NOTE: this is an implementation update currently under development
The Forest Inventory and Analysis Program (FIA) of USDA Forest Service provide tree-level measurements from a systematic grid of field plots across all forest ownerships and land uses in the US.
FIAstemmap is an R package for mapping tree stem locations on FIA plots, modeling individual crown dimensions, and generating plot-level estimates of fractional tree canopy cover. Several stand height metrics can be calculated. Spatial analysis of tree point pattern is facilitated for the standard FIA four-point cluster plot design. Efficient data processing supports national applications. The package provides an updated implementation of the software originally described by Toney et al. 2009 [1]. The original implementation for modeling plot canopy cover from individual tree measurements has supported several applications of FIA data, including:
- reference data for classifying vegetation types and mapping tree canopy cover in the LANDFIRE Program [2, 3, 4, 5]
- National Land Cover Database (NLCD) Tree Canopy Cover (TCC) science and development [6]
- wildlife habitat analyses [7, 8, 9]
- mapping erosion risk in rangelands [10]
- comparative assessments of tree canopy cover estimation methods [11, 12]
Installation
You can install the development version of FIAstemmap with:
# install.packages("pak")
pak::pak("ctoney/FIAstemmap")References
[1] Toney, Chris; Shaw, John D.; Nelson, Mark D. 2009. A stem-map model for predicting tree canopy cover of Forest Inventory and Analysis (FIA) plots. In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 19 p. Available: https://research.fs.usda.gov/treesearch/33381.
[2] LANDFIRE: LANDFIRE Existing Vegetation Cover layer. (LF2024 version released 2025 - last update). U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture. [Online]. Available: https://landfire.gov/vegetation/evc [accessed 2026, Feb 24].
[3] Moore, Annabelle; La Puma, Inga; Dillon, Greg; Smail, Tobin; Schleeweis, Karen; Toney, Chris; Menakis, Jim; Bastian, Henry; Picotte, Josh; Dockter, Daryn; Tolk, Brian. 2024. Twenty years of science and management with LANDFIRE. Connected Science, October 2024. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 2 p. Available: https://research.fs.usda.gov/treesearch/68397.
[4] Vogelmann, Jim & Kost, Jay & Tolk, Brian & Howard, Stephen & Short, Karen & Chen, Xuexia & Huang, Chengquan & Pabst, Kari & Rollins, Matthew. (2011). Monitoring Landscape Change for LANDFIRE Using Multi-Temporal Satellite Imagery and Ancillary Data. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 4. 252 - 264. 10. https://doi.org/10.1109/JSTARS.2010.2044478.
[5] Nelson, K.J., Connot, J., Peterson, B. et al. 2013. The LANDFIRE Refresh Strategy: Updating the National Dataset. Fire Ecology, 9, 80–101. https://doi.org/10.4996/fireecology.0902080.
[6] Toney, Chris; Liknes, Greg; Lister, Andy; Meneguzzo, Dacia. 2012. Assessing alternative measures of tree canopy cover: Photo-interpreted NAIP and ground-based estimates. In: McWilliams, Will; Roesch, Francis A. eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 209-215. Available: https://research.fs.usda.gov/treesearch/41009.
[7] Tavernia, B., Nelson, M., Goerndt, M., Walters, B., & Toney, C. (2013). Changes in forest habitat classes under alternative climate and land-use change scenarios in the northeast and midwest, USA. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 5:2, 135-150(15). Retrieved from https://www.mcfns.com/index.php/Journal/article/view/MCFNS_165.
[8] Rowland, M.M.; Vojta, C.D.; tech. eds. 2013. A technical guide for monitoring wildlife habitat. Gen. Tech. Rep. WO-89. Washington, DC: U.S. Department of Agriculture, Forest Service: 400 p. Available: https://doi.org/10.2737/WO-GTR-89.
[9] Michael C. McGrann, Bradley Wagner, Matthew Klauer, Kasia Kaphan, Erik Meyer, Brett J. Furnas. 2022. Using an acoustic complexity index to help monitor climate change effects on avian diversity. Ecological Indicators, Volume 142, 109271, https://doi.org/10.1016/j.ecolind.2022.109271.
[10] McGwire KC, Weltz MA, Nouwakpo S, Spaeth K, Founds M, Cadaret E. 2020. Mapping erosion risk for saline rangelands of the Mancos Shale using the rangeland hydrology erosion model. Land Degradation & Development. 31: 2552-2564. https://doi.org/10.1002/ldr.3620.
[11] Riemann, R., Liknes, G., O’Neil-Dunne, J., Toney, C., Lister, T. (2016). Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA. Environmental Monitoring and Assessment 188:297. https://doi.org/10.1007/s10661-016-5281-8.
[12] Andrew N. Gray, Anne C.S. McIntosh, Steven L. Garman, Michael A. Shettles. 2021. Predicting canopy cover of diverse forest types from individual tree measurements. Forest Ecology and Management, Volume 501, 119682, ISSN 0378-1127, https://doi.org/10.1016/j.foreco.2021.119682.