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.
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24 January 2017
An erratum to this article has been published.
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Acknowledgments
We thank the National Science Foundation (NSF-HSD-0624177) and the Environmental Protection Agency (EPA-G2006-STAR-H1) for funding this research.
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The original version of this article was revised: One of the authors name was incorrectly listed as “Darrell E. Jenerette” and should be corrected as “G. Darrel Jenerette”.
An erratum to this article is available at https://doi.org/10.1007/s11252-017-0646-5.
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Gillespie, T.W., de Goede, J., Aguilar, L. et al. Predicting tree species richness in urban forests. Urban Ecosyst 20, 839–849 (2017). https://doi.org/10.1007/s11252-016-0633-2
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DOI: https://doi.org/10.1007/s11252-016-0633-2