The potential of geospatial analysis and Bayesian networks to enable i-Tree Eco assessment of existing tree inventories

https://doi.org/10.1016/j.ufug.2020.126801Get rights and content
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Highlights

  • Geospatial analysis to supplement i-Tree Eco attributes in municipal tree inventory.

  • Bayesian networks to scale i-Tree Eco results for entire urban inventory.

  • Spatial differentiation of air pollution levels in i-Tree Eco.

  • Policy relevance for Oslo including awareness-raising.

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.

Abbreviations

ALS
Airborne laser scanning
BN
Bayesian networks
CD
Crown diameter
CA
Crown area
CLE
Crown light exposure
DB
Direction and distance to building
DBH
Stem diameter at breast height
DSM
Digital surface model
DTM
Digital terrain model
ES
Ecosystem services
H
Total tree height
HCB
Height to crown base
HLT
Height to live top
LU
Land use
PCM
Percent crown missing
TLS
errestrial laser scanning

Keywords

Bayesian networks
Economic valuation
Geospatial analysis
i-Tree Eco
Regulating ecosystem services
Tree inventory

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