You are here

Gretchen Moisen

Gretchen Moisen

Research Forester

507 25th Street
Ogden, UT 84401-2450
Contact Gretchen Moisen

Current Research

1. Developing modeling methodologies for the 2011 NLCD tree canopy cover product. 2. Studying US forest disturbance history from landsat. 3. Integrating landsat-based disturbance information into inventory and monitoring estimation procedures. 4. Developing methods to assign cause of disturbance to landsat-based disturbance products. 5. Improving methods for broad scale species distribution modeling. 6. Studying the ffects of projected climate change on distribution patterns of Western North America conifers.

Research Interests

I am interested in four broad areas of research: 1) developing methods for sampling and integrating inventory data with remotely sensed information to improve the quality and efficiency of inventory analysis and reporting products; 2) developing mapping tools and spatial depictions of diverse vegetation conditions; 3) quantifying disturbance effects on various resources; and 4) supporting land management planning and monitoring needs by developing tools to broaden the scope of strategic vegetation inventory applications.

Past Research

Working to improve the quality, efficiency, and breadth of nationwide inventory and monitoring products expands foundational information for a broad spectrum of scientific and managerial applications.

Why This Research is Important

1. Nationwide forest biomass map. 2. Comparing nonlinear and nonparametric modeling techniques for predictive mapping of forest attributes. 3. Improving modeling and validation methodologies for species distribution models.


  • University of New Hampshire, B.S., Forestry, 1982
  • Utah State University, M.S., Statistics, 1990
  • Utah State University, Ph.D., Statistics, 2000
  • Featured Publications


    Healey, Sean P.; Cohen, Warren B.; Yang, Zhiqiang; Brewer, C. Kenneth; Brooks, Evan B.; Gorelick, Noel; Hernandez, Alexander J.; Huang, Chengquan; Hughes, M. Joseph; Kennedy, Robert E.; Loveland, Thomas R.; Moisen, Gretchen; Schroeder, Todd A.; Stehman, Stephen V; Vogelmann, James E.; Woodcock, Curtis E.; Yang, Limin; Zhu, Zhe, 2018. Mapping forest change using stacked generalization: An ensemble approach
    Cohen, Warren B.; Healey, Sean P.; Yang, Zhiqiang; Stehman, Stephen V.; Brewer, C. Kenneth; Brooks, Evan B.; Gorelick, Noel; Huang, Chengqaun; Hughes, M. Joseph; Kennedy, Robert E.; Loveland, Thomas R.; Moisen, Gretchen; Schroeder, Todd A.; Vogelmann, James E.; Woodcock, Curtis E.; Yang, Limin; Zhu, Zhe, 2017. How similar are forest disturbance maps derived from different Landsat time series algorithms?
    McConville, Kelly S.; Breidt, F. Jay; Lee, Thomas C. M.; Moisen, Gretchen, 2017. Model-assisted survey regression estimation with the lasso
    Cooke, Brian; Freeman, Elizabeth; Moisen, Gretchen; Frescino, Tracey, 2017. Painting a picture across the landscape with ModelMap
    Schroeder, Todd A.; Schleeweis, Karen; Moisen, Gretchen; Toney, Chris; Cohen, Warren B.; Freeman, Elizabeth; Yang, Zhiqiang; Huang, Chengquan, 2017. Testing a Landsat-based approach for mapping disturbance causality in U.S. forests
    Holden, Zachary A.; Swanson, Alan; Klene, Anna E.; Abatzoglou, John T.; Dobrowski, Solomon Z.; Cushman, Samuel A.; Squires, John R.; Moisen, Gretchen; Oyler, Jared W., 2016. Development of high-resolution (250 m) historical daily gridded air temperature data using reanalysis and distributed sensor networks for the US northern Rocky Mountains
    Frescino, Tracey; Moisen, Gretchen; Patterson, Paul L.; Freeman, Elizabeth; Menlove, James S., 2016. Nevada Photo-Based Inventory Pilot (NPIP) resource estimates (2004-2005)
    Moisen, Gretchen; Meyer, Mary C.; Schroeder, Todd A.; Liao, Xiyue; Schleeweis, Karen; Freeman, Elizabeth; Toney, Chris, 2016. Shape selection in Landsat time series: A tool for monitoring forest dynamics
    Frescino, Tracey; Patterson, Paul L.; Moisen, Gretchen; Freeman, Elizabeth, 2015. FIESTA—An R estimation tool for FIA analysts
    Matyjasik, Marek; Moisen, Gretchen; Schroeder, Todd A.; Frescino, Tracy; Hernandez, Michael, 2015. Forest disturbances trigger erosion controlled fluxes of nitrogen, phosphorus and dissolved carbon
    Cohen, Warren B.; Andersen, Hans-Erik; Healey, Sean P.; Moisen, Gretchen; Schroeder, Todd A.; Woodall, Christopher W.; Domke, Grant M.; Yang, Zhiqiang; Kennedy, Robert E.; Stehman, Stephen V.; Woodcock, Curtis; Vogelmann, Jim; Zhu, Zhe; Huang, Chengquan., 2015. Integrating field plots, lidar, and landsat time series to provide temporally consistent annual estimates of biomass from 1990 to present
    Toney, J. Chris; Schleeweis, Karen; Dungan, Jennifer; Michaelis, Andrew; Schroeder, Todd; Moisen, Gretchen, 2015. Nationwide disturbance attribution on NASA’s earth exchange: experiences in a high-end computing environment
    Healey, Sean P.; Cohen, Warren B.; Zhiqiang, Yang; Brewer, Ken; Brooks, Evan; Gorelick, Noel; Gregory, Mathew; Hernandez, Alexander; Huang, Chengquan; Hughes, Joseph; Kennedy, Robert; Loveland, Thomas; Megown, Kevin; Moisen, Gretchen; Schroeder, Todd; Schwind, Brian; Stehman, Stephen; Steinwand, Daniel; Vogelmann, James; Woodcock, Curtis; Yang, Limin; Zhu, Zhe, 2015. Next-generation forest change mapping across the United States: the landscape change monitoring system (LCMS)
    Schleeweis, Karen; Moisen, Gretchen; Schroeder, Todd A.; Toney, Chris; Freeman, Elizabeth, 2015. On the road to national mapping and attribution of the processes underlying U.S
    Freeman, Elizabeth; Moisen, Gretchen; Coulston, John W.; Wilson, Barry T. (Ty), 2015. Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance
    Meyer, Mary; Liao, Xiyue; Moisen, Gretchen; Freeman, Elizabeth, 2015. ShapeSelectForest: a new r package for modeling landsat time series
    Schroeder, Todd A.; Moisen, Gretchen; Schleeweis, Karen; Toney, Chris; Cohen, Warren B.; Yang, Zhiqiang; Freeman, Elizabeth, 2015. Using an empirical and rule-based modeling approach to map cause of disturbance in U.S
    Cohen, W.; Andersen, H.; Healey, Sean P.; Moisen, Gretchen; Schroeder, T.; Woodall, C.; Domke, G.; Yang, Z.; Stehman, S.; Kennedy, R.; Woodcock, C.; Zhu, Z.; Vogelmann, J.; Steinwand, D.; Huang, C., 2014. An historically consistent and broadly applicable MRV system based on LiDAR sampling and Landsat time-series
    Matyjasik, M.; Moisen, Gretchen; Combe, C.; Hathcock, T.; Mitts, S.; Hernandez, M.; Frescino, Tracey; Schroeder, T., 2014. Effects of historic forest disturbance on water quality and flow in the Interior Western U.S
    Schroeder, Todd A.; Healey, Sean P.; Moisen, Gretchen; Frescino, Tracey; Cohen, Warren B.; Huang, Chengquan; Kennedy, Robert E.; Yang, Zhiqiang, 2014. Improving estimates of forest disturbance by combining observations from Landsat time series with U.S. Forest Service Forest Inventory and Analysis data
    Frescino, Tracey; Moisen, Gretchen; Adachi, K.; Breidt, J., 2014. Small-area estimation of forest attributes within fire boundaries
    Moisen, Gretchen; Brewer, K.; Czaplewski, R.; Healey, Sean P.; Megown, K.; Finco, M., 2014. The role of remote sensing in U.S. forest inventories: Past, present and future
    Cushman, Samuel A.; Mersmann, Timothy J.; Moisen, Gretchen; McKelvey, Kevin S.; Vojta, Christina D., 2013. Chapter 5. Using Habitat Models for Habitat Mapping and Monitoring
    Holden, Zachary A.; Klene, Anna E.; Keefe, Robert F.; Moisen, Gretchen, 2013. Design and evaluation of an inexpensive radiation shield for monitoring surface air temperatures
    Tipton, John; Opsomer, Jean; Moisen, Gretchen, 2013. Properties of Endogenous Post-Stratified Estimation using remote sensing data
    Schleeweis, Karen; Goward, Samuel N.; Huang, Chengquan; Masek, Jeffrey G.; Moisen, Gretchen; Kennedy, Robert E.; Thomas, Nancy E., 2013. Regional dynamics of forest canopy change and underlying causal processes in the contiguous US
    Masek, Jeffrey G.; Goward, Samuel N.; Kennedy, Robert E.; Cohen, Warren B.; Moisen, Gretchen; Schleweiss, Karen; Huang, Chengquan., 2013. United States forest disturbance trends observed with landsat time series
    Schroeder, Todd A.; Moisen, Gretchen; Healey, Sean P.; Cohen, Warren B., 2012. Adding value to the FIA inventory: combining FIA data and satellite observations to estimate forest disturbance
    Czaplewski, Raymond L. Ph.D.; ; Moisen, Gretchen, 2012. An efficient estimator to monitor rapidly changing forest conditions
    Healey, Sean P.; Patterson, Paul L.; Saatchi, Sassan; Lefsky, Michael A.; Lister, Andrew J.; Freeman, Elizabeth; Moisen, Gretchen, 2012. Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data
    Moisen, Gretchen; Schroeder, Todd A.; Schleeweis, Karen; Toney, Chris; Cohen, Warren B.; Goward, Samuel N., 2012. Attributing causal agents to nationwide maps of forest disturbance
    Moisen, Gretchen; Coulston, John W.; Wilson, Barry T.; Cohen, Warren B.; Finco, Mark V., 2012. Choosing appropriate subpopulations for modeling tree canopy cover nationwide
    Schroeder, Todd A.; Wulder, Michael A.; Healey, Sean P.; Moisen, Gretchen, 2012. Detecting post-fire salvage logging with Landsat change maps and national fire survey data
    Schroeder, Todd A.; Healey, Sean P.; Moisen, Gretchen, 2012. Evaluating the compatibility of American and Mexican national forest inventory data
    Gibson, Jacob; Moisen, Gretchen; Frescino, Tracey; Edwards, Thomas C. Jr., 2012. Expansion and contraction tension zones in western pinon-juniper woodlands under projected climate change
    Coulston, John W.; Moisen, Gretchen; Wilson, Barry T.; Finco, Mark V.; Cohen, Warren B.; Brewer, C. Kenneth, 2012. Modeling percent tree canopy cover: a pilot study
    Jackson, Thomas A.; Moisen, Gretchen; Patterson, Paul L.; Tipton, John, 2012. Repeatability in photo-interpretation of tree canopy cover and its effect on predictive mapping
    Tipton, John; Moisen, Gretchen; Patterson, Paul L.; Jackson, Thomas A.; Coulston, John, 2012. Sampling intensity and normalizations: Exploring cost-driving factors in nationwide mapping of tree canopy cover
    Schleeweis, Karen; Goward, Samuel N.; Huang, Chengquan; Masek, Jeffrey; Moisen, Gretchen, 2012. Understanding trends in observations of forest disturbance and their underlying causal processes
    Frescino, Tracey; Patterson, Paul L.; Freeman, Elizabeth; Moisen, Gretchen, 2012. Using FIESTA , an R-based tool for analysts, to look at temporal trends in forest estimates
    Schroeder, Todd; Wulder, Michael A.; Healey, Sean P.; Moisen, Gretchen, 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data
    Healey, Sean P.; Blackard, Jock A.; Morgan, Todd A.; Loeffler, Dan; Jones, Greg; Songster, Jon; Brandt, Jason P.; Moisen, Gretchen; DeBlander, Larry T., 2009. Changes in timber haul emissions in the context of shifting forest management and infrastructure
    McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray Ph.D., 2009. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT
    Chopping, Mark; Nolin, Anne; Moisen, Gretchen; Martonchik, John V.; Bull, Michael, 2009. Forest canopy height from Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar
    Moisen, Gretchen, 2008. Classification and regression trees
    Chopping, Mark; Moisen, Gretchen; Su, Lihong; Laliberte, Andrea; Rango, Albert; Martonchik, John V.; Peters, Debra P. C., 2008. Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer
    Blackard, J. A.; Finco, M. V.; Helmer, E. H.; Holden, G. R.; Hoppus, M. L.; Jacobs, D.M.; Lister, A. J.; Moisen, Gretchen; Nelson, M. D.; Riemann, R.; Ruefenacht, B.; Salajanu, D.; Weyermann, D. L.; Winterberger, K. C.; Brandeis, T. J.; Czaplewski, R. L. Ph.D.; McRoberts, R. E.; Patterson, Paul L.; Tymcio, R. P., 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
    Uribe, Alberto Sandoval; Healey, Sean P.; Moisen, Gretchen; Rivas, Rigoberto Palafox; Aguilar, Enrique Gonzalez; Tovar, Carmen Lourdes Meneses; Davalos, Ernesto S. Diaz Ponce; Mascorro, Vanessa Silva, 2008. Mexican forest inventory expands continental carbon monitoring
    Freeman, Elizabeth; Moisen, Gretchen, 2008. PresenceAbsence: An R package for presence absence analysis
    Nelson, Mark; Moisen, Gretchen; Finco, Mark; Brewer, Ken, 2007. Forest Inventory and Analysis in the United States: Remote sensing and geospatial activities
    Zarnetske, Phoebe L.; Edwards, Thomas C. Jr.; Moisen, Gretchen, 2007. Habitat classification modelling with incomplete data: Pushing the habitat envelope
    Opsomer, Jean D.; Breidt, F. Jay; Moisen, Gretchen; Kauermann, Goran, 2007. Model-assisted estimation of forest resources with generalized additive models
    Cohen, Warren B.; Healey, Sean P.; Goward, Samuel; Moisen, Gretchen; Masek, Jeffrey G.; Kennedy, Robert E.; Powell, Scott L.; Huang, Chengquan; Thomas, Nancy; Schleeweis, Karen; Wulder, Michael A., 2007. Use of Landsat-based monitoring of forest change to sample and assess the role of disturbance and regrowth in the carbon cycle at continental scales
    Edwards, Thomas C. Jr.; Cutler, D. Richard; Zimmermann, Niklaus E.; Geiser, Linda; Moisen, Gretchen, 2006. Effects of sample survey design on the accuracy of classification tree models in species distribution models
    Moisen, Gretchen; Edwards, Thomas C. Jr.; Osborne, Patrick E., 2006. Further advances in predicting species distributions
    Moisen, Gretchen; Freeman, Elizabeth; Blackard, Jock A.; Frescino, Tracey; Zimmermann, Niklaus E.; Edwards, Thomas C. Jr., 2006. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods
    Scott, Charles T.; Bechtold, William A.; Reams, Gregory A.; Smith, William D.; Westfall, James A.; Hansen, Mark H.; Moisen, Gretchen, 2005. Sample-based estimators used by the forest inventory and analysis national information management system
    Reams, Gregory A.; Smith, William D.; Hansen, Mark H.; Bechtold, William A.; Roesch, Francis A.; Moisen, Gretchen, 2005. The forest inventory and analysis sampling frame
    Ruefenacht, Bonnie; Moisen, Gretchen; Blackard, Jock A., 2004. Forest type mapping of the Interior West
    The “lasso” is a statistical estimator the captures only the best, of many, remotely sensed variables to use in a model. This is one of seven statistical estimators covered in the new tutorial and R package.
    Having precise estimates of our forest characteristics is important if we want to assess the status of our forests, detect change, or monitor trends. New statistical estimators enable us to improve precision by merging forest inventory data with data from a variety of remote sensing instruments, but often pose computational challenges. This new tutorial and R software package makes both old and new survey estimation tools easily accessible.
    A world map displaying the density of ModelMap downloads
    Working in the Forest Inventory and Analysis (FIA) Program, we have access to a valuable collection of detailed information about forests on thousands of sample plots distributed across the country. This information is used to produce summaries of forestland characteristics for a variety of geographic areas such as states or individual national forests. We wanted a simple tool to extend this sample data and make detailed maps of forest characteristics for all the land in between the study locations.
    There is new methodology for fitting ecologically feasible “shapes” to time series of Landsat imagery for modeling, mapping, and monitoring annual forest disturbance dynamics. Through a case study of fire, harvest and bark beetle outbreak, scientists illustrate how resultant fitted values and parameters can be fed into empirical models to map disturbance causal agent and tree canopy cover changes coincident with disturbance events through time.
    Understanding trends in forest disturbance caused by fire, harvest, stress, weather, and conversion is important for many forest management decisions as well as scientific investigations. After a decade of collaborative work between the Forest Service, NASA, University of Maryland and other partners, the North American Forest Dynamics (NAFD) project has processed historic Landsat data. This effort provides a comprehensive annual, wall-to-wall analysis of U.S. disturbance history over the last 25 years. Substantial progress has been made to also identify specific causal agents through time, and nationwide datasets will soon be available for exploring spatial and temporal patterns in U.S. forests.
    A historically consistent and broadly applicable monitoring, reporting, and verification system based on lidar sampling and Landsat time-series (tested in the US, and applied to the US NGHGI reporting system).
    North American Forest Dynamics (NAFD) project is exploiting the Landsat historical record to develop a quantitative understanding of forest disturbance patterns across the conterminous US.
    The Landscape Change Monitoring System (LCMS) is an emerging remote sensing-based system for mapping and monitoring land cover and land use change across the US. Envisioned as a framework for integrating Landsat-based products and other datasets, LCMS  is producing spatially, temporally, and thematically comprehensive data and information from which landscape change can be consistently assessed, documented, and analyzed at the national scale. 
    Forest Inventory ESTimation & Analysis (FIESTA) is a research tool for analysts who use data from the Forest Inventory and Analysis program and work in the open-source, R statistical programming environment.

    National Strategic Program Areas: 
    Inventory and Monitoring
    National Priority Research Areas: 
    Forest Disturbances; Forest Inventory and Analysis
    RMRS Science Program Areas: 
    Inventory and Monitoring