Publications
Batchelor, J. L, RowellE, Prichard, S., Nemans, D., Cronan J., and L. M. Moskal, 2023. Quantifying Forest Litter Fuel Moisture Content with Terrestrial Laser Scanning, Remote Sensing. 15(6), 10.3390/rs15061482.
Qiao, Y.; Zheng, G.; Du, Z.; Ma, X.; Li, J.; Moskal, L. M, 2023. Tree Species Classification and Individual Tree Biomass Model Construction Based on Hyperspectral and LiDAR Data. Remote Sensing., 15(5), 10.3390/rs15051341
Milller, C., B. Harvery, V. R. Kane, L. M. Moskal and E. Alvarado, 2023. Different approaches make comparing studies of burn severity challenging: A review of methods used to link remotely sensed data with the Composite Burn Index, International Journal of Wildland Fire. doi.org/10.1071/WF22050.
Halabisky, M. D. Miller, A. Stewart, D. Lorigan, T. Brasel, L. M. Moskal, 2022. The Wetland Intrinsic Potential tool: Mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators. EGUsphere. 10.5194/egusphere-2022-665
Yun, Z., G. Zheng, Q. Geng, L. M. Moskal, B. Wu, and P. Gong, 2022. Dynamic stratification for vertical forest structure using aerial laser scanning over multiple spatial scales. International Journal of Applied Earth Observation and Geoinformation, 114 (103040), 10.1016/j.jag.2022.103040
Campbell, A.D., T. Fatoyinbo, S. P. Charles, L. L. Bourgeau-Chavez, J. Goes, H. Gomes, M. Halabisky, J. Holmquist, S.Lohrenz, C.Mitchell, L. M. Moskal, B.Poulter, H. Qiu, C. H. R. De Sousa, M. Sayers, M. Simard, A. J. Stewart, D. Singh, C. Trettin, J.Wu, X. Zhang, and D. Lagomasino, 2022. A Review of Carbon Monitoring in Wet Carbon Systems. Environmental Research Letters. Focus on Carbon Monitoring Systems Research and Applications Special Issue. Jan 2022, 10.1088/1748-9326.
Du, Z., G. Zheng, G. Shen and L. M. Moskal, 2021. Characterizing spatiotemporal variations of forest canopy gaps using aerial laser scanning data. International Journal of Applied Earth Observations and Geoinformation, 10.1016/j.jag.2021.102588
Barber, N., E. Alvarado, L.M. Moskal, V. R. Kane, W. E. Mell, 2021. Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors, Sensors, Sensors. 21(19). doi.org/10.3390/s21196350
Shoot, C., H-E., Andersen, L. M. Moskal, C. Babcock, B. Cook, D. Morton, 2021. Classifying Forest Type in the National Forest Inventory Context from a Fusion of Hyperspectral and Lidar Data, Remote Sensing, 13(10). 10.3390/rs13101863
Barnhart, B., P. Pettus, J. Halama, R. McKane, P. Mayer, A. Brookes, K. Djang, L. M. Moskal, 2021. Modeling the hydrologic effects of watershed-scale green roof implementation in the Pacific Northwest, United States. Journal of Environmental Management. 277(111418). 10.1016/j.jenvman.2020.111418.
Xu, Z., G. Zheng and L. M. Moskal, 2020. Stratifying forest overstory for improving effective LAI estimation based on aerial imagery and discrete laser scanning data. Remote Sensing. 12(2126) 10.3390/rs12132126
Endo, Y., M. Halabisky, L. M. Moskal, S. Koshimura, 2020. Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-based Temporal Interpolation. Special Issue on Advances in Remote Sensing for Disaster Research: Methodologies and Applications in Remote Sensing, 12(11). 10.3390/rs12111756
Wang, X., G. Zheng, Z. Yun, Z. Xu, L. M. Moskal, Q. Tian, 2020. Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar. Remote Sensing, 12(7). 10.3390/rs12071071
Wang, X., G. Zheng, Z. Yun, L. M. Moskal, 2020. Characterizing tree spatial distribution patterns using discrete aerial lidar data. Remote Sensing, 12(7). 10.3390/rs12071071
Kato, A., D. Thau, A. Hudak, G. Meigs and L. M. Moskal, 2020, Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality, Remote Sensing of Environment. 237 (111525). 10.1016/j.rse.2019.111525
Barton, I., Czimber, K., Király, G., L.M. Moskal, 2019: Konzisztens Sentinel-2 űrfelvétel idősorozat készítése erdőterületek kiértékeléséhez [Translation: Consistent Sentinel-2 time series construction for evaluating forested areas]; Geomatikai Közlemények [Translation: Geomatics Announcements]. 22, 10.13140/RG.2.2.27511.04006.
Kane, V.R., Bartl-Geller B.N., Kane, J.T., Jeronimo, S.M.A, North, M.P., Collins, B., Lydersen, J., Moskal, L.M. 2019. First-entry fires can create forest tree clump and opening patterns characteristic of historic resilient forests. Forest Ecology and Management. 454:117659.
Richardson, J. C. Torgersen, L. M. Moskal, 2019. LiDAR-based modelling approaches for estimating solar insolation in heavily forested streams. Hydrology and Earth System Sciences. 487. 10.5194/hess-2018-487
Kato, A., L.M. Moskal, J. L. Batchelor, D. Thau and A. A. Hudak, 2019. Relationship between Satellite-Based Spectral Burn Ratios and Terrestrial Laser Scanning, Forests, 10(5); 444. doi.org/10.3390/f10050444
Blomdahl, E. M., C.M. Thompson, J. R. Kane, V. R. Kane, D. Churchill, L. M. Moskal and J. A. Lutz. 2019. Forest structure predictive of fisher (Pekania pennanti) dens exists in recently burned forest in Yosemite, California, USA. Forest Ecology and Management, 44; 174-186. 10.1016/j.foreco.2019.04.024
Halabisky, M., C. Babcock, L. M. Moskal, 2018. Harnessing the temporal dimension to improve object-based image analysis classification of wetlands. Remote Sensing, 10(9), 1467. 10.3390/rs10091467
Walker, L., J. Marzluff, M. Metz, A. Wirsing, L.M. Moskal., D. Stahler, and D. Smith. 2018. Population Responses of Common Ravens to Reintroduced Gray Wolves. Ecology and Evolution. 8(22) 11158-11168.
Vahidi, H., B. Klinkenburg, B. Johnson, L. M. Moskal and W. Yan. 2018. Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-based Approach. Remote Sensing. 10, 1134. 10.3390/rs10071134
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. 10.1016/j.foreco.2017.09.019
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; 14p. 10.3389/fevo.2017.00040
Ma, L., G. Zheng, J. Eitel, T.S. Magney and L.M. Moskal, 2017. Retrieving forest canopy extinction coefficient from terrestrial and airborne lidar. Agricultural and Forest Meteorology, 236; 1-21. 10.1016/j.agrformet.2017.01.004
Zheng, G, L. Ma, J. Eitel, W. He, TS. Magney, L.M. Moskal and M. Li, 2017. Retrieving Directional Gap Fraction, Extinction Coefficient, and Effective Leaf Area Index by Incorporating Scan Angle Information from Discrete Aerial Lidar Data. IEEE Transactions of Geosciences and Remote Sensing, 55(1); 577-590. 10.1109/TGRS.2016.2611651
Johnston, A. and L. M. Moskal, 2017. High-Resolution Habitat Modeling with Airborne LiDAR for Red Tree Voles. Journal of Wildlife Management and Wildlife Monographs, 81(1); 58-72. 10.1002/jwmg.21173
Richardson J. and L. M. Moskal, 2016. An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris. Remote Sensing, 8(9), 778. 10.3390/rs8090778
Ma, L., Zheng, G., Eitel, J., Moskal, L.M., He, W. and H. Huang. 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. 10.1109/TGRS.2015.2459716
Ma, L., Zheng, G., Eitel, J., Magney, T. and L. M. Moskal. 2016. Determining woody-to-total area ratio using terrestrial laser scanning (TLS), Agricultural and Forest Meteorology, 228-229, 217-228. 10.1016/j.agrformet.2016.06.021
Zhang, Z., A. Kazakova, L. M. Moskal, D. Styers and N. Vaughn. 2016. Object-Based Tree Species Classification in Urban Ecosystems using LiDAR and Hyperspectral Data. Forests, 7(6), 122-138. 10.3390/f7060122
Halabisky, M., L. M. Moskal, A. Gillespie, M. Hannam. 2016. Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984 – 2011). Remote Sensing of Environment, 177, 171-183. 10.1016/j.rse.2016.02.040
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. 10.1016/j.ufug.2015.10.006
Zheng, G. Ma, L.X., He, W., Eitel, J.U.H., Moskal, L.M. and Zhang, Z.Y, 2016. 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. 10.1109/TGRS.2015.2481492
Hannam, M, and L. M. Moskal, 2015. Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat, Remote Sensing, 7(3), 3037-3055. 10.3390/rs70303037
Richardson, J. J. Bakker, L.M. Moskal, 2014. Terrestrial Laser Scanning for Vegetation Sampling, Sensors, 5(4); 352-357. 10.3390/s141120304
Styers, D. L. M. Moskal, M. Halabisky and J. Richardson. 2014, Evaluation of the contribution of LiDAR data and post-classification procedures to object-based classification accuracy. Journal of Applied Remote Sensing, 8, 16p. 10.1117/1.JRS.8.083529
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, European Review of Agricultural Economics, pp. 1-29. 10.1093/erae/jbu009
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. 10.1080/2150704X.2014.903350
Richardson, J. and L. M. Moskal, 2014. Assessing the utility of green LiDAR for characterizing bathymetry of heavily forested narrow streams, Remote Sensing Letters, 5(4); 352-357. 10.1080/2150704X.2014.902545
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. 10.1071/WF13086
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. 10.1016/j.ufug.2013.07.003
Moskal, L.M. and M. Jakubauskas, 2013. Monitoring post disturbance forest regeneration with hierarchical object-based image analysis, Forests, Special Issue: LiDAR and Other Remote Sensing Applications in Mapping and Monitoring of Forests Structure and Biomass; 4(4); 808-829. 10.3390/f4040808
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 Issue, 10p.
Gmur, S., D. Vogt, D. Zabowski, and L. M. Moskal, 2012. Hyperspectral Characterization of Soil Series, Nitrogen and Carbon, ** Sensor-Based Technologies and Processes in Agriculture and Forestry, Sensors, 12(8); 10639-10658. 10.3390/s120810639
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. 50(10) 3958-3969. 10.1109/TGRS.2012.2205003
Zheng, G. and L. M. Moskal, 2012. Computational-Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning, IEEE Transactions on Geoscience and Remote Sensing 50(10); 12p. 10.1109/TGRS.2012.2187907
Zheng, G. and L. M. Moskal., 2012. Leaf Orientation Retrieval from Terrestrial Laser Scanning Data, IEEE Transactions on Geoscience and Remote Sensing, 50(10); 10p. 10.1109/TGRS.2012.2188533
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. 10.1016/j.jag.2012.05.002
Vaughn, N., L. M. Moskal and E.C. Turnblom, 2012. Tree Species Detection Accuracy with Airborne Waveform LiDAR, **Special Issue on Laser Scanning in Forests, Remote Sensing, 4(2); 377-403. 10.3390/rs4020377
Moskal, L. M. and Zheng, Guang. 2012. Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sensing, 4(1); 1-20. 10.3390/rs4010001
Moskal, L.M., Styers, D. M. 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. 10.3390/rs3102243
Richardson, J. J. and Moskal, L. M., 2011. Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR, Remote Sensing of Environment, 115(10); 2640-2651. 10.1016/j.rse.2011.05.020
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. 10.1117/1.3563569
Vaughn N., L. M. Moskal and E. Turnblom, 2011. Fourier transformation of waveform LiDAR for species recognition, Remote Sensing Letters, 2(4); 347-356. 10.1080/01431161.2010.523021
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. 10.1016/j.rse.2009.11.002
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. 10.1016/j.rse.2009.02.010
Zheng, G. and L. M. Moskal, 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4); 2719-2745. 10.3390/s90402719
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. 10.1016/j.agrformet.2009.02.007
Moskal, L.M. and S.E. Franklin, 2004. Relationship between airborne multispectral image texture and aspen defoliation, International Journal of Remote Sensing, 2(14); 2710-2711. 10.1080/01431160310001642304
Dunbar, M. D., L. M. Moskal, and 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. 10.1080/10106040408542310
Moskal, L.M. and S.E. Franklin, 2002. Mult-layer forest stand discrimination with multiscale texture from high spatial detail airborne imagery, Geocarto International, 17(4); 53-66. 10.1080/10106040208542254
Franklin, S.E., M.B. Lavigne, L.M. Moskal, M.A. Wulder and T.M. McCaffrey, 2001. Interpretation of Forest Harvest Conditions in New Brunswick Using Landsat TM Enhanced Wetness Difference Imagery (EWDI), Canadian Journal of Remote Sensing, 27(2); 118-128. 10.1080/07038992.2001.10854926
Presutti, M., S.E. Franklin, L.M. Moskal and E.E. Dickson, 2001. Supervised Classification of Multisource Satellite Image Spectral and Texture Data for Agricultural Crop Mapping in Buenos Aires Province, Argentina, Canadian Journal of Remote Sensing, 27(6); 679-684. 10.1080/07038992.2001.10854910
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. 10.5558/tfc76877-6
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. 10.1080/014311600210993
Franklin, S.E., L.M. Moskal, M.B. Lavigne and K. Pugh, 2000. Interpretation and Classification of Partially Harvested Forest Stands in the Fundy Model Forest Using Multitemporal Landsat TM Digital Data, Canadian Journal of Remote Sensing, 26(3); 318-333. 10.1080/07038992.2000.10874783
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. 10.1080/07038992.2000.10874797
Peer Reviewed Book Chapters
Barton, I.; Czimber, K.; Király, G., L.M. Moskal, 2019. Faállomány-típusok térképezése Sentinel-2 űrfelvétel idősorozaton Deep Learning osztályozóval [Translation: Forest type mapping using Sentinel-2 time series with Deep Learning classifier]; In: Király, Gergely; Facskó, Ferenc (szerk.) Soproni Egyetem Erdőmérnöki Kar VII. Kari Tudományos Konferencia : konferencia kiadvány Sopron, Magyarország : Soproni Egyetem Kiadó: 41-47.
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 and Remote Sensing in Mountain Environmental Research, Widacki, W., Bytnerowicz, A. and Riebau, A. (eds). Institute of Geography and Spatial Management of the Jagiellonian University in Krakow and the USDA Forest Service: 219-232.
ORCID ID