title: Mapping carbon storage in urban trees with multi-source remote sensing data: relationships between biomass, land use, and demographics in Boston neighborhoods.
abstract: High resolution maps of urban vegetation and biomass are powerful tools for policy-makers and community groups seeking to reduce rates of urban runoff, moderate urban heat island effects, and mitigate the effects of greenhouse gas emissions. We developed a very high resolution map of urban tree biomass, assessed the scale sensitivities in biomass estimation, compared our results with lower resolution estimates, and explored the demographic relationships in biomass distribution across the City of Boston. We integrated remote sensing data (including LiDAR-based tree height estimates) and field-based observations to map canopy cover and aboveground tree carbon storage at ~1m spatial scale. Mean tree canopy cover was estimated to be 25.5±1.5% and carbon storage was 355Gg (28.8MgCha(-1)) for the City of Boston. Tree biomass was highest in forest patches (110.7MgCha(-1)), but residential (32.8MgCha(-1)) and developed open (23.5MgCha(-1)) land uses also contained relatively high carbon stocks. In contrast with previous studies, we did not find significant correlations between tree biomass and the demographic characteristics of Boston neighborhoods, including income, education, race, or population density. The proportion of households that rent was negatively correlated with urban tree biomass (R(2)=0.26, p=0.04) and correlated with Priority Planting Index values (R(2)=0.55, p=0.001), potentially reflecting differences in land management among rented and owner-occupied residential properties. We compared our very high resolution biomass map to lower resolution biomass products from other sources and found that those products consistently underestimated biomass within urban areas. This underestimation became more severe as spatial resolution decreased. This research demonstrates that 1) urban areas contain considerable tree carbon stocks; 2) canopy cover and biomass may not be related to the demographic characteristics of Boston neighborhoods; and 3) that recent advances in high resolution remote sensing have the potential to improve the characterization and management of urban vegetation.
authors.affiliation: Department of Biology, Hofstra University, Gittleson Hall, Hempstead, NY 11549, United States; Department of Earth and Environment, Boston University, 685 Commonwealth Ave., Boston, MA 02215, United States. Electronic address: Steve.M.Raciti@Hofstra.edu.
authors.forename: Steve M
authors.initials: SM
authors.lastname: Raciti
authors.affiliation: Department of Earth and Environment, Boston University, 685 Commonwealth Ave., Boston, MA 02215, United States.
authors.forename: Lucy R
authors.initials: LR
authors.lastname: Hutyra
authors.affiliation: Department of Earth and Environment, Boston University, 685 Commonwealth Ave., Boston, MA 02215, United States.
authors.forename: Jared D
authors.initials: JD
authors.lastname: Newell
mesh_terms: D018533:Biomass; D001900:Boston; D057965:Carbon Sequestration; D002947:Cities; D003710:Demography; D004784:Environmental Monitoring / Q000379:methods*; D058998:Remote Sensing Technology; D012111:Residence Characteristics; D014197:Trees / Q000502:physiology*
keywords: Carbon cycle; Demographics; High resolution remote sensing; LiDAR; Urban tree canopy; Vegetation biomass