landscape-scale forest carbon measurements for reference sites: the role of remote sensing nicholas...
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Landscape-scale forest Landscape-scale forest carbon measurements for carbon measurements for
reference sites: The role of reference sites: The role of Remote SensingRemote Sensing
Nicholas Skowronski USDA Forest ServiceClimate, Fire and Carbon cycle scienceSilas Little Experimental Forest
OutlineOutline
Background in Remote Sensing Background in Remote Sensing Remote sensing in the context of Remote sensing in the context of
carbon measurementscarbon measurements Basic LiDAR data processing stepsBasic LiDAR data processing steps Focus on LiDAR work at NJ Tier 3 Focus on LiDAR work at NJ Tier 3
sites and beyond. sites and beyond.
Types of Remote Sensing Types of Remote Sensing observationsobservations
Passive Sensors – Spectral Passive Sensors – Spectral Reflectance (sun source)Reflectance (sun source)
Active Sensors – Reflectance and Active Sensors – Reflectance and Echo (sensor source)Echo (sensor source)
Resolution Resolution
Spatial Resolution: How small an object Spatial Resolution: How small an object do you need to see (pixel size) and how do you need to see (pixel size) and how large an area do you need to cover?large an area do you need to cover?
Spectral Resolution: What part of the Spectral Resolution: What part of the spectrum do you want to measure?spectrum do you want to measure?
Radiometric Resolution: How finely Radiometric Resolution: How finely (precisely) do you need to quantify the (precisely) do you need to quantify the data?data?
Temporal Resolution: How often do you Temporal Resolution: How often do you need to measure?need to measure?
GOES
MODIS
LiDAR, RADAR
Landsat, EO-1
IKONOS, Quickbird
Adapted from : Chambers et al. 2007
From: Hostert et al. 2010
Remote Sensing in the Remote Sensing in the Context of Carbon Context of Carbon
MeasurementsMeasurements Land use and land cover ChangeLand use and land cover Change Phenological cyclesPhenological cycles Canopy chemistryCanopy chemistry Crown detection and species Crown detection and species
identificationidentification Forest biomassForest biomass Forest structural attributesForest structural attributes
Land Use Land Use ChangeChange
Landsat TM and Landsat TM and Landsat ETMLandsat ETM
ca. 30 m ca. 30 m vertical vertical resolutionresolution
16 day temporal 16 day temporal resolutionresolution
8 spectral 8 spectral bandsbands
From: Lathrop et al. 2009
Monthly phenology (as illustrated by various vegetation indices) for a single MODIS pixel in 2005 at Harvard Forest, MA, USA. Reed et al. 2009
PhenologyPhenology
MODISMODIS ca. 1 km vertical ca. 1 km vertical
resolutionresolution 1 day temporal 1 day temporal
resolutionresolution 36 spectral bands36 spectral bands
Foliar nitrogen and canopy water in Hawaii Volcanoes National Park from AVRIS.
Asner and Vitousek (2005)
Canopy Canopy ChemistryChemistry AVRISAVRIS ca. 17 m ca. 17 m
vertical vertical resolutionresolution
Aircraft-borneAircraft-borne 224 spectral 224 spectral
bandsbands
Lee et al. 2010
Individual Crown Individual Crown DelineationDelineation
Discrete-return scanning Discrete-return scanning LiDARLiDAR
4 pulses m4 pulses m-2-2
Aircraft-borneAircraft-borne ““Return Cloud” filteredReturn Cloud” filtered
(a) LiDAR-derived maximum canopy height. (b) Aboveground live tree carbon
Gonzalez et al. 2010
Short et al.
Landscape-scale Forest Landscape-scale Forest Biomass Biomass
Discrete-Discrete-return return scanning scanning LiDARLiDAR
4 pulses m-24 pulses m-2 Aircraft-borneAircraft-borne ““Return Return
Cloud” filteredCloud” filtered
Kellndorfer et al. 2010
Statistical Data Fusion for regional forest height
mapping.
Regional-scale Forest Regional-scale Forest BiomassBiomass
Data-fusion Data-fusion approachapproach
LiDAR and LiDAR and Spectral DataSpectral Data
Falkowski et al. 2009
Forest Structural Forest Structural Attributes Attributes
Discrete-return Discrete-return scanning LiDAR scanning LiDAR or full-waveform or full-waveform LIDARLIDAR
Varying data Varying data intensityintensity
Aircraft-borne Aircraft-borne or backpack or backpack borneborne
LiDAR work at the NJ LiDAR work at the NJ Tier 3 sitesTier 3 sites
LiDAR backgroundLiDAR background Calibration using plot-level dataCalibration using plot-level data Landscape-level carbon storageLandscape-level carbon storage Change Detection Change Detection Characterization of Canopy Characterization of Canopy
Structure Structure
LiDAR BasicsLiDAR Basics
Filtering LiDAR returns Filtering LiDAR returns
““Point cloud” that has individual Point cloud” that has individual LiDAR returns as x, y and z co-LiDAR returns as x, y and z co-ordinates ordinates
Filtering and Classifying Filtering and Classifying LiDAR returns LiDAR returns
Start with a point cloud that has Start with a point cloud that has individual LiDAR returns as x, y and individual LiDAR returns as x, y and z co-ordinates z co-ordinates
Using an algorithm we filter these Using an algorithm we filter these points to find “low” points for a points to find “low” points for a given area, these points are given area, these points are classified as ground. classified as ground.
Filtering and Classifying Filtering and Classifying LiDAR returns LiDAR returns
Start with a point cloud that has Start with a point cloud that has individual LiDAR returns as x, y and z individual LiDAR returns as x, y and z co-ordinates co-ordinates
Using an algorithm we filter these points Using an algorithm we filter these points to find “low” points for a given area, to find “low” points for a given area, these points are classified as ground. these points are classified as ground.
Other returns are then classified as Other returns are then classified as vegetation or buildings. Heights are vegetation or buildings. Heights are then transformed from height from the then transformed from height from the sensor to the height above ground. sensor to the height above ground.
Statistical Parameters of a cell
• Mean Return Height• Maximum Return
Height• Percentile Heights• Percent Cover• Kurtosis• Skew• Standard Deviation
Mean Return Height
LiDAR to Plot-Level DataAll
LiDAR Observed Biomass (gC m-2)
0 2000 4000 6000 8000
Allo
me
tric
ally
Pre
dic
ted
Bio
ma
ss (
gC
m-2
)
0
2000
4000
6000
8000
Pine
LiDAR Observed Biomass (gC m-2)
0 2000 4000 6000 8000
Allo
me
tric
ally
Pre
dic
ted
Bio
ma
ss (
gC
m-2
)
0
2000
4000
6000
8000
Oak
LiDAR Observed Biomass (gC m-2)
0 2000 4000 6000 8000
Allo
me
tric
ally
Pre
dic
ted
Bio
ma
ss (
gC
m-2
)
0
2000
4000
6000
8000
1:1
1:1
1:1
Biomass by Land cover type in Burlington and Camden Counties
Cover Class 11
111
211
311
412
013
113
213
314
114
214
314
414
915
116
017
420
021
023
024
124
224
324
424
525
0Tot
al
Bio
ma
ss (
gC m
-2)
0
2000
4000
6000
8000
Scan Profile
144-Pine Biomass
Number of Fires (over ca. 50 years)
0 1 2 3 4 5
Car
bon
(gC
m-2
)
0
2000
4000
6000
8000
141 - Oak Biomass
0 1 2 3 4 5C
arbo
n (g
C m
-2)
0
2000
4000
6000
8000
WildfireRxB
WildfireRxB
Impact of repeated fires on tree biomass
Observed (gC m-2)
0 2000 4000 6000 8000 10000
Pre
dict
ed (
gC m
-2)
0
2000
4000
6000
8000
10000
Multi-Temporal Dataset at the Silas Little Experimental Forest
Extent
2004
2005
Gypsy Moth Defoliation from 2005-2008
Foliage Density Profiles
0
2
4
6
8
10
12
14
16H
eig
ht A
bove
Gro
und
(m)
East-WestNorth-South
2020
0
0
2
4
6
8
10
12
14
16
He
igh
t A
bo
ve G
rou
nd
(m
)
East-West
North- South
2020
0
Apparent cover
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
He
igh
t (m
)
0123456789
101112131415161718
Apparent Cover
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
He
igh
t (m
)
0123456789
101112131415161718
Scanning CHP
Biometric CBD
Profile CHP
Biometric CBD
2a 2b
2c 2d
0.0 0.1 0.2 0.3 0.4 0.5 0.6
0
2
4
6
8
10
12
14
16
18
20
22
24
0.0 0.1 0.2 0.3 0.4 0.5 0.6
0
2
4
6
8
10
12
14
16
18
20
22
24
0.0 0.1 0.2 0.3 0.4 0.5 0.6
0
2
4
6
8
10
12
14
16
18
20
22
24
0.0 0.1 0.2 0.3 0.4 0.5 0.6
0
2
4
6
8
10
12
14
16
18
20
22
24
Gypsy Moth Defoliation
Typical Oak-Pine
Young Pine Regeneration
Pitch-Pine Lowland
Unsupervised Classification of CHPs
144: 5 Unsupervised Classes d90 Distribution
Height (m)
0 5 10 15 20 25
Pix
els
0.0
2.0e+6
4.0e+6
6.0e+6
8.0e+6
1.0e+7
1.2e+7
1.4e+7
141: 5 Unsupervised Classes
LiDAR Derived Cover
0.0 0.1 0.2 0.3 0.4 0.5 0.6
He
ight
(m
)
0
5
10
15
20
25
Class 1
Class 2
Class 3
Class 4
Class 5
144: 5 Unsupervised Classes
LiDAR Derived Cover
0.0 0.1 0.2 0.3 0.4 0.5 0.6
He
ight
(m
)
0
5
10
15
20
25
Class 1
Class 2
Class 3
Class 4
Class 5
141: 5 Unsupervised Classes d90 Distribution
Height (m)
0 5 10 15 20 25
Pix
els
0
1e+6
2e+6
3e+6
4e+6
5e+6
6e+6
7e+6
15c. 15d.
15a. 15b.
Canopy Density profiles stratified by Cover type
Upland Oak Trends
Slope of CHP Trajectory-0.04 -0.02 0.00 0.02 0.04
He
ight
(m
)
0
2
4
6
8
10
12
14
16
18
20
22
24
141 Wild Slope
141 RxB Slope
Upland Pine Trends
Slope of CHP Trajectory-0.04 -0.02 0.00 0.02 0.04
He
ight
(m
)
0
2
4
6
8
10
12
14
16
18
20
22
24
144 Wild Slope
144 RxB Slope
18a.
18b.
Trajectory of Foliage Density Profile given repeated fires
Questions?