Integrated GIS and Remote Sensing Training Course

Participants can organise GIS and imagery datasets for reliable retrieval, exchange and analysis.

• Vector, raster, tabular and point-cloud structures compared for analytical suitability
• GeoPackage, GeoTIFF, GeoJSON and shapefile format strengths and constraints
• Attribute domains, identifiers and relationship classes designed for data integrity
• ISO 19115 metadata elements recorded for spatial datasets and imagery
• File naming, folder structures and version control conventions established
• Data lineage documented from source acquisition through derived outputs

Participants can select, transform and verify coordinate reference systems for integrated datasets.

• Geographic and projected coordinate reference systems distinguished by measurement purpose
• Datum transformations selected using source metadata and area location
• UTM zones evaluated against study extent and distortion requirements
• On-the-fly projection checked before overlaying vector and raster layers
• Ground control points used to georeference scanned maps and imagery
• Root mean square error interpreted during geometric correction review

Participants can choose appropriate earth observation data for a defined analytical question.

• Passive optical, thermal and active radar sensors compared systematically
• Spatial, spectral, radiometric and temporal resolution matched to project needs
• Sentinel-2 and Landsat collections assessed for land-cover monitoring
• Cloud cover, seasonality and phenology considered during image selection
• Acquisition plans specify dates, scene identifiers and quality thresholds
• Licensing conditions checked before distributing imagery-derived products

Participants can prepare imagery and raster datasets for consistent spatial analysis.

• Digital numbers, radiance and surface reflectance distinguished before analysis
• Atmospheric correction purpose assessed using sensor product documentation
• Cloud, shadow and no-data masks applied before spectral analysis
• Raster mosaics created with matching projections, resolutions and pixel alignment
• Resampling methods selected for categorical and continuous raster values
• Clipping and masking rasters to study-area boundaries and analysis extent

Participants can interpret imagery using visual cues and appropriate enhancement methods.

• Tone, texture, pattern, shape and association applied during interpretation
• False-colour composites constructed from meaningful multispectral band combinations
• Histogram stretching improves contrast while preserving analytical interpretation
• Principal component analysis separates correlated spectral information across bands
• Pan-sharpening trade-offs assessed before producing high-resolution display imagery
• Interpretation keys documented with classes, evidence cues and exceptions

Participants can plan reference data collection that supports classification and validation.

• Reference data requirements derived from class definitions and accuracy targets
• Stratified random sampling allocates observations across land-cover classes
• GPS positional accuracy recorded with collection methods and observation dates
• Field forms capture class labels, photographs and confidence assessments
• Training and validation samples separated to avoid optimistic accuracy estimates
• Existing surveys and authoritative datasets evaluated for reference-data fitness

Participants can use vector operations to prepare context layers and spatial evidence.

• Attribute queries isolate features using logical expressions and coded values
• Buffer zones model proximity around roads, assets and protected areas
• Overlay operations combine polygons using intersect, union and erase methods
• Spatial joins transfer attributes between points, lines and polygons
• Topology rules identify gaps, overlaps, dangles and duplicate features
• Dissolve and multipart tools prepare reporting units for summarisation

Participants can derive and interpret spectral indices and raster-based indicators.

• Normalized Difference Vegetation Index calculated from near-infrared and red bands
• Normalized Difference Water Index identifies surface-water and moisture patterns
• Normalized Burn Ratio supports fire severity and vegetation recovery assessment
• Raster algebra combines bands, thresholds and conditional evaluation statements
• Index thresholds calibrated using reference observations and visual inspection
• Derived rasters symbolised with meaningful classes, units and legend labels

Participants can produce a supervised land-cover classification from defined training data.

• Classification schema defines mutually exclusive, observable land-cover classes
• Training polygons digitised over spectrally representative image areas
• Spectral signatures reviewed for separability, variability and sample quality
• Maximum likelihood classification assigns pixels using probability distributions
• Random forest classification compares trees, variables and class predictions
• Post-classification filtering removes isolated pixels without obscuring real features

Participants can assess alternative classification approaches for complex imagery and land patterns.

• K-means clustering groups pixels using spectral similarity measures
• ISODATA clustering adjusts cluster membership through iterative reassignment
• Cluster labels assigned through imagery interpretation and reference evidence
• Image segmentation creates objects from spectral, shape and texture properties
• Object-based classification uses rules, context and object attributes
• Method selection justified against class complexity, imagery resolution and available reference data

Participants can quantify classification reliability and report errors transparently.

• Confusion matrices compare mapped classes against independent reference observations
• Overall accuracy calculated from correctly classified validation samples
• Producer accuracy identifies omission error within reference classes
• User accuracy identifies commission error within mapped classes
• Kappa statistics interpreted cautiously alongside class-specific accuracy measures
• Error patterns mapped to diagnose confused classes and sampling gaps

Participants can detect, quantify and communicate spatial change between image dates.

• Image dates aligned using matching projections, extents and pixel grids
• Post-classification comparison identifies transitions between mapped land-cover classes
• Image differencing measures change in reflectance or spectral indices
• Change thresholds selected using reference samples and distribution inspection
• Transition matrices quantify gains, losses and persistence across reporting classes
• Time-series charts communicate seasonal variation separately from sustained change

Participants can derive terrain and drainage indicators from digital elevation models.

• Digital elevation model sources assessed for resolution, date and vertical datum
• Slope, aspect and hillshade derived using raster surface functions
• Sink filling prepares elevation surfaces for flow-direction modelling
• Flow accumulation identifies probable drainage paths and contributing areas
• Watersheds delineated from pour points and corrected drainage networks
• Terrain outputs checked against contours, imagery and known ground conditions

Participants can build transparent suitability models from weighted spatial criteria.

• Decision criteria defined with stakeholders, constraints and measurable thresholds
• Constraint masks exclude prohibited, unsafe or unsuitable locations
• Reclassification standardises criterion values onto a common suitability scale
• Weighted linear combination combines factors using documented relative weights
• Analytic Hierarchy Process compares criteria through pairwise judgement matrices
• Sensitivity analysis tests how changed weights affect priority areas

Participants can prepare decision-ready maps and documented workflows that others can review.

• Map purpose determines scale, projection, symbology and information hierarchy
• Classification legends use intuitive ordering, labels and visible class distinctions
• Layout elements include title, source, date, scale bar and coordinate grid
• Processing parameters recorded in model histories and technical notes
• Quality assurance checklists test geometry, attributes, raster values and layouts
• Findings reported with uncertainty, limitations and defensible recommendations

Participants can build, review and present a complete GIS and remote sensing analysis for a defined decision.

• Project brief translated into datasets, methods, outputs and review criteria
• Source imagery and GIS layers prepared using documented preprocessing decisions
• Classification, index or suitability analysis selected for the stated problem
• Validation evidence used to assess accuracy, uncertainty and limitations
• Decision-ready map series produced with methods summary and metadata
• Peer review tests assumptions, reproducibility and interpretation of findings
• Final presentation explains conclusions, risks and recommended operational actions