UW Remote Sensing & Geospatial Analysis Laboratory
The Research Laboratory of Dr. L. M. Moskal at the University of Washington
 
   
 
Understanding high spatiotemporal resolution multidimensional ecosystem process, function, monitoring and applications through geospatial techniques
 
 
UW
MY FEATURED NSF PROJECTS
• Center for Advanced Forestry Systems (CAFS) located at The University of Washington, NSF Award # 0855690
• CNH: Collaborative Research: Northern Gulf of Mexico Hypoxia and Land Use in the Watershed: Feedback and Scale Interactions, NSF Award # 1010009
NEWS & EVENTS
SFR Graduate Symposium Videos:

Oct 2013 Dr. Moskal gives a keynote presentation at SilviLaser2013, Beijing, China

March 3, 2011 UW RSGAL Geospatial Canopy Cover Assessment Workshop
Feb. 4, 2011 UW Urban Forest Assessment (IUFA) Geospatial Portal

Mendeley RSGAL Journal Club

RSGAL QUICK LINKS

Link to RSGAL Factsheets

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 (June 2003 - May 2006), it continues to be directed by Dr. L. M. Moskal at the University of Washington since June 2006.

RSGAL
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 more research

RSGAL's research goal is to understand multiscale and multidimensional dynamics of landscape change through the application of remote sensing, GIS and geospatial tools. The lab develops tools necessary to analyze hyper-resolution remotely sensed data by exploiting spatial, temporal and spectral capabilities of the data. RSGAL work focuses on the application of high spatial resolution remote sensing (LiDAR, imagery) to investigate vegetation structure, specifically the utilization of leaf area index in heterogeneous canopies. Other RSGAL research themes involve multi resolution and multi sensor data fusion, spatiotemporal object-based image analysis and geovisualization techniques to communicate research results. Moskal's and RSGAL research has been applied to the following themes: ecosystem services and function, bioenergy/biomass, forest inventories, forest health, change analysis, biodiversity, habitat mapping, spatiotemporal wetland assessment, geostatistical analysis of prairie vegetation communities, urban growth and forest fragmentation.

SELECTED REFEREED PUBLICATIONS more publications

pdfHermosilla, T., Ruiz, L., Kazakova, A. Coops, N. and L. M. Moskal, in-press 2013. Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire, p. 30. pdfMoskal, L.M. and M. Jakubauskas, 2013. Monitoring post disturbance forest regeneration with hierarchical object-based image analysis, in Forests, Special Issue: LiDAR and Other Remote Sensing Applications in Mapping and Monitoring of Forests Structure and Biomass; 4(4); 808-829 pdf Richardson, J. and L. M. Moskal, 2013. Uncertainty in Urban Forest Canopy Assessment: Lessons from Seattle, WA USA, Urban Forestry and Urban Greening, p. 12. pdf Halabisky, M., M. Hannam, A. L. Long, C. Vondrasek and L. M. Moskal, 2013. The Sharper Image: Hyperspatial Remote Sensing in Wetland Science. Wetland Science and Practice, June 2013 Issue, 10p. pdf Gmur, S., D. Vogt, D. Zabowski, and L. M. Moskal, 2012. Hyperspectral Characterization of Soil Series, Nitrogen and Carbon, Sensor, 12(8):10639-10658. pdf Zheng, G., Moskal, L. M. and S-H. Kim, 2012. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning , IEEE Transactions on Geoscience and Remote Sensing, 99; 10p. pdf Zheng, G. and L. M. Moskal, 2012. Computational Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning, IEEE Transactions on Geosciences and Remote Sensing, 50(10); 12p. pdf Zheng, G. and L. M. Moskal, 2012. Spatial variability of terrestrial laser scanning based leaf area index, International Journal of Applied Earth Observation and Geoinformation, 19, 226–237. pdf Zheng, G. and L.M. Moskal, 2012. Leaf Orientation Retrieval from Terrestrial Laser Scanning Data, IEEE Transactions on Geoscience and Remote Sensing, 50(10), 11p. pdf Vaughn, N. and L. M. Moskal, 2012. Tree Species Detection Accuracy with Airborne Waveform Lidar, Special Issue on Laser Scanning in Forests, Remote Sensing, 4(2), 377-403. pdf Moskal, L. M. and G. Zheng, 2012. Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sensing, 4(1), 1-20. pdfMoskal, L.M. and D. M. Styers, 2011. Monitoring Urban Forest Canopies Using Object-Based Image Analysis and Public Domain Remotely Sensed Data. Remote Sensing Special Issue on Urban Remote Sensing, 3 (10); 2243-2262. pdf Richardson J. and L. M. Moskal, 2011. Strengths and Limitations of Assessing Forest Density and Spatial Configuration with Aerial LiDAR, Remote Sensing of Environment, 114(4), 725-737. pdf Halabisky, M., L. M. Moskal and S. A. Hall, 2011. Object-Based Classification of Semi-Arid Wetlands, Journal of Applied Remote Sensing, 5(05351); p.13. pdf Vaughn N., L. M. Moskal and E. Turnblom, 2011. Fourier transformation of waveform LiDAR for species recognition, Remote Sensing Letters, 2(4); 347-356.pdf Erdody T. and L. M. Moskal, 2010. Fusion of LiDAR and Imagery for Estimating Forest Canopy Fuels, Remote Sensing of Environment, 114(4); 725-737. pdf 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. pdf Zheng G. and L.M. Moskal, 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4):2719-2745. pdf 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.
 
 
 
University of Washington, College of the Environment, School of Environmental and Forest Sciences  
Bloedel Hall 357/389, Box 352100
Fax: 206.685.0790
Seattle, WA 98195-2100  
email: rsgal at uw.edu