GIS for Agroforestry Systems Training Course

Participants can structure an agroforestry GIS project using appropriate data models, folders and metadata.

• Vector, raster and tabular data structures used in agroforestry projects
• Geodatabase schema for farms, plots, trees, interventions and observations
• Project folders separating source data, working layers, outputs and documentation
• Attribute domains, unique identifiers and relationship classes for field records
• Metadata fields covering lineage, dates, scale, ownership and processing history
• File naming conventions supporting repeatable team-based spatial analysis

Participants can select coordinate reference systems and assess positional quality for agroforestry datasets.

• Geographic and projected coordinate reference systems for local land analysis
• Datum transformations between WGS 84 and national mapping systems
• UTM zone selection for farm, district and watershed mapping
• Georeferencing scanned maps using ground control points and residual errors
• GPS positional accuracy, multipath error and field-recording implications
• Overlay checks identifying coordinate shifts between boundaries and imagery

Participants can acquire, assess and document spatial datasets suitable for agroforestry analysis.

• Data-source assessment for cadastral, soil, climate, terrain and imagery layers
• Licensing conditions for government data, open imagery and community mapping
• Completeness checks for missing attributes, gaps, duplicates and invalid geometries
• Topology rules detecting polygon overlaps, gaps and boundary inconsistencies
• Attribute validation using range checks, coded values and logical rules
• Quality-control log recording corrections, assumptions, sources and outstanding issues

Participants can design field-data collection protocols for agroforestry mapping and validation.

• Sampling frames for farms, plots, transects and permanent monitoring points
• Stratified random sampling across land cover, elevation and management types
• Tree inventory fields: species, diameter, height, condition and canopy width
• Mobile data forms with required fields, skip logic and coordinates
• Photo protocols linking geotagged images to plots and observations
• Field validation procedures for boundaries, land cover and classification samples

Participants can interpret remote-sensing data characteristics for agroforestry mapping tasks.

• Electromagnetic reflectance patterns for vegetation, soil, water and built surfaces
• Spatial, spectral, temporal and radiometric resolution trade-offs for mapping
• Sentinel-2 and Landsat imagery selection for seasonal agroforestry assessment
• Cloud cover, haze, shadows and atmospheric effects on image interpretation
• True-colour, false-colour and infrared composites for vegetation examination
• Pixel size implications for scattered trees, hedgerows and smallholder plots

Participants can prepare satellite imagery and derive vegetation indicators for agroforestry analysis.

• Surface-reflectance imagery selection using date, cloud cover and phenology
• Cloud and shadow masking before vegetation and land-cover analysis
• Band compositing for vegetation vigour, moisture and bare-soil interpretation
• NDVI calculation for seasonal greenness and vegetation condition mapping
• EVI and NDMI comparison for dense canopy and moisture analysis
• Image mosaicking and clipping to project boundaries without edge artifacts

Participants can create interpretable land-cover maps using visual evidence and classification rules.

• Visual interpretation keys using tone, texture, pattern, shape and context
• Land-cover class definitions separating tree crops, woodland and shrubland
• Minimum mapping units appropriate for farm and landscape assessments
• On-screen digitising of farms, tree belts, riparian buffers and woodlots
• Classification decision rules for mixed tree-crop and fallow systems
• Land-cover legends aligned with FAO Land Cover Classification System principles

Participants can produce and evaluate supervised land-cover classifications for agroforestry landscapes.

• Training-sample design using homogeneous pixels and independent validation locations
• Random Forest classification for multispectral agroforestry land-cover mapping
• Class imbalance management through sample allocation and class consolidation
• Confusion matrices reporting producer, user and overall accuracy
• Kappa limitations and class-specific accuracy for operational map decisions
• Error review identifying spectral confusion among crops, trees and shrubs

Participants can map tree-cover indicators and interpret their limits for biomass-related analysis.

• Canopy-cover thresholds distinguishing open farms, agroforestry and woodland
• Object-based image analysis concepts for crowns, hedgerows and woodlots
• LiDAR-derived canopy height models and their agroforestry applications
• Crown delineation constraints in mixed-species and dense planting systems
• Allometric equations linking tree measurements to aboveground biomass estimates
• Biomass uncertainty sources from species choice, measurements and spatial extrapolation

Participants can derive terrain and hydrological indicators for agroforestry site planning.

• Digital elevation models for elevation, slope, aspect and terrain position
• Slope classification supporting erosion control and machinery-access decisions
• Flow direction and accumulation for drainage-path and gully identification
• Watershed delineation from pour points, streams and elevation surfaces
• Topographic wetness index for moisture-related planting suitability assessment
• Soil texture, depth and drainage layers for species-selection screening

Participants can analyse spatial relationships affecting agroforestry placement and ecological function.

• Buffer analysis around streams, roads, settlements and protected areas
• Euclidean distance surfaces for market access and seedling distribution planning
• Riparian buffer widths based on programme rules and local regulations
• Least-cost paths connecting remnant habitat patches through farm landscapes
• Fragmentation metrics measuring patch size, edge density and isolation
• Conflict mapping between restoration zones, cultivation areas and infrastructure

Participants can build transparent agroforestry suitability models using spatial criteria and weights.

• Suitability criteria derived from species ecology, farming systems and programme objectives
• Boolean exclusion masks for protected areas, water bodies and settlements
• Standardising slope, rainfall, soils and access into comparable scores
• Analytic Hierarchy Process pairwise comparisons for criterion weighting
• Weighted overlay combining environmental, social and operational suitability factors
• Sensitivity analysis testing how weight changes affect priority locations

Participants can translate suitability evidence into species and intervention zones.

• Species ecological envelopes based on rainfall, temperature, soils and elevation
• Matching nitrogen-fixing trees to cropping systems and soil-improvement objectives
• Zoning silvopasture interventions using forage, shade and water-access criteria
• Windbreak placement using prevailing winds, field geometry and settlement exposure
• Riparian restoration zones based on stream order, slope and land cover
• Management-unit maps linking interventions, species mixes and implementation schedules

Participants can measure and communicate agroforestry land-cover changes using repeat imagery.

• Baseline-date selection accounting for seasonality, cloud cover and intervention timing
• Post-classification comparison for land-cover transition and area-change matrices
• NDVI time series for vegetation recovery and seasonal management signals
• Change thresholds distinguishing likely planting gains from image noise
• Sample-based verification of detected changes using field records and imagery
• Monitoring indicators for tree survival, canopy expansion and land-use conversion

Participants can prepare spatial inputs for agroforestry restoration and carbon reporting workflows.

• Spatial baselines defining project areas, reference areas and eligible land
• Stratification methods for carbon sampling and biomass estimation planning
• Activity data from land-cover maps for greenhouse-gas accounting inputs
• Uncertainty registers covering classification, sampling, boundaries and allometric models
• Restoration reporting maps showing intervention areas, survival and canopy trends
• Audit trails connecting source imagery, field evidence, methods and reported figures

Participants can communicate agroforestry analysis through clear maps, charts and technical notes.

• Map layout hierarchy using titles, legends, scale bars and source notes
• Colour schemes distinguishing land-cover classes without misleading visual emphasis
• Classification legends designed for farmers, managers, donors and technical reviewers
• Area summaries and charts reporting intervention targets by administrative unit
• Web-map concepts for sharing controlled project information with stakeholders
• Technical map notes documenting methods, limitations, dates and accuracy results

Participants can document and streamline recurring agroforestry GIS workflows for team use.

• ModelBuilder and graphical processing chains for repeatable geoprocessing tasks
• Batch processing imagery, buffers, zonal statistics and map exports
• Parameterised workflows separating fixed methods from project-specific inputs
• Processing logs recording software versions, settings, dates and data sources
• Standard operating procedures for field imports, validation and reporting cycles
• Version-control principles for scripts, project files and analytical documentation

Participants can plan accountable GIS delivery, data stewardship and review arrangements for programmes.

• Data stewardship roles for collection, approval, storage and publication
• Consent considerations for farm boundaries, household records and community maps
• Access controls for sensitive tenure, beneficiary and conservation data
• Review gates for data quality, model assumptions and map release
• Work plans linking field campaigns, imagery dates, analysis and reporting deadlines
• Procurement specifications for outsourced imagery, mapping and validation services

Participants can build, review and present a complete agroforestry spatial assessment with documented recommendations.

• Project brief defining decisions, users, geographic extent and analytical questions
• Prepared geodatabase containing validated boundaries, observations, imagery and reference layers
• Land-cover classification with accuracy results, limitations and interpretation notes
• Suitability model identifying priority zones, exclusions and sensitivity findings
• Implementation map specifying interventions, species zones, access and monitoring locations
• Technical presentation defending methods, results, assumptions and recommended next actions