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Urban Ecosystems

, Volume 20, Issue 4, pp 839–849 | Cite as

Predicting tree species richness in urban forests

  • Thomas W. GillespieEmail author
  • John de Goede
  • Luis Aguilar
  • G. Darrel Jenerette
  • Geoffrey A. Fricker
  • Meghan L. Avolio
  • Stephanie Pincetl
  • Timothy Johnston
  • Lorraine W. Clarke
  • Diane E. Pataki
Article

Abstract

There has been an increasing interest in urban forests and the levels of biodiversity they contain. Currently there are no spatially explicit maps of tree species richness in urban areas. This research tests and identifies GIS and remote sensing metrics (climate, area, productivity, three-dimensional structure) hypothesized to be associated with species richness in native forests and identifies methods that can be applied to predict and map tree species richness in cities. We quantified tree species richness, floristic composition, and structure in 28 1-ha plots in the city of Los Angeles. Climate and remote sensing metrics from high-resolution aerial imagery (10 cm), QuickBird (60 cm), Landsat (30 m), MODIS (250 m), and airborne lidar (2 m) were collected for each plot. There were 1208 individual stems and 108 trees identified to species. Species richness ranged from 2 to 31 species per ha and averaged 17 species per ha. Tree canopy cover from QuickBird explained the highest portion of variance (54%) in tree species richness followed by NDVI from Landsat (42%). Tree species richness can be higher in residential urban forests than native forests in the United States. Spatially explicit species richness maps at 1 ha can be created and tested for cities in order to identify both hotspots and coldspots of tree species richness and changes in species richness over time.

Keywords

Landsat Lidar QuickBird MODIS Remote sensing Species richness Urban forests 

Notes

Acknowledgments

We thank the National Science Foundation (NSF-HSD-0624177) and the Environmental Protection Agency (EPA-G2006-STAR-H1) for funding this research.

Supplementary material

11252_2016_633_MOESM1_ESM.xlsx (125 kb)
ESM 1 (XLSX 125 kb)
11252_2016_633_MOESM2_ESM.xlsx (15 kb)
ESM 2 (XLSX 15 kb)
11252_2016_633_MOESM3_ESM.docx (2.9 mb)
ESM 3 (DOCX 2998 kb)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Thomas W. Gillespie
    • 1
    • 4
    Email author
  • John de Goede
    • 1
  • Luis Aguilar
    • 1
  • G. Darrel Jenerette
    • 2
  • Geoffrey A. Fricker
    • 1
  • Meghan L. Avolio
    • 3
  • Stephanie Pincetl
    • 4
  • Timothy Johnston
    • 1
  • Lorraine W. Clarke
    • 2
  • Diane E. Pataki
    • 3
  1. 1.Department of GeographyUniversity of California Los AngelesLos AngelesUSA
  2. 2.Department of Botany and Plant SciencesUniversity of California RiversideRiversideUSA
  3. 3.Department of BiologyUniversity of UtahSalt Lake CityUSA
  4. 4.Institute of the Environment and SustainabilityUniversity of California Los AngelesLos AngelesUSA

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