Mapping Soil and Ecosystem Health in Africa

1. Mapping Soil and
Ecosystem Health in the Land Degradation
Africa Surveillance Framework
(LDSF)
Tor-G. Vågen
World Agroforestry Centre (ICRAF), Nairobi, KENYA
Tuesday, April 12, 2011

2. Land degradation has implications beyond the land
Soahany, Madagascar
Tuesday, April 12, 2011

3. Since landscapes are known to exhibit
hierarchically scaled patterns,
a desirable property of
landscape models
is that they simulate or predict
patterns at different scales
Tuesday, April 12, 2011

4. Survey Sampling
by a survey we mean the process of measuring characteristics of some or all members of an actual population
-
the purpose of which is to make quantitative generalizations about the population as a whole, or its subpopulations (or in
some cases its super-populations)
Probability sampling Non-probability sampling
random systematic stratified convenience judgement quota snowball
sampling sampling sampling sampling sampling sampling sampling
purest form, but reduces sampling
with very large error by first
populations pool simple, also stratifying and
tends to become referred to as the then applying may be used in the nonprobability
biased Nth name selection random sampling exploratory phase equivalent of
technique of research stratified
sampling. first
stratification then
convenience or
judgement
sampling of strata
Tuesday, April 12, 2011

5. AfSIS Sentinel Sites
Probability sampling approach.
Stratified random sample of
African landscapes.
Built on the Land Degradation
Surveillance Framework (LDSF).
Unbiased sample of landscapes
across sub-Saharan Africa.
Initially (“phase I”) 60 sentinel
sites and 60 alternate sites.
Target in this phase - 60 sites
characterized and sampled.
Tuesday, April 12, 2011

6. AfSIS Sentinel Sites
Site = 100 km 2
Cluster = 1 km2
Plot 1
Plot = 0.1 ha
Sub-plot = 0.01 ha
Tuesday, April 12, 2011

7. The AfSIS Objective 3 team
Tuesday, April 12, 2011

8. AfSIS Sentinel Site Surveys
50
2011
38
20000 2010 25
17500 2011 13
15000 2009 0
Sites sampled
12500 2011
10000
7500 2010
2011
5000
2010
2500 2010
2009
Plots sampled 2009
NIR library 2009 2010 2011
MIR library 2009
Reference analysis
Tuesday, April 12, 2011

9. AfSIS Sentinel Sites
baselines at landscape scale
Tuesday, April 12, 2011

10. AfSIS Sentinel Site baseline information
2000 Site averages
Kontela Infiltration testing 2000 Average curves for areas with/
TRUE
Chica_b without root-depth restrictions FALSE
Mbinga (TRUE/FALSE)
1500 1500
1000 1000
IR
IR
500 500
0 0
0 50 100 150 200 0 50 100 150 200
Time Time
2000 2000 Average curves for areas with
Average curves for cultivated (1) 1 TRUE
0 dense woody cover (>40%) FALSE
and natural/semi-natural areas (0)
1500 1500
1000 1000
IR
IR
500 500
0 0
0 50 100 150 200 0 50 100 150 200
Time Time
Tuesday, April 12, 2011

11. IR spectroscopy of soils
Regional network of NIR
MPA (NIR) spectrometer in Arusha
spectral laboratories and
spectral libraries
Nairobi
MPA (NIR) spectrometer in Bamako Construction of IR lab in Lilongwe
NIR training, Arusha Field testing of new spectrometer
Tuesday, April 12, 2011

12. IR spectroscopy of soils
Bukwaya Kisongo Chinyanghuku
1.2
1.2
1.2
1.0
1.0
1.0
0.8
0.8
0.8
Absorbance
Absorbance
Absorbance
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
4000 5000 6000 7000 8000 4000 5000 6000 7000 8000 4000 5000 6000 7000 8000
Wavelength (1/cm) Wavelength (1/cm) Wavelength (1/cm)
Kiberashi Pandambili Mbinga
1.2
1.2
1.2
1.0
1.0
1.0
0.8
0.8
0.8
Absorbance
Absorbance
Absorbance
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
4000 5000 6000 7000 8000 4000 5000 6000 7000 8000 4000 5000 6000 7000 8000
Wavelength (1/cm) Wavelength (1/cm) Wavelength (1/cm)
Tuesday, April 12, 2011

13. IR spectroscopy
has a wide range of applications, not limited to soils
Baboon
10
Black Rhino
10
Buffalo
11
Bush buck
12
Cape Hare
10
Elephant
17
Giant Forest
Hog
10
Hyena
5
Leopard
2
Mongoose
15
Reedbuck
10
Suni
2
Unknown
3
Warthog
17
Water buck
9
Zebra
12
Partner: KWS
Tuesday, April 12, 2011

14. AfSIS database structure
Tuesday, April 12, 2011

15. Soil analyses (Nairobi)
Tuesday, April 12, 2011

16. Scientific workflows
Scalability. Parallel execution on
multi-core systems
Simple extensibility via
a well-defined API for Command line version
plugin extensions for "headless" batch
executions
R integration
Mining of NIR and MIR spectral data
Classification
Clustering
Processing and development of models from MIR spectra
Predictive models
Meta workflows (e.g. cross validation)
Data preprocessing
Databases (data management)
Reporting
Cluster execution
Data management
Sentinel site baselines
Tuesday, April 12, 2011

17. Development of prediction models for soil organic
carbon (SOC) using scientific workflows and R
Tuesday, April 12, 2011

18. Mapping soil carbon
Ol Lentille and Kipsing, northern Laikipia, Kenya
Tuesday, April 12, 2011

19. Developing carbon baselines for Mt Kenya
Partners:
KEFRI and KWS
Tuesday, April 12, 2011

20. Classification models for predicting land degradation
risk factors based on NIR/MIR spectral libraries
Tuesday, April 12, 2011

21. Clustering of soil spectra for development of
indices of soil condition
Tuesday, April 12, 2011

22. Mapping soil condition
Sasumua watershed, South Kinangop, Kenya
Tuesday, April 12, 2011

23. Automated reporting on soil properties
soil chemical and physical reference values
Tuesday, April 12, 2011

24. Documentation of AfSIS / LDSF methods and
guidelines for implementation
Tuesday, April 12, 2011

25. Documentation of AfSIS / LDSF methods and
guidelines for implementation
“Toolkits”
sentinel site randomization / modeling / ++
Tuesday, April 12, 2011

26. Processing of satellite imagery
GLS 2000 GLS 2005
and later imagery
Tuesday, April 12, 2011

27. Filled DEM Slope Hydrology
Satellite images and other
spatial covariates
Aspect Specific Wetness
catchment Index
area
Tuesday, April 12, 2011

28. Mapping land cover / vegetation
Thematic layers;
• De-vegetation to enhance soil
background signal
• Soil adjusted vegetation index
• Terrain corrections
• Forest index calculations
• Water index calculations
• Automatic generation of water masks
• Automatic cloud masking
Statistically derived;
• Tree density
Terrain-corrected vegetation index (GRUVI) map
Kwadihombo - north of Morogoro, Tanzania
Tuesday, April 12, 2011

29. Mapping land cover and land use
Tanzania
p(Cultivated)
Tuesday, April 12, 2011

30. Modeling land degradation risk factors
and crop performance
Tuesday, April 12, 2011

31. Modeling land degradation risk factors
and crop performance
Co-locating trials at cluster level Relating maps to crop performance
Kiberashi Sentinel Site, Tanzania
Percent of Total
10
5
0
1000 1100 1200 1300 1400
Elevation (m)
Tuesday, April 12, 2011

32. Modeling land degradation risk factors
and crop performance
Tuesday, April 12, 2011

33. Modeling land degradation risk
factors and crop growth response Presence / absence of trees
Presence / absence of erosion
Presence / absence of
root-depth restrictions
Kiberashi sentinel site (Tanzania)
Thuchila sentinel site (Malawi)
Tuesday, April 12, 2011

34. Mapping eroded landscapes
Kiberashi sentinel site (Tanzania)
1987 (left); 2006 (right)
Tuesday, April 12, 2011

35. Mapping eroded landscapes
Yij ! Bernoulli(pij)
logit(pij) = µ+xij!+Vi
Vi ! iid N(0,"2)
Yij indicates presence/absence of
for example erosion in the ith site
and the jth cluster
Mt. Meru / Arusha / Moshi, Tanzania
Tuesday, April 12, 2011

36. ASANTE!
(thank you!)
Tuesday, April 12, 2011