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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. |
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| 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 |
 Gmur, S., D. Vogt, D. Zabowski, and L. M. Moskal, 2012. Hyperspectral Characterization of Soil Series, Nitrogen and Carbon, Sensor, 12(8):10639-10658. 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. 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. 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. 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. 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. 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. Moskal, 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. 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. 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. Vaughn N., L. M. Moskal and E. Turnblom, 2011. Fourier transformation of waveform LiDAR for species recognition, Remote Sensing Letters, 2(4); 347-356.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. 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. 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. and L.M. Moskal, 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. |
RSGAL Factsheets |
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