Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Land Health Surveillance Information for decision making
1. Land Health Surveillance
Information for decision making
Keith D Shepherd, Markus G Walsh, Ermias Betemariam
Remote Sensing – Beyond Images
Hotel Sevilla Palace, Mexico City, 14-15 December 2013
2. Surveillance Science Principles
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Define target population/area
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Measure frequency of problems and associated risk factors in populations
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Sample units
Probability sampling
Standardized measurement protocols
Case definitions
Rapid screening tests
Risk quantification
Operational surveillance systems built into policy and practice
UNEP. 2012. Land Health Surveillance: An Evidence-Based
Approach to Land Ecosystem Management. Illustrated with a Case Study in the West Africa Sahel.
United Nations Environment Programme, Nairobi.
http://www.unep.org/dewa/Portals/67/pdf/LHS_Report_lowres.pdf
Shepherd KD and Walsh MG (2007) Infrared spectroscopy—enabling an evidence-based diagnostic
surveillance approach to agricultural and environmental management in developing countries. Journal
of Near Infrared Spectroscopy 15: 1-19.
3. Land Health Surveillance
Sentinel sites
Randomized sampling schemes
Consistent field
protocol
Coupling with
Prevalence, Risk factors, Digital mapping remote sensing
Soil spectroscopy
4. AfSIS
✓60 primary sentinel sites
➡ 9,600 sampling plots
➡ 19,200 “standard” soil samples
➡ ~ 38,000 soil spectra
➡ 3,000 infiltration tests
➡ ~ 1,000 Landsat scenes
➡ ~ 16 TB of remote sensing data to date
5. Soil infrared spectra
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Mineral composition
Iron oxides
Organic matter
Water (hydration,
hygroscopic, free)
• Carbonates
• Soluble salts
• Particle size
distribution
Functional
properties
1 = Fingerprint region e.g Si-O-Si stretching/bending
2 = Double-bond region (e.g. C=O, C=C, C=N)
3 = Triple bond (e.g. C≡C, C≡N)
4 = X–H stretching (e.g. O–H stretching)
NIR = Overtones; key features clay lattice and water OH; SOM affects overall
shape
6. Infrared spectroscopy
Dispersive VNIR
Handheld MIR ?
FT-NIR
FT-MIR Robotic
Mobile phone cameras
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FT-MIR Portable
Shepherd KD and Walsh MG. (2002) Development
of reflectance spectral libraries for characterization of
soil properties. Soil Science Society of America
Journal 66:988-998.
Brown D, Shepherd KD, Walsh MG (2006). Global
soil characterization using a VNIR diffuse reflectance
library and boosted regression trees. Geoderma
132:273–290.
Terhoeven-Urselmans T, Vagen T-G, Spaargaren O,
Shepherd KD. 2010. Prediction of soil fertility
properties from a globally distributed soil midinfrared spectral library. Soil Sci. Soc. Am. J.
74:1792–1799
8. • Submit batch of spectra online
• Uncertainties estimated for each
sample
• Samples with large error submitted
for reference analysis
• Calibration models improve as more
samples submitted
• All subscribers benefit
18. Global-Continental Monitoring Systems
Applications
CGIAR pan-tropical sites
Vital signs
AfSIS
Vital Signs
Regional Information Systems
National surveillance
systems
Private sector soil testing
Ethiopia, Nigeria
Project baselines
SLM Cameroon
Parklands Malawi
Rangelands E/W Africa
Cocoa - CDI
MICCA EAfrica
19. What is the decision?
Decisions before Data
• Review of the Evidence on Indicators, Metrics and Monitoring
Systems.
http://r4d.dfid.gov.uk/output/192446/default.aspx
• A Survey and Analysis of the Data Requirements for Stakeholders
in African Agriculture
http://r4d.dfid.gov.uk/Output/193813/Default.aspx
• Government-level programmatic decisions
(fertilizer supply/blending; liming programmes)
• Farmer or local provider decisions (what
fertiliser to apply, where, when)
20. Explicit decision modelling
dealers.
• Uncertainties (risks)
represented
• Value of Information Analysis
• Preferences of stakeholders
Influence diagram
“Make fertilizer recommendations” use case
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Workflows that use consistent data model
Free and open source computer software
Automated analyses of remote sensing and market time series
Maps, monitoring and decision analysis products and services for Africa
Deployable via web and cellular/mobile services
Products to CKW’s, farmer groups, land management policy-makers,
government agencies and agro-input dealers
21. Outputs
Probabilistic impact
projections
Identification of high-value
variables
Aquifer size after 70 years of
abstraction (% of original)
Replenishment
Irrigation growth
Initial irrigated area
Water use per hectare
Aquifer size
Natural water use
Importance threshold
22. Smart data – Smart Decisions
• Inclusion of uncertain variables allows truly holistic
impact assessments
• Efficient way of organizing existing knowledge
• High information value variables are almost always not
those typically measured
• Identifies important metrics for monitoring
• Provides accumulated evidence for impact attribution
• Community of practice & capacity building in decision
analysis under uncertainty
Notas del editor
This is the overall AfSIS schema. At the top is a systematic grid system to which all observations can be spatially referenced. From the left we have georeferenced legacy data, such as soil profile data and newly collected field, spectral and lab reference data. Then we have our remote sensing layers, and planned is greater use of UAVs, or drones, for high resolution work.Spectral predictions can be combined with spatial prediction (e.g. using any residual spatial correlation) as well as modelling to remote sensing layers. The outputs of this are digital soil maps and prediction services. Our current emphasis is on feeding spectral predictions and digital soil maps directly into specific development decisions, and developing applications around this.
AfSIS is in the process of producing Africa wide maps based on the newly collected data, updating the maps already generated from the legacy soil profile data, which are available on the AfSIS web site.We have also been helping governments establish national soil surveillance systems. Ethiopia is the most advanced. We proposed a national level baseline based on sampling at latitude and longitude intersection points (blue points). And this work has started. However the government wanted to fast track characterization of soil fertility in areas that were high priority for food production in specific woredas (green areas). The red crosses are points where observations of whether land is cultivated have been made by volunteers under the IIASA Geowiki scheme.
This is an example of the type of product that is being generated. Here we see the areas with high probability of strong soil acidity, which map out very distinctly and where the government can now target liming programmes.
Use case(s) described in this document can be implemented as workflows that use consistent data models (see: http://en.wikipedia.org/wiki/Data_model) and free and open source computer software (see e.g.: http://www.r-project.org & http://grass.osgeo.org) supported by largely automated analyses of remote sensing and market time series that will produce maps, monitoring and decision analysis products and services for the cropland areas of Africa. Once the envisaged system has been developed, it should be deployable via web and cellular/mobile services that will provide access to data and information products to CKW’s, farmer groups, land management policy-makers, government agencies and agro-input