Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Eileen Helmer; Thomas Ruzycki; Barry Wilson; Kirk Sherrill; Michael Lefsky; Humfredo Marcano-VegaThomas Brandeis; Heather Erickson; Bonnie Ruefenacht
    Date: 2018
    Source: Remote Sensing
    Publication Series: Scientific Journal (JRNL)
    Station: International Institute of Tropical Forestry
    PDF: Download Publication  (21.0 MB)


    We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.

    Publication Notes

    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Helmer, Eileen; Ruzycki, Thomas; Wilson, Barry; Sherrill, Kirk; Lefsky, Michael; Marcano-Vega, Humfredo; Brandeis, Thomas; Erickson, Heather; Ruefenacht, Bonnie. 2018. Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals. Remote Sensing. 10(11): 1724-.


    Google Scholar


    deciduousness, protected areas, leaf thickness, sclerophylly, forest recovery, biophysical and socioeconomic controls, sustainable development goals, tree species richness, Earth system models, cloud forest, mountain habitats, species distribution models, leaf toughness, lithology, endemism

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page