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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%
❖ 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