A Brief Overview of Remote Sensing
Remote sensing is most easily described by the gathering of data about the earth and its landscape from afar using satellites or other airborne sensors. Remote sensing is a broader term which can be broken down into 2 varying types: passive and active. Passive remote sensing responds to external stimuli while producing no stimuli of its own. Passive remote sensing most often receives signals from sunlight energy which has been reflected off the surface of the planet. For this reason, passive remote sensing includes radiometers and spectrometers, which receives energy from electromagnetic waves and the light spectrum respectively. A major limitation of passive sensing is that the light stimuli it detects is unable to go through clouds or dust, meaning the signal can sometimes be blocked from reaching the sensor, decreasing the amount of the image a researcher can then see.
Unlike passive sensing, active sensing creates its own stimuli to then recieve following its reflection. An example of active sensing is laser remote sensing. This form of sensing produces its own laser which it then projects towards the earth’s surface. The sensor then records the duration of time it takes to receive the laser back. This is optimal to measure the verticality of the surface of the earth since it gives an accurate measurement of the time to get there and back. Another example of active remote sensing is Lidar which also sends a stimulus to the environment and measures the time it takes for the stimulus to return. Active remote sensing is able to cut through clouds and other debris since the sensors are focused on the radio wave band of the electromagnetic spectrum.
Remote sensing varies in other ways than just whether or not it is producing its own stimuli, however. For example, satellites which house remote sensors can fly in a variety of orbitals. Polar orbitals allow the remote sensors to fly at a 90 degree angle to the equator. This orientation means that the remote sensor will fly over both poles and get a global view of the surface. Polar orbitals are also sun-synchronous which means they are followed by the sun, allowing there to be sunlight wherever the satellite covers. Non-polar orbitals follow a path which is more diagonal to the equator, say a 60 degree angle. This means that the remote sensor is unable to see the entirety of the globe or the poles. This trade off means that the remote sensors will often fly closer to the surface of the earth, allowing a closer image of whatever the target area is. Lastly, there are geostationary orbitals. These orbitals follow the rotation of the earth, meaning it will stand in one space on the earth. This allows for constant coverage of the same area. Because of this advantage of monitoring one area continuously, geostationary remote sensing is optimal for weather tracking.
The choice of orbitals is juxtaposed with the choice of resolution. There are a variety of different types of resolution, including radiometric, spatial, and temporal. To start, radiometric resolution directly relates to the amount of information in each pixel. The higher the radiometric resolution number, the higher the amount of information in each pixel. That information is represented by the number of bits in a pixel. There can be 2 bit, 4 bit, 8 bit, etc. Bits are exponential, where it is 2^n and n is the number of bits. For example, 2 bit would be 2^2 and 8 bit would 2^8. Likewise, spatial resolution is a representation of the amount of distance that is covered by one pixel of a picture of the earth. For example, a resolution could be 40 meters per pixel, or it could be 150 meters per pixel. Intuitively, 40 meters per pixel produces a clearer image because it means that in a larger picture there are more pixels to represent the land. The more pixels there are, the clearer the image. Lastly, temporal resolution refers to the duration of time it takes for a satellite to loop back around the earth and reach the place it started at. Geostationary orbits can have very short temporal resolutions at 30 to 60 seconds while polar orbits could be closer to 0 to 15 days. This discrepancy in temporal image can be good or bad depending on the human development goal. Tracking the weather in a specific area of the globe requires a very small temporal resolution while subtle changes to water level over an extended period of time can have a larger temporal resolution. The type of resolution, not just temporal, is dependent on the goal at hand and up to the researcher to determine which combination most effectively fits the problem.
Potential applications of remote sensing are nearly limitless. The continued sweep of sensors allow researchers to track the changes in shorelines or the erosion of a coast. Likewise, they can monitor rising sea levels using the verticality determination of active remote sensing. In a similar vein and as aforementioned, geostationary orbits can be used to track weather and other natural hazards like hurricanes or tornadoes. This allows cities to be notified beforehand and assess the risk and potential solutions to problems which have yet to come. Another natural hazard which has topical relevance today is the rampant forest fires which plague the western coast of the US. Using remote sensing, researchers and those who wish to find tangible solutions can track the real time spread of forest fires and predict where they will go using past forest fires as predictors. These countless examples illustrate the real impact remote sensing has on disaster prevention and even more.
A tangible example which I found while completing my annotated bibliography is that of the rise in flooding of Ho Chi Minh city in Vietnam. The researchers tracked the spread of urbanization using remote sensing as well as the verticality of the areas they settled on. They tested 3 different types of resolutions, in this case the spatial resolution, and wanted to see which most accurately captured the change over time. Their conclusion was that a sub 1 meter spatial resolution was the only resolution which gave a clear enough image of the urban sprawl. As well, they were capable of illustrating the alarming flooding which was possible in the future as a result of established residential areas being so close to sea level accompanied by the rising sea level. This tangible example of remote sensing further illustrates the indisputable tool it is when striving for productive human development.