Landscape-Level Forest Biomass
and Residue Estimation
Presenter Lucas Wells
Graduate Research Assistant – University of Montana
Forest Biomass
CDFW
< Slide 1 of 30 >
Biomass
Modeling
Validation
Residue
Forest Biomass
CECE
www.estesparknews.com
OR
< Slide 2 of 30 >
Biomass
Modeling
Validation
Residue
Forest Biomass
Ecologically Available Biomass
Technically Available Biomass
Economically Available Biomass
Biomass Stocks
< Slide 3 of 30 >
Biomass
Modeling
Validation
Residue
Forest Biomass
Above Ground Biomass
Ownership/Management
Topography
Harvesting Period
Existing Road Network
Biomass Feedstock
< Slide 4 of 30 >
Biomass
Modeling
Validation
Residue
Forest Biomass
Estimate Above Ground Biomass
Estimate merchantable stock
For large landscape with stand-level
detail
< Slide 5 of 30 >
Biomass
Modeling
Validation
Residue
Estimating AGB
Classical methods are expensive
Remote sensing as an alternative
Use high resolution imagery for stand level
detail to address management related questions
Link with field data to estimate forest
characteristics based on imagery attributes
< Slide 6 of 30 >
Biomass
Modeling
Validation
Residue
The Uncompahgre
Ownership
BLM
Cover Type
Roads CDOW
Boundary
DECITY
-UP
DECOMMISSIONED
1 - COUNTY
BASIC
CUSTODIALWoodland
CARE (CLOSED), EX - EXISTING
Pinyon-Juniper
2 - JOINT
HIGH CLEARANCE VEHICLES, EX - EXISTING
Aspen
3 - SUITABLE FOR PASSENGER CARS, EX - EXISTING
LAND
TRUST
Ponderosa
Pine
4 - MODERATE DEGREE OF USER COMFORT, EX - EXISTING
PRIVATE
Mixed
Conifer
5 - HIGH
DEGREE
OFMesic
USER COMFORT, EX - EXISTING
DISTRICT
UPSCHOOL
Boundary
Mixed Conifer
Dry
SLB
Limber Bristlecone
USFS - GMUG
UP Boundary
< Slide 7 of 30 >
Biomass
Modeling
Validation
Residue
The Uncompahgre
The Uncompahgre Plateau National Forest
Area Subset
NAIP Color Infrared
< Slide 8 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
< Slide 9 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Probabilistic Classification Model
Polytomous Logistic Regression
Forest Characteristics Model
Use existing field data
Find a relationship between field data, texture,
and probabilistic outputs
< Slide 10 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Texture
Gray Level Co-Occurrence Matrix (GLCM)
Image
Framework (East)
Values
0
0
1
1
0
0
1
1
0
2
2
2
2
2
3
3
0
1
2
3
0
2
2
1
0
1
0
2
0
0
2
0
0
3
1
3
0
0
0
1
< Slide 11 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Texture
Gray Level Co-Occurrence Matrix (GLCM)
East Matrix
+
Transpose West
=
Symmetrical
(Horizontal)
2
2
1
0
2
0
2
0
1
0
4
2
1
0
0
2
0
0
2
0
2
0
0
2
4
0
0
0
0
3
1
1
0
0
3
0
1
1
0
6
1
0
0
0
1
0
0
1
0
1
0
0
1
2
< Slide 12 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Texture
Gray Level Co-Occurrence Matrix (GLCM)
Symmetrical
(Horizontal)
Normalization
Normalized
0.2
0.1
0.05
0
0
0.1
0.2
0
0
6
1
0.05
0
0.3
0.05
1
2
0
0
0.05
0.2
4
2
1
0
2
4
0
1
0
0
0
< Slide 13 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Stage 1 Modeling
Response:
Species Group
Independent variables:
PCA
1-3 data
Training
Principle
Component
Analysis
Horizontal
Homogeneity
GLCMs
Vertical and Horizontal Correlation
< Slide 14 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
< Slide 15 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Stage 2 – Forest Characteristics
Response:
Summarized Field Data for AGB, BAA and TPA
Independent variables:
Principle Component Analysis
GLCMs
< Slide 16 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Aspen
PCA Band 3
Horizontal Correlation
Shadow Probability
Aspen AGB, BAA & TPA
R2 = 0.69 – 0.78
< Slide 17 of 30 >
Biomass
Modeling
Validation
Residue
Modeling AGB
Combine Stage 1 & 2
𝐴𝐺𝐵𝑖 = 𝛼 + 𝛽𝑖𝑗 𝑋𝑗
Probabilistic
X 𝐵𝐴𝐴𝑖 = 𝛼 + 𝛽𝑖𝑗 𝑋𝑗
Outputs
𝑇𝑃𝐴 = 𝛼 + 𝛽𝑖𝑗 𝑋𝑗
< Slide 18 of 30 >
Biomass
Modeling
Validation
Residue
Validation
Land
Cover
Classification
Forest
Forest
Parameters
Type
Species
BAA
TPA
QMD
< Slide 19 of 30 >
Biomass
Modeling
Validation
Residue
Validation
Spatially Distributed Sample
Optimal Sample Intensity
Consistency
< Slide 20 of 30 >
Biomass
Modeling
Validation
Residue
Validation
< Slide 21 of 30 >
Biomass
Modeling
Validation
Residue
Validation
Species
Diameter
Total Height
X, Y Location at subplot 1
< Slide 22 of 30 >
Biomass
Modeling
Validation
Residue
Validation
< Slide 23 of 30 >
Biomass
Modeling
Validation
Residue
Treatment Residues
Management Objectives
Above Ground Biomass
Silvicultural Treatments
Biomass Stocks
< Slide 24 of 30 >
Biomass
Modeling
Validation
Residue
Abio = (T – m)
Rbio = r(T – m)
T = total biomass
m = merchantable stock
Recoverable Residues
r = % recoverable of available Available Residues
26
Treatment Residues
Biomass Stocks = Total AGB – Merchantable Stock – Retention
< Slide 26 of 30 >
Biomass
Modeling
Validation
Residue
Treatment Residues
Regional Sense of Merch
Estimate Merch for Field Data
Categorize by Characteristics
< Slide 27 of 30 >
Biomass
Modeling
Validation
Residue
Treatment Residues
< Slide 28 of 30 >
Biomass
Modeling
Validation
Residue
Recoverable Biomass v1.0
30
Acknowledgements
University of Montana
Dr. Woodam Chung
Dr. David Affleck
University of Kentucky
Dr. Marco Contreras
RMRS-HD
Dr. Nathan Anderson
John Hogland
Jesse Young
31