Shi%ingtheparadigm:Towardreal-5metrackingofcarbonstorageonland

CONAFOR,Agust2-2016

Overview

§  CarbonStockMonitoring§  Background

§  NewAboveground30mcarbondataset§  Abovegroundcarbonloss

§  Landcoverchange(Hansenetal.2013)combinedwithcarbondataset

§  Abovegroundcarbondensitylossandgain§  “Direct”carbondensitychange

§  Changepointanalysisofbiomass5meseries§  LiDAR(GLAS)§  MODIS

§  Results

•  ForestInventories

•  Stra%fy&Mul%ply(SM)Approach–  Assignanaveragebiomassvaluetolandcover/vegeta5ontypemap

(AsneretAl.2010)

•  Combine&Assign(CA)Approach–  ExtensionofSM,GISandmul5-layersinforma5on(Gibbsetal.2007)

•  EcologicalModels(EM)Approach–  Remotesensingtoparameterizethemodel(Hur[etal.2004)

•  DirectRemoteSensing(DR)Approach–  EmpiricalModelswhereRSdataiscalibratedtofieldes5mates(Baccini

etal.2004,2008,Blackardetal.2008)

Goetz,Baccinietal.2009

Large Area Biomass Estimation

4

Forest

InventoryPlots

Youknowaveragecarbondensity(emissionsfactors)Youknowtheareadeforested(ac5vitydata)

5

DRC= 17 Billion t C

CO2Emissions2000-2010

(tC/ha)

Reduce uncertainty in carbon cycle studies Input for REDD/carbon Market Stock Flow Approach

(ha/year)

Biomass2000 Deforesta5on2000-2010

Distribu5onofforestinventorydatainCentralAfrica

Fieldbiomassmeasurements

MODIS1kmNBAR(RGB2,6,1)

The Geoscience Laser Altimeter System (GLAS)

The figure shows 30% of the GLAS L2A (year 2003) shots after screening procedures (1.3 million observations) Screening for cloud attenuation, topographic slope (SRTM), waveform noise thresholds, etc. 30-40% remain after screening.

Vegeta5onstructurefromLiDAR(GLAS)

70m

Lidarmetricshavebeenextensivelyusedtocharacterizevegeta5onstructure(Sunetal.2008,Lefskyetal.2005,Lefskyetal.1999)

Drakeetal.(2003),Lefskyetal.2005,Drakeetal.2002foundastrongrela5onshipbetweenAGBandLidarmetrics(HOME)

SatelliteInforma5on

Twotypesofinforma5on:PointdataandImagedata

ICESat GLAS (Points)

Biomass=30(t/ha)Biomass=78(t/ha)Biomass=205(t/ha)

Co-locatedFieldMeasurements

Fieldobserva5onnetwork&calibra5on>300loca5ons>30,000treesmeasured

• Columbia• Ecuador• Bolivia• Brazil

• Gabon• DRC• Uganda• Tanzania

• Vietnam• Cambodia• Indonesia

0 100 200 300 400 500

0100

200

300

400

500

Field Biomass (Mg/ha)

GLA

S P

redi

cted

Bio

mas

s (M

g/ha

)

Samples(million) TropicalAmerica

TropicalAfrica

TropicalAsia

Total

Available 13.2 18.2 11.8 43.2A%erScreening 2.3 2.5 0.7 5.5

Percent%used 17.4 13.8 5.9 11.5

Standarderror22.6MgC/haAdjustedR-squared:83.2

Biomass=HOME+H10+H60+CANOPY_ENE+H25

The Geoscience Laser Altimeter System (GLAS)

The figure shows 30% of the GLAS L2A (year 2003) shots after screening procedures (1.3 million observations) Screening for cloud attenuation, topographic slope (SRTM), waveform noise thresholds, etc. 30-40% remain after screening.

2009NBARone8dayscomposite

2009NBARone8dayscomposite

Composite2007-2008

Pixelsmosaicofbestqualityreflectanceovertheperiod2000-2003

KeyInputUsed:NADIR,BRDF-AdjustedReflectance

(Schaafetal.,2002;RSE)

Removes artifacts associated with variable view geometry

NBARcomposi5ng

Atmosphericallycorrectedandcloudcleared

– Spa5alresolu5onat500(nadir)However….Ar5factsduetocloudsresidualsand

shadowsarepresentComposi5ngover5mesuccessfullyremove

ar5facts

Composite2007-2008

Pan-TropicalBiomassMap

•  BestqualityMODISmosaic•  Mul5pleyears•  cloudfree

•  ScreenedGLASmetrics•  Seriesofmetrics(treeheight,heightofmedianenergy,etc.)

•  Co-locatedfieldmeasurements

GLASyear2007

MODIS500mcompositeyear2007-2008

23°N

0°

23°SA.Bausch

CT1

Pantropical Forest Carbon Mapped with Satellite and Field Observations

AmazonBasindetailfromthemap

DRCdetailfromthemapPNGdetailfromthemap

Error25MgCha-1 Error19MgCha-1 Error24MgCha-1

Baccinietal.2012

LandsatBasedAbovegroundBiomass

Landsatcircayear2000RGB:4,5,7(Hansenetal.2013)

GLASbasedbiomassdensityes5mates

BasedonsimilarapproachofBaccinietal.2012

LandsatBasedBiomassDensity(Yr.2000)Baccinietal.2016inprepara1on,Zarinetal.2016

ImprovementinSpa5alResolu5on30m resolution500m resolution

0 2010 Kilometers

0 105 Miles

Aboveground Carbon

tonnes per hectare0 >18012550 1005

LandsatBasedBiomassYear2000

PixelLevelUncertainty(PercentError)

LandsatAnnualBiomassLossfromDeforesta5on

Landsatbiomassdensitycirca2000combinedwithHansenetal.2013deforesta5on

Biomassreferenceyear2000

AnnualGrossEmissionsfromDeforesta5on

!

Zarinetal.2015

Na5onalScale

StateScale

JuntasIntermunicipales

Emissionsfactors

•  Statelevelaverage3%

•  Municipali5eslevelaverage10.6%

ID=statecode,municipalitycode,andlandcoverclass

IPCCguidelines

Limita5ons

•  Grossbiomasslossesfromdeforesta5on– Degrada5on?Verydifficultwhenfocusedonareaextent

•  Gains?

•  Toolate

Shifting the Paradigm

NoneedtodefineDeforesta5onandDegrada5on

Carbondensitytrajectoriesover5meandspace 2002

2012

•  Timeseriesapproachbasedon“changepoint”analysis

•  Foreach500mx500mpixelweiden5fythetrajectoryofcarbondensity

2 4 6 8 10 12

050

150

250

Pixel 379226

Year

Biom

ass

(Mg/

ha)

●

●

●

●

●

●

●

●

●

●

2 4 6 8 10 12

050

150

250

Pixel 1800164

Year

Biom

ass

(Mg/

ha)

●

●

●

●●

●

●●

● ●●

2 4 6 8 10 12

050

150

250

Pixel 3762098

Year

Biom

ass

(Mg/

ha)

●

●

●

●●

●●

● ●●

●

Con5nuousspa5allyexplicitcarbondensitychangewithmeasurableuncertainty

High : 128

Low : 1

High : -1

Low : -252

High : 293

Low : 0

Gain

Stable

Loss

Mg/ha

Mg/ha

Mg/ha

190kmx215km

2 4 6 8 10 12

050

150

250

Pixel 3710066

Year

Biom

ass

(Mg/

ha)

●

●●

●●

●● ●

●●

●

Gain=59.2StdEr=24.2P-V=0.041

2 4 6 8 10 12

050

150

250

Pixel 3217595

Year

Biom

ass

(Mg/

ha) ●

● ● ●

● ●●

● ●

● ●

2 4 6 8 10 120

5015

025

0

Pixel 1249653

Year

Biom

ass

(Mg/

ha)

●

● ●●

●

●

●

●

●●

●

●

Loss=-201.2StdEr=8.4P-V=0.003

StdEr=46.1P-V=0.99

High : 128

Low : 1

High : -1

Low : -252

High : 293

Low : 0

Consistentwithdeforesta5onandsensi5veto“degrada5on”?

Deforesta5onLandsatbased(30mresolu5on)Hansenetal.2014

Gain

Stable

Loss

Mg/ha

Mg/ha

Mg/ha

190kmx215km

2 4 6 8 10 12

050

150

250

Pixel 3710066

Year

Biom

ass

(Mg/

ha)

●

●●

●●

●● ●

●●

●

Gain=59.2StdEr=24.2P-V=0.041

2 4 6 8 10 12

050

150

250

Pixel 3217595

Year

Biom

ass

(Mg/

ha) ●

● ● ●

● ●●

● ●

● ●

2 4 6 8 10 120

5015

025

0

Pixel 1249653

Year

Biom

ass

(Mg/

ha)

●

● ●●

●

●

●

●

●●

●

●

Loss=-201.2StdEr=8.4P-V=0.003

StdEr=46.1P-V=0.99

SouthEastAsiaBiomasschange2003-2014

0 105 Kilometers

0 14070 Kilometers

-1 < -500

0 105 Kilometers

SouthEastAsiaBiomasschange2003-2014

0 14070 Kilometers

-1 < -500

Deforesta5onLandsatbased(30mresolu5on)Hansenetal.2014

UncertaintyAssociatedtoChange

“Preliminary result based on GLAS/MODIS

44

MexicoAnnualCarbonNetLossandGainBaccinietal.2016.Inreview

<-200

10204060

80

-40-70-100-150

-10-20

Mg/ha

RegionalBiomassLoss

47

XinguBiomassLoss

48

Democ.Rep.CongoAnnualCarbonNetLossandGain

●

●

●●

● ●

●

●

●●

●

−100

−50

050

100

Congo, DRC

Year

Car

bon

(Tg)

03−0

4

04−0

5

05−0

6

06−0

7

07−0

8

08−0

9

09−1

0

10−1

1

11−1

2

12−1

3

13−1

4

●

●

●

●

● ● ●

●

●

●

●

Net LossNetNet Gain

●

●

●

●

● ●

●

●

●●

●

Baccinietal.2016.Inreview

Valida5on

50

Issueswithexis5ngfielddata•  Size•  Measurements•  Dateofmeasurements•  Geoloca5on

512004 2006 2008 2010 2012

020

4060

8010

0

Sum Delta Biomass Change

Year

Biom

ass

(Mg/

ha)

Parcel B−RSParcel B−FParcel C−RSParcel C−F

Brandoetal.2014

Cumula5

veBiomassloss(Mg/ha)

Valida5on/Assessment?

Valida5on

52

2004 2006 2008 2010 2012

020

4060

8010

0

Year

Cum

ulat

ive

Bio

mas

s C

hang

e (M

g/ha

) RS estimatesField estimates

Valida5onBiomassGrowth

53

Planta5onsmaps(WRI)Stapeetal.2010Biomassincrements16to22Mgha-1y-1

2004 2008 2012

050

150

250

Pixel 4307245

year

Bioma

ss (M

g/ha)

●●

● ●●

●●

●

● ●

●

●

Titulodelapresentación

Geospatial Data Integration for Forest Carbon Monitoring:

Southeast Mexico and Beyond

Wayne Walker1, Alessandro Baccini1, Curtis Woodcock2, Luis Carvalho2, Alicia Peduzzi3, Fabio Gonçalves4, Javier Corral Rivas5, Carlos Lopez Sanchez5

Direct Measurement of Aboveground Carbon Dynamics In Support of Large Area CMS Development

2001-2012 2001-2012 2001-2012

Applications and Impact:

4,100 km2 of LiDAR

1000’s of field plots

Thank you!

Wayne Walker Mary Farina Luis Carvalho Tina Cormier Damien Sulla-Menashe Skee Houghton