2014 Tanana Inventory Pilot:
Project Overview
Hans Andersen, Robert Pattison & Ken Winterberger
Resource Monitoring & Assessment/Forest Inventory & Analysis
Pacific Northwest Research Station
USDA Forest Service
Seattle, WA & Anchorage, AK
handersen@fs.fed.us rrpattison@fs.fed.us kwinterberger@fs.fed.us
Ross Nelson, Bruce Cook & Doug Morton
NASA-Goddard Space Flight Center
Biospheric Sciences Laboratory
Greenbelt, MD
ross.f.nelson@nasa.gov bruce.cook@nasa.gov douglas.morton@nasa.gov
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Forest Inventory and Analysis (FIA)
a very brief history and description

Forest Inventory and Analysis (FIA) started in the U.S. in 1929
Initially aimed at reporting to the U.S. Congress the volume and
area of productive timberland
It has evolved dramatically.
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Currently aimed at measuring and monitoring all forest land
throughout the United States (including Hawaii), Puerto Rico,
and the Pacific Trust Islands
Made up of a Systematic Sample across all lands in two phases
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Phase 1 – remotely sensed
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Phase 2 – ground
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USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Phase 1 and Phase 2 – a very brief
description of the grid in Alaska
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USDA Forest Service PNW Research Station
BLM – 38% forest 62% nonforest
DOD – 89% forest 11% nonforest
FWS – 41% forest 59% nonforest
NPS – 21% forest 79% nonforest
TVSF – 100% forest 0% nonforest
FS – 51% forest 49% nonforest
Major Land Managers – 37%
forest 69% nonforest
The Shebang – 38% forest 62%
nonforest
Forest Inventory and Analysis
What types of information do we need
from a FIA inventory in interior Alaska?
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How much forest land is there, and how is it changing?
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What is the biomass and timber resource for local
communities?
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Species, size class, volume, productivity
Are the forests of interior Alaska a net carbon source or sink?
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Climate change, fire, drought, insects
Growth, mortality, land cover, soils
What is the composition of plants and the quality of wildlife
habitat?
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Wildlife potential, subsistence for local communities
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Interior AK inventory units
2014 Tanana Inventory Pilot project
Pilot/Proof of Concept project will be undertaken in 2014 in the Tanana
valley of interior Alaska
Project Objectives:
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Extend FIA “footprint” in interior AK from BNZ/CPCRW experimental
forests to Tanana Valley State Forest (1.8 M ac) and Tetlin NWR (~700K
ac) – discrete management units, relatively “accessible”
Increase/develop familiarity with interior AK logistics
Test modified interior AK plot protocols (soils, lichens, etc.)
Evaluate utility of airborne lidar+hyperspectral remote sensing
Forested area: ~113,000,000 acres
information
Periodic inventory (5 units)
ValleyNASA
is most “accessible” unit
in interior Alaska
Build on relationships with cooperatorsTanana
(UAF,
(ABoVE,
CMS) &
land management agencies in interior AK (AKDNR, USF&WS, etc.)
Funding provided by a variety of sources (PNW, NASA, etc.)
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Inventory Pilot Project: Overview
Field component
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1/4th intensity regular hexagonal grid (1 plot per 24,000 acres) within TVSF and
Tetlin NWR – 99 total plots
Plots randomly moved (~ 50 m) to preserve confidentiality of locations
Road-, ATV-, and river-accessible plots established by UAF
Helicopter-access plots established by PNW-FIA (Anchorage FSL)
Modified field protocol (based on ANC pilot, experimental forest projects,
USGS input, etc.)
Remote Sensing component
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Cooperation with NASA-Goddard (Bruce Cook, Ross Nelson, Doug Morton) and
Michigan State University (Andrew Finley)
State-of-the-art airborne remote sensing instrument (G-LiHT – Goddard LiDAR
Hyperspectral Thermal)
Airborne RS collected in strip sample (9 km spacing b/n strips) over entire
Tanana inventory unit (~145K sq. km) and covering every FIA plot
Evaluation of several statistical estimation/inference and modeling/mapping
approaches (Bayesian hierarchical, etc.)
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana pilot: Field plots
Tanana Valley State Forest: ~ 72 forested plots
(37 heli-access, 35 ground-access)
Tetlin NWR: ~27 forested plots
(25 heli-access, 2 ground access)
Total: ~99 forested plots
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Inventory Pilot:
Plot Measurements
Key Features:
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Current tree protocols – with additional microplot
Current P2 understory vegetation protocols
Current P2 down woody materials protocols
Ground layer sampling (composition and carbon content)
Soil sampling (current thaw depth and belowground carbon)
High-accuracy GPS plot positions
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Pilot:
Tree and Vegetation Measurements
Current Phase 2 tree measurement
protocols used
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Additional microplot on each
subplot
Purpose of second microplot is to
sample small-diameter trees
24 foot radius
subplot (1/24th
ac),
trees and snags >=
5 inches
Canopy cover in
layer by growth
habit
Current Phase 2 vegetation protocols
used
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Veg. collected on 24-foot radius
subplots
Species and abundance (cover)
for four most abundant species
per growth habit, per subplot
Structure – recorded as cover by
growth habit by layer on each
subplot
USDA Forest Service PNW Research Station
6.8 foot
radius
microplots
(1/300th ac)
seedlings,
saplings
Forest Inventory and Analysis
2014 Tanana Pilot: Down woody material (DWM)
DWM is a valuable indicator of:
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Quality and status of wildlife habitat
Structural complexity of forests
Fuel loading and potential fire
behavior
Amount of carbon stored in forests
Site productivity / nutrient cycling
Harvest residues
24 ft
P2 Down woody material protocol:
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Two 24-foot transects per subplot
(8 per plot)
Measure diameters of dead wood
(stems/branches) that transects
cross
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Pilot: Ground layer sampling
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Developed by Sarah Jovan (PNWRMA Corvallis FSL), Rob Smith
(OSU) and others
Objectives:
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Estimate biomass, C and N
content among terrestrial
bryophytes and lichens
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Estimate the landscape-level
cover and biomass of important
functional groups
Cryptograms sampled at 32
microquads (8 per subplot)
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20 cm × 50 cm Daubenmire plots
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13 nonvascular groups
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Percent cover and depth
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Pilot: Soil sampling protocol
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Current thaw depth
Depth of
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Litter/live moss
Organic soil layers
Mineral soil layer
30 ft
Collect samples for further
analysis in lab (C, bulk density,
etc.)
Thaw depth and soil
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Pilot: High-accuracy GPS
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Accurate plot locations are critical for
matching high-resolution remote
sensing and field data
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In 2-phase sampling designs, error in
plot locations directly influences the
precision of parameter estimates
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Dual-frequency GPS+GLONASS
receivers can acquire coordinates with
< 1 m error in all boreal forest
conditions
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Coordinates obtained at subplot level
(enables comparison at ind tree level)
USDA Forest Service PNW Research Station
Acquiring accurate FIA plot locations
using survey-grade GPS receiver on
Kenai Peninsula (August, 2008)
Courtesy: Ray Koleser
Forest Inventory and Analysis
Field logistics & schedule
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Cooperation with UAF
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Helicopter-access plots established by PNW-FIA crews
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Ground-access plots established by UAF field crews (via road,
trail, river)
3–4 person field crews
Bell 206 Long Ranger (still TBD)
Schedule
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Training in Anchorage in May
Manley Hot Springs/Fairbanks: July 3–8
Delta Junction: July 15–18
Tok: July 19–22
Northway (camp): July 28–August 5
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana Inventory Pilot project
Field component
 1/4th intensity regular hexagonal grid (1 plot per 24000 acres) – approx.
97 plots
 Plots randomly moved (~ 50 m) to preserve confidentiality of locations
 Road-, ATV-, and river-accessible plots established by UAF
 Helicopter-access plots established by PNW-FIA
 Modified field protocol (based on ANC pilot, experimental forest projects)
Remote Sensing component
 Cooperation with NASA-Goddard scientists (Bruce Cook, Ross Nelson,
Doug Morton)
 State-of-the-art airborne remote sensing instrument (G-LiHT – Goddard
LiDAR Hyperspectral Thermal)
 Airborne RS collected in strip sample (9.2 km spacing b/n strips) over
entire Tanana inventory unit (60K sq. miles) & covering every FIA plot
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
2014 Tanana pilot: Field plots
2014 Tanana pilot: Field plots
& G-LiHT flight lines
G‐LiHT LiDAR + hyperspectral strip sample:
9.2 km spacing (covers every FIA field plot)
300 meter swath width
LidDAR covers 3% of total area
More intensive G‐LiHT strip sample collected over FIA plots in Bonanza Creek and Caribou–Poker Creek experimental forests
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
What is G-LiHT?
G-LiHT is a portable, airborne imaging
system that simultaneously maps the
composition, structure, and condition of
vegetation using:
1)
LiDAR to provide 3D information
about the spatial distribution of
canopy elements;
2)
Imaging spectroscopy to discern
species composition and variations
in biophysical variables (e.g.,
photosynthetic pigments, nutrient
and water content); and
3)
Thermal measurements to quantify
surface temperatures and detect
heat and moisture stress.
Scanning/Profiling
LiDAR
G‐LiHT System: Synergy and Key Characteristics
MEASUREMENT CHARACTERISTICS*
Scanning LiDAR
Swath width/FOV
387 m (60°)
Footprint diameter
10 cm (0.3 mrad)
Range precision
5 cm (2 σ)
Sampling density at surface
6 pulses m‐2
Max. returns per pulse
8
Imaging spectrometer
Swath width/FOV
Cross track pixels
Spectral range
Spectral resolution
Bands
Irradiance spectrometer
Swath width/FOV
Spectral range
Sample/Band width
310 m (50°)
1,004
400 to 1000 nm
10 nm
402
hemispheric (180°)
350 to 1,100 nm
1.5 and 1.5 nm
Thermal camera
Swath width/FOV
Imaging array size
Spectral range
173 m (30°)
384 × 288
8 to 14 μm
Spatial resolution of data products
Ht.
30 m
Temp.
35° C
* Flying at 335 m AGL and 110 kt. N
0 m
20°
1 m
G-LiHT Platform & Operations
Mounted
through windshield
Piper Cherokee
Piper Cherokee
Irradiance sensor
G-LiHT installed
Occupants: 3 max. (pilot, operator, observer)
Fuel capacity: 80 gallons avgas
Duration: 4 hours w/o reserves
Range/Speed: 800 km @ 110 knots
Acquisitions: July–August 2014
G-LiHT uninstalled
NASA Carbon Monitoring System (CMS) Project
R. Nelson (PI); B. Cook, D. Morton (NASA);
H. Andersen, R. Pattison (PNW);
A. Finley (Michigan State)
⑤
Bayesian joint probability models
& maps of carbon and uncertainty
④
Fire and forest carbon
stocks and losses
③
Enhanced inventory
with tree variables ②
Biomass & carbon inventory
①
Experimental Design
Plots, G‐LiHT
and Landsat
(146,626 km2)
Burn statistics from
MODIS and Landsat
Tree variables derived from G‐LiHT multi‐sensor data
(e.g., species class , size distribution)
G‐LiHT LiDAR transects
(3780 km2; 3% of total area)
FIA field plots
(0.006 km2) Data Sources &
Upscaling Methodology
Courtesy: Bruce Cook NASA-GSFC
Example: LiDAR-assisted sampling
design for biomass estimation
Stage 1: LiDAR strip sample
Stage 2: Subsample of field plots &
prediction of biomass within strips
via regression
Plot AGB
Total biomass = Estimated
mean biomass in LiDAR
strips × Total Area
(+ correction factor to
remove bias in modelassisted designs)
AGB = f(LiDAR)
LiDAR metrics
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Example: LiDAR-assisted sampling design
for biomass estimation, Kenai Peninsula, Alaska
LiDAR acquisition in May, 2004
Covered 120 FIA plots
N-S swaths spaced 10 km apart
Total linear coverage: approx. 600 km
Total area covered: 19,387 ha (2.3 % of
western Kenai lowlands)
Total cost: $60K
ALASKA
Mobilization: 10%
Reflights: 10%
Total size of raw binary data:
50 GB
Biomass estimation within lidar strips used
area-based regression approach*
*
Li, Andersen & McGaughey.2008. A comparison of statistical methods for
estimating forest biomass from light detection and ranging data. West. J. of Applied
Forestry. 23(4): 223-231.
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Example:
Median
Lidar-based
lidar intensity
individual
by species,
tree
measurement, Kenai
KenaiPeninsula,
Peninsula,AK
AK
Field Spc: Birch
Field Ht: 18.8 m
Lidar Spc: Hardwood
Lidar Ht: 18.1 m
Lidar
Lidarcrown
surface trees
Field–measured
segmentation
model
(> 12.5 cm)
50
50
40
All Data
40
50
Late May Lidar Acquisition
40
Early May Lidar Acquisition
30
20
White spruce
Lidar individual tree
species
classification
20
Lidar Ht: 11.37 m
Birch
30
Median Lidar Intensity
30
Lidar Spc: Conifer
20
Median Lidar Intensity
Field Ht: 11.4 m
Median Lidar Intensity
Field Spc: White spruce
Brown: Hardwood
94
95
98
375
Species Code
746
747
USDA Forest Service PNW Research Station
10
0
10
0
0
10
Green: Conifer
94
95
98
375
Species Code
746
747
94
95
98
375
Species Code
746
747
Forest Inventory and Analysis
Example: LiDAR-assisted sampling design for
biomass estimation, Kenai Peninsula, Alaska (cont.)
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Apply regression model to LiDAR metrics at
each 13-m grid cell to obtain predictions
of biomass over entire LiDAR coverage
250
200
150
100
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50
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Mean LiDAR height
Coefficient of variation of LiDAR canopy
heights
LiDAR-derived canopy cover
Coefficients vary by forest/species type
adj. R2 = 0.68
0
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Lidar-predicted Biomass (MG/ha)
Plot-level LiDAR structural metrics*
300
LiDAR structural metrics are well-correlated
with aboveground biomass at plots
0
50
100
150
200
250
300
FIA Subplot Biomass (MG/ha)
*Li, Andersen & McGaughey. 2008. A comparison of statistical methods for
estimating forest biomass from light detection and ranging data. West. J.
of Applied Forestry 23(4): 223-231.
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Tanana Inventory Pilot:
Statistical estimators and products
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Design-based estimation (Bechtold & Patterson, 2005 - aka “green book”)
can be used for plot-based inventory estimates in TVSF & Tetlin NWR
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Active research underway developing statistical properties of 2-level designs
integrating field plots & LiDAR sampling (Gregoire et al., CJFR 2011; Stähl et
al., CJFR 2011; Mandallaz et al. CJFR 2013)
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Model-assisted (i.e. approximate design-unbiased) vs. model-based approaches
2-stage (i.e., cluster sampling) vs. 2-phase sampling designs
Simulation analyses are helping to further develop and refine variance estimators
for complex LiDAR-assisted inventory designs (Ene et al., RSE 2012)
Inventory products:
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Report (PNW-GTR) describing inventory design and forest resources in TVSF &
Tetlin NWR
Maps of inventory attributes (w/ uncertainty) for entire Tanana unit (via Bayesian
hierarchical modeling (Finley et al.)
Peer-reviewed methods paper(s)
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Thank you!
USDA Forest Service PNW Research Station
Forest Inventory and Analysis