GIS and Remote Sensing for Wildlife Management Training Course

Participants can select, apply and document coordinate reference systems for wildlife projects.

• Coordinate reference systems selected using datum, projection, extent, and measurement purpose
• WGS 84 GPS observations transformed into appropriate projected coordinate systems
• UTM zones, national grids, and local projections compared for field areas
• On-the-fly projection checked against control points and authoritative basemaps
• Georeferencing scanned maps with ground control points and residual error review
• Scale, resolution, and minimum mapping unit matched to management decisions
• Coordinate transformations documented within project metadata and map layouts

Participants can identify, evaluate and prepare authoritative spatial data for conservation analysis.

• Authoritative boundaries sourced from protected-area agencies and national mapping authorities
• Species occurrences screened for provenance, observer expertise, and coordinate precision
• OpenStreetMap, national datasets, and conservation portals compared for suitability
• Data licences reviewed before publication, redistribution, and partner data exchange
• Satellite image archives selected by sensor, revisit period, and cloud cover
• Field survey requirements converted into attribute schemas and validation rules
• Sensitive occurrence data generalised using buffers, aggregation, and access controls

Participants can design field data workflows that produce analysable and auditable wildlife records.

• GPS waypoint accuracy evaluated using satellite geometry, canopy, and multipath effects
• Mobile forms designed for sightings, carcasses, threats, and habitat observations
• Controlled vocabularies reduce inconsistent species names, incident types, and observer entries
• Required fields and range checks prevent incomplete field records
• Offline basemaps prepared for patrol teams operating beyond mobile coverage
• Field validation compares mapped features with observations and GPS tracks
• Survey protocols linked to dates, observers, effort, and detection conditions

Participants can perform vector-based spatial analyses for protected-area management questions.

• Buffer analysis estimates influence zones around waterpoints, roads, and settlements
• Overlay operations identify habitat within management zones and development footprints
• Spatial joins summarise incidents, sightings, and patrol effort by management unit
• Dissolve, clip, intersect, and union tools prepare decision-ready feature layers
• Topology rules detect gaps, overlaps, slivers, and invalid protected-area boundaries
• Proximity analysis ranks villages and farms exposed to wildlife conflict
• Attribute queries isolate priority species, seasons, locations, and incident classes

Participants can derive terrain and environmental variables from raster datasets.

• Raster cell size selected according to ecological process and management scale
• Digital elevation models generate slope, aspect, hillshade, and terrain ruggedness
• Raster reclassification converts continuous environmental variables into habitat classes
• Map algebra combines elevation, distance, vegetation, and water suitability factors
• Euclidean distance surfaces quantify access to roads, rivers, and settlements
• Cost-distance surfaces model movement resistance across heterogeneous landscapes
• NoData values, resampling methods, and raster alignment checked before analysis

Participants can select and interpret satellite imagery for habitat and land-condition assessment.

• Landsat and Sentinel-2 imagery compared by spatial, spectral, and temporal resolution
• Spectral signatures distinguish vegetation, bare ground, water, burned areas, and settlements
• True-colour, false-colour, and infrared composites interpreted for habitat features
• Cloud masking and atmospheric correction applied before quantitative image analysis
• Normalized Difference Vegetation Index calculated for forage and habitat condition
• Normalized Difference Water Index identifies seasonal water bodies and wetland extent
• Image dates matched to phenology, rainfall patterns, and management questions

Participants can create and evaluate land-cover classifications from satellite imagery.

• Training samples selected across representative land-cover classes and spectral variation
• Supervised classification uses Random Forest algorithms and labelled reference samples
• Unsupervised clustering supports preliminary exploration of unknown landscape patterns
• Classification schemes aligned with management objectives and field-observable classes
• Confusion matrices calculate overall accuracy, producer accuracy, and user accuracy
• Reference samples separated from training data for independent validation
• Classification errors interpreted before habitat statistics inform management recommendations

Participants can build transparent habitat suitability models for focal wildlife species.

• Focal species requirements translated into measurable habitat suitability criteria
• Ecological variables include vegetation, water, terrain, disturbance, and land cover
• Weighted overlay models combine standardised criteria using documented expert weights
• Analytic Hierarchy Process supports pairwise weighting of habitat criteria
• Suitability scores classified into unsuitable, marginal, suitable, and core habitat
• Sensitivity analysis tests model response to alternative criteria weights
• Habitat maps validated against independent observations and expert field knowledge

Participants can interpret species distribution models and communicate their limitations responsibly.

• Presence-only records assessed for geographic clustering and observer sampling bias
• Background points selected within ecologically accessible areas for model development
• MaxEnt outputs interpreted as relative suitability rather than confirmed occupancy
• Predictor variables screened for collinearity using correlation matrices and VIF
• Cross-validation separates model training records from evaluation records
• AUC, omission rates, and response curves assessed with ecological judgement
• Model limitations documented for sparse records, seasonal movements, and detection bias

Participants can identify movement routes, barriers and potential wildlife corridors using spatial methods.

• Telemetry tracks cleaned for duplicate fixes, impossible speeds, and missing timestamps
• Home ranges estimated using minimum convex polygon and kernel density methods
• Movement paths compared with roads, fences, farms, waterpoints, and settlements
• Resistance surfaces assign movement costs to land-cover and disturbance classes
• Least-cost paths identify plausible corridors between habitat patches
• Circuit theory maps multiple connectivity pathways and pinch points
• Corridor recommendations distinguish ecological potential from tenure and implementation constraints

Participants can map wildlife survey effort and interpret detection-aware monitoring data.

• Sampling frames define survey coverage across habitats, strata, and management zones
• Systematic and stratified random sampling compared for wildlife monitoring programmes
• Camera-trap stations mapped with deployment dates, effort, and detection histories
• Detection probability distinguished from occupancy and observed species absence
• Survey effort standardised using trap nights, transect length, and observer hours
• Spatial bias identified where roads and access routes shape observations
• Monitoring indicators linked to baselines, thresholds, and management response triggers

Participants can analyse threats and patrol data to prioritise field interventions.

• Incident records coded by threat type, severity, date, and verification status
• Kernel density estimation identifies concentrations of poaching and conflict incidents
• Patrol tracks assessed for coverage, effort, repeat visits, and inaccessible areas
• Risk surfaces combine threat likelihood, biodiversity value, and patrol accessibility
• Hotspot analysis distinguishes meaningful clusters from random incident patterns
• Human-wildlife conflict mapped by crop damage, livestock loss, and injury reports
• Patrol priorities documented with operational constraints, safety risks, and community considerations

Participants can detect and report habitat change using multitemporal spatial data.

• Image pairs selected from comparable seasons to reduce phenological differences
• Post-classification comparison quantifies transitions between land-cover categories
• Image differencing identifies vegetation loss, burn scars, and water changes
• NDVI time series reveal seasonal condition patterns and persistent decline
• Change maps checked against field evidence, high-resolution imagery, and local knowledge
• Deforestation alerts prioritised by proximity to core habitat and access routes
• Monitoring dashboards defined through indicators, update frequency, owners, and thresholds

Participants can create clear maps and reports that support accountable management decisions.

• Map purpose determines projection, scale, symbology, labels, and supporting context
• Visual hierarchy directs attention toward management priorities and analytical findings
• Colour schemes selected for accessibility, print reproduction, and thematic meaning
• Legends, scale bars, north arrows, sources, and dates checked systematically
• Uncertainty communicated through classifications, confidence notes, and methodological statements
• Map series compare baseline conditions, current threats, and recommended interventions
• Management reports connect spatial findings to actions, responsibilities, and monitoring measures

Participants can plan a practical GIS operating model for an ongoing wildlife programme.

• GIS workflows mapped from field collection through analysis, reporting, and archival
• Data dictionaries standardise names, codes, units, and permissible attribute values
• File naming conventions support version control and reliable project retrieval
• Role-based permissions protect sensitive wildlife locations and incident intelligence
• Quality assurance checklists schedule validation before publication and decision meetings
• Update cycles assign responsibility for boundaries, imagery, surveys, and incident records
• Implementation roadmaps define priorities, resources, dependencies, and early deliverables

Participants can build, review and present a complete wildlife management GIS analysis with management recommendations.

• Management question defined with focal species, area, decision, and success criteria
• Spatial datasets prepared with documented projections, metadata, and quality checks
• Remote-sensing indicators selected to address habitat condition or land-cover change
• Vector and raster analyses integrated into a transparent management evidence model
• Map layouts produced for technical review and senior management decision meetings
• Recommendations prioritised using ecological evidence, operational feasibility, and stated uncertainty
• Peer review tests assumptions, cartography, reproducibility, and decision relevance