L. Monika Moskal
- Professor
- Associate Director
- Director, Precision Forestry Cooperative
- 206-225-1510
L. Monika Moskal
- Professor
- Associate Director
- Director, Precision Forestry Cooperative
Research areas
Remote sensing; biospatial analysis
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.
B.E.S., Environmental Studies, University of Waterloo
M.Sc., Geography, University of Calgary
Ph.D., Geography, University of Kansas
Prof. Moskal is currently accepting externally funded students.
Courses
- ESRM 190 | Digital Earth () -
- ESRM 430 | Remote Sensing of the Environment (5) - Autumn
- ESRM 433/SEFS 533 | LiDAR Remote Sensing (5) - Spring
- SEFS 459 | Wildlife Conservation - Spring Break in Yellowstone National Park () -
Current Sponsored Projects
- NASA Carbon Monitoring Systems (CMS): Project Lead: Moskal (CMS 2018): Teal Carbon – Stakeholder-driven Monitoring of Forested Wetland Carbon Collaborator: Hudak (CMS 2018): A bottom-up, stakeholder-driven CMS for regional biomass carbon dynamics: Phase II
- Phase I, II and III for Center for Advanced Forestry Systems (CAFS) located at The University of Washington, NSF Award # 0855690; two prior phases were also funded under 3851013 & 3098329
- Analyzing Environmental Changes in Interior Alaska (1982-2014)
This project will use a unique combination of field data, airborne remote sensing, and satellite time series to characterize the spatial heterogeneity of vegetation changes in interior Alaska since 1982. - Bureau of Land Management Precision Forestry Cooperative
The Precision Forestry Cooperative will continue the refinement and development of LiDAR based forest applications to assess stream and riparian habitats. - PFC Carbon Monitoring Systems RJVA
This research agreement will support the development of an optimal sampling design for carbon monitoring using a combination of specialized field plots, airborne lidar sampling, and Landsat time series data, as well as the analysis of this and other similar data to meet the goals of NGHGI inventory and, more broadly, the Forest Inventory and Analysis (FIA) program. - PhoDar at Panther Creek Research Plots
This project will acquire photogrammetric detection and ranging (PhoDAR) techniques for forest inventory and soil study plots at the Panther Creek research site in the state of Oregon. Quantum Spatial (contractor) will acquire the necessary imagery for the Oregon BLM Harvest Sites Phodar Contractor shall provide one (1) report containing the results on the use of remote sensing and photogrammetric detection and ranging (PhoDAR) techniques on forest resources in the Pacific Northwest. - Using Airborne LiDAR to Assess, Compare, and Contrast Forested Landscapes
In this project, LiDAR scientists in the PNW Research Station, Region 5’s Remote Sensing Laboratory, and the UW Forest RRAMS will work together to prototype, test, and refine a conceptual framework that uses the newly developed methods to measure forest structure and compare forest conditions for different areas.
Selected publications
Dr. Moskal’s latest publications can also be found here.
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