Introduction to Geomatic Principles

Part 1: Geomatic Definition

• Geomatic combines geography and computing to collect, analyze, and share geographic data.
• It includes Geographic Information Systems (GIS), remote sensing, and web mapping.
• Its origins date back to the 19th century with pandemic mapping.
• GIS is a crucial component for managing and analyzing geographic data using tools, methods, and user interfaces.

Part 1: Objectives

• Analyze geographic data spatially, attributively, statistically, and graphically.
• Visualize and model data, including cartography.
• Develop decision support systems.
• Manage databases efficiently and quickly.
• Geomatic encompasses all types of data, not just geographic.

Part 1: Geographic Information

• Geographic information is location-related.
• It can have a geometric form (points, lines, or areas) or exist as tabular data within a GIS.

Part 1: Components

• Semantic (attribute): Descriptive data such as road numbers or geologic layer ages, typically stored in tables.
• Geometric (entity): Data represented by points, lines, and areas.

Part 1: Representation

• Geographic information can be represented through two models.

Part 2: Raster Model

• Represents data as a numerical matrix (pixels).
• Used for imagery, representing spatial data accurately.

Part 2: Vector Model

• Represents data as geometric shapes (points, lines, polygons) with attributes.
• Enables creation, modification, and overlay of data.

Part 2: Georeferencing

• Locates objects using coordinates based on geodetic systems.
• Geodetic systems model Earth's shape.

Part 2: Datums

• Global datums (e.g., WGS 84): Used for worldwide applications (e.g., GPS).
• Local datums (e.g., ED50): Suit specific regional contexts.

Part 2: Projections

• Transforming Earth's 3D surface onto a flat 2D plane.
• Different projections preserve different properties (e.g., area or angles).

Part 2: Common Projection Types

• Equivalent: Preserves areas but distorts angles and distances (e.g., Lambert Cylindrical).
• Conformal: Preserves angles but distorts areas and distances (e.g., Mercator).
• Aphylactic/Equidistant: A balance of area and angle preservation.
• Azimuthal: Best for representing poles or specific regions.

Part 3: Geomatic Application

• Modifying old maps to modern ones.
• Creating thematic maps based on geographic data.
• Analyzing geographic data.
• Developing interactive maps.
• Manage large datasets.

Part 3: Geospatial Analysis

• Links geographic data with specific locations.
• Employs computer tools to understand relationships between geographic features and phenomena (human, environmental, and economic).
• Utilize satellite images and GPS data.

Part 3: Geoprocessing

• Manipulates geographic data for analysis.
• Combines data, adapts to standards, and creates new data.
• Can be used for tasks like analyzing land use, calculating distances, and processing satellite imagery, including identification of vegetation types.

Part 3: Geoprocessing Tools

• ArcGIS and QGIS are common GIS software used for geoprocessing.
• Python is a programming language useful for geoprocessing operations.

Part 4: Multi-Criteria Analysis

• Evaluates several options based on multiple criteria (natural, social, economic).
• Involves defining criteria, collecting data, normalizing it, combining them (like AHP), and interpreting results.
• Relevant to resource management and urban planning.

Part 4: Attribute Database Management

• Stores descriptive information about geographic features (e.g., names, addresses, measurements).
• Field types and data structures must be defined to include data.
• Data can be added manually or through importing it.
• Data can be modified and updated.
• Linked to geography through unique identifiers.

Part 5: Remote Sensing (Definition)

• Studying objects remotely using sensors.
• The human eye is a simple form of remote sensing.
• Processes involve the capture, transmission, and analysis of data collected from a distance (without physical contact).

Part 5: Remote Sensing Process

• Sensors capture energy emitted or reflected by objects.
• Data is transmitted and processed in the form of images or numerical data.
• Analysts interpret the results.

Part 5: Remote Sensing Types

• Passive: Uses natural energy sources (e.g., sunlight).
• Active: Uses artificial energy sources. (e.g., radar).

Part 6: Importance of Remote Sensing

• Global Coverage: Studies phenomena globally.
• Multiple Scales: Studies at different levels of detail.
• Access to Hidden Information: Provides data on aspects not directly observable (e.g., ocean temperatures, air pollution).
• Numerical Processing: Aids in statistical and quantified analyses.
• Limitations: Not as precise as on-site measurements or photographic imagery.

Part 6: Spectral Bands

• Visible Light (0.4-0.7 μm): The part of the electromagnetic spectrum we can see.
• Near-Infrared (0.7-1.1 μm): Reflected light from objects. This, like visible light, is used in remote sensing.
• Middle Infrared (1.1-8 μm): Measures atmospheric gases (e.g., water vapor, ozone).
• Thermal Infrared (8–14 μm): Measures the heat emitted by objects.
• Microwave (mm-m): Penetrates clouds and other atmospheric elements and is used for remote sensing.

Part 6: Sensor Characteristics

• Spectral Resolution: Sensor's ability to detect different wavelengths.
• Spatial Resolution: The area covered by each measurement.
• Radiometric Resolution: The precision/sensitivity in detecting slight differences in energy levels.
• Temporal Resolution: Time between observations of the same area.

Part 7: Data Acquisition Methods

• Photography: A traditional method, even in space missions.
• Radiometers: Measure emitted or reflected energy, creating images.
• Radar Imagery: Uses emitted energy, useful for all-weather operation because radar signals can pass through clouds.

Part 7: Data Acquisition Platforms

• Aerial platforms: Aircraft, helicopters, balloons for detailed but limited area coverage.
• Satellites: Continuous and repetitive coverage but of lower resolution than the aforementioned methods.

Part 7: Earth Observation Missions

• Landsat: NASA/USGS program for monitoring earth's changes.
• SPOT: French program for earth observation.
• Sentinel: European Copernicus program for earth monitoring.
• ALSAT: Algerian satellite program.

Part 8: Image Data Processing

• Gray Scale Visualization: Displays images in varying shades of gray to distinguish different features.
• Color Visualization: Shows images using colors to identify objects.
• False Color Composition: Uses a color scheme that emphasizes features not normally apparent.
• Band Ratios: Manipulates bands to highlight features not obvious in a single image.
• Principal Component Analysis (PCA): Reduces multiple bands into a smaller set of informative components, revealing underlying patterns.

Part 9: Remote Sensing Applications

• Marine (water): Temperature, salinity, currents, water quality.
• Terrestrial (land): Vegetation, agriculture, urban development, and natural disasters.
• Atmospheric (air): Weather forecasting, air quality, and greenhouse gas monitoring.

Explore the foundational concepts of geomatic science, which integrates geography and computing to collect and analyze geographic data. This quiz covers essential topics like GIS, remote sensing, and the history of pandemic mapping. Assess your understanding of data management and visualization techniques in the context of geographic information systems.