Follow lmmoskal on Twitter Dr. L. Monika Moskal
Associate Professor of Remote Sensing
Understanding high spatiotemporal resolution multidimensional ecosystem process, function, monitoring and applications through geospatial techniques


Factsheet6 Halabisky, M., and L.M. Moskal., 2012. Using LiDAR and object based image analysis to map wetlands in Mt. Rainier National Park . Factsheet # 23.
Factsheet6 Kirsch JL, Moskal LM, Fischer DG. 2012. Relationships between fine root productivity and variation of canopy height in a temperate forest. Factsheet #22.
Factsheet6 Kazakova A.N., L.M. Moskal, 2012. Quantifying vertical and horizontal stand structure using Terrestrial LiDAR. Factsheet # 21.
Factsheet6 Moskal, L. M.  and  J. Kirsch, 2012. Calibrating Estimates of Above- and Below- Ground Forests Biomass Using Remotely Sensed Metrics. Factsheet # 20.
Factsheet6 Kazakova, A.N., L.M. Moskal, and D.M. Styers. 2011. Hyperspectral Remote Sensing of Urban Tress. Factsheet # 19.
Factsheet6 Moskal, L. M., D. M. Styers, J. Richardson and M. Halabisky, 2011. Seattle Hyperspatial LULC from LiDAR & NIR Imagery Using OBIA. Factsheet # 18.
Factsheet6 Kazakova A. N., L.M. Moskal, D. M. Styers and D.G. Fischer, 2011. Estimating Tree Species Diversity Using Aerial LiDAR and Multispectral Imagery. Factsheet # 17.
Factsheet6 Kirsch, J.L., L.M. Moskal,  D. M. Styers and D.G. Fischer , 2011. Relationships Between Above- and Belowground Plant Carbon Using Field Metrics and LiDAR. Factsheet #16.
Factsheet6 Hannam, M. and L.M. Moskal, 2011. Coastal Wetlands: Monitoring Estuarine Topographic Change with Terrestrial Laser Scanning. Factsheet # 15.
Factsheet6 Vaughn, N. and L.M. Moskal, 2011. Identification of Individual Tree Species Using  Full Waveform LiDAR. Factsheet 14.
Factsheet6 Stephens, D.R. and L.M. Moskal, 2010. Forestry: Deriving Site Growth Indices From Multi-Temporal Small-Footprint Aerial LiDAR Data. Factsheet # 13.
Factsheet6 Moskal., L. M. and D. M. Styers , 2010. Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications. Factsheet # 12.
Factsheet6 Park, J., F. Krogstad, J. Fridley and L. M. Moskal, 2009. Small Stream Mapping Method: Local Difference Algorithm (LDA) Using LiDAR and LandSAT. Factsheet # 11.
Factsheet6 Halabisky, M. , T. Wheeler and L.M. Moskal., 2009.  Mobile GIS Data Capture. Factsheet #10.
Factsheet6 Vondrasek, C. and L.M. Moskal., 2009. Decadal Analysis of Change in Wetlands in Arid Regions. Factsheet # 9.
Factsheet6 Hannam, M., L.M. Moskal and S. Wyllie-Echeverria, 2009. Predicting an Invasive Species’ Distribution with LiDAR-derived Topography. Factsheet # 8.
Factsheet6 Halabisky, M.A. , and L.M. Moskal, 2009. Monitoring the Spatiotemporal Heterogeneity of Arid Wetlands Factsheet # 7.
Factsheet6 James, L., L. M. Moskal, 2008. Spatiotemporal Analysis of Mountain Goat Habitat. Factsheet # 6.
Factsheet5 Richardson, J. J, L.M. Moskal, S. Kim, 2008. Estimating Urban Forest Leaf Area Index (LAI) from aerial LiDAR. Factsheet # 5.
Factsheet4 Erdody, T.L., and L. M. Moskal, 2008. LiDAR derived canopy fuel metrics for wildfire modeling. Factsheet # 4.
Factsheet3 Kato, A., L. M. Moskal., P. Schiess, M. Swanson, D. Calhoun and W. Stuetzel, 2008. LiDAR based  tree crown surface reconstruction. Factsheet # 3.
Factsheet2 Zheng G. and L. M. Moskal, 2008. Leaf Area Index (LAI) from Terrestrial LiDAR. Factsheet # 2.
Factsheet1 Moskal, L. M. and G. Zheng, 2008. Forest inventory and stem characterization from terrestrial LiDAR. Factsheet # 1.




  1. Hannam, M, and L. M. Moskal, 2015. Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat, Remote Sensing, 7(3), 3037-3055.
  2. Richardson, J. J. Bakker, L.M. Moskal, 2014. Terrestrial Laser Scanning for Vegetation Sampling, Sensors, 5:4, 352-357.
  3. Styers, D. L. M. Moskal, M. Halabisky andJ. Richardson. 2014, Evaluation of the contribution of LiDAR data and post-classification procedures to object-based classification accuracy. Journal of Remote Sensing. Journal of Applied Remote, Volume 8, 16p.
  4. Richardson, J. and L. M. Moskal, 2014. Efficacy of Green LiDAR for Depth Measurements in Heavily Forested Streams, Remote Sensing Letters, 5(4), 352-357.
  5. Hermosilla, T., Coops, N.C., Ruiz, L.A., Moskal, L.M., 2014. Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data. Remote Sensing Letters. 5(4), 332-341.
  6. Kling C.L., Y. Panagopoulos, S. S. Robotyagov, A.M. Valcu, P.A. Gassman, T. Campbell, M. J. White, J.A. Arnold, R. Srinivasan, M.J. Jha, J. J. Richardson, L.M. Moskal, R.E. Turner, and N. N. Rabalais, 2014. LUMINATE: Linking Agricultural Land Use, Local Water Quality and Gulf of Mexico Hypoxia.pp1-29
  7.  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, 23, 224-233.
  8. 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
  9.  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.
  10.  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.
  11.  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.
  12.  Gmur, S., D. Vogt, D. Zabowski, and L. M. Moskal, 2012. Hyperspectral Characterization of Soil Series, Nitrogen and Carbon, Sensor, 12(8):10639-10658.
  13.  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) 3958-3969.
  14.  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.
  15.  Zheng, G. and L.M. Moskal, 2012. Leaf Orientation Retrieval from Terrestrial Laser Scanning Data, IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3970-3979.
  16.  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.
  17.  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.
  18.  Moskal, L.M., D. M. Styers and M. Halabisky, 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.
  19.  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.
  20.  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.
  21.  Vaughn N., L. M. Moskal and E. Turnblom, 2011. Fourier transformation of waveform LiDAR for species recognition, Remote Sensing Letters, 2(4); 347-356.
  22.  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.
  23.  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.
  24.  Zheng G. and L.M. Moskal, 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4):2719-2745.
  25.  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.
  26.  Moskal, L.M. & S.E. Franklin, 2004. Relationship between airborne multispectral image texture and aspen defoliation, International Journal of Remote Sensing, 25:14; 2710-2711.
  27.  Dunbar, M. D., L. M. Moskal, & M. E. Jakubauskas, 2004. 3D Visualization for the analysis of forest cover change, Geocarto International, Special Issue on 100th Anniversary of the Association of American Geographers - Remote Sensing Specialty Group, 19(2); 103-112.
  28.  Moskal, L.M. & S.E. Franklin, 2002. Multistory forest stand discrimination with multiscale texture from high spatial detail airborne imagery, Geocarto International, 17(4); 53-66.
  29.  Franklin, S.E., L.M. Moskal, M.B. Lavigne, M.A. Wulder & T.M. McCaffrey, 2001. Interpretation of partial harvest forest stand conditions using the Enhanced Wetness Difference Image (EWDI), Canadian Journal of Remote Sensing, 27(2): 118-128.
  30.  Presutti, M., S.E. Franklin, L.M. Moskal & E.E. Dickson, 2001. Supervised classification of Landsat TM, RadarSAT W2 Data, & texture derivatives for agricultural crop mapping in Buenos Aires Province, Argentina, Canadian Journal of Remote Sensing, 27(6): 679-685.
  31.  Franklin, S.E., E.E. Dickson, D.M. Farr, M.J. Hansen and L.M. Moskal, 2000. Quantification of landscape change from satellite remote sensing, Forestry Chronicle, 76(6): 877-886.
  32.  Franklin, S.E., R.J. Hall, L.M. Moskal, A.J. Maudie and M.B. Lavigne, 2000. Incorporating texture into classification of forest species composition from airborne multispectral images, International Journal of Remote Sensing, 21(1): 61-79
  33.  Franklin, S.E., L.M. Moskal, M.B. Lavigne and K. Pugh, 2000. Interpretation and classification of partially harvested forest stands using multitemporal Landsat TM digital data, Canadian Journal of Remote Sensing, 26(3): 318-333.
  34. Franklin, S.E., McCaffrey, T.M., Lavigne, M.B., Wulder, M.A., and L. M. Moskal, 2000. An ARC/INFO Macro Language (AML) polygon update program (PUP) integrating forest inventory and remotely-sensed data. Canadian Journal of Remote Sensing, 26(6): 566-575.


  1. Moskal, L.M., M.D. Dunbar, M.E. Jakubauskas, 2004. Visualizing the forest: a forest inventory characterization in the Yellowstone National Park based on geostatistical models, in A Message From the Tatras: Geographical Information Systems & Remote Sensing in Mountain Environmental Research, Widacki,. W., Bytnerowicz, A. & Riebau, A. (eds). Institute of Geography & Spatial Management of the Jagiellonian University in Krakow & the USDA Forest Service: 219-232.


  1. Dunbar, M., L.M. Moskal & M.E. Jakubauskas, 2003. 3D Visualization of Forest Cover Change: Human Impacts in Northeastern Kansas & Natural Disturbances in Yellowstone National Park. Earth Observation Magazines, 12 (7); 6-12.
Dr. Moskal's Office: Bloedel Hall 334
Phone: 206.221.6391
Seattle WA, 98195-2100