Remote Sensing for Disaster Management Training Course

Participants can explain sensor characteristics and match them to disaster information needs.

• Electromagnetic spectrum bands used for land, water, fire and damage detection
• Spatial, spectral, temporal and radiometric resolution compared for operational decisions
• Passive optical sensors distinguished from active radar and LiDAR systems
• Cloud cover, illumination and revisit intervals assessed before image selection
• Ground sampling distance related to buildings, roads and settlement patterns
• Sensor limitations documented before distributing findings to operational stakeholders

Participants can identify, acquire and document suitable satellite and reference datasets.

• Sentinel, Landsat and commercial imagery sources compared by resolution and latency
• Copernicus Emergency Management Service products evaluated for response use
• Acquisition plans specify area of interest, timing, sensor and output scale
• Licensing conditions checked before sharing imagery-derived products with partners
• Metadata records captured for source, acquisition time, processing and permissions
• Reference datasets assembled from administrative boundaries, roads, buildings and population grids

Participants can prepare spatial data for analysis while documenting fitness for purpose.

• Coordinate reference systems selected for local analysis and regional reporting
• Raster and vector datasets aligned using projection, extent and resolution checks
• Georeferencing errors identified through control points and feature comparison
• Attribute tables cleaned using field definitions, null checks and coded values
• ISO 19115 metadata elements recorded for traceable disaster information products
• Quality-control logs capture data source, transformations, errors and approval status

Participants can interpret optical imagery and apply spectral indices to disaster questions.

• True-colour and false-colour composites interpreted using visual image elements
• NDVI identifies vegetation condition, crop stress and post-fire recovery patterns
• Normalized Difference Water Index supports open-water and flood extent screening
• Normalized Burn Ratio estimates burned area and fire severity patterns
• Atmospheric haze, shadows and seasonal variation recognised during image interpretation
• Training samples selected for supervised classification of land-cover features

Participants can interpret SAR backscatter patterns for flood, surface and damage assessment.

• SAR wavelength, polarisation and backscatter explained for disaster image interpretation
• Sentinel-1 acquisition modes selected for rapid wide-area flood monitoring
• Speckle filtering applied while preserving meaningful boundaries and small features
• Radar shadow, layover and foreshortening identified in mountainous terrain
• Pre-event and post-event SAR scenes compared under consistent acquisition conditions
• Backscatter signatures interpreted for open water, urban areas and rough surfaces

Participants can detect and communicate meaningful changes between pre-event and post-event observations.

• Image differencing isolates surface change between aligned pre-event and post-event scenes
• Threshold selection tested against reference features and known event locations
• Post-classification comparison quantifies land-cover changes across mapped categories
• Time-series charts distinguish event impacts from normal seasonal variability
• Baseline periods selected using historical imagery, hazard calendars and land-use patterns
• Change maps include dates, thresholds, confidence notes and interpretation limits

Participants can produce flood extent and impact assessments from satellite and hydrological evidence.

• Flood mapping workflow combines SAR, optical imagery, terrain and river-gauge data
• Permanent water masks separate seasonal flooding from rivers, lakes and reservoirs
• Digital elevation models identify flow paths, depressions and likely inundation zones
• Rainfall estimates compared with river levels and observed flood boundaries
• Urban flooding constraints assessed where radar shadows obscure built-up areas
• Flood extent intersected with settlements, roads, cropland and critical facilities

Participants can use satellite indicators to assess wildfire, drought and extreme-heat conditions.

• Active fire products identify thermal anomalies, confidence levels and detection times
• Burn severity classified using pre-fire and post-fire spectral index values
• Vegetation anomaly maps compare current greenness against historical seasonal baselines
• Land surface temperature interpreted with urban form, vegetation and exposure data
• Drought indicators distinguished between meteorological, agricultural and hydrological conditions
• Fire perimeter products checked against cloud, smoke and sensor-resolution limitations

Participants can identify remote-sensing indicators of rapid-onset hazard impacts and damage.

• Building damage indicators interpreted from optical imagery and radar coherence change
• InSAR coherence loss assessed for earthquake damage and ground disturbance
• Landslide scars identified using terrain, vegetation loss and slope context
• Cyclone damage indicators include debris, roof loss, inundation and vegetation defoliation
• Post-event imagery prioritised using population exposure, access constraints and hazard severity
• Damage signatures separated from pre-existing construction conditions and land-use change

Participants can quantify exposed people, assets and services within hazard impact areas.

• Exposure datasets structured for population, buildings, infrastructure and economic activity
• Hazard footprints intersected with asset layers using spatial overlay methods
• Critical facilities classified by function, capacity, accessibility and service dependency
• Network analysis identifies disrupted routes to hospitals, shelters and distribution points
• Population grids interpreted with resolution limits and daytime mobility assumptions
• Exposure totals disaggregated by administrative area, settlement type and asset class

Participants can combine hazard and exposure evidence into transparent impact estimates.

• Risk equation connects hazard, exposure, vulnerability and coping capacity variables
• Damage functions relate hazard intensity to expected asset loss categories
• Vulnerability indicators selected for housing, livelihoods, health and social protection
• Scenario assumptions recorded for return periods, event magnitude and asset condition
• Impact estimates expressed as ranges when data quality constrains precision
• Priority areas ranked using severity, population exposure, access and service disruption

Participants can design a rapid, repeatable satellite-supported damage assessment process.

• Rapid assessment protocols define damage classes, evidence thresholds and review roles
• Building footprints sampled for roof damage, debris and structural collapse indicators
• Grid-based assessment supports consistent coverage across large affected urban areas
• Remote observations triaged into confirmed, probable and unverified damage categories
• Field verification plans target ambiguous locations and high-consequence facilities
• Damage estimates reconciled with reports from authorities, communities and responders

Participants can connect satellite monitoring to triggers, forecasts and anticipatory action plans.

• Early warning chain links monitoring, forecasting, communication and protective action
• Forecast-based triggers define thresholds, lead times, locations and responsible authorities
• Satellite rainfall and vegetation indicators support anticipatory drought action decisions
• Trigger performance reviewed using false alarms, missed events and response feasibility
• Alert messages distinguish forecast probability, expected impact and recommended actions
• Anticipatory action maps identify priority communities, assets and pre-positioning routes

Participants can create map products and situation-report content suited to operational use.

• Map purpose, audience and decision question defined before layout production
• Cartographic hierarchy uses scale, symbols, labels and visual contrast deliberately
• Essential map elements include source dates, projection, legend, north arrow and disclaimer
• Situation-report graphics communicate affected areas, confidence and priority actions
• Web maps and static products selected according to connectivity and user access
• Version control distinguishes preliminary products from reviewed operational releases

Participants can validate mapped outputs and communicate accuracy and uncertainty appropriately.

• Confusion matrices calculate overall accuracy, omission error and commission error
• Reference samples designed using random, stratified and purposive sampling approaches
• Field observations assessed for location accuracy, timing and observer reliability
• Positional uncertainty considered when intersecting hazard boundaries with small assets
• Confidence statements match evidence quality, sample coverage and analytical assumptions
• Peer review checks methods, source dates, classifications and stated limitations

Participants can apply responsible data practices to disaster remote-sensing work.

• Sensitive locations protected through aggregation, masking and controlled data access
• Do No Harm principles applied to crisis maps and population datasets
• Personal data risks assessed when combining imagery with household-level information
• Data-sharing agreements specify ownership, permitted use, retention and disclosure controls
• Indigenous knowledge and community observations credited within assessment processes
• Bias in exposure data examined for informal settlements and underserved communities

Participants can specify reproducible processing workflows for timely disaster analysis.

• Workflow diagrams document inputs, transformations, outputs, owners and quality gates
• Google Earth Engine collections filtered by dates, bounds and cloud conditions
• Batch processing plans reduce manual steps for repeat incident assessments
• Naming conventions organise imagery, intermediate files, outputs and version history
• Processing parameters recorded for reproducibility, audit review and method transfer
• Automation suitability assessed against urgency, error risk and analyst oversight

Participants can design an organisational plan for integrating remote sensing into disaster operations.

• Capability assessments review staff skills, data access, equipment and operating procedures
• Standard operating procedures define request intake, analysis, review and dissemination
• Service-level targets set for product turnaround, update frequency and approval pathways
• Partnership maps identify national agencies, data providers, universities and responders
• Training plans assign roles for analysts, reviewers, decision-makers and field teams
• Implementation roadmaps sequence quick wins, dependencies, investments and performance measures

Participants can build, review and present a complete satellite-supported disaster assessment for an operational decision.

• Incident brief defines decision question, affected area, users and reporting deadline
• Data plan selects imagery, exposure layers, reference sources and validation evidence
• Analysis workflow produces hazard extent, impact estimates and confidence statements
• Operational map package presents findings, priorities, limitations and recommended actions
• Peer review tests traceability, accuracy, usability and ethical data handling
• Final briefing defends methods, assumptions and response recommendations to decision-makers