GIS and Remote Sensing for Disaster Management Training Course

Participants can structure GIS data for disaster analysis and operational reporting.

• Vector and raster data models compared through disaster mapping use cases
• Feature classes, attribute tables, domains, and relationship classes configured
• Spatial joins, selections, buffers, overlays, and geoprocessing workflows
• Map scale, resolution, extent, and generalisation matched to decisions
• Geodatabase schemas structured for incident, shelter, and damage records
• Metadata captured using ISO 19115 elements and lineage statements

Participants can assess, prepare and integrate spatial and tabular datasets from multiple sources.

• Authoritative basemaps, census boundaries, and critical infrastructure datasets assessed
• Data dictionaries reconcile field names, codes, units, and definitions
• OpenStreetMap features evaluated against authoritative emergency mapping sources
• Tabular incident records geocoded using address matching and gazetteers
• Duplicate features identified through keys, proximity rules, and attribute checks
• Data licensing, attribution, and sharing restrictions documented before deployment

Participants can select and verify coordinate reference systems for reliable disaster mapping.

• Geographic and projected coordinate reference systems selected for analysis
• EPSG codes, datums, projections, and units verified before transformation
• Coordinate transformations tested using known control points and residuals
• UTM zones and local grids matched to operational mapping scales
• GPS positions assessed for accuracy, precision, and positional uncertainty
• Projection distortion examined for distance, area, direction, and bearing calculations

Participants can design field data collection workflows for rapid assessments and operational updates.

• Mobile data collection forms designed for shelters, damage, and needs assessments
• Required fields, coded values, skip logic, and validation rules configured
• Offline synchronisation procedures planned for disrupted communications and power
• Field teams briefed on location capture, photographs, consent, and safety
• Sampling frames defined for rapid assessments and representative damage estimates
• Collected records reviewed through completeness, range, and location checks

Participants can select suitable sensor types and imagery for specific disaster-management questions.

• Electromagnetic spectrum bands related to land, water, vegetation, and built surfaces
• Spatial, spectral, temporal, and radiometric resolution compared for task fit
• Optical, thermal, SAR, and LiDAR sensors matched to disaster questions
• Sentinel, Landsat, and commercial imagery selected by timing and coverage
• Cloud cover, shadows, smoke, and acquisition geometry assessed before use
• Image interpretation keys built from tone, texture, pattern, and context

Participants can prepare satellite imagery for comparison, interpretation and analytical processing.

• Radiometric calibration and atmospheric correction prepared for multispectral comparison
• Orthorectification, co-registration, and resampling checked against reference features
• Cloud masks and SAR speckle filters applied with documented parameters
• Band composites, pansharpening, and contrast stretches prepared for interpretation
• Normalized Difference Vegetation Index calculated for vegetation condition mapping
• Pre-event and post-event scenes aligned for change detection

Participants can classify remote-sensing imagery and report the reliability of resulting outputs.

• Supervised classification training samples selected with class definitions
• Random Forest classification configured for land-cover and damage categories
• Object-based image analysis separates buildings, roads, water, and debris
• Confusion matrices, producer accuracy, user accuracy, and kappa interpreted
• Threshold-based indices identify floodwater, burn scars, and moisture anomalies
• Classification outputs converted into operational polygons with confidence attributes

Participants can create hazard and susceptibility maps using historical, environmental and terrain evidence.

• Historical event inventories cleaned for frequency, magnitude, and location analysis
• Kernel density estimation identifies incident clusters and concentration patterns
• Multi-criteria evaluation weights slope, rainfall, geology, and land cover
• Analytic Hierarchy Process explicitly documents hazard-susceptibility weighting judgements
• Weighted overlay produces ranked hazard zones and sensitivity tests
• Return periods and exceedance probabilities communicated without false precision

Participants can combine hazard and asset data to assess exposure, vulnerability and risk.

• Exposure layers combine population, buildings, roads, utilities, and facilities
• Dasymetric mapping redistributes population using land use and settlement data
• Vulnerability indicators normalised, weighted, and documented with source evidence
• Social vulnerability indices distinguish sensitivity, coping capacity, and access
• Network analysis calculates travel times to hospitals, shelters, and depots
• Risk matrices connect hazard likelihood, consequences, controls, and residual risk

Participants can map, validate and communicate disaster damage and needs assessment findings.

• Rapid damage assessment categories aligned with agency reporting protocols
• Pre-event building footprints compared with post-event imagery and observations
• Change detection flags destroyed structures, blocked roads, and inundated areas
• Damage records linked to photographs, assessor identifiers, timestamps, and confidence
• Sampling-based verification estimates classification error under access constraints
• Situation reports distinguish confirmed impacts, estimates, and information gaps

Participants can use terrain, rainfall and radar imagery to analyse flood hazards and impacts.

• Digital elevation models conditioned for flow direction and accumulation
• Catchment delineation derives drainage boundaries, streams, and outlet points
• Rainfall thresholds combined with antecedent conditions for flood triggers
• Flood extent extracted from SAR imagery using backscatter change methods
• Depth grids intersected with buildings and roads for impact estimates
• Evacuation routes assessed for inundation exposure and alternative access

Participants can apply GIS and remote sensing to wildfire and landslide assessment tasks.

• Burn severity mapped using Normalized Burn Ratio differencing
• Fire perimeters, hotspots, wind forecasts, and assets integrated operationally
• Slope, aspect, soil, and rainfall combined for landslide susceptibility
• Terrain derivatives reveal scarps, drainage convergence, and unstable slopes
• Debris-flow runout zones modelled using elevation and channel constraints
• Field observations validate triggers, boundaries, and priority locations

Participants can build geospatial inputs for early warning thresholds, alerts and response actions.

• Thresholds, triggers, lead times, and actions structured into warning protocols
• Common Alerting Protocol fields prepared for consistent public warning messages
• Rain gauges, river gauges, and satellite feeds integrated as time series
• Forecast uncertainty displayed through scenarios, ranges, and decision thresholds
• Trigger-action matrices assign monitoring, escalation, evacuation, and communication responsibilities
• Warning maps distinguish hazard extent, affected populations, and safe routes

Participants can prepare map products that support incident command and resource allocation decisions.

• Common operational picture requirements defined for incident command decisions
• Operational dashboards combine incidents, resources, access routes, and status updates
• Resource allocation maps compare demand, capacity, travel time, and constraints
• Location-allocation analysis identifies candidate sites for shelters and distribution points
• Map templates apply hierarchy, symbology, labels, scale bars, and disclaimers
• Briefing maps communicate uncertainty, source dates, and operational recommendations

Participants can publish controlled geospatial products for emergency coordination and field updates.

• Web map layers published with scale-dependent rendering and access permissions
• Feature services configured for edits, synchronisation, and field updates
• Web dashboards present indicators, trends, alerts, and geographic filters
• OGC WMS, WFS, and WMTS services assessed for interoperability
• Data-sharing agreements define custodians, release conditions, and retention periods
• Offline map packages prepared for low-bandwidth and disconnected deployments

Participants can apply quality assurance, ethical safeguards and governance controls to disaster datasets.

• Quality assurance plans specify acceptance criteria, checks, and corrective actions
• Positional, attribute, temporal, and logical consistency errors recorded systematically
• ISO 19157 data quality measures applied to emergency datasets
• Version control, edit logs, and approval workflows protect authoritative layers
• Personally identifiable information masked through aggregation, suppression, and access controls
• Ethical mapping reviews assess harm, bias, consent, and representation

Participants can build, review and present a complete spatial intelligence package for a simulated disaster incident.

• Scenario brief establishes earthquake impacts, decision deadlines, and reporting requirements
• Data acquisition plan prioritises authoritative layers, imagery, and field observations
• Hazard, exposure, and accessibility analyses assembled into an incident geodatabase
• Damage estimates and priority areas documented with assumptions and uncertainty
• Operational map series and dashboard storyboard prepared for command briefings
• Peer review checks methods, cartography, data quality, and recommendations
• Final presentation defends decisions, limitations, and next operational actions