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Land Health SurveillanceInformation for decision making
Remote Sensing – Beyond Images
Hotel Sevilla Palace, Mexico City, 14-15 December 2013
Keith D Shepherd, Markus G Walsh, Ermias Betemariam
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Surveillance Science Principles• Define target population/area
• Measure frequency of problems and associated risk factors in populations• 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-BasedApproach 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.
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Land Health Surveillance
Consistent field protocol
Soil spectroscopy
Coupling with remote sensingPrevalence, Risk factors, Digital
mapping
Sentinel sites Randomized sampling schemes
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✓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
AfSIS
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Soil infrared spectra
1 = Fingerprint region e.g Si-O-Si stretching/bending2 = 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
• Mineral composition
• Iron oxides• Organic matter• Water (hydration,
hygroscopic, free)• Carbonates• Soluble salts• Particle size
distribution Functional properties
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Infrared spectroscopy Dispersive VNIR FT-NIR FT-MIR Robotic FT-MIR Portable
Handheld MIR ?Mobile phone cameras ?
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 mid-infrared spectral library. Soil Sci. Soc. Am. J. 74:1792–1799
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Spectral prediction performance
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• 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
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Spectral Lab Network
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Soil-Plant Spectral Diagnostics Lab
• 500 visitors/yr again
• 338 instruction
• 13 PhD, 4 MSc training
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Markus Walsh
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Soil property maps of Africa at 1 km
Legacy soil profiles12,000
locations
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Probability topsoil pH < 5.5 ... very acid soils
prob(pH < 5.5)Africa Soil
Information Servicewww.africasoils.net
Markus Walsh
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Markus Walsh
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ApplicationsVital signs
Cocoa - CDIParklands Malawi
National surveillance systems
Regional Information Systems
Project baselines
Ethiopia, Nigeria
Rangelands E/W AfricaSLM Cameroon MICCA EAfrica
Global-Continental Monitoring Systems
CGIAR pan-tropical sites
AfSIS
Private sector soil testing
Vital Signs
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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 Agriculturehttp://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)
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Explicit decision modelling
dealers.
• Uncertainties (risks) represented
• Value of Information Analysis
• Preferences of stakeholders
“Make fertilizer recommendations” use case
Influence diagram
• 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
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Outputs
Replenishment
Irrigation growth
Initial irrigated area
Water use per hectare
Aquifer size
Natural water use
Importance threshold
Identification of high-value variables
Probabilistic impact projections
Aquifer size after 70 years of abstraction (% of original)
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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