Detecting desertification in savanna systems RJ (Bob) Scholes Council for Scientific and Industrial Research Natural Resources and Environment Pretoria, South Africa bscholes@csir.co.za
What is desertification? [Hyperarid] Arid Semiarid Subhumid Desertification is degradation in dry lands United Nations Convention on Combatting Desertification
Global distribution of savannas (basically the same as the summer-rainfall drylands)
Savannas are mixed tree-grass systems
Some facts about savannas World s largest land biome (~12%) Second largest Net Primary Productivity and carbon store (16.8 x 10 15 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)
Desertification sucks! The single environmental issue affecting the livelihoods of the largest number of people worldwide 41% of Earth s land surface 2 billion inhabitants 90% in developing countries
What is degradation? Does not spontaneously return to historical average within a reasonable time when you reduce the cause of change Degradation is a persistent decrease in the capacity of an ecosystem to deliver ecosystem services The benefits that people get from ecosystems -Millennium Ecosystem Assessment
Ecosystem Services Millennium 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
Summary so far To detect desertification, we are looking for a change in state in savanna ecosystems
Ecohydrological state change (typically on silty soils) - runoff rainfall infiltration + + soil water + plant cover + plant production + - herbivory
Nutrient state change (typically on less-fertile, erodible soils eg sandy loams or loess) - + harvest export from the landscape + rainfall nutrients - plant production + - - + + fire - - - herbivory soil erosion - vegetation and litter cover
Change in the rain use efficiency Measure of plant production Eg ΣFAPAR or similar undegraded Decrease in slope or position degraded Rainfall in growing season
Production in water-limited pastoral systems Tree growth Rainfall soils competition Grass growth Animal production
An electrical analog What controls the brightness of the lamp? productivity Sun nutrients water
In pulsed systems, water availability controls the duration of growth opportunity
Linear relation between grass production and rainfall Grass AG NPP (g/m2/y) 500 400 300 200 100 0 Slope= rain use efficiency Intercept = index of un productive water loss 0 200 400 600 800 1000 Annual rainfall clay soil sand soil The inverse texture hypothesis of Noy-Meir
Interannual variability of grass production is higher on clays than sandy soils 500 Grass AG NPP (g/m2/y) 400 300 200 100 σclay σsand clay soil sand soil 0 σrain 0 200 400 600 800 1000 Annual rainfall
Rain Use Efficiency (g/m 2 /mm) 12 10 8 6 4 2 0 50 60 70 80 90 100 Sand % Rain use efficiency (kg/ha/mm)
Intercept dependent on soil water holding capacity 2000 Intercept of AGGNPP vs Rain (kg/ha/y) 1500 1000 500 0-500 -1000-1500 -2000-2500 0 5 10 15-3000 Rain use efficiency (kg/ha/mm)
Changes in vegetation composition (to species less productive or less useful) + Woody plants - + - browsers rain + - grass fire + + - grazers
Tree mass has a non-linear inverse effect on grass production Annual grass aboveground production (g/m2/y) 1200 900 600 300 0 Walker et al Beale Donaldson & Kelk Aucamp et al 0 0.2 0.4 0.6 0.8 1 Fraction of maximum tree quantity Scholes RJ 2003 Convex relationships in ecosystems containing trees and grass. Environment and Resource Economics 26, 559-574
Graphical stability analysis Open, grassy parkland Grass production Low resilience g=0 w=0 high resilience Tree cover Dense tree cover thicket bush encroached Scholes RJ 2003 Convex relationships in ecosystems containing trees and grass. Environment and Resource Economics 26, 559-574
The actual tree-grass interaction is much more complex seeds Browsers Grazers Flame height Slows growth Supresses Reduces Nutrients Grass biomass Flame height = f(grass fuel) Fire frequency Rainfall
Relation between rainfall and quantity of trees in savannas Sankaran et al 2005 Determinants of woody cover in African savannas Nature 438, 846-9 Upward shift detected by albedo change or phenology change
Vegetation metrics Remote sensing Ecological SAR interferometry biomass LIDAR time-to-pulse height greeness indices BDRF Albedo NDVI SI EVI etc spectral reflectance In key bands intra- or interseasonal variability degradation FAPAR % cover phenology LAI basal area structural ecosystem type & area NPP functional ET spectral features compositional
Greeness Vegetation Indices eg NDVI= (Red-NIR)/(Red+NIR) and many others (SI,SAVI,EVI etc) Stable reference point reflectance Chlorophyll absorption Red = reflectance @ 680nm NIR = reflectance @ 900 nm 400 1000 Wavelength (nm)
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=I 0 e 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 modelling productivity
FAPAR Fraction of Absorbed Photosynthetically-Active Radiation intercepted by the vegetation canopy (range 0-1) GPP= ε Σ(PAR*FPAR) *f (stress,nutrients) NPP=GPP-R a GPP = Gross Primary productivity (g/m 2 ) (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/m 2 ) R a = respiration by plants (~50% of GPP)
Seasonal pattern of LAI Leaf area index (m2/m2) 2.5 2.0 1.5 1.0 0.5 0.0 1 2 3 4 5 6 7 8 9 10 11 12 wet season Tree LAI Grass LAI All LAI Month of the year Skukuza 30% canopy wet season
Unscrambling the tree and grass signals 0.40 2001 0.40 2002 0.35 0.35 0.30 0.30 0.25 0.25 Greenness 0.20 0.15 Greenness 0.20 0.15 0.10 0.10 0.05 0.05 0.00 0.00-0.05 28-Jul-01 16-Sep-01 5-Nov-01 25-Dec-01 13-Feb-02 4-Apr-02 24-May-02 date -0.05 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
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