Remote Sensing Data Visualization Training Course

Participants can select imagery sources by matching sensor characteristics to information requirements.

• Sensor platforms compared: multispectral, hyperspectral, radar, thermal and LiDAR
• Electromagnetic spectrum bands linked to land, water and built surfaces
• Sentinel-2, Landsat and commercial imagery selected against information requirements
• Cloud cover, sun angle and revisit interval assessed before acquisition
• Active radar backscatter interpreted alongside optical reflectance signatures
• Acquisition dates aligned with agricultural, disaster and infrastructure events

Participants can build organised GIS projects that support repeatable imagery visualisation.

• QGIS project structure configured with relative paths and layer groups
• ArcGIS Pro catalog, maps and layouts organised for repeatable production
• Raster datasets loaded through GDAL-supported formats and inspected
• Layer symbology, naming conventions and scale ranges established early
• Processing histories recorded within project documentation and metadata notes
• Geodatabases and GeoPackages selected for portable project exchange

Participants can improve image readability while preserving defensible pixel-value interpretation.

• Histogram stretching reveals contrast without altering underlying pixel values
• Min-max, percent clip and standard-deviation stretches compared visually
• Gamma correction applied to improve midtone interpretation
• Histogram equalisation assessed for enhancement benefits and visual artefacts
• Brightness and contrast adjustments documented for reproducible rendering
• Display statistics recalculated after mosaicking, clipping and masking operations

Participants can create and explain band composites and spectral index visualisations.

• True-colour, false-colour and colour-infrared composites matched to objectives
• RGB band assignments selected using target spectral response
• Band ratios visualised for vegetation, moisture and burn severity
• Normalized Difference Vegetation Index rendered with defensible class breaks
• Principal component composites inspected for correlated-band information reduction
• Raster functions assembled for repeatable composite generation

Participants can design colour schemes and legends that accurately communicate raster patterns.

• Continuous colour ramps matched to sequential physical measurement scales
• Diverging schemes centred on meaningful baselines for change detection
• ColourBrewer palettes assessed for perceptual ordering and colour-vision deficiency
• Classified legends use equal interval, quantile and Jenks natural breaks
• Manual breakpoints tied to policy thresholds and domain conventions
• NoData, saturation and uncertainty encoded without misleading visual emphasis

Participants can combine imagery with contextual GIS layers without obscuring key evidence.

• Vector boundaries, labels and reference features controlled through visual hierarchy
• Scale-dependent rendering prevents clutter across regional and local map scales
• Label placement rules resolve conflicts with imagery and thematic layers
• Transparency, blending modes and masking preserve raster interpretability
• Generalisation methods simplify contextual layers while retaining essential geography
• Basemap selection tested for contrast, authority and licensing restrictions

Participants can prepare consistent imagery inputs for credible visual comparison and display.

• AOIs digitised using operational boundaries, buffers and exclusion zones
• Cloud, shadow and water masks prepared before visual interpretation
• Image mosaics balanced using acquisition dates, radiometry and seamline checks
• Reprojection and resampling methods selected for display versus analysis
• Nearest neighbour, bilinear and cubic resampling effects compared
• Raster clipping produces focused products with documented geographic extent

Participants can visualise temporal patterns and distinguish change from acquisition artefacts.

• Swipe, flicker and transparency comparisons reveal temporal land-cover change
• Image differencing visualises change after radiometric normalisation
• NDVI trajectories chart seasonal patterns and anomalous vegetation response
• Time-enabled layers synchronise imagery, observations and incident records
• Before-and-after layouts distinguish real change from acquisition artefacts
• Change narratives cite dates, sensors, thresholds and confidence limits

Participants can communicate classification results, confidence and accuracy limitations clearly.

• Supervised classification outputs symbolised with class-specific confidence information
• Confusion matrices translated into readable accuracy communication graphics
• Object-based image analysis segments displayed with boundaries and attributes
• Classification probability surfaces reveal ambiguous areas requiring review
• Training samples mapped to expose geographic bias and class imbalance
• Post-classification comparison maps change classes using consistent legends

Participants can create terrain visualisations that reveal landform and elevation evidence.

• DEM hillshade parameters adjusted for terrain form and map purpose
• Multi-directional hillshades reduce directional bias across complex terrain
• Hypsometric tints combined with hillshade using controlled blending
• Slope, aspect and curvature rasters symbolised with appropriate units
• Contour intervals selected from scale, terrain range and audience needs
• LiDAR-derived elevation products assessed for vertical artefacts and voids

Participants can present SAR imagery and derived products with appropriate interpretation controls.

• SAR amplitude and coherence images prepared for interpretable display
• Speckle filtering effects evaluated without concealing operational features
• Polarisation combinations distinguish surface roughness, moisture and structure
• Flood extent maps separate open water from radar shadow
• Interferometric phase visualisation explains deformation patterns and limitations
• Optical and SAR layers combined for cloud-affected incident mapping

Participants can link remote sensing indicators with non-spatial data for decision support.

• Spatial joins connect imagery-derived indicators with asset and census data
• Zonal statistics summarise raster values by administrative or management units
• Charts link map patterns with distributions, trends and outliers
• Graduated symbols and choropleths avoid denominator and classification errors
• Small multiples compare sites, dates or scenarios without visual overload
• Linked map-chart dashboards support exploratory review of priority areas

Participants can design remote sensing visual products around audience needs and decisions.

• Map purpose, audience and decisions defined through a visual requirements brief
• Visual hierarchy directs attention from headline evidence to supporting context
• Gestalt principles applied to grouping, proximity, contrast and figure-ground
• Annotation distinguishes observation, interpretation, recommendation and uncertainty
• Accessible typography, contrast and symbols tested against WCAG guidance
• Scale bars, north arrows and graticules used only when informative

Participants can produce print and digital outputs suited to defined publication channels.

• Print layouts built with grids, alignment rules and controlled whitespace
• Page size, resolution and bleed specified for production outputs
• Web map pop-ups present source dates, units and interpretation notes
• Tile schemes and pyramids improve raster display performance online
• GeoTIFF, Cloud Optimized GeoTIFF and PNG exports selected appropriately
• PDF preflight checks fonts, colour profiles, layers and image resolution

Participants can document remote sensing visual products for responsible reuse and publication.

• ISO 19115 metadata elements captured for datasets and visual products
• FAIR principles applied to findability, access conditions and reuse
• STAC item fields document spatiotemporal assets and processing provenance
• Source citations distinguish sensor products, derived layers and contextual data
• Copyright, licensing and attribution requirements checked before publication
• Uncertainty statements communicate positional, temporal and classification limitations

Participants can review remote sensing products against analytical, cartographic and stakeholder criteria.

• Visual QA checklist tests alignment, legends, scale and interpretive claims
• Peer review separates cartographic defects from analytical validity concerns
• Acceptance criteria trace stakeholder requirements to finished map products
• Version control records revisions to data, styles and layouts
• Reproducible processing models reduce manual steps and undocumented changes
• Feedback logs assign corrective actions, owners and completion evidence

Participants can build, review and present a complete, documented remote sensing visual product for a defined decision.

• Project brief defines decision, users, area, dates and output format
• Source imagery assessed for fitness, licensing, metadata and temporal coverage
• Processing workflow produces documented composites, indices and contextual layers
• Map series applies symbols, annotations, uncertainty statements and accessible design
• QA review tests analytical claims, visual clarity and export specifications
• Presentation explains findings, limitations, methods and recommended operational actions
• Peer critique records revisions against agreed acceptance criteria