Transcript Application I - i-Tree

Tree Inventories
and
Sampling
Overview
Inventories
Inventories in i-Tree
differ
 Scope
 Type
Sampling
Pervasive in i-Tree
Concept important
www.treesaremyfriends.org/.../ 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
Types of Inventories
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 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.
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
Concepts IV
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.
Are you done yet?!
Source: blogaloutre http://www.ontabec.com/fatigue.jpg
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