IIED, Paris, Dec 4th 2015

REDD+ baselines: what are the crucial ingredients?

Casey Ryan, Iain McNicol, Becky Stedham, Yaqing Gou, Neha Joshi and Nick Berry

Senior Lecturer, University of Edinburgh, School of GeoScience, UK

In collaboration with the TREDD team, coordinated by IIED

The ingredients of a useful baseline1. historical information about land cover change

2. understanding of the causes and drivers of change

3. an extrapolation into the future, taking account of potential new drivers

challenge no.1: good historical information on land cover and drivers

challenge no.2: “[Social] scientists are good historians. … But they have not been good forecasters” (Lipset 1983:157)

Woodland cover

Time

1991 2000

2007

2027?

ProjectA

B

Baseline

C

Historical land cover information

❖ ingredient 1: trend in woody carbon stocks over time

1. Landcover issues for TREDD❖ Existing global biomass maps

inaccurate in woodland ecosystems by a factor of 2 (Hill, Ryan, et al, 2013). And, only 1 time period

❖ land cover change data only describes deforestation, not degradation (e.g. Hansen or in most part FAO FRA).

❖ creating consistent time series of land cover data in seasonal woodlands (e.g. with Landsat) is extremely challenging - when comparing over time, classification errors are greater than the change you are looking for

McNicol, Ryan, in prepD

efor

esta

tion

rate

(%) FAO FRA Hansen et al radar-based

TREDD approach: radar based biomass maps

❖ radar backscatter is an indirect measure of woody biomass in woodlands

❖ needs field data for calibration

❖ data available from 2007-10, and 2014 onwards

❖ method developed and tested in central Mozambique ALOS PALSAR

L-band radarRyan et al, 2012 Glob. Change Biol.

Radar backscatter (m2/m2)w

oody

bio

mas

s g

roun

d m

easu

rem

ent

tC/h

a

Afforestation!

Degradation!

Deforestation!

Total!

-200! -150! -100! -50! 0! 50! 100!

Biomass loss (GgC)!

Clearance of forest (farming)

Extraction of biomass (fuel, timber)

Fallow regrowth

errorbars show 1 standard deviation from the 30,000 bootstraps

Reduction in forest area

Reduction in forest biomass density

2.3±1.5 % biomass lost per year (Ryan et al, 2012, Global Change Biology)

Advantages of the approach❖ No cloud contamination❖ Spatially explicit biomass

estimates (no land cover classes needed)

❖ Can detect small changes over large areas (low bias)

❖ First estimates of degradation losses in African woodlands

❖ Resolution (25 m) allows detection of most kinds of land cover change

A

B

B

C

D

E

F

G

A

0 0.50.25

Kilometers

Change in biomass(2010 as % of 2007)

200 %

0 %

100 %

A

B

B

C

D

E

F

G

A

0 0.50.25

Kilometers

Change in biomass(2010 as % of 2007)

200 %

0 %

100 %

Scaling up to the Beira Corridor

❖ tested at small scales (Gorongosa) and districts (Sussendenga)

❖ then to Sofala, Manica and Zambezia: the TREDD area

❖ currently for all of Southern African woodlands

Metric that combines deforestation and

degradation:

The area that has lost >50% biomass in 3 years (1km2 pixels)

Loss rates from 2-4%/yr at district and provincial scale

Patchy!

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M A L AW IM A L A W I

Z I M B A BW EZ I M B A BW E

G A Z AG A Z A I N H A M B A N EI N H A M B A N E

M A N I C AM A N I C A

N A M P U L AN A M P U L AN I A S S AN I A S S A

S O F A L AS O F A L A

T E T ET E T E

Z A M B E Z I AZ A M B E Z I A

TETE

CHIMOIO

BEIRA

QUELIMANE

Biomass lossArea affected over 3 years

20%

0%

McNicol et al, in prep

Biom

ass l

oss (

area

affe

cted

ove

r 3 y

ears

, %)

2. Causes of land cover change

❖ ingredient 2: data on which activities lead to biomass loss

2a 2b 2c

1a 1b 1c

3a 3b 3c

2a 2b 2c

1a 1b 1c

3a 3b 3c

2a 2b 2c

1a 1b 1c

3a 3b 3c

2a 2b 2c

1a 1b 1c

3a 3b 3c

2a 2b 2c

1a 1b 1c

3a 3b 3c

bar = 500 m

Radar change map high-res optical ground truth

Ryan, et al (2014), Applied Geography

linking activities to biomass loss

!1# !0.8# !0.6# !0.4# !0.2# 0#

!Small!Scale!Agriculture!!

!Construc2on!ac2vi2es!!

!Charcoal!!

!Logging!!

!Large!Scale!Agriculture!!

Carbon!loss!over!3!years!(TgC)!

C!loss!by!land!use!ac2vity!❖ Small scale agriculture: 46±17% of biomass loss

❖ Biomass loss: 44±26% deforestation vs 56±33% degradation

Ryan, et al (2014), Applied Geography

3. How can this data be used to create baselines?

❖ crystal ball ?

❖ land use change modelling?

❖ “Despite the sophistication of our projection, it is slightly less accurate than if we had assumed no forest-cover change over 2000–2008” (Sloan and Pelletier, 2012, using GEOMOD)

❖ Ingredient 3: Scenarios and horizon scanning (futures research)

Scenarios built on driver-activity linkages

Activity Driver

Small scale agric Total Population

Charcoal Urban Pop

Logging Export of timber

Construction etc Rural Pop

Large scale agric Planned expansion 2000 20501950 2100

Data from INE (2012), and Global Timber (2011)

Assume cause-driver link constant

BAU (existing drivers)

New Drivers??

Lessons learned: scenarios

❖ the existing drivers are accelerating - so BAU is not linear; simple historical extrapolations are risky

❖ new driver on horizon is commercial agriculture - very uncertain rates and locations

Lessons learned: land cover change❖ existing land cover data is not v.

informative - esp re degradation❖ Currently used deforestation

rates (0.7% for Manica) are much lower than observed biomass loss rates (2.8±1.9%)

❖ degradation accounts for around ~50% of biomass loss

❖ patchy nature of land cover change means wall-to-wall mapping is needed

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M A L AW IM A L A W I

Z I M B A BW EZ I M B A BW E

G A Z AG A Z A I N H A M B A N EI N H A M B A N E

M A N I C AM A N I C A

N A M P U L AN A M P U L AN I A S S AN I A S S A

S O F A L AS O F A L A

T E T ET E T E

Z A M B E Z I AZ A M B E Z I A

TETE

CHIMOIO

BEIRA

QUELIMANE

Biomass lossArea affected over 3 years

20%

0%

casey.ryan@ed.ac.uk

❖ all of the TREDD team, for fruitful discussions and collaborations

❖ PhD students: Yaqing Gou, Daniel Jones

❖ Researchers: Iain McNicol, Neha Joshi, Nick Berry

❖ Funders: JAXA, ESA, IIED, NORAD, Norwegian embassy in Maputo

Thanks!

Hill, …, & Ryan (2013)

ground-based plus radar

global maps

Metric that combines deforestation and

degradation:

AL, the area that has lost >50% biomass in 3

years (1km2 pixels)

Loss rates from 2-4%/yr

Patchy!

Large assumption!Change in urban

pop (2007-10)

Biomass lost due to charcoal (2007-10)

Change in urban pop (2010-20)

Biomass lost due to charcoal (2010-20)

=

etc for all drivers....

5.33.8

7.2

Loss, TgC (2010-2020)

Cause - driver linkages held constant

Cause-driver linkages modified

Ryan, et al (2014), Applied Geography