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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