Trees provide numerous benefits for urban residents, including reduced energy usage, improved air quality, stormwater management, carbon sequestration, and increased property values. Quantifying these benefits can help justify the costs of planting trees. In this paper, we use i-Tree Streets to quantify the benefits of street trees planted by nonprofits in three U.S. cities (Detroit, Michigan; Indianapolis, Indiana, and Philadelphia, Pennsylvania) from 2009 to 2011. We also use both measured and modeled survival and growth rates to “grow” the tree populations 5 and 10 years into the future to project the future benefits of the trees under different survival and growth scenarios. The 4059 re-inventoried trees (2864 of which are living) currently provide almost $40,000 (USD) in estimated annual benefits ($9–$20/tree depending on the city), the majority (75%) of which are increased property values. The trees can be expected to provide increasing annual benefits during the 10 years after planting if the annual survival rate is higher than the 93% annual survival measured during the establishment period. However, our projections show that with continued 93% or lower annual survival, the increase in annual benefits from tree growth will not be able to make up for the loss of benefits as trees die. This means that estimated total annual benefits from a cohort of planted trees will decrease between the 5-year projection and the 10-year projection. The results of this study indicate that without early intervention to ensure survival of planted street trees, tree mortality may be significantly undercutting the ability of tree-planting programs to provide benefits to neighborhood residents.

Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of street tree inventories, but there are fundamental uncertainties regarding the level of data quality that can be expected from these emerging approaches to data collection. We asked 16 volunteers to inventory street trees in suburban Chicago using Google Street View^TM imagery, and we assessed data quality by comparing their virtual survey data to field data from the same locations. We also compared virtual survey data quality according to self-rated expertise by measuring agreement within expert, intermediate, and novice analyst groups. Analyst agreement was very good for the number of trees on each street segment, and agreement was markedly lower for tree diameter class and tree identification at the genus and species levels, respectively. Interrater agreement varied by expertise, such that experts agreed with one another more often than novices for all four variables assessed. Compared to the field data, we observed substantial variability in analyst performance for diameter class estimation and tree identification, and some intermediate analysts performed as well as experts. Our findings suggest that virtual surveys may be useful for documenting the locations of street trees within a city more efficiently than field crews and with a high level of accuracy. However, tree diameter and species identification data were less reliable across all expertise groups, and especially novice analysts. Based on this analysis, virtual street tree inventories are best suited to collecting very basic information such as tree locations, or updating existing inventories to determine where trees have been planted or removed. We conclude with evidence-based recommendations for effective implementation of this type of approach.