mapping soil and ecosystem health in africa
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Mapping Soil and Ecosystem Health in
Africa
Tor-G. VågenWorld Agroforestry Centre (ICRAF), Nairobi, KENYA
the Land Degradation Surveillance Framework
(LDSF)
Tuesday, April 12, 2011
Soahany, Madagascar
Land degradation has implications beyond the land
Tuesday, April 12, 2011
Since landscapes are known to exhibit hierarchically scaled patterns,
a desirable property of landscape models
is that they simulate or predictpatterns at different scales
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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)
Survey Sampling
Probability sampling Non-probability sampling
random sampling
systematic sampling
stratified sampling
convenience sampling
judgement sampling
quota sampling
snowball sampling
purest form, but with very large
populations pool tends to become
biased
reduces sampling error by first
stratifying and then applying
random sampling
simple, also referred to as the
Nth name selection technique
the nonprobability equivalent of
stratified sampling. first
stratification then convenience or
judgement sampling of strata
may be used in exploratory phase
of research
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AfSIS Sentinel SitesProbability 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.
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AfSIS Sentinel Sites
Sub-plot = 0.01 ha
Site = 100 km2
Cluster = 1 km2
Plot = 0.1 ha
Plot 1
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The AfSIS Objective 3 team
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Sites sampled
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Plots sampled
NIR library
MIR library
Reference analysis
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AfSIS Sentinel Site Surveys
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AfSIS Sentinel Sitesbaselines at landscape scale
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Time
IR
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2000KontelaChica_bMbinga
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Site averages Average curves for areas with/without root-depth restrictions (TRUE/FALSE)
AfSIS Sentinel Site baseline informationInfiltration testing
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Average curves for cultivated (1) and natural/semi-natural areas (0)
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Average curves for areas with dense woody cover (>40%)
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IR spectroscopy of soils
Nairobi
Regional network of NIR spectral laboratories and
spectral libraries
NIR training, Arusha
MPA (NIR) spectrometer in Bamako
Field testing of new spectrometer
MPA (NIR) spectrometer in Arusha
Construction of IR lab in Lilongwe
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IR spectroscopy of soils
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Bukwaya
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Absorbance
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Chinyanghuku
Wavelength (1/cm)
Absorbance
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Kiberashi
Wavelength (1/cm)
Absorbance
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Kisongo
Wavelength (1/cm)
Absorbance
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Mbinga
Wavelength (1/cm)
Absorbance
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Pandambili
Wavelength (1/cm)
Absorbance
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IR spectroscopyhas a wide range of applications, not limited to soils
Baboon10Black Rhino10Buffalo11Bush buck12Cape Hare10Elephant17Giant Forest Hog10Hyena5Leopard2Mongoose15Reedbuck10Suni2Unknown3Warthog17Water buck9Zebra12
Partner: KWSTuesday, April 12, 2011
AfSIS database structure
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Soil analyses (Nairobi)
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Scientific workflows
Processing and development of models from MIR spectra
Data managementSentinel site baselines
Scalability.
Simple extensibility via a well-defined API for plugin extensions
Parallel execution on multi-core systems
Command line version for "headless" batch executions
R integration
Mining of NIR and MIR spectral data
Classification
Clustering
Predictive models
Meta workflows (e.g. cross validation)
Data preprocessing
Databases (data management)
Reporting
Cluster execution
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Development of prediction models for soil organic carbon (SOC) using scientific workflows and R
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Mapping soil carbon
Ol Lentille and Kipsing, northern Laikipia, KenyaTuesday, April 12, 2011
Developing carbon baselines for Mt Kenya
Partners:KEFRI and KWS
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Classification models for predicting land degradation risk factors based on NIR/MIR spectral libraries
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Clustering of soil spectra for development of indices of soil condition
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Mapping soil condition
Sasumua watershed, South Kinangop, Kenya Tuesday, April 12, 2011
Automated reporting on soil properties soil chemical and physical reference values
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Documentation of AfSIS / LDSF methods and guidelines for implementation
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“Toolkits”
sentinel site randomization / modeling / ++
Documentation of AfSIS / LDSF methods and guidelines for implementation
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Processing of satellite imagery
GLS 2000 GLS 2005and later imagery
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Satellite images and other spatial covariates
Filled DEM Slope Hydrology
Aspect Specific catchment
area
Wetness Index
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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) mapKwadihombo - north of Morogoro, Tanzania
Tuesday, April 12, 2011
Mapping land cover and land useTanzania
p(Cultivated)
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Modeling land degradation risk factors and crop performance
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Co-locating trials at cluster level Relating maps to crop performance
Kiberashi Sentinel Site, Tanzania
Elevation (m)
Per
cent
of T
otal
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Modeling land degradation risk factors and crop performance
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Modeling land degradation risk factors and crop performance
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Presence / absence of erosion
Presence / absence of trees
Presence / absence of root-depth restrictions
Modeling land degradation risk factors and crop growth response
Kiberashi sentinel site (Tanzania)Thuchila sentinel site (Malawi)
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Kiberashi sentinel site (Tanzania)1987 (left); 2006 (right)
Mapping eroded landscapes
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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, TanzaniaTuesday, April 12, 2011
ASANTE!(thank you!)
Tuesday, April 12, 2011
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