Changshan Wu

 (414) 229-4860
 Bolton Hall 482

Web Site:


PhD, Geography, The Ohio State University, 2003
MS, Institute of Remote Sensing Applications, Chinese Academy of Sciences, 1999
BS, Urban and Environmental Sciense, Peking University, China, 1995

Office Hours

M 11:00 am – 12:00 pm

Courses Taught

Geog 403 – Remote Sensing: Environmental and Land Use Analysis Syllabus
Geog 430 – Geography of Transportation Syllabus
Geog 525 – Geographic Information Science Syllabus
Geog 547 – Spatial Analysis Syllabus
Geog 625 – Intermediate Geographic Information Science Syllabus
Geog 725 – Advanced Geographic Information Science: Geographic Modeling Syllabus
Geog 750 – Remote Sensing and Urban Analysis Syllabus

Research Interests

I am a broadly trained geographer with substantive interests in remote sensing, geographic information science, spatial analysis, and their applications in urban environments. Specially, my research focuses on two aspects: 1) remote sensing image analysis and applications, especially for urban impervious surface and population estimation, as well as urban growth modeling, and 2) spatial analysis and modeling, and their applications in housing market analysis, public health, and transportation studies.

One emphasis of my research is to develop innovative methods for better extracting urban biophysical and socio-economic information, and applying the derived information for planning and management of urban and natural environments. Especially, my research in the urban remote sensing field includes 1) urban imperviousness estimation, in particular using the spectral mixture analysis method, 2) small area population interpolation and estimation using high-resolution remote sensing and GIS dataset, and 3) urban growth monitoring and modeling using spatial modeling techniques. In addition to remote sensing and urban analysis, my other research area involves GIS and spatial modeling (spatial optimization, spatial statistics, etc.) and their applications in housing market analysis, public health, and transportation studies.

Representative Publications

Deng, Y., & Wu, C. (2016, April (2nd Quarter/Spring)). Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments. Remote Sensing, 8(4), 349.
Li, W., & Wu, C. (2016, January (1st Quarter/Winter)). A geostatistical temporal mixture analysis approach to address endmember variability for estimating regional impervious surface distributions. GIScience & Remote Sensing, 53(1), 102-121.
Song, Y., & Wu, C. (2016, January (1st Quarter/Winter)). Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect. Advances in Space Research, 57(1), 96-109.
Li, W., & Wu, C. (2015, March). Incorporating land use land cover probability information into endmember class selections for temporal mixture analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 163-173.
Li, M., Zang, S., Wu, C., & Deng, Y. (2015, March). Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness. Advances in Space Research, 55(5), 1307-1315.
Yingbin, D., & Wu, C. (2015, July (3rd Quarter/Summer)). RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments, International Journal of Applied Earth Observation and Geoinformation. International Journal of Applied Earth Observation and Geoinformation, 39, 40-48.
Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014, June). A review of remote sensing image classification techniques: the role of spatio-contextual information. European Journal of Remote Sensing, 47, 389-411.
Li, W., & Wu, C. (2014). Phenology based temporal mixture analysis for estimating large scale impervious surface distribution. International Journal of Remote Sensing, 35(2), 779-795.