University of Tennessee, Knoxville
Geospatial Data Science

Geographic Information Science (GIS), Remote Sensing, Environmental Modeling, Big Geospatial Data Analytics, Wetland Mapping, Surface Water Hydrology, LiDAR, GIS Programming, Python, R, Google Earth Engine

Tell Me More

About Me

Qiusheng Wu, PhD
（中文名：吴秋生）

Assistant Professor
University of Tennessee
Curriculum Vitae
My faculty page @ UTK
My faculty page @ UTK Geography

I am an Assistant Professor in the Department of Geography at the University of Tennessee, Knoxville (田纳西大学). My research interests include Geographic Information Science (GIS), remote sensing, and environmental modeling. More specifically, I am interested in applying geospatial big data, machine learning, and cloud computing (e.g., Google Earth Engine) to study environmental change, especially surface water and wetland inundation dynamics. I am a strong advocate of open science and reproducible research. I have developed and published various open-source packages for advanced geospatial analysis, such as geemap, lidar, whitebox-python, whiteboxR, and WhiteboxTools-ArcGIS. Check out my research and blog for more information about what I am currently working on.

Academic Profiles:
Google Scholar | ResearchGate | ORCID | Publons | GitHub | Medium | Figshare | Blog | YouTube

Education:
• 2015   Ph.D. in Geography. University of Cincinnati, Ohio, USA
• 2011   M.A. in Geography. University of Cincinnati, Ohio, USA
• 2007  B.S. (with high honor) in GIS. Sun Yat-sen University, Guangzhou, China

Appointments:
• 2019–now   Assistant Professor, Department of Geography, University of Tennessee
• 2018–2019   Graduate Director, Department of Geography, Binghamton University (SUNY)
• 2015–2019   Assistant Professor, Department of Geography, Binghamton University (SUNY)
• 2014–2015   Remote Sensing Specialist, U.S. Environmental Protection Agency (EPA)

Editorships:
• 2018–present   Associate Editor, Remote Sensing, MDPI
• 2016–present   Associate Editor, Wetlands, Springer

Open-source Packages/Apps for Geospatial Analysis:
• R Packages: whiteboxR
• Python Packages: geemap | lidar | whitebox | geehydro | geoscale | pygis | python-geospatial
• Google Earth Engine: Wetland Inundation Mapping | Earth Engine Apps | QGIS EE Examples
• ArcGIS Toolboxes: Depression Identification Analyst | Wetland Hydrology Analyst | Drumlin Extraction Toolbox | Level-Set Toolbox | WhiteboxTools-ArcGIS

My YouTube channel for Earth Engine Python Tutorials:
youtube

Research

Geospatial Data Science: the science that investigates the 'where' and 'why' of various human and natural phenomena!
As geographers, we discover deeper insights, make better decisions, and take them to action.

GIScience

Spatial Analysis, Geostatistics, WebGIS, GIS Programming, Python, R

Remote Sensing

LiDAR, Microwave, Big Data, Deep Learning, Google Earth Engine, Azure

Environmental Modeling

Hydrological Modeling, Wetland Hydrology, Spatiotemporal Analytics

Software Tools

Open-source packages/apps for geospatial and hydrological analysis

Tutorials, Maps, and Data

Various tutorials, maps, and datasets for reproducible research

Google Slides Show

Demos of my research projects and refereed publications

Publications

Total: 43; First-author: 12; Citations: Google Scholar; Source code: Figshare

  1. Amani, M., Ghorbanian, A., Ahmadi, A., Kakooei, M., ..., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
  2. Chen, W., Zhou, Y., Wu, Q., Chen, G., Huang, X., & Yu, B. (2020). Urban building type mapping using geospatial data: a case study of Beijing, China. Remote Sensing, 12(17), 2805. https://doi.org/10.3390/rs12172805
  3. Aybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). rgee: An R package for interacting with Google Earth Engine. The Journal of Open Source Software. 5(51), 2272. https://doi.org/10.21105/joss.02272 ( source code )
  4. Wu, Q. (2020). geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software. 5(51), 2305. https://doi.org/10.21105/joss.02305 ( source code )
  5. Wu, B., Yu, B., Shu, S., Wu, Q., Zhao, Y., & Wu, J. (2020). A spatiotemporal structural graph for detecting land cover changes. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2020.1778706
  6. Rajib, A., Golden, H. E., Lane, C. R., & Wu, Q. (2020). Surface depression and wetland water storage improves major river basin hydrologic predictions. Water Resources Research. https://doi.org/10.1029/2019WR026561
  7. Liu, X., Huang, Y., Xu, X., Li, X., et al., & Wu, Q., Huang, K., Estes, L., & Zeng, Z. (2020). High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability. https://doi.org/10.1038/s41893-020-0521-x
  8. Berhane T., Lane, C. R., Mengistu, S., Christensen, J. R., Golden, H. E., Qiu, S., Zhu, Z., & Wu, Q. (2020). Land-cover changes to surface-water buffers in the Midwestern USA: 25 years of Landsat analyses (1993-2017). Remote Sensing, 12(5), 754. https://doi.org/10.3390/rs12050754
  9. Wang, C., Chen, Z., Yang, C., Li, Q., Wu, Q., Wu, J., Zhang, G., & Yu, B. (2020). Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data. International Journal of Applied Earth Observation and GeoInformation, 85, 101989. https://doi.org/10.1016/j.jag.2019.101989
  10. Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick B., Zhang, X., & Brown, M. (2019). Detect change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, 111181. https://doi.org/10.1016/j.rse.2019.04.034 ( source code )
  11. Li, X., Zhou, Y., Meng, L., Asrar, G. R., Lu, C., & Wu, Q. (2019). A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth System Science Data, 11(2), 881-894. https://doi.org/10.5194/essd-11-881-2019
  12. Golden, H. E., Rajib, A., Lane, C. R., Christensen, J. R., Wu, Q., & Mengistu, S. (2019). Non-Floodplain Wetlands Affect Watershed Nutrient Dynamics: A Critical Review. Environmental Science & Technology, 53 (13), 7203-7214. https://doi.org/10.1021/acs.est.8b07270
  13. Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015 ( source code )
  14. Wu, B., Yu, B., Yao, S., Wu, Q., Chen, Z., & Wu, J. (2019). A surface network based method for studying urban hierarchies by night time light remote sensing data. International Journal of Geographical Information Science, 33(7), 1377-1398. https://doi.org/10.1080/13658816.2019.1585540
  15. Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., & Liu, H. (2019). Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method. JAWRA Journal of the American Water Resources Association, 55(2), 354-368. https://doi.org/10.1111/1752-1688.12689 ( source code )
  16. Beck, R., Xu, M., Zhan, S., Johansen, R., Liu, H., Tong, S., ... & Wu, Q. (2019). Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations. Journal of Great Lakes Research, 45(3), 413-433. https://doi.org/10.1016/j.jglr.2018.09.001
  17. Yu, B., Tang, M., Wu, Q., Yang, C., Deng, S., Shi, K., ... & Chen, Z. (2018). Urban built-up area extraction from log-transformed NPP-VIIRS nighttime light composite data. IEEE Geoscience and Remote Sensing Letters, 15(8), 1279-1283. https://doi.org/10.1109/LGRS.2018.2830797
  18. Berhane, T. M., Lane, C. R., Wu, Q., Autrey, B. C., Anenkhonov, O., Chepinoga, V., & Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sensing, 10(4), 580. https://doi.org/10.3390/rs10040580
  19. Wu, Q. (2018). GIS and Remote Sensing Applications in Wetland Mapping and Monitoring. In: Huang, B. (Ed.), Comprehensive Geographic Information Systems, Vol. 2, pp. 140-157. Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.10460-9
  20. Berhane, T. M., Lane, C. R., Wu, Q., Anenkhonov, O., Chepinoga, V., Autrey, B. C., & Liu, H. (2018). Comparing pixel-and object-based approaches in effectively classifying wetland-dominated landscapes. Remote Sensing, 10(1), 46. https://doi.org/10.3390/rs10010046
  21. Wu, B., Yu, B., Wu, Q., Chen, Z., Yao, S., Huang, Y., & Wu, J. (2018). An extended minimum spanning tree method for characterizing local urban patterns. International Journal of Geographical Information Science, 32(3), 450-475. https://doi.org/10.1080/13658816.2017.1384830
  22. Ye, Z., Liu, H., Chen, Y., Shu, S., Wu, Q., & Wang, S. (2017). Analysis of water level variation of lakes and reservoirs in Xinjiang, China using ICESat laser altimetry data (2003–2009). PloS One, 12(9), e0183800. https://doi.org/10.1371/journal.pone.0183800
  23. Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017 ( source code )
  24. Jia, Y., Huang, Y., Yu, B., Wu, Q., Yu, S., Wu, J., & Wu, J. (2017). Downscaling land surface temperature data by fusing Suomi NPP-VIIRS and landsat-8 TIR data. Remote Sensing Letters, 8(12), 1132-1141. https://doi.org/10.1080/2150704X.2017.1362125
  25. Chen, Z., Yu, B., Song, W., Liu, H., Wu, Q., Shi, K., & Wu, J. (2017). A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6305-6319. https://doi.org/10.1109/TGRS.2017.2725917
  26. Wang, S., Wu, Q.*, & Ward, D. (2017). Automated delineation and characterization of drumlins using a localized contour tree approach. International Journal of Applied Earth Observation and GeoInformation, 62, 144-156. https://doi.org/10.1016/j.jag.2017.06.006 ( source code )
  27. Beck, R., Xu, M., Zhan, S., Liu, H., Johansen, R., Tong, S., ... & Wu, Q. (2017). Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing, 9(6), 538. https://doi.org/10.3390/rs9060538
  28. Wu, B., Yu, B., Wu, Q., Yao, S., Zhao, F., Mao, W., & Wu, J. (2017). A graph-based approach for 3D building model reconstruction from airborne LiDAR point clouds. Remote Sensing, 9(1), 92. https://doi.org/10.3390/rs9010092
  29. Wu, Q., Su, H., Sherman, D. J., Liu, H., Wozencraft, J. M., Yu, B., & Chen, Z. (2016). A graph-based approach for assessing storm-induced coastal changes. International Journal of Remote Sensing, 37(20), 4854-4873. https://doi.org/10.1080/01431161.2016.1225180
  30. Wu, B., Yu, B., Wu, Q., Huang, Y., Chen, Z., & Wu, J. (2016). Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. International Journal of Applied Earth Observation and Geoinformation, 52, 82-94. https://doi.org/10.1016/j.jag.2016.06.003
  31. Wu, Q., Deng, C., & Chen, Z. (2016). Automated delineation of karst sinkholes from LiDAR-derived digital elevation models. Geomorphology, 266, 1-10. https://doi.org/10.1016/j.geomorph.2016.05.006 ( source code )
  32. Beck, R., Zhan, S., Liu, H., Tong, S., Yang, B., Xu, M., ... & Wu, Q. (2016). Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing of Environment, 178, 15-30. https://doi.org/10.1016/j.rse.2016.03.002
  33. Wu, Q., & Lane, C. R. (2016). Delineation and quantification of wetland depressions in the Prairie Pothole Region of North Dakota. Wetlands, 36(2), 215-227. https://doi.org/10.1007/s13157-015-0731-6 ( source code )
  34. Wu, B., Yu, B., Huang, C., Wu, Q., & Wu, J. (2016). Automated extraction of ground surface along urban roads from mobile laser scanning point clouds. Remote Sensing Letters, 7(2), 170-179. https://doi.org/10.1080/2150704X.2015.1117156
  35. Wu, Q., Liu, H., Wang, L., & Deng, C. (2016). Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and GeoInformation, 45, 187-199. https://doi.org/10.1016/j.jag.2015.10.011
  36. Su, H., Liu, H., & Wu, Q. (2015). Prediction of water depth from multispectral satellite imagery—The regression kriging alternative. IEEE Geoscience and Remote Sensing Letters, 12(12), 2511-2515. https://doi.org/10.1109/LGRS.2015.2489678
  37. Wu, Q., Liu, H., Wang, S., Yu, B., Beck, R., & Hinkel, K. (2015). A localized contour tree method for deriving geometric and topological properties of complex surface depressions based on high-resolution topographic data. International Journal of Geographical Information Science, 29(12), 2041-2060. https://doi.org/10.1080/13658816.2015.1038719 ( source code )
  38. Lane, C. R., Liu, H., Autrey, B. C., Anenkhonov, O., Chepinoga, V., & Wu, Q. (2014). Improved wetland classification using eight-band high resolution satellite imagery and a hybrid approach. Remote Sensing, 6(12), 12187-12216. https://doi.org/10.3390/rs61212187
  39. Wu, Q., Lane, C. R., & Liu, H. (2014). An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sensing, 6(11), 11444-11467. https://doi.org/10.3390/rs61111444
  40. Townsend‐Small, A., Pataki, D. E., Liu, H., Li, Z., Wu, Q., & Thomas, B. (2013). Increasing summer river discharge in southern California, USA, linked to urbanization. Geophysical Research Letters, 40(17), 4643-4647. https://doi.org/10.1002/grl.50921
  41. Beck, R. A., Hinkel, K. M., Eisner, W. R., Whiteman, D., Arp, C. D., Machida, R., ... & Wu, Q. (2013). Contrasting Historical and Recent Breakup Styles on the Meade River of Arctic Alaska in the Context of a Warming Climate. American Journal of Climate Change, 2(02), 165-172. https://doi.org/10.4236/ajcc.2013.22016
  42. Liu, H., Wang, L., Sherman, D. J., Wu, Q., & Su, H. (2011). Algorithmic foundation and software tools for extracting shoreline features from remote sensing imagery and LiDAR data. Journal of Geographic Information System, 3(2), 99-199. https://doi.org/10.4236/jgis.2011.32007
  43. Liu, H., Wang, L., Sherman, D., Gao, Y., & Wu, Q. (2010). An object-based conceptual framework and computational method for representing and analyzing coastal morphological changes. International Journal of Geographical Information Science, 24(7), 1015-1041. https://doi.org/10.1080/13658810903270569

Awards and Honors

Teaching

I was the winner of Binghamton University's Harpur College of Arts and Sciences 2017 Teaching Award - Best Instructor of Graduate Classes, receiving the most nominations among 384 different Harpur instructors nominated by graduate students. Selected comments from students on teaching evaluation forms:

  • “Very articulate and creative teaching method. Never a dull moment in class. Always makes students feel appreciated and welcomed.”
  • “Professional and practical. Extremely versed in all fields of subject matter. Pushes students to achieve more than they ever thought was possible. Always approachable and supportive.”
  • “Dr. Wu is an awesome guy, and someone I honestly look to as a standard of professionalism and self-motivation. I highly recommend him as a professor to anyone in the Geography Department. He knows pretty much everything about ArcGIS, programming, stats … you name it. Thanks to him, I’ve learned much more about my field and topics I had tried to tackle on my own to no avail.”
  • “It’s honestly not hard to see why professor Wu would win such a prestigious award. He is fiercely intelligent, yet humble. He is genuinely devoted to his job and goes above and beyond to make sure his students are prepared to succeed.”
  • “Dr. Wu, I just wanted to thank you for giving me the opportunity to learn Python. It has come in handy so many times in my new position as a GIS Analyst. I am able to solve data integration and standardizing issues that otherwise would make processes impossible. So thank you very much!!”

First Steps in GIS Programming

GEOG-312 @ University of Tennessee

Statistics for Geography

GEOG-533 @ Binghamton University

Global Climate Change

GEOG-221 @ Binghamton University

Physical Geography

GEOG-121 @ Binghamton University

Web GIS

GEOG-550 @ Binghamton University

Spatial Statistics

GEOG-566 @ Binghamton University

Professional Services

I have reviewed 145+ times for 40+ journals since 2015. I won the Sentinel of Science Award (2016) and Top 1% of Peer Reviewers in Multidisciplinary (2017). Check out my verified peer review record at Publons.

Wetlands

Associate Editor (2016–present)

Remote Sensing

Associate Editor (2018–present)

Cogent Geoscience

Editor (2016–2019)

Graduate Students

I am looking for self-motivated students interested in geospatial data science. Please contact me if you are interested in working in my research lab.
Find out more information about the application guideline from our Graduate School and the Geography Department.

Current Grad Students

UTK Brochure (English)

UTK Brochure (Chinese)

Contact Me

Qiusheng Wu, Ph.D.
Assistant Professor
Department of Geography
University of Tennessee
Knoxville, TN 37996-0925
Email: qwu18@utk.edu
Web: https://wetlands.io
Blog: https://lidarblog.com
Phone: 865-974-6033
Office: 309 Burchfiel Geography Building
Location: Google Map

I look forward to hearing from you