Forest Inventory & Analysis

• Ph.D Mathematical Sciences - Statistics, Utah State University, 2000
• M.S. Statistics, Utah State University, 1990
• B.S. Forestry, University of New Hampshire, 1982

Members of the Techniques Research Team in IW-FIA and numerous collaborators are currently working to improve the quality, efficiency, and breadth of nationwide inventory and monitoring products. We are investigating a number of statistical methods to integrate remotely sensed information into inventory and monitoring estimation procedures. We are mapping and analyzing US forest disturbance history using historic landsat data. Coupled with that, we are developing methods to assign cause of disturbance to these landsat-based disturbance products, and analyzing temporal and spatial patterns caused by various disturbance agents. We are working to improve computational methods for broad scale species distribution and tree attribute modeling. In addition, we are studying the effects of projected climate change on distribution patterns of Western North America conifers. 

 

 

 

Past research has also involved improving the quality, efficiency, and breadth of nationwide inventory and monitoring products. Previous projects enhancing FIA’s estimation processes include: improved stratification methods, extension into model-assisted estimation, and integration of large-scale photography into inventory processes. Previous mapping projects include: developing modeling methodologies for the 2011 NLCD tree canopy cover product, developing the first nationwide forest biomass map, comparing modeling methodologies, and improving modeling and validation methodologies for species distribution models. Past work related to forest disturbance includes: forest disturbance and regrowth analyses for the North American Carbon Program as well as for specific management issues, small area estimation for fires, and design of targeted disturbance sampling projects. Finally, past work has involved expanding applications for inventory information to wildlife habitat modeling.

 

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. By developing methods for sampling and integrating inventory data with remotely sensed information, we are improving the quality and efficiency of inventory analysis and reporting products. By developing and improving mapping tools, we are enabling the production of better maps of the diverse vegetation conditions across the country. By mapping and analyzing historic forest disturbance across the US, we are opening doors to many untold stories about how US forests are changing in response to natural and anthropogenic forces. Finally, by developing tools to broaden the scope of strategic vegetation inventory applications we are supporting land management planning and monitoring needs.

 

 

• Coulston, J.W., Moisen, G.G., Wilson, B.T., Finco, M.V., Cohen, W.B., Brewer, C.K.  2012.  Modeling percent tree canopy cover:  A pilot study.  Photogrammetric Engineering and Remote Sensing 78(7).  715-727
• Schleeweis, K., Goward, S.N., Huang, C., Thomas, N., Riswami, K., Powell, S.L., Kennedy, R.E., & Moisen, G.G. [In Press]. Regional Dynamics of Forest Canopy Change and Underlying Causal Processes in the Conterminous US. Journal of Geophysical Research - Biogeosciences.
• Schroeder, T.A., M.A. Wulder, S.P. Healey, and G.G. Moisen. [In Press]. Detecting post-fire salvage logging with Landsat change maps and national fire survey data. Remote Sensing of Environment.
• Healey, S.P., E. Lapoint, G. G. Moisen, S. L. Powell. 2011. Maintaining the confidentiality of plot locations by exploiting the low sensitivity of forest structure models to different spectral extraction kernels. International Journal of Remote Sensing Vol. 32, Issue 1, 2011.
• Main-Knorn , M., G.G. Moisen, S.P. Healey, W.S. Keeton, E.A. Freeman, and P. Hostert. 2011. Evaluating the remote sensing and inventory-based estimation of biomass in the Western Carpathians. Remote Sensing 3:1427-1446.
• Schroeder, T. A., M.A. Wulder, S.P. Healey, G.G, Moisen. 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sensing of the Environment 115:1421–1433.
• Stueve, K.M., C.H. Perry, M.D. Nelson, S.P. Healey, A.D. Hill, G.G. Moisen, W.B. Cohen, D.D. Gormanson, and C. Huang. 2011. Ecological importance of intermediate windstorms rivals large, infrequent disturbances in the northern Great Lakes. Ecosphere 2:art2.
• Powell, S.L., S.P. Healey, W.B. Cohen, R.E. Kennedy, G.G. Moisen, K.B. Pierce, and J.L. Ohmann.  2010.  Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches.  Remote Sensing of Environment 114(5) 1053 – 1068.
• Healey,S. P., J. A. Blackard, T. A. Morgan, D. Loeffler, G. Jones, J. Songster, J. P. Brandt, G. G. Moisen and L. T. DeBlander. 2009.  Changes in timber haul emissions in the context of shifting forest management and infrastructure. Carbon Balance and Management 4:9.
• Chopping, M., A. Nolin, G. G. Moisen, J. V. Martonchik, and M. Bull. 2009.  Forest canopy height from multiangle imaging spectroradiometer (MISR) assessed with high resolution discrete return lidar. Remote Sensing of Environment 113: 2172 – 2185.
• Uribe, A. S., S. P. Healey, G. G. Moisen, R. P. Rivas, E. G. Aguilar, C. L. M. Tovar, E. S. Davalos, and V. S. Mascorro. The Potential Impact of Mexico’s New Forest Inventory on Consistent Monitoring of Continental-Scale Carbon Dynamics. EOS Transactions 89 (47), 18 November, 2008.
• Freeman, E. A. and G. G. Moisen. 2008.  A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa. Ecological Modelling 217, 48-58.
• Chopping, M., G. G. Moisen, L. Sul, A. Laliberte, A. Rango, J. V. Martonchik, and D. P. C. Peters. 2008.  Large area mapping of southwestern forest crown cover, canopy height, and biomass using MISR. Remote Sensing of Environment 112: 2051-2063.
• Moisen, G. G. Classification and regression trees. 2008. In: Sven Erik Jørgensen and Brian D. Fath (Editor-in-chief), Ecological Informatics.  Encyclopedia of Ecology, Volume 1: 582-588.
• Zimmermann, N. E., G. G. Moisen, T. C. Edwards, Jr., T. S. Frescino, and J. A. Blackard. 2007. Testing the partial contributions of remotely-sensed and topo-climatic predictors for tree species modeling in Utah.  Journal of Applied Ecology, 44: 1057-1067.
• Goward, S. N., J. G. Masek, W. B. Cohen, G. G. Moisen, G. J. Collatz, S. Healey, R. Houghton, C. Huang, R. Kennedy, B. Law, D. Turner, S. Powell, and M. Wulder. 2008.  Forest disturbance and North American carbon flux. EOS Transactions 89(11), 11 March, 2008.
• Blackard, J., M. Finco, E. Helmer, G. Holden, M. Hoppus, D. Jacobs, A. Lister, G. G. Moisen, M. Nelson, R. Riemann, B. Ruefenacht, D. Salajanu, D. Weyermann, K. Winterberger, T. Brandeis, R. Czaplewski, R. McRoberts, P. Patterson, and R. Tymcio. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment 112: 1658-1677.
• Freeman, E. A. and G. G. Moisen. 2008.  PresenceAbsence: An R package for presence absence analysis. Journal of Statistical Software, Volume 23, Issue 11.
• Zarnetske, P. L., T. C. Edwards, Jr., and G. G. Moisen.  2007.  Habitat classification modelling with incomplete data: Pushing the habitat envelope.  Ecological Applications, 17(6): 1714-1726
• Nelson, M.D., G. G. Moisen, M. Finco, and K. Brewer. 2007. Forest Inventory and Analysis in the United States: Remote sensing and geospatial activities. Photogrammetric Engineering and Remote Sensing 73(7):729-732
• Opsomer, J. D., F. J. Breidt,  G. G. Moisen, and G. Kauermann. 2007. Model-assisted estimation of forest resources with generalized additive models.Journal of the American Statistical Association, 102:400-416
• Freeman, E. and G. G. Moisen. 2007.  Evaluating kriging as a tool to improve moderate resolution maps of forest biomass. Environmental Monitoring and Assessment, 128:395-410
• Moisen, G. G., E. A. Freeman, J. A. Blackard, T. S. Frescino, N. E. Zimmermann, and T. C. Edwards, Jr.  2006. Predicting tree species presence in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological Modelling, 199:176-187
• Edwards, T. C., Jr., D. R. Cutler, N. E. Zimmermann, L. Geiser, and G. G. Moisen. 2006. Effects of sample survey design on the accuracy of classification models in ecology. Ecological Modelling, 199:132-141
• Moisen, G. G., T. C. Edwards, Jr., P. E. Osborne. 2006. Further advances in predicting species distributions. Ecological Modelling, 199:129-131
• Moisen, G. G., and T. S. Frescino. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157: 209-225.
• Edwards, T. C., Jr., G. G. Moisen, T. S. Frescino, and J. J. Lawler. 2002. Modelling multiple ecological scales to link landscape theory to wildlife conservation. In: J. A. Bissonette and I. Storch, editors. Landscape ecology and resource management: Making the linkages. Island Press, Covelo, California, USA
• Frescino, T. S., T. C. Edwards, Jr., and G. G. Moisen. 2001. Modeling spatially explicit structural attributes using generalized additive models. Journal of Vegetation Science 12:15-26
• Moisen, G. G., R. D. Cutler, and T. C. Edwards, Jr. 2000. Generalized linear mixed models for analyzing error in a satellite-based vegetation map of Utah. In: H. T. Mowrer and R. Congalton, editors. Quantifying Spatial Uncertainty in Natural Resources: Theory and Application for GIS and Remote Sensing. Ann Arbor Press, Chelsea, Michigan, p. 37-44
• Moisen, G. G. and T. C. Edwards, Jr. 1999. Use of generalized linear models and digital data in a forest inventory of Utah. Journal of Agricultural, Biological  and Environmental Statistics 4(4): 372-390
• Edwards, T. C., Jr., G. G. Moisen, and D. R. Cutler. 1998. Assessing map uncertainty in ecoregion-scale cover maps. Remote Sensing of Environment 63:73-83
• Moisen, G. G., A. R. Stage, and J. D. Born. 1995. Point sampling with disjunct support near population boundaries. Forest Science Monograph 31:62-82
• Moisen, G. G., T. C. Edwards, Jr., and D. R. Cutler. 1994. Spatial sampling to assess classification accuracy of remotely sensed data. Pages 161-178 in J. Brunt, S. S. Stafford, and W. K. Michener, editors. Environmental Information Management and Analysis: Ecosystem to Global Scales, Taylor and Francis, Limited