land health surveillance information for decision making

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

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.

Land Health Surveillance

Consistent field protocol

Soil spectroscopy

Coupling with remote sensingPrevalence, Risk factors, Digital

mapping

Sentinel sites Randomized sampling schemes

✓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

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

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

Spectral prediction performance

• 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

Spectral Lab Network

Soil-Plant Spectral Diagnostics Lab

• 500 visitors/yr again

• 338 instruction

• 13 PhD, 4 MSc training

Markus Walsh

Soil property maps of Africa at 1 km

Legacy soil profiles12,000

locations

Africa Soil Information Service

www.africasoils.net

Markus Walsh

Probability topsoil pH < 5.5 ... very acid soils

prob(pH < 5.5)Africa Soil

Information Servicewww.africasoils.net

Markus Walsh

Markus Walsh

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

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)

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

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)

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