Holistic Conservation Programme for Forests in Madagascar

Roissy – 9 novembre 2010

REDD+ in Madagascar Carbon stocks assessment and baseline setting:

project-level inputs to national level!-!

Side-Event at COP 17! Durban - 2 dec 2011

Speakers:

Romuald Vaudry: GoodPlanet Foundation Ghislain Vieilledent: CIRAD Clovis Grinand: GoodPlanet Foundation Jean-Roger Rakotoarijaona: National Office for the Environment Naomi Swickard: Verified Carbon Standard Charles Rakotondrainibe: Madagascar National Parks

Roissy – 9 novembre 2010

!!!Keywords -  5M€ project exclusively funded by Air France -  Implemented by GoodPlanet and WWF Madagascar

-  Oct. 2008 - Feb. 2012 -  Grant based project: no carbon credits issued by the end of the project

-  Carbon accounting: testing innovative tools/methods

Holistic Conservation Programme for Forests!

In Madagascar !

© WWF Madagascar

Objectives

!

§  Aims to reduce deforestation and forest degradation and resulting greenhouse gas (GHG) emissions

§  Specific goals: •  Improve living conditions of local communities •  Improve knowledge concerning the verifiable and effective measurement of the

ground activities that aim to reduce GHG emissions •  Fully integrate the preservation of the Madagascar’s exceptional biodiversity

© Yann Arthus-Bertrand

© GoodPlanet/Romuald Vaudry

Intervention Sites 515 000 ha on 5 sites !!

390 000 ha of moist forests

© WWF Canon/Edward Parker

125 000 ha of spiny forests


Legend

HCPF Sites Background © Missouri Botanical Garden

© PHCF / Sitraka Ranoeliarivao

Andapa/Bealanana

Fandriana/Marolambo

Ivohibe

Fort-Dauphin

Vondrozo

§  Creation of new protected areas: 350 000 ha

§  Transfer of natural resources management to local communities: 205 000 ha

§  Restoration of degraded forest: 20 000 ha

§  Reforestation: 3 000 ha

§  Promotion of alternative practices: all sites

§  Carbon knowledge and accounting: all sites "

Project activities !Conservation activities and carbon accounting"


© WWF Canon/John E Newby


© Spot Image

Above-Ground Biomass Assessment

"

1.  Land-Use analysis"

2.  LiDAR technology"

3.  Allometric models development"

4.  Final results"

5.  Key inputs for national level"

"

Above-Ground Biomass Assessment"

Autres terres (sol nu)

Prairies

Terres cultivées

Forêts secondaires Forêts denses

Forêts claires Etablissements (Village)

Forêts ripicoles

© HCPF / Sitraka Ranoeliarivao

Other land (bare soil)

Grassland

Cropland

Secondary regrowth

Dense forests Clear forests

Settlements (village)

Riparian forests

Grassland

Dense forests Typical Land-Use – Northern site"

AGB AssessmentLand-Use Analysis"

Original scene"Fandriana Region"24th march 2009"

© Spot Image

Spot Image very high resolution images (2.5-meter colour) "

acquisition on all sites"


Stratified image (oct 2009)""""""""""

In partnership with "Antananarivo University"

© HCPF / Tahiana Rajosarimalala


"






"


LiDAR = Light Detection And Ranging! "

Measures properties of scattered light to find range and/or other information of a distant target"

Relevance: "•  Height measurement => 3D structure""

•  Allow to reduce considerably "the number of field plots"

Covered Area: 50,000 ha" (10% of the total project area)"

© Carnegie Airborne Observatory

AGB AssessmentLiDAR technology!

Maps of forest types" x "

Soil cartography "x "

Digital Elevation Model""""

LiDAR Sampling!



Forest clearing Secondary forest

Forest degrada2on


Couverture du site de Fort-Dauphin"


Field Inventories sampling!"

Objective: measuring the widest range of forest carbon stocks"

•  47 plots in moist forests "•  36 plots in spiny forests"


AGB AssessmentField inventories!

"






"


-  To switch from conventional forestry measures (diameter, height) in carbon stocks"

-  Africa: 30% of the tropical forests""-  No generic allometric model for Africa"

-  The choice of the allometric model is the most important source of error regarding AGB assessment (Keller 2001, Chave 2004)

"=> We made the decision to develop models for

Madagascar in partnership with the CIRAD and the Antananarivo University "

"

""

"

AGB AssessmentAllometric Models Development!

Materials: ""•  50 genera studied"

•  480 trees measured and entirely weighed, from stems to leaves"

•  2000 wood samples analysed in Cirad lab""""


Results:""•  Models used in Madagascar (including DBH only) strongly overestimate the AGB of: "

" """Brown: "+ 35,5% in moist forests "Chave: "+ 36,9% in moist forests"

" " "+ 91,1% in spiny forests " "+ 63,3% in spiny forests""•  Models now available for any project proponent in Madagascar"""


Results:""•  Compared to our regional models, Chave’s models including DBH, Height and Wood

Density can also provide accurate tree biomass estimates.


Results:""•  Compared to our regional models, Chave’s models including DBH, Height and Wood

Density can also provide accurate tree biomass estimates.

•  When allometric models are not available (e.g in Africa) and since height is time-consuming to measure, a simple H-DBH allometry can feed the above model to accurately estimate biomass and carbon stock from plot inventories.

For more details: A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models - Vieilledent G et al (2011). Ecological Applications.


"






"


AGB AssessmentFinal results!

Very high correlation between Mean Canopy Height (m) and Above Ground Carbon density (Mg C ha -1) Uncertainty: 12% only

Mean Stocks"

Spiny forests"17 tC/ha"

Moist Forests"90 tC/ha"

Southern Mountains (East) and Plains (West)!

10% uncertainty"at the pixel level (1ha)"


Moist Forests"88 tC/ha"

Northern Mountains Region!

35% uncertainty"at the pixel level (1ha)"

For more details: Human and Environmental Controls over Aboveground Carbon Storage in Madagascar - Asner et al (2011). Carbon Balance Management (in review)


Northern Mountains Region! Costs

0,15 $/ha of forest This cost does not include allometric models development

Can easily drop around: 0,06-0,07 $/ha of forest Because: -  Our sites are scattered in the country -  24 field plots of the 83 plots were

enough to calibrate the MCH/ACD model

-  Basal area measurement can replace DBH/H (Mascaro et al.2011)

-  LiDAR coverage: 5% of the project area is usually enough (vs 10% in our case)

± 300 000 $ for all the Malagasy moist forests (4,7 M ha)


Key inputs for (sub)national level •  Malagasy colleagues trained to deal with Very High Resolution

imagery

•  Critical information provided regarding forest definition (dry/spiny forests)

•  Allometric models now available for any project proponent in Madagascar"

•  LiDAR technology: a very interesting option to map carbon stocks of the remote moist forests of Madagascar (mainly located in mountains, on steep slopes). "

AGB Assessment!

Modeling deforestation and associated CO2 emissions

Introduction

Materials and methods Results Discussion

Outline

1- Modeling the deforestation process: l  Deforestation rates 2000-2010 l  Factors of deforestation

2- Forecasting deforestation 2010-2030 3- Estimating CO2 emissions associated to deforestation

Introduction Objectives

Introduction Area

4 study areas - 3 in moist forest (North, Middle, South) - 1 in spiny dry forest (South) Surface 7 800 000 ha

2 141 520 ha of forest in 2010

Introduction

Materials and methods l  Land use change observation l  Uncertainty l  Deforestation model l  Projected deforestation Results Discussion

Outline

Photo interpretation - Definition of forest: 0.1ha, 10% cover, 5m height - Landsat images (+GoogleEarth VHR images) - Training forest plots (1 for 500 ha of forest) - Land use change observations at 3 dates: FFF, FDD, FFD - Free Open Source (FOS) software: QGIS

Materials and Methods Land use change observation

Sept. 2001 April 2005 Nov. 2010

Land use change model


Photo interpretation Landsat images

model

FFF, FDD, FFD + cloud, water and shade

Pixel characteristics - NIR, VIS - NDVI - ...

FOS software: R with randomForest classification algorithm

Projected land use change on the whole image

Materials and Methods Land use chane observation

Landsat images

model

FFF, FDD, FFD + cloud, water and shade

Pixel characteristics - NIR, VIS - NDVI - EVI - ...

FOS software: R + GRASS GIS


Overland

Sept. 2001 April 2005 Nov. 2010

Past deforestation 2001-2005 and 2005-2010

Originality of the approach - Integrates expert knowledge (photo interpretation) - Automated statistical treatment of the images with an advanced algorithm (randomForest) - Direct classification of land use change (FFF, FDD) - Using Free Open Source software


Introduction


Outline

Uncertainty - Uncertainty associated to land use change observation - Assessed with cross-validation procedure → Land use accuracy: 85% → Land use change accuracy: 70% Consistency assessment - Comparison with other approaches: point sampling (pixel level)

Materials and Methods Uncertainty

Point sampling - Regular grid of 2 x 2 km - Pixel level - Photo interpretation on Landsat images (+ GoogleEarth VHR images)


Comparison


Introduction


Outline

Logistic regression model

Materials and Methods Deforestation model

Land-use change observations: Zit={1,0} 1: deforested and 0: forest i: pixel et t: date Zit ~Binomial(θit) logit(θit)=f(factors, β) θit: probability of deforestation for pixel i at date t β: model parameters to be estimated

Factors of deforestation - Landscape factor: forest fragmentation - Anthropogenic factor: population density - Policy factor: protected area network


Parameter estimates Inference in a hierarchical Bayesian framework Iterations = 5001:9996 Thinning interval = 5 Number of chains = 1 Sample size per chain = 1000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE (Intercept) -2.149186 0.068773 2.175e-03 5.039e-03 as.factor(fragindex)2 -0.607695 0.100512 3.178e-03 7.411e-03 as.factor(fragindex)3 -2.053594 0.087759 2.775e-03 8.137e-03 as.factor(fragindex)4 -3.244258 0.313901 9.926e-03 3.165e-02 as.factor(fragindex)5 -6.777683 0.829412 2.623e-02 9.915e-02 as.factor(aire_prot)2 -0.807469 0.134398 4.250e-03 1.300e-02 dens 0.004556 0.001107 3.499e-05 7.449e-05

FOS software: R and C++ code


Introduction


Outline

Projecting explicative variables in the future

Population : Using demographic models Protected areas : Unchanged Forest fragmentation : Iterative method, forest fragmentation is re-computed at each time step

Materials and methods Projected deforestation

Computing probability of deforestation in the future logit(θit)=β0+β1j Fragmentationit+β2 DensPopit+β3k AireProtit θit: probabilité de déforestation du pixel i à la date t blue-green-yellow-orange-red θit=0 -----------------------> θit=1 We simulate the deforestation following the estimated probability of deforestation FOS software: R + GRASS GIS



Deforestation and associated C/CO2 emissions


Deforestation 2010-2030

Introduction Materials and methods Results Discussion

Outline

Carbon released on project areas

Results

Project area Forest surface 2010 (ha)

Deforestation rate 2000-2010

C from AGB 2010-2030 (t)

ANDAPA 197,763.13 0.44 1,332,641.00

Ivohibe 111,759.00 0.80 1,824,165.00

Fandriana 25,342.00 2.19 859,040.00

FD moist 51,433.00 0.84 529,995.00

FD dry 119,736.00 0.35 139,588.00

TOTAL 4,685,429.00

Introduction Materials and methods Results Discussion

Outline

Key inputs for the (sub)national level Work done on large areas (~25% of national forest) Methodological framework is now clearly defined Free Open-Source softwares with all scripts freely available Building capacity: students + engineers from national institutes ⇒ Work can be easily and rapidly transposed to other forest areas

Discussion

Soil Organic Carbon Assessment

Soil carbon accountancy at project scale

1.  Introduction

2.  Measuring soil carbon reference level

3.  Spatial estimation of soil carbon stocks

4.  Monitoring soil-C change

Why taking soil into account?

Values in billions of tC (PgC)

Soil is a major carbon pool



Higher stocks in tropical and boreal forests Soil is a major carbon pool



Dry subhumid, semiarid, arid and hyperarid lands

Higher stocks in tropical and boreal forests Soil is a major carbon pool


1.  Introduction




Measuring soil carbon reference level

Objective : Soil carbon stock estimation at plot level Methodology : Soil collection and measurement from a 10x10 m plot, stratified by forest type, land use category and elevation Results were aggregated for 0-30 cm and 0-100 cm soil layer (IPCC requirements) Main activities : - Field survey - Formation of local survey team - Measurement of soil bulk density - Soil collection and database - Traditional lab measurement of C concentration - Infra red spectroscopy - Soil carbon stock estimation

Measuring soil carbon reference level

Some figures: - 121 sample plots -  302 kg of soil collected -  750 soil carbon measure using infra red spectroscopy

Infra red spectroscopy is used to produce accurate and cost-effective carbone measurement

Measuring soil carbon reference level C

sto

cks

(tC/h

a)

From 1,6 up to 3,8 times more carbon is stored on soil (1 m) compared to biomass

Humid Forest Spiny Forest

Soil layer


1.  Introduction




Spatial estimation of soil carbon stocks

How much carbon is stored in the soil?

and here?


Use of soil carbon inventory coupled with RS data and spatially explicit soil

factor


© Clovis Grinand / IRD

Elevation

Land cover

Annual precipitation

Vegetation index

Number of dry month

Mean annual temperature

Importance relative des facteurs bio-physiques

Relative Importance of RS data and other spatially explicit factor

Moist Forests

Croplands

Savannah

Spiny Forests

Savannah

Savannah

Google Earth view


Moist Forests

Croplands

Savannah

Spiny Forests

Savannah

Savannah

Land cover classification - 2005


Moist Forests

Croplands

Savannah

Spiny Forests

Savannah

Savannah

Soil carbon stocks - 2005


Remote sensing can be used to derive soil carbon information

Estimation is at 10 meter resolution

Mean uncertainty is +/- 20 tC/ha for 30 cm and +/- 42 tC/ha for 100 cm

Digitial soil carbon map is the best way to take into account soil variability compared with traditional soil map

Soil carbon stocks 1m (tC/ha)


(Grinand et al, forthcoming)


1.  Introduction




Monitoring Soil-C change after deforestation

Soil carbon dynamic is difficult to assess due to interaction of many biological, physical and chemical process.

Traditional study involves long term permanent trial plot, but that are not running in Madagascar

Another way to assess carbon loss after deforestation is to measure carbon stock at different time after conversion.


Soil carbon dynamic is difficult to assess due to interaction of many biological, physical and chemical process.

Traditional study involves long term permanent trial plot, but that are not running in Madagascar

Another way to assess carbon loss after deforestation is to measure carbon stock at different time after conversion.

To what extent the carbon store in the soil is lost and after how many years?


Year after conversion Year after conversion

C lo

ss p

erce

ntag

e

C lo

ss p

erce

ntag

e

Our results are consistent with the IPPC default emission factor 40%

Maximum loss is reach around the 15th year after conversion

Losses are different according to the soil layer

Spiny region Humid region

(Razakamanarivo et al, forthcoming)


Baseline soil & above ground biomass emission

Humid region Spiny region

In humid region, soil carbon is 30% of the biomass emission

In spiny region, soil carbon mitigation potential is higher than biomass (Razakamanarivo et al, forthcoming)


Key inputs for (sub) national level


Soil carbon spectroscopy is a rapid and cost-effective tool for carbon measurement and is available in Madagascar

à Allow to set up reference level of carbon stock efficiently





Spatial estimation of soil carbon stock is possible also at landscape scale using remote sensing and easy to access ancillary dataset

à The method can be easily replicated to other regions





Spatial estimation of soil carbon stock is possible also at landscape scale using remote sensing and easy to access ancillary dataset

à The method can be easily replicated to other regions

Soil carbon losses values are important input to estimate reduction potential at national scale

à They can be used for rough estimate as Malagasy soil in humid climate region have similar properties


Misaotra Merci

Thank you"


Holistic Conservation Programme for Forests in Madagascar

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