Remote sensing

Remote sensing makes use of the interaction of energy waves to measure objects or materials from a distance, without physical contact. It is an analytic technique with wide-ranging applications in many fields, including meteorology, resource management, national security, environmental monitoring, agriculture, and mapping.

What Is Remote Sensing?

As its name implies, remote sensing is the collection of data remotely, or from a distance, whether of an object or area. In most cases, remote sensing involves the active or passive reading of a variety of radiations from the electromagnetic spectrum as they are emitted, reflected, or scattered from the object or phenomenon being observed. Other propagated signals (such as sound waves) also can be used.

Because remote sensing can be performed from a large distance, it has wide-reaching applications in many different areas of study. It is particularly useful in applications involving the environment, agriculture, meteorology, mapping, and national security.

Environmental remote sensing is aimed at understanding natural and human-made changes to Earth’s ecosystems. One major environmental application of remote sensing is land conservation, which involves monitoring deforestation and land usage, tracking glacial decline, overseeing urban growth, and keeping hazardous waste in check. Environmental remote sensing also can be used in the conservation of natural resources, both renewable (oceans, soil, forests, wetlands) and nonrenewable (natural gas, minerals, oil).

Remote sensing also has direct applications to agriculture. It can be used to monitor crop conditions, predict crop yield, and measure soil erosion. (The monitoring of soil erosion is crucial; preventive measures do exist and can be implemented when needed.) Remote sensing is useful for monitoring crop damage from weeds, insects, wind, hail, and herbicides, and the resulting information can help farmers know which sections of a farm, for example, need fertilizer or pesticides.

In meteorology, remote sensing can be used to predict weather patterns and track short-term and long-term weather trends, such as hurricanes and tropical storms. It also is an essential technique for monitoring ozone depletion and global warming.

Remote sensing is widely used in map creation, particularly in terms of topography, the study of Earth’s surface features (such as mountain ranges and other landforms). In addition to mapping surface features, remote sensing can help to map coastal and ocean depths through a technique known as depth sounding. The study of these depths is called bathymetry. Because remote sensing does not require physical contact, it is useful in the exploration of dangerous or inaccessible areas. Remote sensing is an integral aspect of geographic information systems (GIS).

Finally, as national security often requires the detailed knowledge of inaccessible and hazardous areas, remote sensing is an essential tool for military surveillance and strategy. Security-related remote sensing often requires particularly powerful tools that have both a high temporal resolution (timely results) and a high spatial resolution (finely detailed results).

The History of Remote Sensing

Early remote sensing has its basis in the fields of photography and aviation. The modern history of remote sensing began in 1858, when French photographer and balloonist Gaspard-Félix Tournachon (also known by the pseudonym Félix Nadar) took photos of Paris from his hot-air balloon, in which he had a makeshift darkroom. For approximately the next century, camera photography was essentially the only method of overhead remote sensing, and the technology developed alongside the development of flight technology. In addition to balloons, other platforms were used to carry cameras, including messenger pigeons, kites, and early rockets.

Other forms of remote sensing began in the 1950s with the growing availability of satellite platforms, electro-optical sensor systems, and quantitative analytical tools for measuring and interpreting the resulting photographic and electro-optical images. The year 1959 marked the first time photos of Earth were taken from space, from a nonorbital space flight of Explorer 6. In 1961 an unpiloted Mercury-Atlas flight (MA-4) took the first color photos of Earth from space. The Television Infrared Observation Satellite (TIROS-1) began monitoring weather and the global environment from space in 1960. The Cold War also was a driving factor in the development of remote sensing during this time; systematic aerial photography became a necessary defensive tool.

The early 1970s brought about the further development of space-based remote sensing. The first Landsat multispectral scanner system, for example, launched in 1972 to begin the systematic acquisition of satellite imagery of Earth. Although quite advanced at the time, its features and resolution are modest by twenty-first century standards. Groups such as the National Aeronautics and Space Administration (NASA), the Jet Propulsion Laboratory (a NASA program), and the US Geological Survey (USGS) have continued to drive the development of remote sensing. Improved computer technology and mobile devices have vastly improved the quality and accessibility of the field in the twenty-first century.

Remote Sensing and the Electromagnetic Spectral Range

Remote sensing depends on the interaction of energy and the matter that is being measured; the observed energy, more specifically, is radiation that is present in the electromagnetic spectrum (including the visible spectrum; that is, what humans can see). Electromagnetic radiation refers to energy waves traveling at the speed of light; the spectrum is divided into regions by wavelength.

The visible spectrum, for example, has wavelengths between 380 nanometers (nm) and 760 nm (790-400 terahertz). Radio waves, though, are much larger, with wavelengths reaching hundreds of meters. Remote sensing instruments can measure electromagnetic radiation that is emitted, reflected, or scattered by an object. Many materials found on Earth and in the atmosphere have a unique spectral signature—a “fingerprint” that allows conclusions to be drawn from the measurements taken by a remote sensing instrument.

Remote sensing can be divided into three classes with respect to the wavelength region being detected. These classes are visible and reflective infrared, thermal infrared, and microwave. Visible and reflective infrared remote sensing is mainly limited to passive, not active, remote sensing because the energy source is the sun (and not the sensing instrument). This type of remote sensing is particularly useful for materials at Earth’s surface, where reflected energy from the sun exceeds the earth’s own emitted energy. This region of the spectrum includes wavelengths between 0.4 and 3 micrometers (µm), encompassing the visible, near infrared, and short-wave infrared regions of the electromagnetic spectrum.

Thermal (long-wave) infrared remote sensing measures energy radiated from objects, beginning at a wavelength around 5 µm and peaking at 10 µm. Because this type of remote sensing does not depend on the reflectance of solar energy, thermal infrared remote-sensing can be done at night. Forward-looking infrared cameras (found on military planes) use this region of the spectrum to aid operators in steering at night.

Microwave remote sensing deals with wavelengths on a larger order, typically between 1 millimeter and 30 centimeters. While some passive remote sensing systems can detect microwaves, active remote sensing also can be used by measuring the way the observed material backscatters the radiation emitted by the sensing system.

Passive Remote Sensing Techniques and Tools

Remote sensing can be described as passive or active. Passive remote sensing involves the detection of radiation emitted or reflected by the observed object, material, or phenomenon; the sensing system measures but does not actively emit radiation itself. Passive remote sensing can encompass visible and reflective infrared, thermal infrared, and some microwave remote sensing. Radiation sources include reflected solar energy and energy inherent in the ground, atmosphere, and clouds.

Passive remote sensing techniques and sensor types include film and digital photography, multispectral scanners, hyperspectral imaging tools, passive sonar, radiometers, photometers, and seismometers. For the purposes of remote sensing, film photography is typically aerial (shot from aircraft or satellites). The use of film photography for remote sensing skyrocketed after World War I as aircraft and photographic technology were both progressing rapidly. Color photography became more popular after World War II, but black and white (panchromatic) photography remained in use as color photography was more expensive.

Traditionally even more expensive is infrared photography, which has clear advantages. Like color film, infrared photography detects three distinct colors. Where color film detects blue, green, and red, infrared film detects green, red, and infrared. The advantage to infrared is that this particular set of wavelengths shows more clearly the distinction between land and water and between healthy and unhealthy vegetation.

While analog film photography established the field of remote sensing, it was essentially replaced by digital photography as that technology grew more affordable. Many digital imaging techniques rely on sensors called charge-coupled devices, which allow for the movement of a charge to a format that can be digitally analyzed.

Other forms of remote sensing use other specialized sensors. NASA’s Landsat program, which began in 1972 with the launch of remote sensing satellite Landsat 1, made use of a multispectral scanning system that could detect four spectral bands of reflected sunlight. One satellite that launched later in the program, Landsat 3, could detect an additional band: thermal infrared radiation. While multispectral imaging covers a number of discrete spectral bands, hyperspectral imaging is similar but covers a whole spectral range without breaks between bands. NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) uses this technology to detect more than 220 contiguous spectral bands between wavelengths of 400 and 2,500 nm.

Other useful remote sensing tools include radiometers, which measure radiant flux, and photometers, which measure luminous flux. While radiant flux describes the total power of electromagnetic radiation emitted from or landing on an object, luminous flux describes only the power of the visible light emitted from the object. (Radiant flux includes infrared and ultraviolet light, not just visible light.)

The term remote sensing is often defined as being dependent on the electromagnetic spectrum. However, the more general idea of measuring something without physical contact also encompasses techniques such as passive sonar (using the propagation of sound waves to navigate or detect objects) and seismography (using seismic waves to determine information about earthquakes, volcanic eruptions, and other ground movements).

Active Remote Sensing Techniques and Tools

Unlike passive remote sensing systems, active remote sensing systems actually create an artificial radiation source as a probe, emitting energy toward the object to be observed and then measuring the reflection or backscattering. Radar (originally an acronym for “radio detection and ranging”) and LIDAR (light detection and ranging) are two of the most common forms of active remote sensing, but the definition can be expanded to include active sonar, which uses sound waves rather than electromagnetic radiation.

Radar techniques make use of radio waves or microwaves to gather information about objects, including their position, speed, and direction. The radar dish or antenna transmits energy wave pulses, which bounce off objects; a sensor at the dish or antenna detects the energy that is returned. Radar signals work especially well with highly conductive materials such as metals and wetlands.

Common uses of radar techniques involve weather monitoring, air traffic control, and security (antimissile systems, for example). It is important to note that although radar has a wide variety of uses, it has a number of limitations: signal noise, interference and clutter from irrelevant objects, intentional or unintentional jamming of the signal, and the curvature of a radar beam path from atmospheric effects.

One common type of radar is Doppler radar, a specialized tool that focuses on the velocity of objects. For example, police can use Doppler radar to detect speeding cars. This technology is based on the Doppler effect, the shift between an emitted wave frequency and the wave frequency observed by someone or something moving relative to the wave source. Consider an ambulance speeding past with siren blaring. The siren sounds differently as it approaches, passes, and moves farther away; the siren itself is emitting a stable wave frequency. Doppler radar puts this into practice by detecting the change in the frequency of the energy reflected back by the moving object in question and then using this information to calculate the object’s velocity.

Another common type of radar is synthetic aperture radar (SAR), which is often used for terrain mapping, environmental monitoring, and military purposes. SARs emit a radiation beam from a moving source, enabling the generation of extraordinarily high-resolution images by utilizing the movement of the radar platform in a way that simulates a large antenna or aperture. Because of the movement required, these radars are most commonly found on aircraft or spacecraft. SAR Earth-observation satellites include RADARSAT from Canada (a satellite pair), TerraSAR-X from Germany, and NASA’s Magellan.

LIDAR works in much the same way as radar except that it uses infrared, visible, or ultraviolet light instead of microwaves or radio waves. LIDAR is more accurate than radar when it comes to the detection of object heights and features because its light beam reacts with a wider range of materials than does a radar beam. Drones may be equipped with LIDAR. Ukraine used drones with LIDAR during its war with Russia. The drones were able to detect ground disturbances such as trenches.

LIDAR applications are varied and include agriculture, archaeology, meteorology, and military. In agriculture, for example, LIDAR can be used to make detailed topographic maps of farmland, giving farmers a detailed look at which sections of their crops get less sun and need more fertilizer. In the military, LIDAR can be used to detect landmines and applications of biologic warfare, among other uses. Beyond the electromagnetic spectrum, active sonar is useful for underwater measurements, particularly the distance to an object.

The Resolutions of Remote Sensing Systems

Remote sensing systems work on the premise of what is known as the inverse problem: determining information about an object or phenomenon by analyzing observed measurements about the object or phenomenon. (Consider, for example, figuring out a type of animal based on its observed footprints.) The quality of the observations of a remote sensing system depends on four main types of resolution: spatial, spectral, radiometric, and temporal.

Spatial resolution refers to the pixel size in an image generated by a remote sensing system and its relationship to the sample size it represents in the area being observed. Low resolution is good for meteorology, for instance, because meteorologists are interested in weather patterns occurring over large sections of land. Meteorology would not need a resolution that can make buildings clear, for example. The equipment in the Landsat program, for instance, tends to have moderate resolution, enough to show large human-made structures (like the Great Wall of China) but not enough to show houses, because that information is barely relevant to satellite images of the earth as a whole. For mapping and military purposes, however, it is generally important for a remote sensing system to have high resolution to provide an accurate and detailed look at small pieces of a large area.

Spectral resolution refers to the wavelength width of the frequency bands recorded by a remote sensing system. Spectral resolution is usually discussed in terms of how many bands a system records, particularly in the case of multispectral systems, which measure discrete bands (as opposed to hyperspectral imaging, which looks at contiguous bands of a spectrum without breaks between the bands). For example, NASA’S MODIS, a sensing instrument aboard two Earth-observation satellites, detects thirty-six spectral bands. Band 1 detects wavelengths between 620 and 670 nm, which corresponds to chlorophyll in vegetation, so this band contributes to information about land boundaries. Meanwhile, bands 17 through 19 cover three discrete bands between wavelengths of 890 and 965 nm, corresponding to cloud and atmospheric properties.

Similarly, radiometric resolution refers to the system’s ability to differentiate between different magnitudes of energy, described in binary data format. A remote sensing system with high radiometric resolution can detect small differences in energy magnitudes.

Finally, temporal resolution refers to the frequency at which a plane or satellite flies over the object or area being observed, when applicable. In meteorology and military applications, high temporal resolution is necessary to provide up-to-date information. Low resolution is fine for many kinds of mapping, however, because they do not generally require time-sensitive information.

Information Extraction from Remote Sensing Systems

Remote sensing systems can provide a wide range of data. Depending on the goal of the remote sensing, the extraction of information takes different forms. For mapping, for example, information extraction tends to be image-centered, focusing on the spatial relationships among images on the ground to create a larger picture. Image-centered information extraction, called photointerpretation, still relies heavily on the human eye, even with modern technology automating part of the process. Photointerpretation involves the analysis of shapes, sizes, tones, textures, patterns, and shadows.

For purposes other than mapping, a data-centered approach is often appropriate, involving analysis of the spectral absorption features of the observed object or area and the fractional abundances of the isotopes in the materials. Long-term monitoring of the environment tends to combine these two approaches. Although scientists need data referring to the composition of the objects in question, it is necessary to put the information in a spatial framework.

Principal Terms

backscattering: a reflection of energy waves by an object

bathymetry: the study of underwater depth

depth sounding: determining the depth of a specific point underwater; the data are used in bathymetry

electromagnetic radiation: energy emitted and absorbed by charge particles; travels in a wave-like manner

hyperspectral imaging: a remote sensing technique that detects electromagnetic radiation across a wide spectrum of wavelengths, including visible light

inverse problem: determining information about an object or phenomenon by analyzing observed measurements about the object or phenomenon

light detection and ranging: a remote sensing technique involving the emission of beams of light

multispectral scanning: a remote sensing technique that detects discrete bands of electromagnetic radiation

radar: a remote sensing technique involving the emission of radio waves or microwaves

sonar: a remote sensing technique for object detection and navigation that utilizes the propagation of sound waves

spectral signature: a unique identifier of a material based on how the material absorbs or reflects electromagnetic radiation wavelengths

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