1. An Introduction to Remote Sensing

This is a learning diary for week 1.
Let’s get started with remote sensing to explore our world 🌏

1.1 Summary

1.1.1 Remote sensing 📡

  • Acquiring information from a distance through sensors such as satellites, planes(aerial imagery), drones etc. - e.g. satellites collect data on the same points on Earth every day to every 16 days.
  • Type of orbit
    • geosynchronous orbit (GSO): satellite matches the Earth’s rotation
    • geostationary orbit: holds the same position

1.1.2 Types of waves 〰️

  • An electromagnetic wave has two components, an electric field(moving up and down) and a magnetic field(moving left and right).

    Types How to work Emission Examples
    Passive use available energy
    (from the sun)    		 Nothing Human eye, camera,
    satellite sensor
    Active have an energy source for illumination emits electromagnetic waves and then waits to receive Radar, X-ray, LiDAR

Source: Nadhir Al-Ansari

  • Electromagnetic radiation(EMR) from the sun isn’t automatically reflected. It interacts with Earth’s surface and chchangesefore hitting the sensor.- e.g. being absorbed by the surface, transmitted through the surface, or (sunlight is) scattered by particles in the atmosphere.

  • Sunlight

    • An electromagnetic wave emitted by the sun.
    • The shorter the wavelength,
      • the more robust the electromagnetic wave energy
      • the bluer it is 🌈
    • The visible colour usually changes depending on the distance light reaches and the size of particles in the atmosphere.
    Types Feature
    Visible light

    • has a rainbow spectrum

    • Particles or molecules scatter unabsorbed ultraviolet rays in the atmosphere, and we can only observe this.
    Infrared light
    • invisible
    Ultraviolet light

    • invisible

    • absorbed mainly by the ozone layer

Source: NASA

1.1.3 Light scattering & methods to gauge distance 📐

  • A phenomenon in which light with straightness is scattered in all directions by rough surfaces or tiny particles.

  • It makes distance measurement difficult (e.g. atmosphere, clouds) → we need alternative methods to gauge distance

    • Synthetic aperture radar (SAR): Polarization, Fluoresence

    • Bidirectional Reflectance Distribution Function (BRDF): reflects visible and invisible parts simultaneously depending on the satellite angle

    • Atmospheric correction: remove what affected by the atmospheric scattering

      • Low resolution means it has a large pixel size
      • Four elements to decide the resolution of Remotely sensed data
      Types Feature Examples
      Spatial
      • the size of the raster grid per pixel
      		 20cm / 30m
      Spectral
      • the number of bands it records data in
      		 -
      Temporal
      • the time it revisits
      		 daily / weekly / on demand
      Radiometric

      • identify differences in light or reflectance

      • lower the radiometric resolution, the lower the quality of the image

      		 4/8/11-bit sensor

1.2 Application

  • In forestry: Forest (soil) survey, pest damage investigation, forest fire damage investigation, tree species discrimination (confirming the distribution of coniferous or broadleaf trees) by taking seasonal aerial photographs.

  • In architecture: to crack down on illegal buildings or check the boundaries of buildings / cadastral lines.

  • to identify or prevent the degree of damage from natural disasters such as air pollution and floods.

  • to check time-series changes in distances or spaces at specific locations.

  • aerial photographs of famous tourist attractions are interpreted as artwork.

  • When choosing the most appropriate sensor for the analysis, the following factors should be considered: Size of features, date range, revisit requirement, spectral sensitivity, cost etc.

1.3 Reflection

I tend to check the location in advance with satellite images through Google Maps before I go to an unfamiliar place. I also used annual aerial photographs to check the phenomenon of the urban sprawl of Suwon City when I worked in South Korea. Learning about remote sensing data in Week 1 reminded me that I have been unconsciously using tremendously advanced technology so far. Still, I have not recognised it because it is commonly used.

Also, I thought that remote sensing data was an optimized task for dealing with massive spaces, such as forests, oceans, rivers, urban areas, rural areas etc. In particular, it may also be commonly used related to agriculture🌾 in rural areas.

p.s. One interesting imagination 🙃

Considering that remote sensing data(e.g. aerial photographs) taken at equal intervals can soon be used as historical data for specific spaces, I thought it would be interesting to make future museums fill with 3D aerial photographs, not relics!