TreeMap: A tree-level model of the forests of the United States

Machine learning matched forest plot data with biophysical characteristics of the landscape to produce a seamless tree-level forest map.

The TreeMap 2016 dataset is a spatial model of the trees in continental US forests. It provides detailed spatial information on forest characteristics including a list of trees for each pixel (with tree species, DBH, height, and live or dead status), and summary information for each pixel including forest type, number of live and dead trees, biomass, and carbon. TreeMap covers the entire forested extent of the continental United States at 30 x 30m resolution, enabling analyses at fine scales. Inputs to TreeMap include detailed forest plot data measured by Forest Inventory Analysis (FIA, https://www.fia.fs.usda.gov/) and national gridded maps of forest cover, height, and vegetation type provided by the LANDFIRE project (https://www.landfire.gov/). TreeMap 2016 includes disturbance as a response variable, resulting in increased accuracy in mapping disturbed areas.

Using random forests (a type of machine learning algorithm) we match the forest plot data to the gridded vegetation maps, producing a seamless model of the trees of the forests of the US. Specifically, the result is a map of plot ID numbers, which identify the best-matching forest plot for each 30x30 pixel in the map. The map of plot ID numbers can be linked back to the FIA databases (https://apps.fs.usda.gov/fia/datamart/datamart.html) to generate maps of any number of forest characteristics, ranging from basal area to biomass to species types.

Some current uses of the dataset include estimation of:

  • Forest carbon
  • Wildfire risk to forest carbon
  • Volume of harvestable wood generated by fuel treatments
  • Hydrologic effects of fuel treatments

Read more about the background and development of TreeMap in this Science You Can Use in 5: Seeing the forest AND the trees: TreeMap provides a tree-level forest model.

View the interactive, web-based platform to explore TreeMap products without having to download the full dataset at https://apps.fs.usda.gov/lcms-viewer/treemap.html

Access the TreeMap dataset here: https://www.fs.usda.gov/rds/archive/Catalog/RDS-2021-0074

Snag Hazard Map

Derived from TreeMap 2016, Snag Hazard is a map that classifies forested areas into categories of low, moderate, high, or extreme snag hazard based on snag density (number of dead trees/acre) and height. The Snag Hazard map can help wildland fire managers identify hazardous snag conditions and avoid exposing fire responders in these areas. This Science You Can Use in 5 "Heads Up in a Dead Forest: Using the Snag Hazard Map to Support Safety and Strategic Planning for Fire Responders" describes how the Snag Hazard map can be used to support safety and strategic planning for first responders.

Click on photos for larger view.

Images

The project workflow. FIA forest plots (reference data) and raster landscape data from LANDFIRE (target data) were employed in a random forests algorithm that imputed the plot data to all forested pixels on the CONUS-wide landscape.
Top Image: Plot identifiers for a subset of the Mogollon Rim of Arizona. Each unique color corresponds to a different plot. Bottom Image: Live tree carbon for the same subset of the Mogollon Rim.

Select Publications & Products

Publications

Riley, Karin L., Isaac C. Grenfell, John D. Shaw, and Mark A. Finney. 2022. TreeMap 2016 Dataset Generates CONUS-Wide Maps of Forest Characteristics Including Live Basal Area, Aboveground Carbon, and Number of Trees per Acre. Journal of Forestry 120(6): 607-632. 
https://doi.org/10.1093/jofore/fvac022

Riley, Karin L., Christopher D. O’Connor, Christopher J. Dunn, Jessica R. Haas, Richard D. Stratton, and Benjamin Gannon. 2022. A National Map of Snag Hazard to Reduce Risk to Wildland Fire Responders. Forests 13(8): 1160. https://doi.org/10.3390/f13081160

Riley, Karin L.; Grenfell, Isaac C.; Finney, Mark A.; Shaw, John D. 2021. TreeMap 2016: A tree-level model of the forests of the conterminous United States circa 2016. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2021-0074

Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, Jason M. Wiener, and Rachel M. Houtman. 2019. Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014, https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0026

Karin L. Riley, Isaac C. Grenfell, Mark A. Finney, Jason M. Wiener. 2018. Fire Lab tree list: A tree-level model of the western US circa 2009 v1. https://doi.org/10.2737/RDS-2018-0003

Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping forest vegetation for the western US using modified random forests imputation of FIA forest plots. Ecosphere 7(10): 1-22. https://www.fs.usda.gov/treesearch/pubs/53114

Karin L. Riley, Isaac C. Grenfell, Mark A. Finney, and Nicholas L. Crookston. 2014. Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses. In: Viegas, D.X. (editor). Proceedings of the VII International Conference on Forest Fire Research. Associacao para o Desenvolvimento da Aerodinamica Industrial. November 17-20, 2014, Coimbra, Portugal. https://www.fs.usda.gov/treesearch/pubs/49563

Science You Can Use in 5:

Seeing the forest AND the trees: TreeMap provides a tree-level forest model

Heads up in a dead forest: using the Snag Hazard Map to support safety and strategic planning for fire responders

Conference presentations

Riley, Karin L., Isaac C. Grenfell, John D. Shaw, and Mark A. Finney. 2021. TreeMap lays foundation for analysis of risk to terrestrial carbon from wildland fire and fuel treatment. Association for Fire Ecology, 9th International Fire Ecology and Management Congress: November 30 - December 3, 2021, Virtual.

Riley, Karin L., Isaac C. Grenfell, Mark A. Finney. 2018. Mapping forest vegetation and biomass for the continental United States using modified random forests imputation of FIA forest plots. Society for Ecological Restoration and Society of Wetland Scientists, Restoring Resilient Communities in Changing Landscapes: October 15-18, 2018, Spokane, Washington. 

Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and Nicholas L. Crookston. 2014. Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses. VII International Conference on Forest Fire Research. Associacao para o Desenvolvimento da Aerodinamica Industrial. November 17-20, 2014, Coimbra, Portugal.

Riley, Karin L., Isaac Grenfell, and Nicholas Crookston. 2014. Random forests imputation of forest plot data for landscape-level analyses. 2014 Society of American Foresters National Convention. Society of American Foresters. October 9-11, 2014, Salt Lake City, Utah.

Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, Alan A. Ager, and Nicholas L. Crookston. 2012. Random Forests imputation of forest plot data for landscape-level wildfire analyses. Association for Fire Ecology 5th Fire Ecology Congress: Uniting Research, Education, and Management. December 3-7, 2012, Portland, Oregon.

Seminars/webinars

Riley, Karin L. and Isaac C. Grenfell. 2022. Mapping live and dead trees, fire responder hazard, carbon, and more with TreeMap, Snag Hazard, and FuelMap datasets. Presented for the U.S. Forest Service Missoula Fire Sciences Lab Seminar Series on November 3, 2022. https://firelab.org/event/1106 

Riley, Karin L. and Christopher D. O’Connor. 2022. National dataset of snag hazard for fire responders.  Presented for the LANDFIRE Office Hours on October 26, 2022. https://youtu.be/-THS16iMoVg

Riley, Karin L., Isaac C. Grenfell, John D. Shaw, and Mark A. Finney. 2021. TreeMap: Mapping forest vegetation for the continental United States using modified random forests imputation of FIA forest plots. Presented for the Forest Inventory Analysis National Users’ Group Meeting on June 21-23, 2021. Presentation begins at 44:15: https://www.youtube.com/watch?v=5p5GTX0V0Sg 

Riley, Karin L. 2018. Mapping forest vegetation for the continental United States using modified random forests imputation of FIA forest plots. Presented to the Society for Ecological Restoration, Pacific Northwest Chapter, webinar series on June 26, 2018. 

Riley, Karin L., Isaac Grenfell, and Mark A. Finney. 2016. Imputation of forest plot data for landscape-level wildfire analyses. Presented at the U.S. Forest Service Fire Sciences Laboratory, as part of the Missoula Fire Lab Seminar Series, on February 4, 2016.