Collaborative Research: Improving Small Area Population Estimation with High-Resolution Remote Sensing National Science Foundation Award # 0822489 August 1, 2008 - January 31, 2012
3-D visualization of LiDAR altimetry data	Principal Investigator: Le Wang Department of Geography State University of New York at Buffalo Co-Principal Investigator: Peter Rogerson Department of Geography State University of New York at Buffalo Collaborative Investigators: Changshan Wu Department of Geography University of Wisconsin-Milwaukee Frederick Day Department of Geography Texas State University-San Marcos
Summary Small-area population estimates are essential for understanding and responding to many social, political, economic, and environmental problems. Population estimates are critical in decisions concerning resource allocation, market area delineation, new facility/transportation development, as well as generating diagnostic indicators for environmental and socioeconomic assessments. Despite its significance and the update frequency needed, detailed and accurate population and socioeconomic information is only available for one date per decade through the national census. The recent advancements in remote sensing technology coalesced with existing knowledge in the field of applied demography can lead to developing an effective method to project population more accurately in different applications. For example, the launch of IKONOS in 1999 provided new opportunities to investigate urban physical configurations at a fine spatial scale from very high resolution (VHR) optical images. Likewise, the advent of Airborne Light detection and Ranging (LiDAR) sensors for measuring the vertical information has complemented the information provided by optical VHR imagery in many urban studies. The overall objective of this project is to develop detailed (census block level) and accurate population estimates through integrating the traditional housing unit methods and remote sensing technologies.
Call for Papers IJRS Special Issue on “Population Estimation Using Remote Sensing and GIS Technologies” Special Paper Sessions for the 2009 AAG Annual Meeting: March 22-27, Las Vegas

Previous Publications on this Research Topic

1. Wu, S., L. Wang, and X. Qiu, 2008. Incorporating GIS building data and census housing statistics for sub-block-level population estimation, Professional Geographers, 60(1):121--135.

2. Silván-Cárdenas, J.L., and L. Wang, 2008. The sub-pixel confusion-uncertainty matrix for assessing soft classifications, Remote Sensing of Environment, 112(3), 1081--1095.

3. Wu, S., J.L. Silván-Cárdenas, and Wang, L., 2007. Per-field urban land use classification based on tax parcel boundaries, International Journal of Remote Sensing, 28 (12) 2777--2801.

4. Tang, J.M., L. Wang, and S. Myint, 2007. Improving urban classification through fuzzy supervised classification and spectral mixture analysis, International Journal of Remote Sensing, 28(18):4047--4063.

5. J.L. Silván-Cárdenas and L. Wang, 2006. A Multi-resolution Approach for Filtering LiDAR Altimetry Data, ISPRS Journal of Photogrammetry and Remote Sensing, 61(1): 11--22.

6. Wu, S., X. Qiu and L. Wang, 2006. Using Semi-variance Image Texture Statistics to Model Population Densities, Cartography and Geographic Information Science, 33(2): 127--140.

7. Wu, S., B. Xu, amd L. Wang. 2006. Urban land use classification using variogram-based analysis with an aerial photograph, Photogrammetric Engineering and Remote Sensing, 72(7):813--822.

8. Wu, S., X. Qiu, and L. Wang. 2005. Population estimation methods in GIS and remote sensing: a review, GIScience and Remote Sensing, 42:58--74.