Tree Inventories and

Sampling

Overview

InventoriesInventories in i-Tree

differScopeType

SamplingPervasive in i-TreeConcept important

www.treesaremyfriends.org/.../ photos1.htm

Scope of inventories Individual treeMCTIDay-to-day managementGoal: accurate data for

every tree

Population of treesSTRATUM, UFORELong-term planningGoal: accurate analysis

of forest

Inventory typesComplete 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 benefitsIncrease public safetyFacilitate short- and long-term planningImprove public relationsJustify budgetsEstimate tree benefits

Large gain for small investment

i-Tree promotes the value of sampling

Sampling I

Traditional sampling techniques valid, but tedious for larger areasi-Tree v. 1.0 will include applications to automate the process for two types of plots:Linear (street) plots/segments

STRATUM/MCTI, SDAPSpatial (park, any area) plots

UFORE

Sampling II

Linear plot selectorsSTRATUM/MCTISDAP

RequirementsArcView 3.x or ArcMap 8.3 or 9.0GIS files

Polygon file delimiting study area boundary

Road shape file (TIGER/Line data)

TIGER/Line filesTopologically 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. Shape files free from ESRI for use in a GIS

Sampling IIISpatial plot selectorUFOREFinal testing

RequirementsArcMap 8.3 or 9.0Study area boundary Sub-areas or strata--e.g.,

land uses Digital aerial photos

(optional)

Concepts IRandom sampleData 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 IIVariance (= 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 sizeHow 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 http://www.ontabec.com/fatigue.jpg

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