REDD+ For the Guiana Shield, 3rd Working Group Meeting, Design of a Multipurpose National

Status of forest biomass and

carbon stock assessment in

South and Central America

Matieu Henry (FAO) & Lars Gunnar Marklund (FAO)

Role of South American forests in

global climate change

Status of national

communications to the

UNFCCC (%)

https://unfccc.int/2860.php

•LUCF sector contributes to 8% of global GHG emissions (Le Quéré et al. 2013)

•1/3 of the tropical C sink results from the South America tropical forests (Malhi, 2010)

Large source of uncertainties

related to the combination of

various sources of data and

various methodologies, from

bottom-up ecosystem

inventories to land fluxes,

biogeochemical models, and

atmospheric inversions.

Nat. Com. Non-Annex I LA Countries

1st 94 95

2nd 65 81

3d 3 10

4th 1 5

5th 1 5

Reporting GHG estimates under the UNFCCC

(2) National Communication, submitted by all Parties (however it is mandatory, every 4 years, for Annex I Parties only).

• Under the UNFCCC there are 2 channels for GHG data acquisition:

The review procedure:

Assists Annex I Parties in improving the quality

Ensures that the COP has adequate and reliable information

>> subject to review & accounting procedure under KP

>> subject to review

Future requirements under the UNFCCC (CP.16)

The first biennial report due by 1 January 2014 and then every 2 yrs

• Annex I Parties should submit a biennial report

>>subject to an International assessment and review process

The first biennial update reports due by December 2014 &then every 2 yrs

• Non-Annex I Parties should submit a biennial update report

>> subject to an international consultations and analysis

(1)GHG Inventory, annually submitted by Annex I Parties only (it is mandatory). A GHGI

contains information on GHG emissions and removals.

Data are published on the UNFCCC website;

http://unfccc.int/national_reports/items/1408.php

Their consistency with other official statistics prepared

by the Party under other national (e.g. statistical

yearbook) and international initiative (e.g. FRA) has to

be ensured.

Data published are official data of the Party

Published data are used by the UNFCCC to assessing

country progresses towards commitments and for assessing

global trends of anthropogenic emissions and removals.

National Forest Resources Assessment in South and Central America

• Data at national level

• FRA 2010 dataset – data officially submitted by countries to FAO. Note that FRA data come from the countries, they are not FAO data.

Guidelines for country reporting available at:

http://www.fao.org/docrep/010/k1276e/k1276e00.htm

Countries are requested to provide best possible

estimates:

• NFI or RS data on forest area;

• NFI data on stocks per hectare when available;

• National data / equations on volume and biomass whenever possible;

• If not available, best possible volume estimate and application of national

BCEF or as a last resort, default BCEF values from IPCC 2006.

The bottleneck is, and has always been, the availability of robust country data

How have the countries reported to FRA 2010?

• This brief analysis covers all LA countries excluding the Caribbean Islands

• In total 21 countries and territories.

• Of these countries, 17 reported on growing stock and 16 reported on biomass and carbon - the following analysis will focus on these 16 countries.

• Among those not reporting we find countries like Ecuador, El Salvador, Paraguay & Venezuela. The reason given is lack of information.

Forest area estimates

• Of 16 countries reporting on biomass and carbon, 3 have estimated total forest area from NFI data, remaining 13 from RS derived maps

• Several countries do not have trend data on forest area, and have assumed an annual deforestation rate – a couple of countries even assumed zero deforestation

Growing stock estimates • 7 countries have fairly recent NFI data, 1 more have

very old NFI data.

• No any country reports using tree allometric equations in FRA reports, although they may have used tree AE.

• Two countries have estimated volume from biomass figures, applying a BCEF “backwards”.

• Remaining countries have estimated growing stock per hectare from other partial inventories, management plans, etc. It is unclear when equations have been used and when not.

Above-ground biomass estimates

• 3 countries (Mexico, Guatemala, Honduras) have applied biomass equations on NFI data

• 4 countries have applied nationally estimated stocks per hectare

• Remaining countries have applied either an estimate based on D x BEF or directly a BCEF. Of these, a few countries (Argentina, Brazil) have made more elaborated estimation of BEFs, by region or by biome. Some have used the equation by Brown to calculate BEF.

Below-ground biomass

• One country (Mexico) seems to have applied equations, but it is unclear how estimates were derived

• Two countries/territories have national estimates on BGB per hectare – it is unclear how these were derived (Guyana / French Guiana)

• Remaining have used IPCC default values, usually 0.24. A few countries have applied sub-national or biome values (Argentina, Brazil)

Carbon estimates

• Most countries have applied a carbon fraction of 0.47 as of IPCC (2006)

• A few countries have applied a carbon fraction of 0.5 (IPCC, 2003)

• Brazil is using different carbon fractions for different biomes (between 0.47 and 0.5)

Conclusions – data reported to FRA

• Growing stock data still very weak. Some important countries (Venezuela, Colombia, Ecuador) completely lack growing stock data useful for national estimates.

• Other important countries rely on partial inventory data for their growing stock estimates (Brazil, Peru, Bolivia).

• Biomass and carbon estimates are in most cases based on the growing stock figures.

• Only a few countries have used national biomass data, and it is uncertain how these data were obtained and processed.

Recent development

• Several countries in process of planning or implementing a national forest inventory

– Brazil

– Ecuador

– Panama

– Paraguay

– Costa Rica

• Many countries are updating or elaborating new forest maps, so better area and area change estimates will soon be available

Other sources for national data

• Saacthi pantropical study on forest carbon stocks

– Satellite LIDAR data calibrated with ground plots

– Tabulated national estimates cannot be compared “as is” with FRA data, as areas are inconsistent

– Saatchi gives slightly lower carbon estimates per ha than what’s reported to FRA, but likely due to different area figures – Saatchi seems to include more “bushland” as forest. Typical example is Argentina.

Comparisons with the Saatchi study

Argentina 2,396 1,306 81.5 16.5 29,400 79,000

Belize 138 158 99.1 79.0 1,393 2,000

Bolivia 3,582 4,850 62.6 65.5 57,196 74,000

Brazil 52,745 47,526 101.5 79.7 519,522 596,000

Chile 1,124 1,028 69.2 39.5 16,231 26,000

Colombia 5,488 8,054 90.7 95.9 60,499 84,000

Costa Rica 192 333 73.6 83.3 2,605 4,000

French Guiana 1,344 328 166.3 109.3 8,082 3,000

Guatemala 227 576 62.0 72.0 3,657 8,000

Guyana 1,348 2,401 88.7 126.4 15,205 19,000

Honduras 266 514 51.2 57.1 5,192 9,000

Mexico 1,688 2,243 26.0 33.0 64,802 68,000

Nicaragua 282 674 90.5 84.3 3,114 8,000

Panama 296 469 91.1 78.2 3,251 6,000

Peru 6,903 9,666 101.5 120.8 67,992 80,000

Suriname 2,553 1,815 173.0 129.6 14,758 14,000

Forest

area

(1 000 ha)

Saatchi

Carbon in

AGB (t/ha)

FRA

Carbon in

AGB

(million t)

Saatchi

Carbon in

AGB (t/ha)

Saatchi

Forest

area

(1 000 ha)

FRACountry/area

Carbon in

AGB

(million t)

FRA

Final remarks

• It is essential to improve country data in many aspects, and in particular

– Better data on area and in particular area trends. Changes often assessed by comparing independent studies or maps, and can therefore have substantial errors

– National forest inventories are the basis for stock assessments. Still, many of the most important countries in the region do not have a NFI; however many countries are planning or have started the process of implementing a NFI.

Remarks

• NFI data need to be complemented with good allometric equations for volume and biomass.

• There is very little data on below-ground biomass.

• Airborne LIDAR is a promising technology for non-destructive estimation of biomass. Still quite expensive. Does not replace a NFI as the NFI covers many more aspects than just biomass and carbon.

Status of national forest inventory in the Guiana Shield

1 000 ha

% of

land area %a %a %a

Suriname 3 14,758 95 0 0 -0.02 214 3,158

French Guiana ** 3 8,082 98 -0.09 -0.04 -0.04 204 1,649

Brazil ** 23 519,522 62 -0.51 -0.57 -0.42 121 62,862

Guyana 4 15,205 77 0 0 0 107 1,627

Average LA 47 80,350 49 0.59 0.60 0.62 115 6,350

Country/area

Forest 2005-2010Pop.

Density

/km 2

C stock t/

ha

Total C

stock (Mt

C)

1990-2000 2000-2005

Suriname Guiana Brazil French_Guiana

Forest land area

Map: NARENA SBB

1998

Guyana Forestry

Commission, FRIU 2005 -

2007 (But estimates for year

1999)

Maps of the

Vegetable Cover of

Brazilian Biomes –

PROBIO (MMA,

2007)

Recensement agricole, Direction de

l’Agriculture et de la Forêt, 1990.

Granville et al., 1993, Recensement

agricole, 2001, IFN 2008, Gond V.,

2002, Eva, H., et al. 2000

NFI NA NA NA NA

Deforestation NA NA Annual

Linear Extrapolation based on 1990

and 2006

Forest categories

Classif.: Lindeman &

Molenaa 1957,

National Forest

Policy 2003, Veg.

Class 1998, SBB

2000-2008

Guyana Forestry

Commission, Forest

Resources Information Unit

2005 - 2007

PortalBio/MMA

2002 ONF 1968 - 2008

Afforestation/refore

station NA NA

ABRAF of 2006,

2007, 2008, and

2009 & IBGE, 2008,

Bracelpa 2002

Plantations ECEREX 1990, 2000,

2005 et 2010

Status of national forest inventory in the Guiana Shield

Status of national forest inventory in the Guiana Shield

Suriname Guiana Brazil French_Guiana

Volume

Growing stock:

Surinam Forest

Inventory 1973,

Extraction: LBB

yearly statistics

1999-2007 Default

FELFILI (1994, 1997, 2001),

PEREIRA,(1987), BRASILEIRO (1988),

REZENDE 2002: (1988-1992), Higuchi et

al (1997-2000), Andrade and Higuchi

(2000, 2002, 2004, 2006)

Silva (1994), Carvalho, Oliveira (1993),

Sá (1998), Carvalho, Zákia (1994), Isaia,

Verslype, Gariglio (1992)

ONF-CTFT 1970,

ONF-CTFT 1972,

ONF 2000-2004,

Gazel 1983, ONF

1998

Biomas

s

Poels (1987), K.

Tjon (1998) Default

REZENDE (2002), SANTOS et al (2001),

CASTRO (1996), FELFILI (1994),

FELFILI, SILVA (2001), HIGUCHI et al

(1994), HIGUCHI etal (1998, p. 153-166),

HIGUCHI et al (1998, p.153-166),

HIGUCHI M (2004), FEARNSIDE (1994),

HIGUCHI et al (1994)

Utilisation de

données sources

des années 1970-

1980 (inventaires

papetiers)

BCEF NA Default Specific NA

RS Default Default Specific Specific

Dead

Wood Default Default Specific Specific

Carbon

content Default Default Specific Default

Litter Default Default Specific Default

SOC Default Default Specific Default

Status of national forest inventory in the Guiana Shield

Suriname Guiana Brazil French Guiana

Fires

Forest sector

environmental

assessment plan

2003 NA

IBAMA (1988-1992,

1998-2002, 2003-

2007), INPE (1998-

2002, 2003-2007) ONF, 1990 - 2000

Perturbati

ons NA NA

EMBRAPA

FLORESTAS (1988-

2003) ONF, 1990 - 2000

Volume

extracted SBB 2000 & 2005

Forest Sector

Information

Report, Guyana

2005-2007 IBGE (1990-2007) ONF (annual)

22

The regional workshop is divided into 4 sessions:

Accounting & reporting objectives of REDD+

• Report the anthropogenic impact on carbon-stock changes in, and other emissions from, forest carbon pools

• Account for the change, in carbon-stock changes and other emissions, associated with mitigation actions

• To ensure comparability among accounted quantities, we need to calculate estimates that are systematically neither over nor under the true value so far as can be judged and with an associated uncertainty reduced so far as practicable

• There is not any reference to conservativeness in COP decision related to REDD+ activities

• However, COP Decision taken in Warsaw on FRL ask for providing justification of why omitted pools and/or activities were deemed not significant; which means that non-significant pools may be excluded from accounting

Designing a NFI for REDD+ purposes

Carbon stock assessment for REDD+ under the

UNFCCC

“….requested developing country Parties aiming to undertake the REDD+ activities, […], to develop:

– A national strategy or action plan

– Forest reference emission level and/or forest reference level

– A robust and transparent national forest monitoring system for the monitoring and reporting REDD+ activities

– A system for providing information on how the safeguards are being addressed and respected”

The Cancun Agreements (Decision 1/CP.16)

Elements requested to be developed

“…establish, according to national circumstances and capabilities, robust and

transparent national forest monitoring systems and, if appropriate, sub-national

systems as part of national forest monitoring systems that:

• Use a combination of remote sensing and ground-based forest carbon

inventory approaches for estimating, as appropriate, anthropogenic forest-related

greenhouse gas emissions by sources and removals by sinks, forest carbon stocks

and forest area changes;

According to Decision 4/CP15: to use the most recent IPCC guidance and guidelines, as

adopted or encouraged by the COP.

Establish & maintain IA

Ensure Designate Make

available

Assign responsi-

bilities

Elaborate a QA/QC plan

Establish processes

Improve-ment plan

Analyze Estimate in accordance

with GL

Complete data

collection

Assess quality

Report Control Review

Improve-ment based on review

Archive Make

accessible Transpa-

rency Security

Linking the different elements/ functions of the

national system for the preparation of the GHG

inventory

Sustainability of the forest monitoring system

Forestry

Border Mapping

National Defense

Techno.& scientific progresses can support different sectors/national

objectives

ADOPTABLE ADAPTABLE FEASIBLE

AN ADEQUATE INSTITUTIONAL & LEGAL FRAMEWORK

BUT, they need to be

AND REQUIRE

Biodiversity

Cross-cutting issues

– Regional networking;

– Data availability and accessibility;

– Regional collaboration;

– Communication.

– Etc.