A presentation by Casey Ryan, from the University of Edinburgh, at a workshop held in Paris from Thursday, 3 December to Friday, 4 December during the 21st Conference of the Parties (COP21). The event organised by the International Institute for Environment and Development aimed to share the findings of its research to inform a wider debate on how REDD+ is contributing to addressing the drivers of land use and land use change. The presentation focused on land cover change and creating baselines. More details: http://www.iied.org/redd-paris-what-could-be-it-for-people-forests

1. 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

2. The ingredients of a useful baseline
1. 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?
Project
A
B
Baseline
C

3. Historical land cover information
❖ ingredient 1: trend in woody carbon stocks over time

4. 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 prep
Deforestationrate(%)
FAO FRA Hansen et al radar-based

5. 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 radar
Ryan et al, 2012 Glob. Change Biol.
Radar backscatter (m2/m2)
woodybiomass
groundmeasurement
tC/ha

9. 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)

10. 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
0 0.50.25
Kilometers
Change in biomass
(2010 as % of 2007)
200 %
0 %
100 %
0 0.50.25
Kilometers
Change in biomass
(2010 as % of 2007)
200 %
0 %
100 %

11. 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

12. 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 A W IM A L A W I
Z I M B A B W EZ I M B A B W 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 loss
Area affected over 3 years
20%
0%

13. McNicol et al, in prep
Biomassloss(areaaffectedover3years,%)

14. 2. Causes of land cover change
❖ ingredient 2: data on which activities lead to biomass
loss

15. 2a 2b 2c
1a 1b 1c
3a 3b 3c
2a 2b 2c
1a 1b 1c
3a 3b 3c
2a 2b 2c
1a 1b 1c
2a 2b 2c
1a 1b 1c
2a 2b 2c
1a 1b 1c
bar = 500 m
Radar change map high-res optical ground truth
Ryan, et al (2014),
Applied Geography

16. 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

17. 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)

18. 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)

19. Assume cause-driver link constant
BAU
(existing drivers)
New
Drivers??

20. 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

21. 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 A W IM A L A W I
Z I M B A B W EZ I M B A B W 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 loss
Area affected over 3 years
20%
0%

22. 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!

23. Hill, …, & Ryan (2013)
ground-based
plus radar
global maps

24. 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!

25. 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....

26. 5.3
3.8
7.2
Loss, TgC
(2010-2020)
Cause - driver linkages held constant
Cause-driver linkages modified
Ryan, et al (2014),
Applied Geography