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dc.contributor.authorCimburova, Zofie
dc.contributor.authorBarton, David Nicholas
dc.coverage.spatialOslo, Norge, Norwayen_US
dc.description.abstractValuing 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 lowcost 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 iTree 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. Bayesian networks Economic valuation Geospatial analysis i-Tree Eco Regulating ecosystem services Tree inventoryen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectBayesian networksen_US
dc.subjectEconomic valuationen_US
dc.subjectGeospatial analysisen_US
dc.subjecti-Tree Ecoen_US
dc.subjectRegulating ecosystem servicesen_US
dc.subjectTree inventoryen_US
dc.titleThe potential of geospatial analysis and Bayesian networks to enable i-Tree Eco assessment of existing tree inventoriesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2020 The Authors.en_US
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210en_US
dc.source.journalUrban Forestry & Urban Greeningen_US
dc.relation.projectNorges forskningsråd: 160022en_US
dc.relation.projectNorges forskningsråd: 255156en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal