Tree Inventories and Sampling

Overview Inventories Inventories in i-Tree differ Scope Type Sampling Pervasive in i-Tree Concept important photos1.htm

Scope of inventories Individual tree MCTI Day-to-day management Goal: accurate data for every tree Population of trees STRATUM, UFORE Long-term planning Goal: accurate analysis of forest

Complete Inventory Day-to-day field management Costly, time-consuming Partial Inventory Complete inventory of some segment Sample Inventory Randomly-selected trees inventoried for large-scale interpretation Cost-efficient Good for planning Types of Inventories

Types II Sample inventory benefits Increase public safety Facilitate short- and long-term planning Improve public relations Justify budgets Estimate tree benefits Large gain for small investment i-Tree promotes the value of sampling

Sampling I Traditional sampling techniques valid, but tedious for larger areas i-Tree v. 1.0 includes applications to automate the process for two types of plots: Linear (street) plots/segments STRATUM/MCTI, SDAP Spatial (park, any area) plots UFORE

Sampling II Linear plot selectors STRATUM/MCTI SDAP Requirements ArcView 3.x (legacy program) OR ArcMap 8.3 or 9.x GIS files Polygon file delimiting study area boundary Road shape file (TIGER/Line data) Manual selection also possible

TIGER/Line files Topologically Integrated Geographic Encoding and Referencing, or TIGER/Line Format used by the United States Census Bureau to describe land attributes such as roads, buildings, rivers, and lakes.United States Census Bureau Shape files free from ESRI for use in a GIS

Sampling III Spatial plot selector UFORE Still testing… Requirements ArcMap 8.3 or 9.x Study area boundary Sub-areas or strata--e.g., land uses Digital aerial photos (optional) Manual methods also possible

Concepts I Random sample Data collection in which every member of the population has an equal chance of being selected Can sometimes break population into subgroups (stratification) for better numbers Mind tricks easily, so need rigorous method

Concepts II Variance (= square of SD) Measure of how much individual samples vary The less the individual measurements vary from the mean (average), the more reliable the mean In an urban forest, different traits to investigate (variables) may have different variances E.g., species distribution (high?) vs. population size (low)

Source: Dave Nowak and Jeff Walton, personal communication (DRG data)

Concepts III Sample size How big? Sample size depends on The relationships to be detected (weak  more) The significance level sought (high  more) The size of the smallest subgroup (small  more) The variance of the variables (high  more) Can be smaller as these factors change, especially as variance goes down

Source: Dave Nowak, personal communication

Standard error (SEM) The Standard Error (Standard Error of the Mean) calculates how accurately a sample mean estimates the population mean. Formula: SEM = SD/  N, where SD = “standard deviation” of the sample, and N = sample size. Note that as SD goes down or N goes up, SEM gets smaller—i.e., estimate becomes better. Commonly represented by “±” after a number. Concepts IV

Source: blogaloutre Are you done yet?!

Final sampling thoughts Sampling is our friend Both tool and product in i-Tree The validity of i-Tree depends critically on understanding the process and capability of sampling