CONTEXTField conditions and systems being assessed
Data Processing
Satellite Imagery
Marine remote sensing technology is currently developing rapidly, along with advances in sensor technology for detecting objects on the water surface.
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
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ANALYSISIntegrated data, methods, and modelling
DECISIONVisual outputs and actionable recommendations
Executive Brief
Clarity before a decision is made
01Evidence-led
02Traceable assumptions
03Decision-ready outputs
04Methods proportionate to risk
Executive Brief
Satellite Imagery
Clarity before a decision is made
Marine remote sensing technology is currently developing rapidly, along with advances in sensor technology for detecting objects on the water surface.
In general, satellite imagery data have very large data volumes, depending on the resolution and spatial coverage of the imagery. There are two approaches to satellite imagery processing for modeling purposes.
Decision Supported
Define the approach, priorities, and actions for satellite imagery using traceable evidence.
Risk Controlled
Errors in projection, units, timing, quality control, interpolation, and interpretation.
Success Criteria
Reproducible processing, verified results, and usable output formats.
Analysis Scope
What is assessed and why it matters
Data inventory and inspection
This aspect is assessed to clarify its implications for satellite imagery.
Cleaning, correction, and standardization
This aspect is assessed to clarify its implications for satellite imagery.
Spatial, temporal, and unit transformation
This aspect is assessed to clarify its implications for satellite imagery.
Statistical, spatial, or image analysis
This aspect is assessed to clarify its implications for satellite imagery.
Cross-validation and quality control
This aspect is assessed to clarify its implications for satellite imagery.
Visualization, documentation, and export
This aspect is assessed to clarify its implications for satellite imagery.
Data & Methods
A traceable evidence base
Observations
Field surveys, in-situ measurements, laboratory results, historical records, and operating information as required.
Remote sensing & GIS
Satellite imagery, mapping, spatial analysis, temporal change, and integration of multiple data sources.
Modeling & scenarios
Model setup, calibration, validation, existing–planned–extreme scenarios, and sensitivity analysis.
Quality assurance
Metadata, quality controls, assumptions, limitations, data versions, and processing lineage are documented.
Core Deliverables
Decision-ready information
Initial assessment & data gaps
Objectives, study area, available data, additional needs, initial risks, and recommended level of detail.
Datasets, maps & indicators
Quality-controlled data, thematic maps, time series, indicators, and comparable visualizations.
Scenarios & risk evaluation
Comparison of existing conditions, alternatives, extremes, sensitivities, consequences, and mitigation options.
Report & executive brief
Methods, results, limitations, recommendations, action priorities, and stakeholder presentation materials.
Decision Value
Benefits for decision makers and policy leaders
Reduce uncertainty
Assumptions, data, variability, and limitations are stated so decision risk is not hidden.
Compare options objectively
Alternative locations, designs, operations, or policies are assessed using consistent indicators.
Optimize cost and time
Data needs and analysis depth are proportionate to risk so resources are used efficiently.
Increase stakeholder confidence
Findings and recommendations are transparent for technical, management, regulatory, and partner review.
Delivery Path
A clear process from need to recommendation
- 01
Need definition
Objectives, users, location, project phase, problems, constraints, and the decision to support.
- 02
Scope & work plan
Methods, data, surveys, models, schedule, team, deliverables, review gates, and resource estimate.
- 03
Acquisition & quality control
Collection, inspection, harmonization, documentation, and data-sufficiency assessment.
- 04
Analysis & scenario testing
Processing, modeling, validation, option comparison, sensitivity, and risk evaluation.
- 05
Recommendation & handover
Maps, report, executive brief, presentation, supporting data, and follow-up plan.
Full technical basis and contextOpen this section to read the complete source technical narrative.
Marine remote sensing technology is currently developing rapidly, along with advances in sensor technology for detecting objects on the water surface. Various datasets provided by international research institutions can be used by combining satellite imagery with model outputs. The purpose is to make various types of analysis easier by integrating remote sensing technology and modeling technology. Satellite imagery can be used to verify and validate models. In addition, satellite imagery data can also be used as model input data, such as QuikSCAT and SeaWinds satellite data, which provide wind speed and wind direction over the sea surface.
In general, satellite imagery data have very large data volumes, depending on the resolution and spatial coverage of the imagery. There are two approaches to satellite imagery processing for modeling purposes. The first approach processes satellite imagery as image-based information, where the data are qualitative. The second approach processes satellite imagery as data that contain numerical values for each pixel, where the data are quantitative.
The first approach is used in modeling as additional supporting information for model outputs. For example, hyperspectral satellite imagery can be used to map coastal vegetation types. This information is useful for identifying areas that are sensitive to oil spills based on results produced by an Oil Spill model module. The second approach is directly involved in the numerical calculations of the model. For example, wind data from QuikSCAT and SeaWinds satellite imagery can be used as model input data and as comparison data in the verification and validation process.
Various commercial and non-commercial satellite image processing software packages are widely available. The right choice depends on user needs and the capability of the software to meet image processing requirements. Using the appropriate software can provide a high level of efficiency and effectiveness to support modeling activities quickly and accurately.
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Learn more →Next Step
Share the need, location, available data, and the decision to be supported.
The CORZ team will review the objective, scope, data availability, risk level, schedule, and required outputs to prepare a proportionate approach.
Useful initial information
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule