GIS and Remote Sensing for Food Security Training Course

Participants can organise spatial datasets and select appropriate coordinate reference systems for analysis.

• Vector, raster and tabular data models for agricultural analysis
• Coordinate reference systems selected using area, distance and data-source requirements
• Geographic versus projected coordinates for district and national mapping
• Geodatabases structured with feature classes, domains and naming conventions
• Attribute tables cleaned using field types, null values and coded categories
• Metadata records documented with source, date, scale, lineage and restrictions

Participants can source, assess and document datasets for food-security analysis.

• Administrative boundaries checked against authoritative versions and reporting units
• Field observations prepared with unique identifiers, coordinates and collection dates
• Crop calendars sourced from agricultural ministries, FAO and local surveys
• Rainfall and temperature datasets assessed for temporal coverage and resolution
• OpenStreetMap features reviewed for roads, markets, schools and water points
• Data quality screened through completeness, positional accuracy, consistency and timeliness

Participants can configure a QGIS project and produce readable agricultural reference maps.

• QGIS projects organised with groups, layer names and relative paths
• Symbology selected for administrative, agricultural, hydrological and infrastructure layers
• Labels configured using scale ranges, expressions and priority settings
• Map layouts built with titles, legends, scale bars and source notes
• Classification methods compared using equal interval, quantile and natural breaks
• Reference maps exported as PDF and image files

Participants can interpret satellite raster properties and select imagery for agricultural questions.

• Raster cells, bands, spatial resolution and temporal resolution explained
• Electromagnetic spectrum bands linked to vegetation, water and bare soil
• Sentinel-2 acquisition schedule, band characteristics and agricultural applications
• Landsat imagery compared with Sentinel-2 for historical crop monitoring
• Digital numbers, reflectance and surface reflectance distinguished accurately
• Cloud, haze, shadow and mixed-pixel effects recognised during interpretation

Participants can create analysis-ready satellite image stacks for a defined agricultural area.

• Areas of interest created from administrative boundaries and field polygons
• Sentinel-2 scenes filtered by date, cloud cover and tile coverage
• Cloud masks applied using scene classification layers and quality bands
• Raster bands clipped, resampled and aligned to common grid specifications
• Band composites assembled for natural colour, false colour and vegetation display
• Processing records maintained through filenames, parameters and source identifiers

Participants can calculate and interpret spectral indices for crop and water monitoring.

• NDVI calculated from near-infrared and red reflectance bands
• NDWI applied to surface-water and crop-moisture interpretation questions
• SAVI selected where sparse vegetation and exposed soil affect NDVI
• Index thresholds tested against known crop, soil and water conditions
• Colour ramps designed to avoid misleading interpretation of index values
• Vegetation index maps compared with field observations and seasonal expectations

Participants can analyse seasonal vegetation patterns and identify crop-condition anomalies.

• Seasonal NDVI profiles constructed for crops, rangelands and reference areas
• Baseline periods selected using comparable seasons and adequate historical observations
• Vegetation anomalies calculated against median, mean and percentile baselines
• Phenological stages identified from green-up, peak and senescence patterns
• Time-series charts interpreted alongside planting dates and rainfall events
• Anomaly findings qualified using cloud contamination and land-cover change checks

Participants can prepare a supervised classification workflow for land cover or crop-area mapping.

• Classification schemes defined using decision purpose, classes and minimum mapping units
• Training samples collected from imagery, field records and high-resolution reference data
• Spectral signatures compared across crops, woodland, settlements and bare ground
• Random forest classification configured with labelled samples and predictor bands
• Post-classification filtering applied to remove isolated pixels and noise
• Crop-area estimates calculated from classified raster cells and administrative boundaries

Participants can assess classification accuracy and plan ground-validation data collection.

• Reference samples designed using random, stratified and systematic sampling approaches
• Confusion matrices constructed from predicted classes and reference observations
• Producer accuracy, user accuracy and overall accuracy calculated correctly
• Kappa interpretation considered alongside class imbalance and sampling design
• Ground-truth protocols defined for coordinates, photographs and crop observations
• Accuracy limitations reported with class-specific errors and confidence considerations

Participants can combine rainfall and vegetation evidence to assess agricultural drought conditions.

• CHIRPS rainfall data aggregated by dekad, month and growing season
• Standardized Precipitation Index calculated for defined accumulation periods
• Rainfall anomalies mapped against seasonal climatology and agricultural thresholds
• Vegetation condition index derived from historical NDVI minimum and maximum values
• Drought indicators interpreted with crop stage, soil type and irrigation context
• Water-stress findings converted into monitored areas and response priorities

Participants can derive terrain variables and build transparent agricultural suitability models.

• Digital elevation models processed into slope, aspect and flow accumulation
• Slope thresholds applied to mechanisation, erosion and cultivation suitability
• Soil texture, depth and drainage layers prepared for suitability scoring
• Weighted overlay models built using documented criteria and suitability weights
• Multi-criteria decision analysis tested through alternative weighting scenarios
• Suitability maps reviewed against existing land use and local knowledge

Participants can map flood exposure and estimate affected agricultural assets using spatial overlays.

• Flood extent extracted from radar or optical imagery with quality checks
• River buffers, elevation and drainage patterns used for flood susceptibility
• Cropland, settlements and infrastructure intersected with mapped flood extent
• Affected area summaries produced by district, livelihood zone and crop type
• Pre-event and post-event imagery compared for visible agricultural damage
• Rapid-assessment maps annotated with dates, sources and confidence statements

Participants can assess physical access to markets and agricultural services using network methods.

• Road networks cleaned for connectivity, travel assumptions and route restrictions
• Travel-time surfaces constructed from roads, land cover and slope
• Cost-distance analysis applied to markets, storage sites and extension centres
• Service catchments mapped using distance thresholds and travel-time bands
• Market-access results compared with production zones and vulnerable communities
• Infrastructure priorities identified from underserved agricultural production areas

Participants can combine hazard, exposure and vulnerability layers into transparent risk maps.

• Risk models structured around hazard, exposure, vulnerability and coping capacity
• Livelihood zones linked with crop dependence, assets and seasonal hazards
• Composite indicators standardised using min-max scaling and documented directionality
• Weighted indices constructed with explicit assumptions and stakeholder review
• Priority areas ranked using thresholds, sensitivity checks and operational constraints
• Targeting maps distinguished from household-level beneficiary selection decisions

Participants can interpret climate projections and map agricultural planning implications cautiously.

• Climate normals distinguished from weather observations and seasonal forecasts
• Representative Concentration Pathways interpreted as scenario-based emissions assumptions
• Downscaled temperature and rainfall projections assessed for spatial uncertainty
• Growing-season changes analysed through onset, cessation and dry-spell indicators
• Climate exposure mapped for rainfed crops, rangelands and irrigation systems
• Adaptation options screened against climate hazards, feasibility and beneficiary needs

Participants can identify spatial patterns while avoiding common interpretation errors.

• Spatial autocorrelation explained using Moran's I and clustered observations
• Getis-Ord Gi-star hotspots mapped for production losses or service gaps
• Kernel density estimation applied to point incidents and market locations
• Modifiable areal unit problem considered when aggregating district indicators
• Ecological fallacy avoided when interpreting area-level food-security statistics
• Spatial patterns triangulated with qualitative reports and field verification

Participants can manage sensitive spatial information and document reproducible analysis workflows.

• Data-sharing agreements reviewed for ownership, access and permitted use
• Sensitive household coordinates protected through aggregation, masking and access controls
• Informed consent considerations applied to field-collected location data
• Version control conventions used for datasets, scripts and map outputs
• Standard operating procedures documented for recurring monitoring workflows
• Uncertainty statements written for imagery quality, models and data gaps

Participants can communicate geospatial findings through decision-focused maps and briefs.

• Audience needs translated into map purpose, scale and key messages
• Map layouts designed for senior decision-makers and technical reviewers
• Methods sections written with datasets, dates, assumptions and limitations
• Decision briefs structured around findings, implications, options and recommended actions
• Charts paired with maps to explain trends, comparisons and uncertainty
• Quality assurance checklists applied before publishing spatial decision products

Participants can build, review and present a complete geospatial food-security assessment for a defined area and decision.

• Assessment question scoped around a food-security decision and target area
• Spatial datasets assembled, documented and checked against analytical requirements
• Satellite indicators processed for crop condition, rainfall and hazard evidence
• Risk and accessibility analyses combined into transparent priority-area findings
• Map portfolio and decision brief reviewed using technical quality criteria
• Findings presented with assumptions, uncertainty, recommendations and implementation actions