Remote sensing

Page protected with pending changes
Source: Wikipedia, the free encyclopedia.

Synthetic aperture radar image of Death Valley colored using polarimetry

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about Earth and other planets. Remote sensing is used in numerous fields, including geophysics, geography, land surveying and most Earth science disciplines (e.g. exploration geophysics, hydrology, ecology, meteorology, oceanography, glaciology, geology). It also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others.

In current usage, the term remote sensing generally refers to the use of

propagated signals (e.g. electromagnetic radiation). It may be split into "active" remote sensing (when a signal is emitted by a satellite or aircraft to the object and its reflection is detected by the sensor) and "passive" remote sensing (when the reflection of sunlight is detected by the sensor).[1][2][3][4]

Overview

This video is about how
Landsat was used to identify areas of conservation in the Democratic Republic of the Congo, and how it was used to help map an area called MLW
in the north.

Remote sensing can be divided into two types of methods: Passive remote sensing and Active remote sensing. Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas. Reflected

radiometers. Active collection, on the other hand, 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.

Illustration of remote sensing

Remote sensing makes it possible to collect data of dangerous or inaccessible areas. Remote sensing applications include monitoring

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.

Orbital platforms collect and transmit data from different parts of the

El Niño and other natural long and short term phenomena. Other uses include different areas of the earth sciences such as natural resource management, agricultural fields such as land usage and conservation,[5][6] greenhouse gas monitoring,[7] oil spill detection and monitoring,[8] and national security and overhead, ground-based and stand-off collection on border areas.[9]

Types of data acquisition techniques

The basis for multispectral collection and analysis is that of examined areas or objects that reflect or emit radiation that stand out from surrounding areas. For a summary of major remote sensing satellite systems see the overview table.

Applications of remote sensing

Radar image of Aswan Dam, Egypt taken by Umbra
  • Conventional radar is mostly associated with aerial traffic control, early warning, and certain large-scale meteorological data.
    Magellan
    ).
  • Laser and radar altimeters on satellites have provided a wide range of data. By measuring the bulges of water caused by gravity, they map features on the seafloor to a resolution of a mile or so. By measuring the height and wavelength of ocean waves, the altimeters measure wind speeds and direction, and surface ocean currents and directions.
  • Ultrasound (acoustic) and radar tide gauges measure sea level, tides and wave direction in coastal and offshore tide gauges.
  • Light detection and ranging (LIDAR) is well known in examples of weapon ranging, laser illuminated homing of projectiles. LIDAR is used to detect and measure the concentration of various chemicals in the atmosphere, while airborne LIDAR can be used to measure the heights of objects and features on the ground more accurately than with radar technology. Vegetation remote sensing is a principal application of LIDAR.[10]
  • emission spectra
    of various chemicals, providing data on chemical concentrations in the atmosphere.
Examples of remote sensing equipment deployed by
or interfaced with oceanographic research vessels.[11]

Geodetic

  • InSAR, LIDAR, etc.[27]

Acoustic and near-acoustic

  • Sonar: passive sonar, listening for the sound made by another object (a vessel, a whale etc.); active sonar, emitting pulses of sounds and listening for echoes, used for detecting, ranging and measurements of underwater objects and terrain.
  • Seismograms taken at different locations can locate and measure earthquakes
    (after they occur) by comparing the relative intensity and precise timings.
  • Ultrasound: Ultrasound sensors, that emit high-frequency pulses and listening for echoes, used for detecting water waves and water level, as in tide gauges or for towing tanks.

To coordinate a series of large-scale observations, most sensing systems depend on the following: platform location and the orientation of the sensor. High-end instruments now often use positional information from

satellite navigation systems. The rotation and orientation are often provided within a degree or two with electronic compasses. Compasses can measure not just azimuth (i. e. degrees to magnetic north), but also altitude (degrees above the horizon), since the magnetic field curves into the Earth at different angles at different latitudes. More exact orientations require gyroscopic-aided orientation
, periodically realigned by different methods including navigation from stars or known benchmarks.

Data characteristics

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 in side length from 1 to 1,000 metres (3.3 to 3,280.8 ft).
Spectral resolution
The wavelength 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 infrared
spectrum, ranging from a spectral resolution of 0.7 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.

Data processing

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. For example, in conventional photographs, distances are accurate in the center of the image, with the distortion of measurements increasing the farther you get from the center. Another factor is that of the platen against which the film is pressed can cause severe errors when photographs are used to measure ground distances. The step in which this problem is resolved is called

georeferencing
and involves computer-aided matching of points in the image (typically 30 or more points per image) which is extrapolated with the use of an established benchmark, "warping" the image to produce accurate spatial data. As of the early 1990s, most satellite images are sold fully georeferenced.

In addition, images may need to be radiometrically and atmospherically corrected.

Radiometric correction
Allows avoidance of radiometric errors and distortions. The illumination of objects on the Earth's surface is uneven because of different properties of the relief. This factor is taken into account in the method of radiometric distortion correction.[28] Radiometric correction gives a scale to the pixel values, e. g. the monochromatic scale of 0 to 255 will be converted to actual radiance values.
Topographic correction (also called terrain correction)
In rugged mountains, as a result of terrain, the effective illumination of pixels varies considerably. In a remote sensing image, the pixel on the shady slope receives weak illumination and has a low radiance value, in contrast, the pixel on the sunny slope receives strong illumination and has a high radiance value. For the same object, the pixel radiance value on the shady slope will be different from that on the sunny slope. Additionally, different objects may have similar radiance values. These ambiguities seriously affected remote sensing image information extraction accuracy in mountainous areas. It became the main obstacle to the further application of remote sensing images. The purpose of topographic correction is to eliminate this effect, recovering the true reflectivity or radiance of objects in horizontal conditions. It is the premise of quantitative remote sensing application.
Atmospheric correction
Elimination of atmospheric haze by rescaling each frequency band so that its minimum value (usually realised in water bodies) corresponds to a pixel value of 0. The digitizing of data also makes it possible to manipulate the data by changing gray-scale values.

Interpretation is the critical process of making sense of the data. The first application was that of aerial photographic collection which used the following process; spatial measurement through the use of a light table in both conventional single or stereographic coverage, added skills such as the use of photogrammetry, the use of photomosaics, repeat coverage, Making use of objects' known dimensions in order to detect modifications. Image Analysis is the recently developed automated computer-aided application that is in increasing use.

Object-Based Image Analysis (OBIA) is a sub-discipline of GIScience devoted to partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale.

Old data from remote sensing is often valuable because it may provide the only long-term data for a large extent of geography. At the same time, the data is often complex to interpret, and bulky to store. Modern systems tend to store the data digitally, often with

ultrafiche, usually in typefonts such as OCR-B
, or as digitized half-tone images. Ultrafiches survive well in standard libraries, with lifetimes of several centuries. They can be created, copied, filed and retrieved by automated systems. They are about as compact as archival magnetic media, and yet can be read by human beings with minimal, standardized equipment.

Generally speaking, remote sensing works on the principle of the inverse problem: while the object or phenomenon of interest (the state) may not be directly measured, there exists some other variable that can be detected and measured (the observation) which may be related to the object of interest through a calculation. 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 (such as carbon dioxide) in that region. The frequency of the emissions may then be related via thermodynamics to the temperature in that region.

Data processing levels

To facilitate the discussion of data processing in practice, several processing "levels" were first defined in 1986 by NASA as part of its Earth Observing System[29] and steadily adopted since then, both internally at NASA (e. g.,[30]) and elsewhere (e. g.,[31]); these definitions are:

Level Description
0 Reconstructed, unprocessed instrument and payload data at full resolution, with any and all communications artifacts (e. g., synchronization frames, communications headers, duplicate data) removed.
1a Reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and georeferencing parameters (e. g., platform ephemeris) computed and appended but not applied to the Level 0 data (or if applied, in a manner that level 0 is fully recoverable from level 1a data).
1b Level 1a data that have been processed to sensor units (e. g., radar backscatter cross section, brightness temperature, etc.); not all instruments have Level 1b data; level 0 data is not recoverable from level 1b data.
2 Derived geophysical variables (e. g., ocean wave height, soil moisture, ice concentration) at the same resolution and location as Level 1 source data.
3 Variables mapped on uniform spacetime grid scales, usually with some completeness and consistency (e. g., missing points interpolated, complete regions mosaicked together from multiple orbits, etc.).
4 Model output or results from analyses of lower level data (i. e., variables that were not measured by the instruments but instead are derived from these measurements).

A Level 1 data record is the most fundamental (i. e., highest reversible level) data record that has significant scientific utility, and is the foundation upon which all subsequent data sets are produced. Level 2 is the first level that is directly usable for most scientific applications; its value is much greater than the lower levels. Level 2 data sets tend to be less voluminous than Level 1 data because they have been reduced temporally, spatially, or spectrally. Level 3 data sets are generally smaller than lower level data sets and thus can be dealt with without incurring a great deal of data handling overhead. These data tend to be generally more useful for many applications. The regular spatial and temporal organization of Level 3 datasets makes it feasible to readily combine data from different sources.

While these processing levels are particularly suitable for typical satellite data processing pipelines, other data level vocabularies have been defined and may be appropriate for more heterogeneous workflows.

History

The TR-1 reconnaissance/surveillance aircraft
The 2001 Mars Odyssey used spectrometers and imagers to hunt for evidence of past or present water and volcanic activity on Mars.

The modern discipline of remote sensing arose with the development of flight. The balloonist G. Tournachon (alias

Nadar) made photographs of Paris from his balloon in 1858.[32]
Messenger pigeons, kites, rockets and unmanned balloons were also used for early images. With the exception of balloons, these first, individual images were not particularly useful for map making or for scientific purposes.

Systematic aerial photography was developed for military surveillance and reconnaissance purposes beginning in World War I.[33] After WWI, remote sensing technology was quickly adapted to civilian applications.[34] This is demonstrated by the first line of a 1941 textbook titled "Aerophotography and Aerosurverying," which stated the following:

"There is no longer any need to preach for aerial photography-not in the United States- for so widespread has become its use and so great its value that even the farmer who plants his fields in a remote corner of the country knows its value."

— James Bagley, [34]

The development of remote sensing technology reached a climax during the

OV-1 series both in overhead and stand-off collection.[35] A more recent development is that of increasingly smaller sensor pods such as those used by law enforcement and the military, in both manned and unmanned platforms. The advantage of this approach is that this requires minimal modification to a given airframe. Later imaging technologies would include infrared, conventional, Doppler and synthetic aperture radar.[36]

The development of artificial satellites in the latter half of the 20th century allowed remote sensing to progress to a global scale as of the end of the Cold War.

Magellan spacecraft provided detailed topographic maps of Venus, while instruments aboard SOHO allowed studies to be performed on the Sun and the solar wind, just to name a few examples.[38][39]

Recent developments include, beginning in the 1960s and 1970s, the development of

ESL Inc. developed Fourier transform techniques leading to the first notable enhancement of imagery data. In 1999 the first commercial satellite (IKONOS) collecting very high resolution imagery was launched.[44]

Training and education

Remote Sensing has a growing relevance in the modern information society. It represents a key technology as part of the aerospace industry and bears increasing economic relevance – new sensors e.g.

natural disasters. As an example, 80% of the German students use the services of Google Earth; in 2006 alone the software was downloaded 100 million times. But studies have shown that only a fraction of them know more about the data they are working with.[45] There exists a huge knowledge gap
between the application and the understanding of satellite images. Remote sensing only plays a tangential role in schools, regardless of the political claims to strengthen the support for teaching on the subject.[46] A lot of the computer software explicitly developed for school lessons has not yet been implemented due to its complexity. Thereby, the subject is either not at all integrated into the curriculum or does not pass the step of an interpretation of analogue images. In fact, the subject of remote sensing requires a consolidation of physics and mathematics as well as competences in the fields of media and methods apart from the mere visual interpretation of satellite images.

Many teachers have great interest in the subject "remote sensing", being motivated to integrate this topic into teaching, provided that the curriculum is considered. In many cases, this encouragement fails because of confusing information.

learning modules and learning portals. Examples include: FIS – Remote Sensing in School Lessons,[49] Geospektiv,[50] Ychange,[51] or Spatial Discovery,[52]
to promote media and method qualifications as well as independent learning.

Software

Remote sensing data are processed and analyzed with computer software, known as a

remote sensing application
. A large number of proprietary and open source applications exist to process remote sensing data.

Remote Sensing with gamma rays

There are applications of gamma rays to mineral exploration through remote sensing. In 1972 more than two million dollars were spent on remote sensing applications with gamma rays to mineral exploration. Gamma rays are used to search for deposits of uranium. By observing radioactivity from potassium, porphyry copper deposits can be located. A high ratio of uranium to thorium has been found to be related to the presence of hydrothermal copper deposits. Radiation patterns have also been known to occur above oil and gas fields, but some of these patterns were thought to be due to surface soils instead of oil and gas.[53]

Satellites

Six Earth observation satellites comprising the A-train satellite constellation as of 2014.

An

GNSS radio occultation
.

The first occurrence of satellite remote sensing can be dated to the launch of the first artificial satellite, Sputnik 1, by the Soviet Union on October 4, 1957.[54] Sputnik 1 sent back radio signals, which scientists used to study the ionosphere.[55] The United States Army Ballistic Missile Agency launched the first American satellite, Explorer 1, for NASA's Jet Propulsion Laboratory on January 31, 1958. The information sent back from its radiation detector led to the discovery of the Earth's Van Allen radiation belts.[56] The TIROS-1 spacecraft, launched on April 1, 1960, as part of NASA's Television Infrared Observation Satellite (TIROS) program, sent back the first television footage of weather patterns to be taken from space.[54]

In 2008, more than 150 Earth observation satellites were in orbit, recording data with both passive and active sensors and acquiring more than 10 terabits of data daily.[54] By 2021, that total had grown to over 950, with the largest number of satellites operated by US-based company Planet Labs.[57]

Most

SMOS spacecraft of European Space Agency are observing the Earth from an altitude of about 700 km (430 mi). The Earth observation satellites of UAE, DubaiSat-1 & DubaiSat-2 are also placed in Low Earth Orbits (LEO) orbits and providing satellite imagery of various parts of the Earth.[58][59]

To get global coverage with a low orbit, a polar orbit is used. A low orbit will have an orbital period of roughly 100 minutes and the Earth will rotate around its polar axis about 25° between successive orbits. The ground track moves towards the west 25° each orbit, allowing a different section of the globe to be scanned with each orbit. Most are in Sun-synchronous orbits.

A geostationary orbit, at 36,000 km (22,000 mi), allows a satellite to hover over a constant spot on the earth since the orbital period at this altitude is 24 hours. This allows uninterrupted coverage of more than 1/3 of the Earth per satellite, so three satellites, spaced 120° apart, can cover the whole Earth. This type of orbit is mainly used for meteorological satellites.

See also

References

  1. from the original on 1 May 2016. Retrieved 15 November 2015.
  2. from the original on 24 April 2016. Retrieved 15 November 2015.
  3. (PDF) from the original on 19 July 2018. Retrieved 27 October 2021.
  4. . Retrieved 2 April 2023.
  5. ^ "Saving the monkeys". SPIE Professional. Archived from the original on 4 February 2016. Retrieved 1 January 2016.
  6. S2CID 120031016
    .
  7. .
  8. .
  9. ^ "Science@nasa - Technology: Remote Sensing". Archived from the original on 29 September 2006. Retrieved 18 February 2009.
  10. .
  11. ^ Just Sit Right Back and You'll Hear a Tale, a Tale of a Plankton Trip Archived 10 August 2021 at the Wayback Machine NASA Earth Expeditions, 15 August 2018.
  12. S2CID 214254543
    .
  13. .
  14. .
  15. ^ Goldberg, A.; Stann, B.; Gupta, N. (July 2003). "Multispectral, Hyperspectral, and Three-Dimensional Imaging Research at the U.S. Army Research Laboratory" (PDF). Proceedings of the International Conference on International Fusion [6th]. 1: 499–506.
  16. ISSN 0924-2716
    .
  17. .
  18. .
  19. (PDF) from the original on 13 July 2021. Retrieved 27 October 2021.
  20. .
  21. ^ "Begni G. Escadafal R. Fontannaz D. and Hong-Nga Nguyen A.-T. (2005). Remote sensing: a tool to monitor and assess desertification. Les dossiers thématiques du CSFD. Issue 2. 44 pp". Archived from the original on 26 May 2019. Retrieved 27 October 2021.
  22. S2CID 197567301
    .
  23. .
  24. .
  25. .
  26. ^ Carfagna, E. (2005). "Using remote sensing for agricultural statistics". International statistical review. 73 (3): 389–404.
  27. ^ "Geodetic Imaging". Archived from the original on 2 October 2016. Retrieved 29 September 2016.
  28. .
  29. ^ NASA (1986), Report of the EOS data panel, Earth Observing System, Data and Information System, Data Panel Report, Vol. IIa., NASA Technical Memorandum 87777, June 1986, 62 pp. Available at http://hdl.handle.net/2060/19860021622 Archived 27 October 2021 at the Wayback Machine
  30. ^ C. L. Parkinson, A. Ward, M. D. King (Eds.) Earth Science Reference Handbook – A Guide to NASA's Earth Science Program and Earth Observing Satellite Missions, National Aeronautics and Space Administration Washington, D. C. Available at http://eospso.gsfc.nasa.gov/ftp_docs/2006ReferenceHandbook.pdf Archived 15 April 2010 at the Wayback Machine
  31. ^ GRAS-SAF (2009), Product User Manual, GRAS Satellite Application Facility, Version 1.2.1, 31 March 2009. Available at http://www.grassaf.org/general-documents/products/grassaf_pum_v121.pdf Archived 26 July 2011 at the Wayback Machine
  32. ^ Maksel, Rebecca. "Flight of the Giant". Air & Space Magazine. Archived from the original on 18 August 2021. Retrieved 19 February 2019.
  33. ISSN 0307-1235. Archived from the original
    on 18 April 2014. Retrieved 19 February 2019.
  34. ^ a b Bagley, James (1941). Aerophotography and Aerosurverying (1st ed.). York, PA: The Maple Press Company.
  35. ^ "Air Force Magazine". www.airforcemag.com. Archived from the original on 19 February 2019. Retrieved 19 February 2019.
  36. ^ "Military Imaging and Surveillance Technology (MIST)". www.darpa.mil. Archived from the original on 18 August 2021. Retrieved 19 February 2019.
  37. .
  38. ^ "In Depth | Magellan". Solar System Exploration: NASA Science. Archived from the original on 19 October 2021. Retrieved 19 February 2019.
  39. ^ Garner, Rob (15 April 2015). "SOHO - Solar and Heliospheric Observatory". NASA. Archived from the original on 18 September 2021. Retrieved 19 February 2019.
  40. .
  41. .
  42. ^ Fussell, Jay; Rundquist, Donald; Harrington, John A. (September 1986). "On defining remote sensing" (PDF). Photogrammetric Engineering and Remote Sensing. 52 (9): 1507–1511. Archived from the original (PDF) on 4 October 2021.
  43. JSTOR 2569553
    .
  44. ^ Colen, Jerry (8 April 2015). "Ames Research Center Overview". NASA. Archived from the original on 28 September 2021. Retrieved 19 February 2019.
  45. ^ Ditter, R., Haspel, M., Jahn, M., Kollar, I., Siegmund, A., Viehrig, K., Volz, D., Siegmund, A. (2012) Geospatial technologies in school – theoretical concept and practical implementation in K-12 schools. In: International Journal of Data Mining, Modelling and Management (IJDMMM): FutureGIS: Riding the Wave of a Growing Geospatial Technology Literate Society; Vol. X
  46. ^ Stork, E.J., Sakamoto, S.O., and Cowan, R.M. (1999) "The integration of science explorations through the use of earth images in middle school curriculum", Proc. IEEE Trans. Geosci. Remote Sensing 37, 1801–1817
  47. S2CID 62414447
    .
  48. ^ Digital Earth
  49. ^ "FIS – Remote Sensing in School Lessons". Archived from the original on 26 October 2012. Retrieved 25 October 2012.
  50. ^ "geospektiv". Archived from the original on 2 May 2018. Retrieved 1 June 2018.
  51. ^ "YCHANGE". Archived from the original on 17 August 2018. Retrieved 1 June 2018.
  52. ^ "Landmap – Spatial Discovery". Archived from the original on 29 November 2014. Retrieved 27 October 2021.
  53. .
  54. ^ .
  55. .
  56. ^ "James A. Van Allen". nmspacemuseum.org. New Mexico Museum of Space History. Retrieved 14 May 2018.
  57. ^ "How many Earth observation satellites are orbiting the planet in 2021?". 18 August 2021.
  58. ^ "DubaiSat-2, Earth Observation Satellite of UAE". Mohammed Bin Rashid Space Centre. Archived from the original on 17 January 2019. Retrieved 4 July 2016.
  59. ^ "DubaiSat-1, Earth Observation Satellite of UAE". Mohammed Bin Rashid Space Centre. Archived from the original on 4 March 2016. Retrieved 4 July 2016.

Further reading

External links