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 bands8 spectral bands

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 co-individual LiDAR returns as x, y and z co-ordinates 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

met

rical

ly P

redi

cted

Bio

mas

s (g

C m

-2)

0

2000

4000

6000

8000

Pine

LiDAR Observed Biomass (gC m -2)

0 2000 4000 6000 8000

Allo

met

rical

ly P

redi

cted

Bio

mas

s (g

C m

-2)

0

2000

4000

6000

8000

Oak

LiDAR Observed Biomass (gC m -2)

0 2000 4000 6000 8000

Allo

met

rical

ly P

redi

cted

Bio

mas

s (g

C 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

0Tota

l

Bio

mas

s (g

C 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 (g

C 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

eigh

t Abo

ve G

roun

d (m

)

East-WestNorth-South

2020

0

0

2

4

6

8

10

12

14

16

Hei

ght A

bove

Gro

und

(m)

East-West

North- South

2020

0

Apparent cover0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Hei

ght (

m)

0123456789

101112131415161718

Apparent Cover0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Hei

ght (

m)

0123456789

101112131415161718

Scanning CHPBiometric CBD

Profile CHPBiometric 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

Hei

ght (

m)

0

5

10

15

20

25

Class 1Class 2Class 3Class 4Class 5

144: 5 Unsupervised Classes

LiDAR Derived Cover

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Hei

ght (

m)

0

5

10

15

20

25

Class 1Class 2Class 3Class 4Class 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

Hei

ght (

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

Hei

ght (

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?