sampling in i-tree concepts, techniques and applications

Sampling in i-Tree

Concepts, techniques and applications

Introduction

Sampling is so pervasive in i-Tree that we have factored it out for a separate discussionOverviewConceptsTechniquesApplications

Concepts IRandom sampleData collection in which every member of the

population has an equal chance of being selectedPopulation = the set of people or entities to which

findings are to be generalized. The population must be defined explicitly before a

sample is taken

Can sometimes break population into subgroups (stratification) for better numbers

Mind tricks easily, so need rigorous method

Source: http://www.negrdc.org/counties/madison/comprehensive-plans/newcomp/maps/8_01ExistLandUseMadisonCo.jpg

Concepts IIVariance = (SD)2

Measure of how spread out the distribution is, i.e., 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

Species distribution (high?) vs. population size (low) Hurricane debris (high?) vs. ice storm debris (low)

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

Concepts III

Sample sizeWill need to be larger

the weaker the relationships to be detected the higher the significance level being sought the smaller the population of the smallest subgroup the greater the variance of the variables

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

Techniques IGet random numbersTablesTelephone book

(final digits!)Electronic

randomizersOnline DesktopPDA

Techniques IISelect plotsUse map techniques

Grid overlay for maps/photosSimple edge rulers also work

Pick randomly from listStreet, with replacementBlock number

Create random coordinates SpreadsheetGIS

Techniques II

Easy way to get random list of street segments

Bring TIGER/Line files as shape file from ESRI into a GIS

Details in Appendix B of the Manual

Techniques IIIReserveCreate more plots than neededSomething like 10%Take replacements from list in order

when plot must be thrown outNon-existentUnfindable Inaccessible

No bias!

Application I

Inventory typesComplete Inventory

Costly, time-consumingPartial Inventory

Complete inventory of some forest segmentSample Inventory

Randomly-selected trees inventoried for large-scale interpretation

Cost-efficient Good for planning Not suitable for day-to-day field management

Application I

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

Applications II

Manual 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

Applications II

Linear plot selectorSTRATUM/MCTISDAP

Final testingRequirementsArcMap 8.3 or 9.0Polygon file delimiting study area

boundaryRoad shape file (TIGER/Line data)

Applications II

Spatial plot selectorUFOREFinal testing

RequirementsArcMap 8.3 or 9.0Polygon file delimiting study area

boundaryRaster-based file of strata (e.g., land

uses) within study area Digital aerial photos (optional)

Final sampling thoughts

Sampling is our friendBoth tool and product in i-TreeUnderstanding of validity of what i-Tree offers will depend critically on understanding the process and capability of sampling