Abstract
Valuing the ecosystem services of urban trees is important for gaining public and political support for urban tree conservation and maintenance. The i-Tree Eco software application can be used to estimate regulating ecosystem services provided by urban forests. However, existing municipal tree inventories may not contain data necessary for running i-Tree Eco and manual field surveys are costly and time consuming. Using a tree inventory of Oslo, Norway, as an example, we demonstrate the potential of geospatial and machine learning methods to supplement missing and incomplete i-Tree Eco attributes in existing municipal inventories for the purpose of rapid low-cost urban ecosystem accounting. We correlate manually surveyed stem diameter and crown dimensions derived from airborne laser scanning imagery to complete most structural attributes. We then use auxiliary spatial datasets to derive missing attributes of trees’ spatial context and include differentiation of air pollution levels. The integration of Oslo’s tree inventory with available spatial data increases the proportion of records suitable for i-Tree Eco analysis from 19 % to 54 %. Furthermore, we illustrate how machine learning with Bayesian networks can be used to extrapolate i-Tree Eco outputs and infer the value of the entire municipal inventory. We find the expected total asset value of municipal trees in Oslo to be 38.5–43.4 million USD, depending on different modelling assumptions. We argue that there is a potential for greater use of geospatial methods in compiling information for valuation of urban tree inventories, especially when assessing location-specific tree characteristics, and for more spatially sensitive scaling methods for determining asset values of urban forests for the purpose of awareness-raising. However, given the available data in our case, we question the accuracy of values inferred by Bayesian networks in relation to the purposes of ecosystem accounting and tree compensation valuation.
Highlights
More than half of the world’s population lives in cities
Species were recorded for 57 % and diameter at breast height (DBH) for 19 % of trees
I-Tree Eco analysis of the mu nicipal dataset is possible for 19 % of trees, i.e. all trees with both species and DBH recorded
Summary
The pro portion is predicted to rise to 68 % by 2050 globally and from 75 % in 2020 to nearly 84 % in 2050 in Europe (UN, 2018), leading to increased demand for living space This results in the conversion of natural ve getation cover to artificial surfaces and soil sealing (European Environment Agency (EEA, 2006). Urban green infrastructure com prising all types of vegetation provides ecosystem services (ES) to urban populations (European Commission, 2013; Gomez-Baggethun and Barton, 2013). Urban forests and individual trees are the major com ponents of urban green infrastructure, delivering provisioning, cultural and regulating services (Mullaney et al, 2015; Nesbitt et al, 2017; Nowak et al, 2008; Song et al, 2018) with social, economic, health and visual aesthetic benefits to humans (Roy et al, 2012). The health benefits of trees and forests in the coterminous US were valued at 1.5–13 billion USD, mostly occurring in urban areas (Nowak et al, 2014)
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