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Karen G. Schleeweis

Karen G. Schleeweis
Inventory and Monitoring
507-25th Street
Ogden, UT 84401-2450
United States
Current Research
I am an Ecologist in the Forest Inventory and Analysis (FIA) Unit of the Rocky Mountain Research Station. My current work focuses on developing and applying geospatial technical methods to guide natural resources-related land management decisions:
  • Support LANDFIRE forest structure and vegetation mapping with FIA data.
  • Characterize disturbance regimes and forest disturbances for the USFS Resource Planning Act (RPA) Assessment.
  • Use spatial patterns and metrics to give context to forest plots, pixels and trends.  
  • Characterize the implications and impacts of data quality and quantity, from FIA and imaged data, on forest structure mapping across enivornmental gradients and for multiple applications.
Past Research
International Outreach  -  I have served multiple temporary assignments with USDA Forest Service International Programs. Through these assignments, I have co-led workships on remote sensing and land cover mapping, advanced GIS techniques, and landscape patterns analyses with forest cover maps and conducted a land use change assessment on the border lands between Haiti and the Dominican Republic. 

Annual Forest History Mapping across CONUS – I had the good fortune to be part of a research collaboration spanning multiple federal agencies and Universities that lasted over a decade: the NAFD (North American Forest Dynamics) study, a core project of the NASA North American Carbon Program (NACP).

I was able to serve multiple roles through this project's life cycle including collaborative coder on the NASA JPL supercomputers, research support, project coordinator and independent researcher. The study contributed techniques and CONUS wide data across a diverse set of topics supporting forest history modeling and mapping while creating national statistical annual estimates and maps of forest change (this only lists the projects pubs i contributed to directly):

  • Insights into Natural Resource Applications from time-series forest history and Disturbance Regimes maps (Gong et al. 2022; Riitters et al. 2020; Huang et al., 2009)
  • Mapping attribution of causal agents in forest change events (Schleeweis et al. 2020; Schroeder et al. 2017; Schleeweis et al. 2013)
  • Mapping intensity of forest disturbance events (Lu et al. 2022; Tao et al. 2019)
  • Mapping the timing and location of forest mortality events (Zhao et al. 2019; Goward et al. 2016; Masek et al. 2013)
  • High volume image processing and QA (Zhao et al. 2019; Schleeweis et al. 2016; Huang et al. 2009)
  • Validation of time-series maps (Zhao et al. 2019; Thomas et al. 2011)
Research Interest

A geographer at heart, I get excited about the relevance of spatial relationships and landscape heterogeniety in natural resource applications.  Research interests include cartography, visualization, biogeography, spatial pattern analysis, scaling, forest disturbance regimes, and domain breaks of drivers and constraints within forest dynamics.  I have been lucky to collaborate with teams that integrate geospatial techniques, using FIA plot data, image time-series, geostatistics, GIS and large volume data computing, to characterize forest trends as they relate to questions of natural resource monitoring and management. 

I currently serve as the FIA liason for LANDFIRE, a USDA Forest Service and USGS joint program, that produces over 28+ National geospatial layers for wildfire decision support. To read more about the LANDFIRE program and FIA a 15 year interagency partnership follow this link to the StoryMap .

I currently serve as the FIA science team lead for the National Land Cover Database (NLCD) tree canopy cover product. The Forest Service is a member of the Multi-Resolution Land Characteristics Consortium (MRLC) and is responsible for producing the tree canopy dataset for the United States using Landsat imagery and maintaining the consortium's product cycle.


Why This Research Is Important

In order for the Forest Inventory and Analysis program to continue its efficacy, continue to learn and improve, and advance forest inventory science, we need to refine existing and develop new techniques. It is perhaps more important than ever to share the most recent information on the status of and trends in our forests in an engaging and and meaningful manner across a diverse range of audiences. Working on techniques development and technology transfer methods advances these goals and can benefit the broader forest science community.

The FIA program fills critical forest information needs for a variety of customers in state government, the timber industry, environmental organizations, and many more. As a neutral provider of data, FIA seeks to constantly improve the inventories efficiency and utility in order to maximize the return on taxpayer dollars. Integrating geospatial and remote sensing technologies into inventory processes aids in achieving this objective.
  • University of Maryland, College Park, Ph.D. Geography, Remote Sensing and GIS for Natural Resource Applications, 2012
  • University of Maryland, Baltimore County, Bachelor Of Science, Geography, 2006
Professional Organizations
  • International Association for Landscape Ecology,  Current
Featured Publications
Other Publications
Citations of Non-Forest Service Publications
  • Tao, X.; Huang, C.; Zhao, F.; Schleeweis, K.; Masek, J.; Liang, S. Mapping forest disturbance intensity in North and South Carolina using annual Landsat observations and field inventory data. Remote Sensing of Environment 2019, 221, 351-362.

    Zhao, F.; Huang, C.; Goward, S.N.; Schleeweis, K.; Rishmawi, K.; Lindsey, M.A.; Denning, E.; Keddell, L.; Cohen, W.B.; Yang, Z., et al. Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP). Remote Sensing of Environment 2018, 209, 312-326, doi:

    Huang, C.; Goward, S.N.; Schleeweis, K.; Thomas, N.; Masek, J.G.; Zhu, Z.L. Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States. Remote Sensing of Environment 2009, 113, 1430-1442.

    Thomas, N.E.; Huang, C.; Goward, S.N.; Powell, S.; Rishmawi, K.; Schleeweis, K.; Hinds, A. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sensing of Environment 2011, 115, 19-32, doi:10.1016/j.rse.2010.07.009.

    Huang, C.; Schleeweis, K.; Thomas, N.; N, G.S. Forest dynamics within and around the Olympic National Park assessed using time series Landsat observations. In Remote Sensing of Protected Lands, Wang, Y., Ed. Taylor & Francis: London, 2011; pp. 75-94.


  • Masek, J.G.; Goward, S.N.; Kennedy, R.E.; Cohen, W.B.; Moisen, G.G.; Schleeweis, K.; Huang, C. United States Forest Disturbance Trends Observed Using Landsat Time Series. Ecosystems 2013, 1-18.

    Huang, C.; Goward, S.N.; Masek, J.G.; Gao, F.; Vermote, E.F.; Thomas, N.; Schleeweis, K.; Kennedy, R.E.; Zhu, Z.; Eidenshink, J.C., et al. Development of time series stacks of Landsat images for reconstructing forest disturbance history. International Journal of Digital Earth 2009, 195-218.

    Goward, S.N., C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J.L. Dungan, and A. Michaelis. 2015. NACP NAFD Project: Forest Disturbance History from Landsat, 1986-2010. ORNL DAAC, Oak Ridge, Tennessee, USA.

  • Hansen, C., Yonavjak, L. Clarke,C., Minnemeyer, S., Boisrobert, L., Leach, A., Schleeweis, K. (2010) Southern Forests for the Future. World Resources Institute: Washington D.C.
Research Highlights

Mapping Causes of Disturbance in U.S. Forests

Year: 2020
A collaborative project between the USFS Forest Inventory and Analysis Program (FIA), NASA, and several universities developed a national dataset mapping the location, timing, and cause of canopy loss events due to removals, fire, stress, wind, and land use conversion, as well as unperturbed forest ...

ModelMap Predicts Forest Characteristics Over Any Geographic Extent

Year: 2016
Forest Service scientists created a tool, ModelMap, that can combine the Forest Inventory and Analysis plot data with remote sensing satellite images to predict forest characteristics (such as species composition, crown cover, and forest disturbances) over any geographic extent.