Thermal Remote Sensing Applications Training Course

Participants can select thermal sensors according to spatial, temporal and spectral requirements.

• Landsat TIRS bands, revisit cycle and spatial resolution
• MODIS thermal products for regional monitoring and time-series analysis
• ECOSTRESS observations for diurnal temperature and evapotranspiration studies
• ASTER thermal bands and multispectral temperature-emissivity separation
• Airborne thermal cameras for asset and infrastructure inspection
• Drone thermal surveys, flight altitude and ground sampling distance

Participants can define a thermal imagery acquisition plan aligned to a decision question.

• Problem statements translated into measurable thermal analysis questions
• Sensor selection matrix using resolution, revisit, cost and licensing criteria
• Acquisition timing matched to diurnal cycles and seasonal conditions
• Cloud cover, atmospheric humidity and scene-quality screening
• Field reference requirements for temperature, land cover and weather
• Metadata review using acquisition geometry and processing-level information

Participants can convert thermal image values into calibrated radiance and brightness temperature.

• Digital number conversion using published radiometric rescaling coefficients
• Spectral radiance calculation from Landsat thermal band metadata
• Top-of-atmosphere brightness temperature derived using thermal calibration constants
• Unit checks for radiance, wavelength, temperature and spatial resolution
• Pixel value inspection using image histograms and statistics
• Calibration errors traced to metadata, scaling factors and band selection

Participants can prepare spatially aligned thermal datasets and identify unusable observations.

• Coordinate reference systems selected for local measurement and reporting
• Raster alignment checked through control features and overlay comparison
• Resampling methods evaluated for thermal raster transformation
• Cloud, cloud-shadow and cirrus masks applied from quality-assurance bands
• No-data values, scan-line gaps and edge artefacts removed
• Study-area clipping and pixel-footprint checks before temperature retrieval

Participants can assess atmospheric influences and choose an appropriate correction approach.

• Atmospheric absorption, scattering and path radiance in thermal bands
• Water vapour effects on satellite-derived surface temperature
• Radiative transfer equation components for thermal image correction
• Atmospheric profile data obtained from reanalysis and weather stations
• Single-channel correction parameters matched to sensor wavelength
• Correction assumptions recorded for audit and technical reporting

Participants can estimate surface emissivity using vegetation and land-cover information.

• Emissivity defined as surface efficiency in thermal energy emission
• NDVI threshold method for vegetation fraction and emissivity estimates
• Land-cover emissivity tables applied with documented source values
• Fractional vegetation cover calculated from NDVI minimum and maximum
• Mixed pixels assessed for roofs, soil, water and vegetation
• Emissivity uncertainty propagated into surface temperature estimates

Participants can calculate land surface temperature from corrected thermal imagery.

• Single-channel algorithm applied to calibrated Landsat thermal imagery
• Split-window algorithm using paired thermal bands and emissivity differences
• Land surface temperature calculated in Celsius and Kelvin
• Temperature retrieval parameters documented in processing models
• Raster calculator expressions checked against sample pixel calculations
• Output temperature ranges reviewed against weather and land-cover evidence

Participants can combine thermal outputs with supporting geospatial layers for interpretation.

• Land surface temperature overlaid with land cover and imperviousness
• Digital elevation models used to assess topographic temperature effects
• Zonal statistics summarised by districts, parcels and management units
• Buffer analysis applied around roads, factories and water bodies
• Time-enabled layers organised for repeated temperature observations
• Attribute joins link thermal metrics with demographic and asset records

Participants can identify urban heat patterns and define priority areas for intervention.

• Urban heat island indicators derived from daytime surface temperature
• Reference rural areas selected using land cover and elevation
• Hotspot analysis using Getis-Ord Gi* temperature clusters
• Heat exposure maps combined with population and vulnerability indicators
• Cool roofs, tree canopy and surface materials interpreted thermally
• Priority neighbourhoods ranked using heat intensity and exposure criteria

Participants can interpret thermal indicators of crop water stress and irrigation performance.

• Canopy temperature interpreted against crop stage and weather conditions
• Crop Water Stress Index calculated from thermal observations
• Thermal imagery paired with NDVI for vegetation condition assessment
• Evapotranspiration concepts linked to surface energy balance components
• Irrigation anomalies identified through field-level temperature differences
• Field observations used to distinguish stress from soil background effects

Participants can map surface water temperature while accounting for shoreline and atmospheric effects.

• Water surface temperature retrieval using thermal infrared imagery
• Shoreline mixed pixels excluded through water masks and buffers
• Thermal plumes identified near outfalls, intakes and industrial discharges
• Seasonal temperature profiles compared across lakes, rivers and reservoirs
• In-situ gauge readings used for water temperature validation
• Temperature gradients interpreted with flow, depth and meteorological context

Participants can use thermal data to support fire detection, assessment and recovery monitoring.

• Active fire detection using thermal anomalies and confidence flags
• Fire Radiative Power interpreted as an indicator of combustion intensity
• Pre-fire and post-fire temperature comparisons for affected landscapes
• Normalized Burn Ratio combined with thermal evidence after fire events
• Cloud, smoke and hot bare soil false alarms assessed
• Incident maps prepared with acquisition times and confidence statements

Participants can assess thermal anomalies associated with built assets and industrial operations.

• Building heat-loss patterns interpreted from airborne thermal imagery
• Industrial heat anomalies screened using baseline temperature comparisons
• Pipeline and utility corridor inspection constraints for thermal surveys
• Roof moisture indicators distinguished from solar heating effects
• Substation hotspots assessed with sensor resolution and safety limits
• Inspection priorities ranked by anomaly magnitude, persistence and asset criticality

Participants can conduct preliminary thermal screening for geothermal and geological features.

• Thermal lineaments interpreted alongside faults and geological maps
• Night-time imagery selected to reduce solar heating influence
• Persistent anomalies compared across seasons and acquisition dates
• Geothermal targets screened with field evidence and hydrogeological data
• Topographic effects separated from potential subsurface thermal signals
• Exploration limitations stated before recommending ground investigation

Participants can analyse repeated thermal observations to identify trends, cycles and anomalies.

• Time-series stacks organised by acquisition date and sensor
• Seasonal decomposition separates recurring cycles from temperature anomalies
• Trend estimation using linear regression and Sen's slope
• Baseline periods defined for anomaly detection and comparison
• Missing observations managed through quality flags and documented exclusions
• Change maps distinguish persistent warming from single-scene variation

Participants can validate thermal products and communicate uncertainty appropriately.

• Validation plan matches field measurements with image acquisition timing
• Root mean square error calculated for temperature estimates
• Mean bias error used to identify systematic temperature differences
• Spatial representativeness assessed between point sensors and image pixels
• Uncertainty sources documented across calibration, atmosphere and emissivity assumptions
• Confidence statements written for maps, reports and decision briefings

Participants can classify thermal patterns using transparent thresholds and statistical methods.

• Standard deviation thresholds identify unusually hot and cold pixels
• Quantile classification supports comparative temperature priority mapping
• K-means clustering groups similar thermal response patterns
• Local outlier analysis identifies isolated temperature anomalies
• Thresholds tested against land cover, weather and operational records
• Classification rules recorded for repeatable monitoring workflows

Participants can produce clear thermal maps and reports that state methods and limitations.

• Colour ramps selected to avoid misleading temperature interpretation
• Map legends specify units, dates, sensor and processing method
• Class breaks aligned with decision thresholds and uncertainty
• Methods sections document inputs, algorithms and quality-control procedures
• Findings linked to operational actions, inspections and monitoring priorities
• Technical limitations presented without overstating thermal evidence

Participants can build, review and present a complete thermal remote sensing analysis for a defined operational question.

• Project brief defines decision question, users, geography and success criteria
• Data inventory specifies sensors, dates, ancillary layers and field references
• Processing workflow applies calibration, masking, emissivity and temperature retrieval
• Validation evidence quantifies error, bias and interpretation limitations
• Decision map identifies priorities using documented thermal analysis rules
• Peer review tests assumptions, reproducibility and reporting clarity
• Presentation communicates findings, recommended actions and implementation requirements