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Random point sampling to detect gain and loss in tree canopy cover in response to urban densification
journal contributionposted on 2023-05-19, 15:21 authored by Kaspar, J, David Kendal, Sore, R, Livesley, SJ
Urban tree canopy cover (UTC) is a simple, and common, measure of urban forest resource. Urban infill development is likely to lead to losses in UTC under private tenure, at a time when local governments are setting ambitious targets to increase UTC overall. Simple, statistically rigorous methods are required to benchmark and track change in UTC, whilst identifying which land-use types or tenures experience change. We estimated UTC in six Melbourne suburbs in 2010 and 2015 by randomly sampling 2000 points across public land, public streetscapes and private land. We were able to detect a net change in UTC of <2% over five years to a 95% level of confidence. A significant net decrease in UTC (−2.4%) was only detected in one of the six suburbs. Two suburbs had a net increase in UTC by +2.7% over five years. On private land, there was often areas of UTC loss, but this was generally offset by canopy gain in other areas of the private realm as well as in streetscapes and public land. Losses in UTC on private land were mainly due to tree removal, with or without subsequent construction works. This study describes a simple, but statistically rigorous, method to quantify UTC change and the drivers of change in different land-use types and tenure. Despite studying two suburbs will high rates of infill development, only one suburb showed evidence of net UTC decrease. The ‘dynamic equilibrium’ in UTC, whereby canopy losses area approximately offset by concurrent canopy gain, means that ambitious targets being set by local governments to increase UTC may be difficult to achieve without changes in tree protection and infill development policy and planning.
Publication titleUrban Forestry and Urban Greening
Department/SchoolSchool of Geography, Planning and Spatial Sciences
Place of publicationGermany
Rights statementCopyright 2017 Elsevier GmbH