Using lidar for forest assessment and
monitoring
Hans-Erik Andersen
Forest Inventory and Analysis
USDA Forest Service
PNW Research Station
Anchorage, Alaska
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Cost of helicopter-access plots on Kenai
Peninsula, AK: ~ $6000/plot
Status of FIA inventory in
Alaska Boreal Forests
We estimate that Alaska has 17% of
U.S. forest land
?
Roughly 112 million acres of forest in
“interior” Alaska.
9 Vast
9 Mostly low productivity
9 Inaccessible
9 Difficult and expensive to
inventory
Currently there is no FIA inventory in
interior Alaska
Remote sensing will necessarily play an
integral role in any future inventory
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Importance of FIA inventory in
Alaska Boreal Forests
Fore
st N
PP
CO 2
So
il t
e
Fires
Insect
Snowdamage
epth
1955d
-2004
1989-2005
Climate Change
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e
USDA Forest Service PNW Research Station
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Forest Inventory and Analysis
Proposed Periodic Inventory in the
Tanana Basin of interior AK
Systematic grid for forest inventory in Tanana unit (36 mill ac)
27 million acres of forest (estimated from remote sensing data)
1-in-5 option (~ one plot per 30,000 acres)
Fairbanks
1-in-5 option: 1220 plots, about 900 forested
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
What type of information can lidar provide in
forest inventory?
Lidar is VERY good at measuring certain forest attributes:
z
z
z
Overstory tree heights
Canopy cover
Vertical canopy structure
In certain conditions, lidar CAN also provide other information:
z
z
z
z
Species class (conifer vs. hardwood, esp. in leaf-off conditions)
Mortality (esp. in leaf-on conditions)
Understory cover (esp. with high density lidar)
Surface fuels (esp. with high density lidar)
Lidar CANNOT (yet) directly measure other important forest attributes:
z
z
z
z
z
z
z
Understory individual tree heights
Species
Stem diameter
Age
Defect/decay class
Lichens/mosses/indicator species/understory vegetation
Soils, permafrost, etc. etc.
PNW-FIA (Anchorage FSL) is carrying out a pilot project on the Kenai peninsula to
investigate the utility of lidar as a tool in the Alaska resource inventory
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Lidar-derived
Bare-Earth Surface Model
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Lidar-derived
Canopy Surface Model
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Lidar-derived canopy cover
An estimate of canopy cover is
generated from first-return lidar
points:
# lidar first returns in canopy (13)
Total # lidar first returns (20)
USDA Forest Service PNW Research Station
= 65%
Forest Inventory and Analysis
Kenai Lidar Flight
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Lidar strips &
FIA plots
Anchorage
Acquisition in May, 2004
Covered 120 FIA plots
N-S swaths spaced 10 km apart
Total linear coverage: approx.
600 km
Alaska
Total area covered:
Kenai Peninsula
19,387 ha (2.3 % of western
Kenai lowlands)
Total cost: $60K
z
z
‹
Mobilization: 10%
Reflights: 10%
Total size of raw binary data:
50 GB
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Kenai Lidar Flight Parameters
And System Settings
•
•
•
•
•
•
Optech ALTM 3070
Platform: fixed-wing
Flying height: 1200 m
Flying speed: 250 km/h
Scanning swath: ~350 m
Laser pulse density: 4
pulses/m2
• Laser pulse rate: 70,000
pulses/second
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Lidar forest measurement, Kenai Peninsula, AK
FIA
plot
& over
Lidar
terrain
Lidar strips &
Lidar
canopy
Lidar
swath
Quickbird
model
surfacesatellite
modelFIA plots
FIA
plotbirch/spruce
image,
forest on Kenai
Peninsula, AK
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Lidar-based
Median
individual
lidar intensity
tree measurement,
by species,
Kenai
KenaiPeninsula,
Peninsula,AK
AK
Field Spc:
Spc: Birch
Field Ht: 18.8 m
FCrownArea:34 m2
Lidar Spc:: Hardwood
All DataSpc
50
50
40
Lidar
crown
Lidar
surface
Ht: 18.1 m
Field–measured treesLidar
LCrownArea:31 m
segmentation
model
(> 12.5 cm)
2
40
50
Late May Lidar Acquisition
40
Early May Lidar Acquisition
20
White spruce
LCrownArea:
LCrownArea: 2 m2
30
Lidar Ht: 11.37 m
Lidar individual tree
species
classification
20
Lidar Spc:
Spc: Conifer
Birch
30
Median Lidar Intensity
30
FCrownArea:
FCrownArea: 3 m2
20
Median Lidar Intensity
Field Ht: 11.4 m
Median Lidar Intensity
Field Spc:
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
Accurate GPS survey of FIA plot locations
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Accurate plot locations are
critical for matching LIDAR &
field data
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Average error of original FIA
plot coordinates was 6 m
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Accurate (<1 m error) plot
coordinates acquired over 32
plots on Kenai using surveygrade GPS
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More plots to be GPSed in
summer, 2008
USDA Forest Service PNW Research Station
Acquiring GPS location at FIA plot on Kenai Peninsula
Forest Inventory and Analysis
Methods: Biomass estimation using
FIA plot data & lidar strip sample
Use FIA plot data from entire Kenai to
simulate lidar-derived forest structure
metrics at plot level
z
z
z
z
z
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Maximum tree/segment height
Mean tree/segment height
Canopy cover (% plot area > 2 m ht)
Tree/segment density
Coefficients vary by forest/species type
Comparison with lidar metrics on GPSed
field plots shows very good match with max
ht, more discrepancies in canopy cover
Current set of (road-accessible) GPSed
plots are not representative of entire
western Kenai – need random sample for
validation!
USDA Forest Service PNW Research Station
25 100
25
20 80
20
15 60
15
10 40
10
Regression relationships using plot-level
structural metrics
5 5 20
‹
0 0
z
Due to edge effect and recently
fallen
trees
R2 dead
= 0.83
Plot-based
SQRT(biomass(tons/ha))
canopy
cover
Field-based
maximum
ht
(m) 15
5Field-based
10
z
Apply edge correction to reduce bias in canopy cover
Generate simulated lidar canopy height model for
each subplot
Apply lidar measurement algorithms to simulated
canopy height model
0
z
Field
vs.field
lidar
biomass
Lidar
vs.
canopy
cover
Lidar
vs.
field
max
ht
Plot
-level
biomass
vs.
structure
metrics
Field-based SQRT(Biomass (tons/ha)
‹
0
0
00
20
5 5
5
40
60
80
100
10 10
15 15 20
25
10
15 20
Lidar-based canopy cover
Lidar-based
maximum(tons/ha))
ht (m)
Predicted
SQRT(biomass
Lidar-based SQRT(Biomass (tons/ha))
Forest Inventory and Analysis
LANDFIRE
Results: Biomass Estimation on western Kenai using
Lidar Strip Sample
Lidar-based Biomass
Estimates (13 m cells)
Black: Low Biomass
White: High Biomass
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Model-assisted Design for Biomass
Estimation
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Lidar strip sample and plot data can be used as components of
model-assisted sampling design
Approx. design-unbiased regression estimator (Särndal, 1992):
∑
t yˆi
sΙ
π Ιi
+ ∑s
yk − yˆ k
πk
Model-based estimate + Adjustment term
(based on lidar strips) (corrects for bias
using plot data )
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Highly efficient if model fits well
Still approx. unbiased even if model does not hold
Precision further increased w/ post-stratification
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Results: Assessing Lidar Strip Sampling
Efficiency via Simulation
Simulation used to assess sampling
error for various plot and stripbased estimates
Detailed PI-based forest stand
classification layer used to
generate continuous biomass
map over entire western Kenai
Precision = RMSE/tot.biomass:
Simulated
Strip
Simulated
Strip
FIA
sampling
Sample
frame Map
Sample
Biomass
5 km
15
km
10
km
spacing
spacing
350-m wide N-S strip samples:
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5 km spacing: 0.015
10 km spacing: 0.032
15 km spacing: 0.032
20 km spacing: 0.05
Systematic plot samples:
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5 km spacing: 0.033
10 km spacing: 0.084
15 km spacing: 0.126
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Future research: Post-stratification of lidar strip
sample using LANDFIRE veg class, height, and
canopy cover layers
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Future research: Monitoring growth with
multi-temporal lidar
Individual tree lidar data, Capitol Forest, WA
2003
1999
1998
3 m height growth
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Conclusions & Future Directions
Lidar has the potential to be a valuable sampling tool for large area resource
inventory
Lidar may be even more useful as a monitoring tool
z
Lidar surveys could significantly decrease remeasurement interval
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z
In periodic interior AK inventory, 20 - 25 years between field measurements
Plots could be “visited” with lidar every 8 - 10 years
Assessment of carbon emitted from fires, etc.
Future Research
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Assessment of model-assisted sampling design (w/ additional GPSed plots, poststratification w/ LANDFIRE)
Forest biomass assessment with spaceborne L-band imaging radar (w/ NASA-JPL &
UAF Geophysical Inst.)
Assessment of biomass/carbon in NA boreal forests using combination of plot data,
airborne profiling lidar & GLAS satellite lidar (w/ Ross Nelson, NASA-Goddard)
Investigation of using lidar & NN imputation (w/ Oregon St. Univ.)
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Questions/Discussion?
USDA Forest Service PNW Research Station
Forest Inventory and Analysis
Future research: Monitoring long-term changes in
Interior AK forests with AIRIS Large-Scale Photography
and LIDAR
2007
1982aerial
CIR aerial
photo
photo
Eutrification of lake
USDA Forest Service PNW Research Station
Forest Inventory and Analysis