Mapping Soil and Ecosystem Health in Africa

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


Since landscapes are known to exhibit hierarchically scaled patterns,

a desirable property of landscape models

is that they simulate or predictpatterns at different scales


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


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.


AfSIS Sentinel Sites

Sub-plot = 0.01 ha

Site = 100 km2

Cluster = 1 km2

Plot = 0.1 ha

Plot 1


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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2010 2011

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AfSIS Sentinel Site Surveys


AfSIS Sentinel Sitesbaselines at landscape scale


Time

IR

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2000KontelaChica_bMbinga

Time

IR

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Site averages Average curves for areas with/without root-depth restrictions (TRUE/FALSE)

AfSIS Sentinel Site baseline informationInfiltration testing

Time

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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%)


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


IR spectroscopy of soils

4000 5000 6000 7000 8000

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Bukwaya

Wavelength (1/cm)

Absorbance

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Chinyanghuku

Wavelength (1/cm)

Absorbance

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Kiberashi

Wavelength (1/cm)

Absorbance

4000 5000 6000 7000 80000.0

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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


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


Soil analyses (Nairobi)


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


Development of prediction models for soil organic carbon (SOC) using scientific workflows and R


Mapping soil carbon

Ol Lentille and Kipsing, northern Laikipia, KenyaTuesday, April 12, 2011

Developing carbon baselines for Mt Kenya

Partners:KEFRI and KWS


Classification models for predicting land degradation risk factors based on NIR/MIR spectral libraries


Clustering of soil spectra for development of indices of soil condition


Mapping soil condition

Sasumua watershed, South Kinangop, Kenya Tuesday, April 12, 2011

Automated reporting on soil properties soil chemical and physical reference values


Documentation of AfSIS / LDSF methods and guidelines for implementation


“Toolkits”

sentinel site randomization / modeling / ++

Documentation of AfSIS / LDSF methods and guidelines for implementation


Processing of satellite imagery

GLS 2000 GLS 2005and later imagery


Satellite images and other spatial covariates

Filled DEM Slope Hydrology

Aspect Specific catchment

area

Wetness Index


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


Mapping land cover and land useTanzania

p(Cultivated)


Modeling land degradation risk factors and crop performance


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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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)


Kiberashi sentinel site (Tanzania)1987 (left); 2006 (right)

Mapping eroded landscapes


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!)


Mapping Soil and Ecosystem Health in Africa

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