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Le Wang photo

Assistant Professor

Geography

University at Buffalo

110 Wilkeson Quad

Buffalo, NY 14261

716-645-0474

 

National Center for Geographic Information and Analysis

University at Buffalo

301 Wilkeson Quad

Buffalo, NY 14261

716-645-2545

 

Dr. Le Wang

webpage: http://www.buffalo.edu/~lewang and http://www.ncgia.buffalo.edu

Research Interests:

Dev. of new remote sensing methods; Land cover & use classification & change detection; Coastal mangrove forest characterization; Invasive species spread modeling; Urban population estimates; LiDAR & hyperspectral remote sensing; auto. feature extraction

Summary of Recent Relevant Research:

Dr. Wang is currently conducting two NSF-funded projects: 1) Population Estimation from Remote Sensing: Small-area population estimates are essential for understanding and responding to many social, political, economic, and environmental problems. However, 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 and accurate population estimates through integrating the traditional housing unit methods and remote sensing technologies. 2) Characterization of coastal mangroves from remote sensing: Mangroves are a unique forest type that provide critical “ecosystem services”, one of which was recently evidenced in the 2004 Indian Ocean Tsunami, i.e. areas with intact seaward mangrove forests suffered much less human death and property destruction than otherwise. In this research, I intend to map and monitor the spatial distribution, species composition, and health of coastal mangrove forests through linking local, stand-level measurements with remotely sensed patterns of canopy composition and dynamics at the landscape level.

figure 1

3-D visualization of rasterized LiDAR altimetry data used for building volume estimation (left) and 2000 IKONOS Imagery illustrating three different mangrove species in our study site at Panama (right)