Estimating basal area, trees, and above ground biomass per acre for common tree species across

41 Pages·2012·2.16 MB·English

by  Matthew Fonseca

Estimating basal area, trees, and above
ground biomass per acre for common tree
species across the Uncompahgre Plateau
using NAIP CIR imagery
By
John Hogland
Nathaniel Anderson
Greg Jones
Objective
• Develop a flexible and transferable
methodology to estimate basal area per acre,
trees per acre, and above ground biomass per
acre
– Use consistent and readily available data sources
– Generate outputs that can be used to model
multiple management scenarios
– Fine grained detail (1m2)
Project Area
²
Southwest Colorado
580,000 Acres
Major Tree Species
• Quaking Aspen • Pinyon Pine
• Ponderosa Pine • Rocky Mountain Juniper
• Lodge Pole • Utah Juniper
• Subalpine Fir • Mountain Mahogany
• Corkbark Fir • Gambel Oak
• Douglass Fir
• Engelmann Spruce
• Blue Spruce
Tree Species Groups
• Quaking Aspen • Pinyon Pine
• Ponderosa Pine • Rocky Mountain Juniper
• Lodge Pole • Utah Juniper
• Subalpine Fir • Mountain Mahogany
• Corkbark Fir • Gambel Oak
• Douglass Fir
Pinyon/Juniper
• Engelmann Spruce
Shrubs
• Blue Spruce

Aspen
Pine
Spruce/Fir
Datasets
• Imagery
– NAIP (CIR)
– DEM ?

• Stratified Random sample
– Visually interpreted from imagery

• Field Data
– Plot data (FIA)
– Fixed radius
2 Stage Approach
• Probabilistic classification using Polytomous
Logistic Regression (grain size 1 m)
– Identifying tree species groups using spectral
bands and texture
• Multivariate Regression (BAA, TPA, and AGB)
– Window Circle with radius of 7 meters -> FIA plot
size
• Develop texture metrics that quantify trees vs. open
ground/shadow for the probabilistic classification
2 Stage Approach
• Probabilistic classification using Polytomous
Logistic Regression (grain size 1 m)
– Identifying tree species groups using spectral
bands and texture
• Multivariate Regression (BAA, TPA, AGB)
– Window Circle with radius of 44 meters -> FIA plot
size
• Develop texture metrics that quantify trees vs. open
ground/shadow for the probabilistic classification
Maximum Likelihood Classification
(Discriminant function analysis)
  
  1
MD  D (i)  x  x S x  x i 1,...,n j  1,...,p
j i i
1 1     j 1,...,n
  1
QDS  D i   lnS  x x S x x ln p
j j i j j i j j
2 2 p  prior for class j
j
 1 
exp D (i)
j
 2 
 
PP j |i 
j  1 
exp D (i)
j
 2 
i1
where x and x are the observed vector and mean vector, respectively, and S
i
and S are the pooled sampled covariance matrix and the sample
j
covariances, respectively (Johnson and Wichern, 2002).
Polytomous logistic regression
Estimated class (response) probabilities {π(x)} are determined by manipulating
j
baseline category logits as follows:
J1
       
given  x 1, j 1,...,J-1,  x  1-  x .
j J h
j h1
 
 x 
j  
ln   xβ x   1, x i 1,...p
   j i
 x
 J 
 
exp xβ 1
 x j  x
j J1 J J1
1expxβ  1expxβ 
h h
h1 h1
Where π(x) is the mean probability of group j, with class means and variances
j
equal to nπ and nπ(1-π), respectively (Agresti, 2002).
j j j