Welcome to Dr. L. M. Moskal's Remote Sensing and Geospatial Analysis Laboratory (RSGAL), the remote sensing and geospatial research partner of the Precision Forestry Cooperative in the College of the Environment, School of Forest Resources at the University of Washington. The laboratory was established in 2003 and originally located at Missouri State University (2003-2006), it continues to be directed by Dr. L. Monika Moskal.

Mission

To provide a research rich environment and exceptional resources that drive the understanding of multiscale dynamics of landscape change through the innovative application of remote sensing & geospatial tools. RSGAL research promotes a transdisciplinary approach for sustainable management solutions to pressing environmental issues.

Current Research

• Object based hierarchical approach for wetland identification
• Development of a LiDAR-driven forest inventory
• Leaf Area Index (LAI) from aerial & terrestrial LiDAR
• Estimating forest susceptibility to Pine Bark Beetle in Eastern WA
• Forest fire modeling variable estimation with LiDAR
• Invasive species mapping with hyperspectral & hyperspatial remote sensing
• Biomass estimation from remote sensing for bioenergy

more research...

Selected Recent Peer Reviewed Publications

• Moskal, L. M., T. Erdody, A. Kato, J. Richardson, G. Zheng and D. Briggs, 2009. Aerial and Terrestrial LiDAR Applications in Precision Forestry, SilviLaser2009 Conference Proceedings, Collage Station, TX.
• Erdody T. and L. M. Moskal. Fusion of LiDAR and Imagery for Estimating Forest Canopy Fuels, Remote Sensing of Environment, (in press).
• Kato, A. Moskal L.M., Schiess, P. Swanson, M., Calhoun, D. and W. Stuetzle, 2009. Capturing Tree Crown Formation through Implicit Surface Reconstruction using Airborne LiDAR Data, Remote Sensing of Environment, 113(6); 1148-1162.
• Zheng G., Moskal L.M, 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4):2719-2745.
• Richardson, J., Moskal, L. M. and S. Kim, 2009. Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LiDAR, Agricultural and Forest Meteorology 149, 1152-1160.

more publications...