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3.13. Remote Sensing (TLD)

This knowledge area covers the acquisition and interpretation of information about the Earth's surface from satellite, airborne, and UAV sensors: remote sensing fundamentals, digital image processing, active sensing (LiDAR and radar), and remote sensing applications in civil engineering.

Knowledge Area (KA)	Core Tier1	Core Tier2
3.13.1 Remote Sensing Fundamentals	Elective
3.13.2 Digital Image Processing	Elective
3.13.3 Active Remote Sensing: LiDAR and Radar	Elective
3.13.4 Remote Sensing Applications in Civil Engineering	Elective

3.13.1. TLD/Remote Sensing Fundamentals ↑ Back to top

The electromagnetic spectrum and its interaction with Earth surface materials, passive and active sensor types, the resolution concepts that characterize a sensor, and the platforms that carry them.
Topics:
Core

• The electromagnetic spectrum and spectral signatures of Earth surface materials
• Passive versus active sensor types
• Spatial, spectral, temporal, and radiometric resolution concepts
• Satellite, airborne, and UAV remote sensing platforms
• Atmospheric effects on remotely sensed signals

Learning Outcomes:
Core:

1. Explain the electromagnetic spectrum and the spectral signature concept [Familiarity]
2. Distinguish passive from active remote sensing and identify example sensors of each [Familiarity]
3. Select a sensor and platform based on required spatial, spectral, and temporal resolution [Usage]
4. Describe the remote sensing platforms available for a given application [Familiarity]
5. Account for atmospheric effects when interpreting remotely sensed data [Assessment]

3.13.2. TLD/Digital Image Processing ↑ Back to top

Radiometric and geometric preprocessing of remotely sensed imagery, image enhancement, derived spectral indices, and the image classification techniques used to extract thematic information.
Topics:
Core

• Radiometric correction of remotely sensed imagery
• Geometric correction and orthorectification of imagery
• Image enhancement techniques: contrast stretching and filtering
• Spectral band combinations and indices (e.g. NDVI)
• Supervised and unsupervised image classification

Learning Outcomes:
Core:

1. Apply radiometric correction to a remotely sensed image [Usage]
2. Orthorectify imagery using ground control and a terrain model [Assessment]
3. Enhance an image using contrast stretching and spatial filtering [Usage]
4. Compute spectral indices (e.g. NDVI) from multispectral imagery [Assessment]
5. Classify an image into thematic categories using supervised or unsupervised methods [Assessment]

3.13.3. TLD/Active Remote Sensing: LiDAR and Radar ↑ Back to top

Operating principles and derived products of LiDAR and synthetic aperture radar, including the InSAR techniques used for ground deformation monitoring.
Topics:
Core

• LiDAR operating principles: ranging, scanning, and point density
• LiDAR-derived products: classified point clouds, DEMs, and canopy models
• Synthetic Aperture Radar (SAR) fundamentals
• Interferometric SAR (InSAR) for ground deformation monitoring
• Point cloud processing and feature extraction from active sensors

Learning Outcomes:
Core:

1. Explain LiDAR operating principles and the factors governing point density [Familiarity]
2. Derive a classified point cloud, DEM, or canopy model from LiDAR data [Usage]
3. Describe Synthetic Aperture Radar fundamentals and its advantages over optical sensing [Familiarity]
4. Monitor ground deformation using InSAR time-series analysis [Assessment]
5. Process and extract features from a point cloud acquired by an active sensor [Usage]

3.13.4. TLD/Remote Sensing Applications in Civil Engineering ↑ Back to top

Application of remote sensing to land cover and change detection, environmental and hazard monitoring, infrastructure monitoring, and its integration with GIS for engineering decision-making.
Topics:
Core

• Land cover classification and multi-temporal change detection
• Environmental and natural hazard monitoring using remote sensing
• Infrastructure condition and deformation monitoring using remote sensing
• Integration of remote sensing products with GIS for analysis and decision-making
• End-to-end remote sensing application workflows for an engineering project

Learning Outcomes:
Core:

1. Classify land cover and detect multi-temporal change from a satellite image series [Usage]
2. Monitor an environmental or natural hazard condition using remote sensing data [Assessment]
3. Monitor infrastructure condition or deformation using remote sensing techniques [Usage]
4. Integrate remote sensing products into a GIS-based engineering analysis [Assessment]
5. Design an end-to-end remote sensing application workflow for an engineering project [Assessment]