L. Monika Moskal

L. Monika Moskal

Associate Professor / Associate Director / Director, Precision Forestry Cooperative
Remote sensing; Biospatial analysis

Office: Bloedel 382
Phone: 206-225-1510 
Email: lmmoskal@uw.edu


B.E.S., Environmental Studies, University of Waterloo, 1996
M.Sc., Geography, University of Calgary, 2000
Ph.D., Geography, University of Kansas, 2005

My research lab, the Remote Sensing and Geospatial Analysis laboratory (RSGAL), is focused on driving the understanding of multiscale dynamics of landscape change through the innovative application of remote sensing and geospatial tools.

For a comprehensive list of publications, please see my facutly website.

Courses Taught:Quarter offered:
ESRM 430 Hyperspatial Remote Sensing in Natural Resource Management (5)Winter
ESRM 433 Airborne Lidar for Remote Sensing of Vegetation and Geomorphology (5)Spring
SEFS 533 Airborne Lidar for Remote Sensing of Vegetation and Geomorphology (5)Spring
Current Sponsored Research:
Analyzing Environmental Changes in Interior Alaska (1982-2014)
Bureau of Land Management Precision Forestry Cooperative
PFC Carbon Monitoring Systems RJVA
PhoDar at Panther Creek Research Plots
Using Airborne LiDAR to Assess, Compare, and Contrast Forested Landscapes
Recent Publications:
Guang Zheng, Lixia Ma, Jan U. H. Eitel, , Wei He, Troy S. Magney, L. Monika Moskal, Mingshi Li. 2017. Retrieving directional gap fraction, extinction coefficient, and effective leaf area index by incorporating scan angle information from discrete aerial lidar data. Transactions on Geoscience and Remote Sensing 55(1): 577-590.
Johnston, A, and L. M. Moskal. 2017. High-Resolution Habitat Modeling with Airborne LiDAR for Red Tree Voles. The Journal of Wildlife Management 81(1): 58-72 doi: 10.1002/jwmg.21173
Ma, L., G. Zheng, J. Eitel, T.S. Magney and L.M. Moskal. 2017. Retrieving forest canopy extinction coefficient from terrestrial and airborne lidar. IEEE Transactions on Geoscience and Remote Sensing 236: 1-21.
North, M. P., J. T. Kane, V. R. Kane, G. A. Asner, W. Berigan, D. J. Churchill, S. Conway, R.J. Gutierrez, S. Jeronimo, J. Keane, A. Koltunov, T. Mark, L. M. Moskal, T. Muton, Z. Peery, C. Ramirez, R. Sollman, A. M. White and S. Whitmore. 2017. Cover of tall trees best predicts California spotted owl habitat. Forest Ecology and Management 405: 166-178.
Shyrock, B, J. Marzluff and L. M. Moskal. 2017. Urbanization alters the influence of weather and an index of forest productivity on avian community richness and guild abundance in the Seattle metropolitan area. Frontiers Ecology and Evolution 5(40): 14.
Halabisky, M., L. M. Moskal, A. Gillespie, M. Hannam. 2016. Reconstructing wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite imagery (1984 - 2011). Remote Sensing of Environment 177:171-183.
Lixia Ma,Guang Zheng, Jan H.U. Eitel, Troy S. Magney, L. M. Moskal. 2016. Retrieving forest canopy extinction coefficient from three dimensional point cloud data. Agricultural and Forest Meteorology 228-229:217-228.
Richardson J. and L. M. Moskal. 2016. Urban Food Crop Production Capacity and Competition with the Urban Forest. Urban Forestry and Urban Greening 15: 58-64.
Richardson, J. & L. M. Moskal. 2016. An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris. Remote Sensing 8(9):778-791.
Zhang, Z., A. Kazakhova, D. Styers and L. M. Moskal. 2016. Object-Based Tree Species Classification in Urban Ecosystems using LiDAR and Hyperspectral Data. Forests 7(6):122-138.
Zheng, G., M. Lixia Ma, U.H. J. U.H. Eitel, T. S. Magney, and L. M. Moskal. 2016. Improved Salient Feature-Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies. IEEE Transactions on Geoscience and Remote Sensing 54(2):679-696.
Hannam, M, L.M. Moskal. 2015. Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat. Remote Sensing 7(3): 3037-3055.
Zheng, G. Ma, L.X., He, W., Eitel, J.U.H., Moskal, L.M., Zhang, Z.Y. 2015. Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning (TLS) data. IEEE Transactions on Geoscience and Remote Sensing 54(3):1474-1484.
Hermosilla, T., Coops, N., Ruiz Fernandes, L.A. and L. M. Moskal. 2014. Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data. Remote Sensing Letters 5(4): 332-341.
Jeffery Richardson and L. Monika Moskal. 2014. Efficacy of Green LiDAR for Depth Measurements in Heavily Forested Streams. Remote Sensing Letters 5(4): 352-357
Richardson, J.J. Bakker, L.M. Moskal. 2014. Terrestrial Laser Scanning for Vegetation Sampling. Sensors 5(4): 352-357.
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, July 2013, 1-31.
Hermosilla, T., Ruiz, L., Kazakova, A. Coops, N. and L. M. Moskal, 2013. Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire 22:224-233.
Moskal, 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
Richardson, J. and L. M. Moskal, 2013. Uncertainty in Urban Forest Canopy Assessment: Lessons from Seattle, WA USA, Urban Forestry and Urban Greening 13(1):152-157.
Zheng, G., L. M. Moskal and S-H. Kim, 2013. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning, IEEE Transactions on Geoscience and Remote Sensing, 51(2): 777-786.