Measuring trees and leaf area

There are different ways to make choices about trees and shrubs, including quantitative and qualitative methods. You can use a mix of methods to find a local solution that meets your needs.

Estimating tree canopy cover

In Step 1.8, we discussed trees and temperature, including the role of the tree canopy in providing shade.

The definition of tree canopy cover is: the area of ground (m2) covered by the crown of a tree (Parmehr et al. 2016).

There are different methods to estimate urban tree canopy cover.

The non-field-based methods are:

• Random point sampling: estimates urban tree canopy cover based on a random sample of a limited number of points or plots in a geographic area. For example, the i-Tree canopy, was developed by the United State Department of Agriculture (USDA) Forest Service.
• Remote sensing methods: estimates urban tree canopy cover based on remotely sensed data. For example, Light Detection And Ranging (LiDAR) and multi-spectral aerial imagery data can provide 2D and 3D maps that can be integrated to automatically identify urban tree canopy cover over a large area (Parmehr et al. 2016).

In this step, we will look at iTree, which is used internationally because it is free, easy to use and effective.

Determining the value of a tree using iTree

iTree is a method for calculating the value of trees in an urban environment. The method uses the ‘big leaf’ model to calculate a tree’s absorption of air pollutants and benefits in monetary terms.

iTree involves:

1. Visiting the website and clicking ‘get started’.
2. Finding your location. iTree uses aerial and satellite images from Google Maps.
3. Selecting existing geographic boundaries or drawing your own, and then iTree generates random sample points.
4. Manually categorising the area as ‘tree’ and ‘non-tree’ to assess the tree canopy cover coverage in the sample points.
5. Reviewing the outputs, which include tree cover by percent or area, and the benefits. Accuracy is dependent on the operator’s ability to interpret the images and the image quality.

Precision depends on the number of random sample points.

The iTree website suggests using 500–1000 sample points, however, precision increases with an increase in the number of sample points. The number of sample points used will need to be selected based on the need for precision, project budget and time, and project objective.

Your task

Read through the following questions, and then spend some time reflecting on them in your own personal journal. These questions are designed to help reinforce the skills you are learning, and the greatest benefit will come from consideration of these questions over time.

Note: We encourage you to reach out and interact with your peers for guidance, assistance and support if you have difficulty with any aspect of this task.

Visit iTree and tell us about your experience in the comments below.

• What did you do?
• What did you find?
• Was the tool useful to you?

Commenting isn’t compulsory for completing the course, but it would be brilliant to hear your insights and for your fellow learners to benefit from your perspectives.

Academic references (Abstract only free to the public)

• Parmehr, E.G., Amati, M., Taylor, E.J., and Livesley, S.J., (2016), Estimation of urban tree canopy cover using random point sampling and remote sensing methods Urban Forestry & Urban Greening, Volume 20, pp.160-171, https://doi.org/10.1016/j.ufug.2016.08.011.

In the next step Dr Timpe shares some insights on co-design and co-production when planning your green corridor.