framework challenged by cropland expansion in protected areas. Nat Sustain (2023). https://doi.org/10.1038/s41893-023-01093-w
      • Dai Y. et al. Coastal phytoplankton blooms expand and intensify in the 21st century. Nature 615, 280–284 (2023). https://doi.org/10.1038/s41586-023-05760-y
        • Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., & Townshend, J.R. (2018). Global land change from 1982 to 2016. Nature, 560, 639-643
          • Eigenbrod, F., Beckmann, M., Dunnett, S., Graham, L., Holland, R.A., Meyfroidt, P., Seppelt, R., Song, X.P., Spake, R., Vaclavik, T., & Verburg, P.H. (2020). Identifying Agricultural Frontiers for Modeling Global Cropland Expansion. One Earth, 3, 504-514
            • Hansen, M.C., Wang, L., Song, X.-P., Tyukavina, A., Turubanova, S., Potapov, P.V., & Stehman, S.V. (2020). The fate of tropical forest fragments. Science Advances, 6, eaax8574
              • Dr. Song's philosophy centers on fostering community, supporting students and colleagues, and advancing the shared mission of the department and university.

              Campus - University of Maryland High-Performance Computing Allocations and Advisory Committee member

               

              Personal Website(s)

              XiaopengSong

              Xiaopeng Song
              Assistant Professor

              | 4600 River Road, Suite 363 (send mail to 2181 LeFrak)

              | xpsong@umd.edu

              | (301) 405-0809