Tree Inventory, Assessment, and Urban Forest Analysis
This domain covers how municipal tree data are collected, which fields matter, and how the numbers become management information. Questions test inventory design, condition and diversity metrics, canopy cover methods, and the limits of each data set.
Core rules
- A complete inventory supports work planning and risk tracking for every tree. A stratified random sample supports population estimates with confidence intervals but cannot serve as a work list.
- Standard fields: location, species, DBH, condition, canopy, site type, maintenance need, risk rating, and ownership.
- DBH is measured at 4.5 ft (1.37 m) above grade; multi-stem trees are measured per written policy and the method is documented.
- Diversity targets commonly follow the 10-20-30 rule: no more than 10 percent of one species, 20 percent of one genus, 30 percent of one family.
- i-Tree Eco quantifies ecosystem services from field data; i-Tree Canopy estimates canopy cover from photo-interpreted points.
- Canopy cover is a percentage of land area, not a tree count.
Worked example
A sample of 400 trees drawn from 20,000 public trees finds 25 percent needing pruning. The standard error of that proportion is the square root of (0.25 times 0.75 divided by 400) = 0.022, so the 95 percent confidence interval is about plus or minus 4.3 percent, roughly 4,140 to 5,860 trees. That range is defensible for budgeting but cannot tell you which trees to prune; for that you need a complete inventory.
Traps
Using sampled data as a work list. Mixing condition scales between inventories. Reporting canopy cover as a tree count. Assuming a low stocking level means abundant planting sites without checking site suitability.