LB 32-310 - How many leaves on an urban tree? Validating methods to estimate tree leaf area

Background/Question/Methods:

Urban trees provide ecosystem services which benefit society, such as helping to keep urban areas cooler in hot temperatures and improving air quality. The provision of benefits like these is closely linked to the total leaf area of a tree. A tree’s leaf area is an important metric used in models to estimate urban tree benefits, such as in the i-Tree Eco model. Broader datasets on the leaf area and leaf area index of urban trees could improve the estimation the ecosystem services for individual trees. A range of methods are available to collect such information, but range in usability and cost. Destructive sampling, while the most accurate approach, is time-consuming and necessitates destroying a tree. Indirect methods provide faster, non-destructive estimates of leaf area, but are often designed for use in closed canopy conditions and with little validation of for use on urban trees. This study will provide a comprehensive assessment of multiple indirect methods to assess urban tree leaf area, validated against destructive samples. The methods assessed include allometry, a smartphone app, ceptomtery, hemispherical photography and terrestrial laser scanning.

Results/Conclusions:

Initial results will be reported for destructive samples of at least seven trees of three different species ranging from 7-14m in height. Destructive samples of trees provided leaf area estimates of 60m² to 260m². Overall, hemispherical photography and a smartphone app provided the greatest accuracy, with mean absolute errors of 16-33%. The poorest method was allometry, which for some species over-estimated leaf area by 100%. Many methods were sensitive to timing, sky conditions and interference from neighbouring objects, reducing the usability of these tools for time-constrained field projects. Evaluating the accuracy and usability of these different methods can help future urban tree project select the most appropriate tool for their analysis. Should rapid and low-cost methods, such as the smartphone app, provide similar results to other methods could make leaf area measurement accessible to large-scale projects and to citizen science approaches. Such approaches could help to rapidly build a database of leaf areas for urban trees to better estimate their ecosystem service provision and inform their management.