Detecting desertification in savanna systems · 2013-10-24 · Detecting desertification in savanna...
Transcript of Detecting desertification in savanna systems · 2013-10-24 · Detecting desertification in savanna...
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Detecting desertificationin savanna systems
RJ (Bob) ScholesCouncil for Scientific and Industrial Research
Natural Resources and EnvironmentPretoria, South Africa
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What is desertification?
[Hyperarid]Arid
SemiaridSubhumid
Desertification is degradation in dry lands
United Nations Convention on Combatting Desertification
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Global distribution of savannas(basically the same as the summer-rainfall drylands)
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Savannas are mixed tree-grass systems
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Some facts about savannas
• World’s largest land biome (~12%)• Second largest Net Primary Productivity and
carbon store (16.8 x 1015 gC/y)• Large ‘natural’ impact on atmosphere through fire,
carbon cycle and dust• Home to ~1 billion people; source of food and
energy to most of them• Centre of biodiversity (7000 species in Africa
alone)
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Desertification sucks!The single environmental issue affecting the livelihoods of
the largest number of people worldwide
41% of Earth’s land surface 2 billion inhabitants90% in developing countries
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What is degradation?
Degradation is a persistent decrease in the capacity of an ecosystem to deliver ecosystem services
Does not spontaneously return to historical average within a reasonable time when you
reduce the cause of change
The benefits that people get from ecosystems
-Millennium Ecosystem Assessment
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Ecosystem ServicesMillennium Ecosystem Assessment Classification
Provisioning
•Crops•Grazing and livestock
•Timber and fuel•Fisheries
•Water•Medicine
Regulating
•Climate•Floods
•Pest regulation •Disease control
Cultural
•Aesthetic•Recreation
•Amenity•Spiritual
•Educational
Supporting• Net Primary Production
• Nutrient cycling
Biodiversity: the variety of Life on Earth
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Summary so far…
To detect desertification, we are looking for a change in state in savanna ecosystems
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Ecohydrological state change(typically on silty soils)
runoff-
rainfall infiltration+
+ soil water+
plant productionplant cover++
herbivory-
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Nutrient state change(typically on less-fertile, erodible soils eg sandy loams or loess)
rainfall plant production
vegetation andlitter cover
soil erosion
+
herbivory
-
+
-
fire
-
-
nutrients
harvest+
--
export from thelandscape
+
-
+
-
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Change in the rain use efficiency
undegraded
degraded
Decrease in slopeor position
Measure of plant productionEg ΣFAPAR or similar
Rainfall in growing season
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Production in water-limited pastoral systems
Tree growth
Rainfall
Grass growth
competitionAnimal
productionsoils
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An electrical analogWhat controls the brightness of the lamp?
productivity
Sun nutrients
water
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In pulsed systems, water availability controls the duration of growth opportunity
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Linear relation betweengrass production and rainfall
0
100
200
300
400
500
0 200 400 600 800 1000
Annual rainfall
Gra
ss A
G N
PP
(g/m
2/y)
clay soilsand soil
The ‘inverse texturehypothesis’ ofNoy-Meir
Slope=‘rain use efficiency’
Intercept = index of unproductive water loss
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Interannual variability of grass production is higher on clays than
sandy soils
0
100
200
300
400
500
0 200 400 600 800 1000
Annual rainfall
Gra
ss A
G N
PP
(g/m
2/y)
clay soilsand soil
σclay
σsand
σrain
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Rain Use Efficiency(g/m2/mm)
0
2
4
6
8
10
12
50 60 70 80 90 100
Sand %
Rai
n us
e ef
ficie
ncy
(kg/
ha/m
m)
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Intercept dependent on soil water holding capacity
-3000
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
0 5 10 15
Rain use efficiency (kg/ha/mm)
Inte
rcep
t of A
GG
NPP
vs
Rai
n
(kg/
ha/y
)
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Changes in vegetation composition(to species less productive or less useful)
Woodyplants
grass grazers
browsers
firerain
+
+
-+
--
+
+
-
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Tree mass has a non-linear inverse effect on grass production
0
300
600
900
1200
0 0.2 0.4 0.6 0.8 1Fraction of maximum tree quantity
Ann
ual g
rass
abo
vegr
ound
pr
oduc
tion
(g/m
2/y)
Walker et alBealeDonaldson & KelkAucamp et al
Scholes RJ 2003 Convex relationships in ecosystems containing trees and grass. Environment and Resource Economics 26, 559-574
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Graphical stability analysis
Tree cover
Gra
ss p
rodu
ctio
nLow resilience
high resilience
Open, grassy ‘parkland’
∆w=0
∆g=0
Dense tree cover thicket‘bush encroached’
Scholes RJ 2003 Convex relationships in ecosystems containing trees and grass. Environment and Resource Economics 26, 559-574
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The actual tree-grass interaction is much more complex
Flame height
Supresses
Grass biomass
Flame height = f(grass fuel)
GrazersBrowsers
Fire frequencyRainfall
ReducesSlows growth
seeds
Nutrients
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Relation between rainfall and quantity of trees in savannasSankaran et al 2005 Determinants of woody cover in African savannas Nature 438, 846-9
Upward shift detectedby albedo changeor phenology change
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Vegetation metricsRemote sensing Ecological
SAR interferometry biomass
% cover
FAPAR NPP
ETLAI
phenology
height
Albedobasal area
NDVISIEVIetc
intra- or inter-seasonalvariability
LIDAR time-to-pulse
ecosystemtype & area
‘degradation’
‘functional’spectral reflectanceIn key bands
BDRF ‘structural’
greenessindices
‘compositional’spectral features
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‘Greeness’ Vegetation Indices
eg NDVI= (Red-NIR)/(Red+NIR)and many others (SI,SAVI,EVI etc)
Wavelength (nm)400 1000
Chlorophyllabsorption
Stable reference point
Red = reflectance @ 680nmNIR = reflectance @ 900 nm
reflectance
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Leaf Area Index• The one-sided leaf area per unit of ground area• Can go down to near 0 in the dry season• Theoretical limit about 6
I=I0e –K*LAI (Beer’s Law)therefore for LAI=6, k=0.5: I=5% of Io
• Hard to measure in forests due to saturation above ~ 3
• Seldom above 3 in drylands– Typically ~1 in savannas
• Useful for estimating evaporation and interception, but FPAR has fewer assumptions for modellingproductivity
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FAPARFraction of Absorbed Photosynthetically-Active Radiation
intercepted by the vegetation canopy (range 0-1)
GPP= ε Σ(PAR*FPAR)
NPP=GPP-Ra
*f (stress,nutrients)
GPP = Gross Primary productivity (g/m2) (dry matter = 42% C)ε = Radiation use efficiency (gC/MJ),
species dependent (0.3-0.8 gC/MJ)NPP = Net Primary Productivity (g/m2) PAR = Photosynthetically-active radiation (400-700 nm) (W/m2)Ra = respiration by plants (~50% of GPP)
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Seasonal pattern of LAI
0.0
0.5
1.0
1.5
2.0
2.5
1 2 3 4 5 6 7 8 9 10 11 12
Month of the year
Leaf
are
a in
dex
(m2/
m2)
Tree LAIGrass LAIAll LAI
wet season wet season
Skukuza30% canopy
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Unscrambling the tree and grass signals
2001
28-Jul-01 16-Sep-01 5-Nov-01 25-Dec-01 13-Feb-02 4-Apr-02 24-May-02
date
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Gre
enne
ss
2002
13-Jul-02 1-Sep-02 21-Oct-02 10-Dec-02 29-Jan-03 20-Mar-03 9-May-03 28-Jun-03
date
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Gre
enne
ss
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Conclusions• It is possible to define desertification in a
rigorous and operational way, via the concept of persistent loss of ecosystem services
• There are mechanisms by which state change in ecosystem service delivery can occur: desertification is a real and widespread phenomenon
• These should be detectable using remote sensing, but require additional non-remote sensing data (soil and rainfall), time series, and often the unmixing of the tree and grass signal