Data for Monitoring, Reporting, and

Verification: Remote sensing, Inventories, and Intensive sites

Richard Birdsey Carbon Monitoring

Workshop 17 September, 2010

Measurement, Monitoring, and Verification For….

• National Greenhouse Gas Inventories

• REDD and REDD+

• Participation in Carbon Markets (Projects)

• Research and Education

Guías y Requerimientos de Monitoreo

• Existen varias guías, por ejemplo: – IPCC “Guías de Buenas Prácticas” y reportes especiales

– Programas como GEO, GOFC-GOLD, UN-REDD

• Incluir deforestación, degradación forestal y manejo forestal (los métodos pueden ser diferentes)

• Necesidad de flexibilidad para permitir amplia participación, pero los resultados deben ser consistentes

• Necesidad de proyecciones confiables de la línea de base para establecer la adicionalidad

El Enfoque “Multi-tier” de Monitoreo: Observaciones Extensivas con Estudios Intensivos de Procesos del

Ecosistema

• Sensores remotos

• Inventario nacional forestal

• Modelaje

• Sitios de referencia para validación

Selected Land Variables and Measurement Methods

Variable

Remote Sensing

Forest Inventory

Intensive Sites

Land cover X X X

Leaf area X X X

Disturbance X X X

Live biomass X X

Stand structure X X

Species composition X X

Growth, removals, mortality X X

Litter fall X

Soil CO2 flux X

Runoff X

Dissolved Organic C X

Net Ecosystem Exchange of CO2 X

Forest Type Map from Remote Sensing and Inventory

Basic Forest

Inventory Approach

Phase I – Remote sensing to stratify area

Phase 2 – Field inventory

Forest Inventory Phase 3 Sample

• Direct monitoring of additional variables • Not yet included in U.S. greenhouse gas

inventory • Additional variables on subset of P2: soil carbon, down dead wood, forest floor carbon

Condition B = Nonforest Land Use

Condition A =Forest Land Use

Old 1/5-acre plot

US Forest Greenhouse Gas Inventory Data: Currently Based on Phase 2 plots and Ecosystem Models

Carbon estimates are based on tree species and dimensions, forest type, volume of growing stock, and stand age.

Use of Data From Intensive Sites Stand structure and composition Diameter and height

Tree age

Leaf area index

Tree density

Species composition

Carbon pools

Live biomass

Woody debris

Forest floor

Mineral soil

Carbon fluxes

Biomass increment

Litterfall

Forest floor decomposition

Net ecosystem carbon balance

Link to forest classification from inventory (Scaling up)

Data for empirical models based on inventory data

Data for ecosystem process and forest dynamic models

Generalized Biomass Equations by Combining Data from Multiple Studies

0

2000

4000

6000

8000

0 20 40 60 80 100

Ab

ov

eg

rou

nd

bio

ma

ss

(k

g)

aspen/cottonwood

hardmaple/oak

cedar/larch

Douglas-fir

pine

woodland

dbh (cm)

SOURCE: Jenkins and others, 2003

Biomass Related to Volume of Growing Stock: Fitted equation and data points for live trees, Maple-

Beech-Birch, NE region

0

100

200

300

400

0 100 200 300 400

Growing stock volume (m /ha) 3

Bio

mas

s (T

/ha

dry

wt.)

SOURCE: Smith and others, 2003

Forest floor carbon accumulation, decay, and total Example: Southern pines

0

10

20

30

0 25 50 75

Years

Car

bon

mas

s de

nsity

(Mg/

ha)

Mixed or unknown age

SOURCE: Smith and Heath, 2002

accumulation

TOTAL

decomposition

Some Uses of the Data

CO2 Emissions from U.S. Forest Fires Compared with Net CO2 Flux from Forestry and Land-use Change

(From Heath and Smith in US Greenhouse Gas Inventory, 2009)

0

200

400

600

800

1,000

1,200

1,400

1,600

1990 1995 2000 2005

Forest Fire Emissions

Net Forest Sequestration

Million tons CO2 yr-1

-2000

-1000

0

1000

2000

3000

4000

1700 1800 1900 2000 2100

The Carbon Budget of the U.S. Forest Sector

(Forest Ecosystems and Wood Products)

Net Emissions

Net Sequestration

From Birdsey 2006

Year

Mill

ion

to

ns

CO

2 p

er

year

National baseline: -800 MtCO2/yr offsetting 12% of fossil fuel emissions

Basic Information: Total Forest Carbon

Includes all forest ecosystem carbon components, based on FORCARB2 and 2002 RPA Forest Data

Analyze Activities in the Forest Sector to Increase Carbon Sequestration or Reduce Emissions

• Increase forest land area – Avoiding deforestation – Afforestation

• Increase carbon stocks – Mine land reclamation – Forest restoration – Improved forest management – Agroforestry – Urban forestry

• Increase use of wood – Biomass energy plantations – Use wood residues for energy – Substitute wood for other materials

Calculate Emission Factors and Lookup Tables

• Work well when individual reports are summed over a large domains equivalent to that used for derivation of factors

• Tend to smooth over interannual variability over time

• Can be consistently applied at low cost to reporters and verifiers

• May not matter if estimates are consistently wrong (biased) as long as change is accurately estimated

• Reporting and verification burden shifts to documentation of “activity” levels

Sample Output from Landscape Monitoring

• Series of equations: NEP = f(condition, age)

• Hundreds of equations required for diverse conditions

• Example - aggregated estimates for U.S. regions:

-4

-3

-2

-1

0

1

2

3

4

5

6

0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50

Age Class

t C

per h

a p

er y

ea

r

Southeast

South Central

Northeast

North Central

Rocky Mountain

Pacific Coast

From Pan et al. 2010

Voluntary Reporting Program • Project and entity reporting • Department of Energy, Energy Information

Administration • USDA improving agriculture and forestry

accounting rules and guidelines

Years0 10 20 30 40

Car

bon

(t/ha

)0

50100150200250age 2 carbon

0

50

100

150

200

250

300

0 50 100 150

livec_tphfrom ARG

Pine plantation,

SC

Carbon OnLine Estimator (COLE)

The principal applications of COLE: Greenhouse gas inventories

•Regions, states, groups of counties •User-defined domains •Carbon pools of forest ecosystems

Calculations for greenhouse gas registries

•National registry: 1605(b) •Regional and state registries •Chicago Climate Exchange

COLE is a web-based decision-support tool that queries the U.S. forest inventory database and estimates forest carbon stocks using national standard methodology. Users may select an area of interest as small as several counties, and target specific forest types or conditions.

Source of

Estimate:

Mean NPP

(g C m-2 yr-1)

Std Dev

CASA 491 86

PnET – CN 417 35

Towers 350 75

FIA 250 100

Validation

Teaching and Research Example: Components of NEP Estimation

NEP = (ANPP – RW) + (ΔCFR + ΔCCR + ΔCS – L) Where ANPP = aboveground NPP RW = respiration from woody debris ΔCFR = net change in fine root C ΔCCR = net change in coarse root C ΔCS = net change in mineral soil C L = annual litterfall (From Law et al. 2004)

Potential additional flux terms depending on disturbance and scale (= NBP):

DOC, DIC, VOCs, CH4, particulates,herbivory, tree harvest

Coordination and Consistency for MRV Among Countries With Different Circumstances

!

!

! Hidalgo

Silas Little

Parker tract

Hidalgo

Silas Little

Parker tract

Tropic of Cancer

Summary and Conclusions

• Main products:

– Statistical estimates and maps of carbon stocks and productivity for representative landscapes

– Improved ecosystem models at ecoregion and stand scales

– Decision-support tools for carbon management

– Carbon management research and demonstration sites

• Outcomes of improved forest carbon management:

– Landowners may claim carbon credits

– Cleaner air and lower risk of climate change

• Additional benefit:

– Basis for “early warning” system to detect initial impacts of climate change