Lecture 8c - Remote Sensing

Introduction to Remote Sensing

  • Remote sensing is a field that involves using various methods to gather information about objects or areas from a distance, typically from airborne or spaceborne platforms.
  • This lecture provides a brief introduction to essential aspects of remote sensing relevant for interpreting geophysical surveys.

Key Concepts in Remote Sensing

Data Collection Platforms
  • Remote Sensing Platforms:
    • Satellite Data:
    • Offers global coverage at low cost, typically funded by governments or international consortiums.
    • Satellites can be geostationary (viewing the same area continuously, lower resolution) or orbiting (higher resolution).
    • Some satellites also collect data from extraterrestrial bodies like Mars.
    • Airborne Remote Sensing:
    • Uses closer camera systems to provide higher resolution data (e.g., down to centimeters).
    • More responsive data collection following events of interest (e.g., natural disasters).
    • Issues may include aircraft availability and cost limitations.
    • Ground-Based Systems:
    • Useful for frequent surveys and smaller scale details.
Classification of Remote Sensing Systems
  • Remote sensing systems can be classified based on:
    • Data Collection Method:
    • Standard Cameras with megapixel sensors (common in consumer devices).
      • Example: Mobile phones have sensors ranging from 10 to 50 megapixels.
    • Specialized Cameras (e.g., 200 megapixels for airborne sensing).
    • Sensor Type:
    • Image construction methods vary; single sensors (scanners) build images incrementally rather than all at once.
    • Cross Track Scanning: uses mirrors to scan sideways and forwards.
    • Down Track Scanning: a linear array of sensors captures images as the platform moves.
    • Sensitivity Spectrum:
    • Monochromatic Cameras: Sensitive to visible light (RGB bands).
    • Multispectral Sensors: Collect data over multiple electromagnetic spectrum bands (e.g., 10 bands).
    • Hyperspectral Sensors: Capture hundreds of bands per pixel leading to extensive data complexity.

Data Processing Workflow

  • Steps involved in converting raw data into useful information:
    1. Georeferencing and Geometric Corrections:
    • Determines the geographical location of sampled data and corrects inaccuracies due to the capture process.
    • For example, aerial imagery might change from rectangular to irregularly shaped post-correction.
    1. Radiometric Corrections:
    • Adjusts at-sensor radiance values to represent actual ground surface radiance, considering atmospheric effects.
    1. Calculation of Property Values:
    • Derive property values from radiance (e.g., albedo, temperature).
    1. Analysis and Information Extraction:
    • Classifying land use types or detecting thermal anomalies (e.g., volcano monitoring).
Thermal Image Interpretation
  • Thermal Imaging:
    • Based on radiance received; temperature estimation relies on the assumption of surface homogeneity at the pixel level, complicating interpretations.
    • Stefan-Boltzmann Law: Relates power loss to temperature as Pext(radiation)extextisproportionaltoT4P ext{ (radiation)} ext{ } ext{is proportional to } T^4, where TT is temperature.
    • Subpixel Features: Brightness in thermal images can misrepresent the temperature range due to non-homogenous coverage within a pixel.
Case Study: MODIS
  • MODIS Sensor: Frequently used for volcanic activity detection due to multiple daily overpasses.
    • Identifies hot spots, even with a resolution of around 1 km per pixel, useful for monitoring smaller volcanic features.

Visualization Techniques

  • Visualization is essential for interpreting data, especially in more complex datasets (multispectral/hyperspectral).
  • Techniques:
    • Mapping single or multiple bands to RGB channels.
    • For hyperspectral images, choosing specific bands for visualization can highlight features (e.g., mineral detection).
    • Use tools like GIS for flexibility in combining remote sensing data with other geophysical measurements.

Conclusion

  • Remote sensing involves comprehensive data processing steps to transform raw data into valuable information.
  • Effective visualization is crucial to simplify interpretation and aid in decision-making, especially in applications like mineral exploration or environmental monitoring.