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Sustainable cities depend on urban forests. City trees—pillars of urban forests—improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about city tree communities as ecosystems, particularly regarding spatial composition, species diversity, tree health, and the abundance of introduced species. Here, we assembled and standardized a new dataset ofN= 5,660,237 trees from 63 of the largest US cities with detailed information on location, health, species, and whether a species is introduced or naturally occurring (i.e., “native”). We further designed new tools to analyze spatial clustering and the abundance of introduced species. We show that trees significantly cluster by species in 98% of cities, potentially increasing pest vulnerability (even in species-diverse cities). Further, introduced species significantly homogenize tree communities across cities, while naturally occurring trees (i.e., “native” trees) comprise 0.51–87.4% (median = 45.6%) of city tree populations. Introduced species are more common in drier cities, and climate also shapes tree species diversity across urban forests. Parks have greater tree species diversity than urban settings. Compared to past work which focused on canopy cover and species richness, we show the importance of analyzing spatial composition and introduced species in urban ecosystems (and we develop new tools and datasets to do so). Future work could analyze city trees alongside sociodemographic variables or bird, insect, and plant diversity (e.g., from citizen-science initiatives). With these tools, we may evaluate existing city trees in new, nuanced ways and design future plantings to maximize resistance to pests and climate change. We depend on city trees.
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Analyzing Domain Knowledge for Big Data Analysis: A Case Study with Urban Tree Type Classification.
The goals of this research were to create a labeled dataset of tree shadows and to test the feasibility of shadow-based tree type identification using aerial imagery. Urban tree big data that provides information about individual trees can help city planners optimize positive benefits of urban trees (e.g., increasing wellbeing of city residents) while managing potential negative impacts (e.g., risk to power lines). The continual rise of tree type specific threats, such as emerald ash borer, due to climate change has made this problem more pressing in recent years. However, urban tree big data are time consuming to create. This paper evaluates the potential of a new tree type identification method that utilizes shadows in aerial imagery to survey larger regions of land in a shorter amount of time. This work is challenging because there are structural variations across a given tree type and few verified tree type identification datasets exist. Related work has not explored how tree structure characteristics translate into a profile view of a tree’s shadow or quantified the feasibility of shadow-only based tree type identification. We created a consistent and accurate dataset of 4,613 tree shadows using ground truthing procedures and novel methods for ensuring consistent collection of spatial shadow data that take binary and spatial agreement between raters into account. Our results show that identifying trees from shadows in aerial imagery is feasible and merits further exploration in the future.
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- Award ID(s):
- 1737633
- PAR ID:
- 10184558
- Date Published:
- Journal Name:
- Big Data Analytics. BDA 2019. Lecture Notes in Computer Science
- Volume:
- 11932
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-37188-3_11
