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Remote Sensing – an emerging latest technology

REMOTE SENSING is the getting of information about an object or phenomenon without making physical contact with the object. In modern usage, the remote sensing generally refers to the use of aerial sensor technologies to detect and classify objects on Earth’s surface and in the atmosphere (including oceans) by means of electromagnetic radiation emitted from aircraft or satellites (propagated signals). Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring in areas such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of coastal and ocean depths. Military collection during the Cold War made use of stand-off collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed.

There are two main types of remote sensings - passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas.The most common source of radiation measured by passive sensors is reflected sunlight.

Examples of passive remote sensors include film photography, infrared, charge-coupled devices, and radiometers. On the other hand, active collection emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target. RADAR and LiDAR are examples of active remote sensing where the time delay between emission and return is measured, establishing the location, speed and direction of an object. The uses of remote sensing include different areas of the earth sciences such as natural resource management, agricultural fields such as land usage and conservation, and national security and overhead, ground-based and stand-off collection on border areas.

Generally, remote sensing works on the principle of the inverse problem. While the object or phenomenon of interest may not be directly measured, there exists some other variable that can be detected and measured, which may be related to the object of interest through the use of a data-derived computer model. The common analogy given to describe this is trying to determine the type of animal from its footprints. For example, while it is impossible to directly measure temperatures in the upper atmosphere, it is possible to measure the spectral emissions from a known chemical species. The frequency of the emission may then be related to the temperature in that region via various thermodynamic relations. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions.

SPATIAL RESOLUTION: The size of a pixel that is recorded in a raster image-typically pixels may correspond to square areas ranging inside length from 1 to 1,000 metres.

SPECTRAL RESOLUTION: The wavelength width of the different frequency bands recorded – usually, this is related to the number of frequency bands recorded by the platform. Current Landsat collection is that of seven bands, including several in the infra-red spectrum, ranging from a spectral resolution of 0.07 to 2.1 µm. The Hyperion sensor on Earth Observing-1 resolves 220 bands from 0.4 to 2.5 µm, with a spectral resolution of 0.10 to 0.11 µm per band.

RADIOMETRIC RESOLUTION: The number of different intensities of radiation the sensor is able to distinguish. Typically, this ranges from 8 to 14 bits, corresponding to 256 levels of the gray scale and up to 16,384 intensities or “shades” of colour, in each band. It also depends on the instrument noise.

TEMPORAL RESOLUTION: The frequency of flyovers by the satellite or plane, and is only relevant in time-series studies or those requiring an averaged or mosaic image as in deforesting monitoring. This was first used by the intelligence community where repeated coverage revealed changes in infrastructure, the deployment of units or the modification/introduction of equipment. Cloud cover over a given area or object makes it necessary to repeat the collection of said location.

In order to create sensor-based maps, most remote sensing systems expect to extrapolate sensor data in relation to a reference point including distances between known points on the ground. This depends on the type of sensor used.

The most common application of remote sensing is, Conventional radar which is mostly associated with aerial traffic control, early warning, and certain large scale meteorological data. Doppler radar is used by local law enforcements’ monitoring of speed limits and in enhanced meteorological collection such as wind speed and direction within weather systems. And some other common applications are, light detection and ranging (LIDAR) is well known in examples of weapon ranging, Earth observing satellites, Radiometer and Photometer, Aerial photographs and Hyperspectral imaging.

NASA is providing an opportunity of internship for students to know about applications of remote sensing. Working through NASA, this programme give students experience in real-world remote sensing applications, as well as providing valuable training. (More information can be found on the NASA DEVELOP website.

The writer is undergraduate student of Electrical Engineering, Quaid-e-Awam University of Engineering Science & Technology (QUEST), Nawabshah, Sindh, Pakistan. He can be reached at <>


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