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{{short description|Acquisition of information at a significant distance from the subject}}
{{distinguish|remote viewing}}
{{more citations needed|date=September 2023}}▼
{{other uses}}
▲{{more citations needed|date=September 2023}}
{{pp-pc}}
{{Use dmy dates|date=August 2020}}
[[File:Death-valley-sar.jpg|thumb|right|upright|[[Synthetic aperture radar]] image of [[Death Valley]] colored using [[polarimetry]]]]
'''Remote sensing''' is the acquisition of [[information]] about an [[physical object|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 [[planet]]s. 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]])
In current usage, the term ''remote sensing'' generally refers to the use of [[satellite]]- or aircraft-based [[sensor]] technologies to detect and classify objects on Earth. It includes the surface and the [[atmosphere]] and [[oceans]], based on [[wave propagation|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).<ref>{{cite book |last=Schowengerdt|first=Robert A. |title=Remote sensing: models and methods for image processing |publisher=[[Academic Press]] |edition=3rd |date=2007 |isbn=978-0-12-369407-2 |page=2 |url=https://books.google.com/books?id=KQXNaDH0X-IC&pg=PA2 |access-date=15 November 2015 |archive-date=1 May 2016 |archive-url=https://web.archive.org/web/20160501133641/https://books.google.com/books?id=KQXNaDH0X-IC&pg=PA2 |url-status=live}}</ref><ref>{{cite book |last=Schott|first=John Robert |title=Remote sensing: the image chain approach |publisher=[[Oxford University Press]] |edition=2nd |date=2007 |isbn=978-0-19-517817-3 |page=1 |url=https://books.google.com/books?id=uoXvgwOzAkQC&pg=PT20 |access-date=15 November 2015 |archive-date=24 April 2016 |archive-url=https://web.archive.org/web/20160424152943/https://books.google.com/books?id=uoXvgwOzAkQC&pg=PT20 |url-status=live}}</ref><ref>{{cite journal
|doi=10.1117/1.JRS.8.084597
|title=Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model
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|last5=Zhang
|first5=Ying
|issue=1
|bibcode=2014JARS....8.4597G
|s2cid=28430037
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Remote sensing makes it possible to collect data of dangerous or inaccessible areas. Remote sensing applications include monitoring [[deforestation]] in areas such as the [[Amazon Basin]], [[glacier|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 [[electromagnetic spectrum]], which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such as [[El Niño]] and other natural long and short term phenomena. Other uses include different areas of the [[earth science]]s such as [[natural resource management]], agricultural fields such as land usage and conservation,<ref>{{cite web|title=Saving the monkeys|url=https://spie.org/membership/spie-professional-magazine/journal-of-applied-remote-sensing-saving-monkey-habitat|publisher=SPIE Professional|access-date=1 January 2016|archive-date=4 February 2016|archive-url=https://web.archive.org/web/20160204011902/https://spie.org/membership/spie-professional-magazine/journal-of-applied-remote-sensing-saving-monkey-habitat|url-status=live}}</ref><ref>{{cite journal|author=Howard, A.|display-authors=etal|title=Remote sensing and habitat mapping for bearded capuchin monkeys (Sapajus libidinosus): landscapes for the use of stone tools|journal=Journal of Applied Remote Sensing|date=19 August 2015|volume=9|issue=1|pages=096020|doi=10.1117/1.JRS.9.096020|s2cid=120031016}}</ref> [[greenhouse gas monitoring]],<ref name=":2">{{Cite journal|last1=Innocenti|first1=Fabrizio|last2=Robinson|first2=Rod|last3=Gardiner|first3=Tom|last4=Finlayson|first4=Andrew|last5=Connor|first5=Andy|date=2017|title=Differential Absorption Lidar (DIAL) Measurements of Landfill Methane Emissions|journal=Remote Sensing|language=en|volume=9|issue=9|pages=953|doi=10.3390/rs9090953|bibcode=2017RemS....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.
{{Prose|section|date=October 2024}}
=== Applications of remote sensing ===
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* Laser and [[radar altimeter|radar]] [[altimeter]]s 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
* [[Radiometer]]s and [[photometer]]s are the most common instrument in use, collecting reflected and emitted radiation in a wide range of frequencies. The most common are visible and infrared sensors, followed by microwave, gamma-ray, and rarely, ultraviolet. They may also be used to detect the [[emission spectra]] of various chemicals, providing data on chemical concentrations in the atmosphere.
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* Remotely sensed multi- and hyperspectral images can be used for assessing biodiversity at different scales. Since the spectral properties of different plants species are unique, it is possible to get information about properties that relates to biodiversity such as habitat heterogeneity, spectral diversity and plant functional trait.<ref>{{Cite journal |last1=Wang |first1=Ran |last2=Gamon |first2=John A. |date=2019-09-15 |title=Remote sensing of terrestrial plant biodiversity |url=https://www.sciencedirect.com/science/article/pii/S0034425719302317 |journal=Remote Sensing of Environment |language=en |volume=231 |pages=111218 |doi=10.1016/j.rse.2019.111218 |bibcode=2019RSEnv.23111218W |s2cid=197567301 |issn=0034-4257}}</ref><ref>{{Cite journal |last1=Rocchini |first1=Duccio |last2=Boyd |first2=Doreen S. |last3=Féret |first3=Jean-Baptiste |last4=Foody |first4=Giles M. |last5=He |first5=Kate S. |last6=Lausch |first6=Angela |last7=Nagendra |first7=Harini |last8=Wegmann |first8=Martin |last9=Pettorelli |first9=Nathalie |date=February 2016 |editor-last=Skidmore |editor-first=Andrew |editor2-last=Chauvenet |editor2-first=Alienor |title=Satellite remote sensing to monitor species diversity: potential and pitfalls |url=https://onlinelibrary.wiley.com/doi/10.1002/rse2.9 |journal=Remote Sensing in Ecology and Conservation |language=en |volume=2 |issue=1 |pages=25–36 |doi=10.1002/rse2.9 |bibcode=2016RSEC....2...25R |s2cid=59446258 |issn=2056-3485|hdl=11585/720672 |hdl-access=free }}</ref><ref>{{Cite journal |last1=Schweiger |first1=Anna K. |last2=Cavender-Bares |first2=Jeannine |last3=Townsend |first3=Philip A. |last4=Hobbie |first4=Sarah E. |last5=Madritch |first5=Michael D. |last6=Wang |first6=Ran |last7=Tilman |first7=David |last8=Gamon |first8=John A. |date=June 2018 |title=Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function |url=https://www.nature.com/articles/s41559-018-0551-1 |journal=Nature Ecology & Evolution |language=en |volume=2 |issue=6 |pages=976–982 |doi=10.1038/s41559-018-0551-1 |pmid=29760440 |bibcode=2018NatEE...2..976S |s2cid=256718584 |issn=2397-334X}}</ref>
* Remote sensing has been used to detect rare plants to aid in conservation efforts. Prediction, detection, and the ability to record biophysical conditions were possible from medium to very high resolutions.<ref>{{Cite journal |last1=Cerrejón |first1=Carlos |last2=Valeria |first2=Osvaldo |last3=Marchand |first3=Philippe |last4=Caners |first4=Richard T. |last5=Fenton |first5=Nicole J. |date=2021-02-18 |title=No place to hide: Rare plant detection through remote sensing |journal=Diversity and Distributions |volume=27 |issue=6 |pages=948–961 |doi=10.1111/ddi.13244 |s2cid=233886263 |issn=1366-9516|doi-access=free |bibcode=2021DivDi..27..948C }}</ref>
* [[Remote sensing for statistics|Agricultural and environmental statistics]], usually combining classified satellite images with [[ground truth]] data collected on a sample selected on an [[area sampling frame]]<ref>{{Cite journal |last=Carfagna |first=E. |date=2005 |title=Using remote sensing for agricultural statistics |journal=International Statistical Review |volume=73 |issue=3 |pages=389–404|doi=10.1111/j.1751-5823.2005.tb00155.x }}</ref>
=== Geodetic ===
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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.
==Applications==
[[Satellite imagery|Satellite images]] provide very useful information to produce statistics on topics closely related to the territory, such as agriculture, forestry or land cover in general. The first large project to apply Landsata 1 images for statistics was LACIE (Large Area Crop Inventory Experiment), run by NASA, [[National Oceanic and Atmospheric Administration|NOAA]] and the [[United States Department of Agriculture|USDA]] in 1974–77.<ref>{{Cite journal |last=Houston |first=A.H. |title=Use of satellite data in agricultural surveys |journal=Communications in Statistics. Theory and Methods |issue=23 |pages=2857–2880}}</ref><ref>{{Cite journal |last=Allen |first=J.D. |title=A Look at the Remote Sensing Applications Program of the National Agricultural Statistics Service |journal=Journal of Official Statistics |volume=6 |issue=4 |pages=393–409}}</ref> Many other application projects on crop area estimation have followed, including the Italian AGRIT project and the MARS project of the [[Joint Research Centre]] (JRC) of the [[European Commission]].<ref>{{Cite book |last=Taylor |first=J |title=Regional Crop Inventories in Europe Assisted by Remote Sensing: 1988-1993. Synthesis Report |publisher=Office for Publications of the EC |year=1997 |location=Luxembourg |publication-date=1997}}</ref> Forest area and deforestation estimation have also been a frequent target of remote sensing projects,<ref>{{Cite journal |last=Foody |first=G.M. |date=1994 |title=Estimation of tropical forest extent and regenerative stage using remotely sensed data |journal=Journal of Biogeography |volume=21 |issue=3 |pages=223–244|doi=10.2307/2845527 |jstor=2845527 |bibcode=1994JBiog..21..223F }}</ref><ref>{{Cite journal |last=Achard |first=F |date=2002 |title=Determination of deforestation rates of the world's humid tropical forests |journal=Science |volume=297 |issue=5583 |pages=999–1002|doi=10.1126/science.1070656 |pmid=12169731 |bibcode=2002Sci...297..999A }}</ref> the same as land cover and land use<ref name=":0">{{Cite journal |last=Ambrosio Flores |first=L |date=2000 |title=Land cover estimation in small areas using ground survey and remote sensing |journal=Remote Sensing of Environment |volume=74 |issue=2 |pages=240–248|doi=10.1016/S0034-4257(00)00114-0 |bibcode=2000RSEnv..74..240F }}</ref>
[[Ground truth]] or reference data to train and validate image classification require a field survey if we are targetting [[Annual plant|annual crops]] or individual forest species, but may be substituted by [[Aerial photographic and satellite image interpretation|photointerpretation]] if we look at wider classes that can be reliably identified on [[Aerial photography|aerial photos]] or satellite images. It is relevant to highlight that probabilistic sampling is not critical for the selection of training pixels for image classification, but it is necessary for accuracy assessment of the classified images and area estimation.<ref>{{Cite book |last2=Green | first2=Kass | last1=Congalton |first1=Russell G. |url=https://www.taylorfrancis.com/books/mono/10.1201/9780429052729/assessing-accuracy-remotely-sensed-data-russell-congalton-kass-green |title=Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Third Edition |date=2019-01-25 |publisher=CRC Press |isbn=978-0-429-05272-9 |edition=3 |location=Boca Raton |doi=10.1201/9780429052729}}</ref><ref>{{Cite journal |last=Stehman |first=S. |date=2013 |title=Estimating Area from an Accuracy Assessment Error Matrix |journal=Remote Sensing of Environment |volume=132 |issue=132 |pages=202–211|doi=10.1016/j.rse.2013.01.016 |bibcode=2013RSEnv.132..202S }}</ref><ref>{{Cite journal |last=Stehman |first=S. |date=2019 |title=Key issues in rigorous accuracy assessment of land cover products |journal=Remote Sensing of Environment |volume=231 |issue=231|doi=10.1016/j.rse.2019.05.018 |bibcode=2019RSEnv.23111199S }}</ref> Additional care is recommended to ensure that training and validation datasets are not spatially correlated.<ref>{{Cite journal |last=Zhen |first=Z |date=2013 |title=Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification |journal=International Journal of Remote Sensing |volume=34 |issue=19 |pages=6914–6930|doi=10.1080/01431161.2013.810822 |bibcode=2013IJRS...34.6914Z }}</ref>
We suppose now that we have classified images or a [[Land cover maps|land cover map]] produced by visual photo-interpretation, with a legend of mapped classes that suits our purpose, taking again the example of wheat. The straightforward approach is counting the number of pixels classified as wheat and multiplying by the area of each pixel. Many authors have noticed that [[estimator]] is that it is generally [[Bias of an estimator|biased]] because [[Type I and type II errors|commission and omission errors]] in a [[confusion matrix]] do not compensate each other <ref>{{Cite journal |last=Czaplewski |first=R.L. |title=Misclassification bias in areal estimates |journal=Photogrammetric Engineering and Remote Sensing |issue=39 |pages=189–192}}</ref><ref>{{Cite journal |last=Bauer |first=M.E. |date=1978 |title=Area estimation of crops by digital analysis of Landsat data |journal=Photogrammetric Engineering and Remote Sensing |issue=44 |pages=1033–1043}}</ref><ref>{{Cite journal |last=Olofsson |first=P. |date=2014 |title=Good practices for estimating area and assessing accuracy of land change |journal=Remote Sensing of Environment |volume=148 |issue=148 |pages=42–57|doi=10.1016/j.rse.2014.02.015 |bibcode=2014RSEnv.148...42O |url=http://eprints.nottingham.ac.uk/44846/ }}</ref>
The main strength of classified satellite images or other indicators computed on satellite images is providing cheap information on the whole target area or most of it. This information usually has a good correlation with the target variable (ground truth) that is usually expensive to observe in an unbiased and accurate way. Therefore it can be observed on a [[Sampling (statistics)|probabilistic sample]] selected on an [[area sampling frame]]. Traditional [[survey methodology]] provides different methods to combine accurate information on a sample with less accurate, but exhaustive, data for a covariable or [[Proxy (statistics)|proxy]] that is cheaper to collect. For agricultural statistics, field surveys are usually required, while photo-interpretation may better for land cover classes that can be reliably identified on aerial photographs or high resolution satellite images. Additional uncertainty can appear because of imperfect reference data (ground truth or similar).<ref>{{Cite journal |last=Mcroberts |first=R |date=2018 |title=The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions |journal=ISPRS Journal of Photogrammetry and Remote Sensing |volume=142 |issue=142 |pages=292–300|doi=10.1016/j.isprsjprs.2018.06.002 |bibcode=2018JPRS..142..292M |doi-access=free }}</ref><ref>{{Cite journal |last=Foody |first=G.M. |date=2010 |title=Assessing the accuracy of land cover change with imperfect ground reference data |journal=Remote Sensing of Environment |volume=114 |issue=10 |pages=2271–2285|doi=10.1016/j.rse.2010.05.003 |bibcode=2010RSEnv.114.2271F |url=http://eprints.nottingham.ac.uk/2274/ }}</ref>
Some options are: [[ratio estimator]], [[regression estimator]],<ref>{{Cite journal |last=Sannier |first=C |date=2014 |title=Using the regression estimator with landsat data to estimate proportion forest cover and net proportion deforestation in gabon |journal=Remote Sensing of Environment |volume=151 |issue=151 |pages=138–148|doi=10.1016/j.rse.2013.09.015 |bibcode=2014RSEnv.151..138S }}</ref> [[calibration estimators]]<ref>{{Cite journal |last=Gallego |first=F.J. |date=2004 |title=Remote sensing and land cover area estimation |journal=International Journal of Remote Sensing |volume=25 |issue=5 |pages=3019–3047|doi=10.1080/01431160310001619607 |bibcode=2004IJRS...25.3019G }}</ref> and [[Small area estimation|small area estimators]]<ref name=":0" />
If we target other variables, such as [[crop yield]] or [[Leaf area index|leaf area]], we may need different indicators to be computed from images, such as the [[Normalized difference vegetation index|NDVI]], a good proxy to [[chlorophyll]] activity.<ref>{{Cite journal |last=Carfagna |first=E. |date=2005 |title=Using remote sensing for agricultural statistics |journal=International Statistical Review |volume=73 |issue=3 |pages=389–404 |doi=10.1111/j.1751-5823.2005.tb00155.x}}</ref>
== History ==
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The development of remote sensing technology reached a climax during the [[Cold War]] with the use of modified combat aircraft such as the [[P-51]], [[P-38]], [[RB-66]] and the [[F-4C]], or specifically designed collection platforms such as the [[Lockheed U-2|U2/TR-1]], [[SR-71]], [[A-5 Vigilante|A-5]] and the [[OV-1]] series both in overhead and stand-off collection.<ref>{{Cite web|url=http://www.airforcemag.com/MagazineArchive/Pages/1999/October%201999/1099recon.aspx|title=Air Force Magazine|website=www.airforcemag.com|access-date=2019-02-19|archive-date=19 February 2019|archive-url=https://web.archive.org/web/20190219133225/http://www.airforcemag.com/MagazineArchive/Pages/1999/October%201999/1099recon.aspx|url-status=live}}</ref> 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.<ref>{{Cite web|url=https://www.darpa.mil/program/military-imaging-and-surveillance-technology|title=Military Imaging and Surveillance Technology (MIST)|website=www.darpa.mil|access-date=2019-02-19|archive-date=18 August 2021|archive-url=https://web.archive.org/web/20210818140014/https://www.darpa.mil/program/military-imaging-and-surveillance-technology|url-status=live}}</ref>
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.<ref>{{Cite report|title=The Indian Society of International Law - Newsletter: VOL. 15, No. 4, October - December 2016| date=2018 | publisher=Brill |doi = 10.1163/2210-7975_hrd-9920-2016004}}</ref> Instrumentation aboard various Earth observing and weather satellites such as [[Landsat program|Landsat]], the [[Nimbus program|Nimbus]] and more recent missions such as [[RADARSAT]] and [[Upper Atmosphere Research Satellite|UARS]] provided global measurements of various data for civil, research, and military purposes. [[Space probe]]s to other planets have also provided the opportunity to conduct remote sensing studies in extraterrestrial environments, synthetic aperture radar aboard the [[Magellan probe|Magellan]] spacecraft provided detailed topographic maps of [[Venus]], while instruments aboard [[Solar and Heliospheric Observatory|SOHO]] allowed studies to be performed on the [[Sun]] and the [[solar wind]], just to name a few examples.<ref>{{Cite web|url=https://solarsystem.nasa.gov/missions/magellan/in-depth|title=In Depth {{!}} Magellan|website=Solar System Exploration: NASA Science|access-date=2019-02-19|archive-date=19 October 2021|archive-url=https://web.archive.org/web/20211019095913/https://solarsystem.nasa.gov/missions/magellan/in-depth/|url-status=live}}</ref><ref>{{Cite web|url=http://www.nasa.gov/mission_pages/soho/index.html|title=SOHO - Solar and Heliospheric Observatory|last=Garner|first=Rob|date=2015-04-15|website=NASA|access-date=2019-02-19|archive-date=18 September 2021|archive-url=https://web.archive.org/web/20210918060134/https://www.nasa.gov/mission_pages/soho/index.html|url-status=live}}</ref>
Recent developments include, beginning in the 1960s and 1970s, the development of [[image processing]] of [[satellite imagery]]. The use of the term "remote sensing" began in the early 1960s when [[Evelyn Pruitt]] realized that advances in science meant that aerial photography was no longer an adequate term to describe the data streams being generated by new technologies.<ref>{{Cite book |last1=Campbell |first1=James B. |url= |title=Introduction to Remote Sensing |last2=Wynne |first2=Randolph H. |date=2011-06-21 |publisher=The Guilford Press |isbn=978-1-60918-176-5 |edition=5th |location=New York London |language=English}}</ref><ref>{{Cite book |last=Ryerson |first=Robert A. |url=http://archive.org/details/whywheremattersu0000ryer |title=Why 'where' matters : understanding and profiting from GPS, GIS, and remote sensing : practical advice for individuals, communities, companies and countries |date=2010 |publisher=Manotick, ON : Kim Geomatics Corp. |others=Internet Archive |isbn=978-0-9866376-0-5}}</ref> With assistance from her fellow staff member at the Office of Naval Research, Walter Bailey, she coined the term "remote sensing".<ref>{{Cite journal |last1=Fussell |first1=Jay |last2=Rundquist |first2=Donald |last3=Harrington |first3=John A. |date=September 1986 |title=On defining remote sensing |url=https://www.asprs.org/wp-content/uploads/pers/1986journal/sep/1986_sep_1507-1511.pdf |journal=Photogrammetric Engineering and Remote Sensing |volume=52 |issue=9 |pages=1507–1511 |archive-url=https://web.archive.org/web/20211004200606/https://www.asprs.org/wp-content/uploads/pers/1986journal/sep/1986_sep_1507-1511.pdf |archive-date=October 4, 2021}}</ref><ref name="ELP">{{Cite journal |last=Pruitt |first=Evelyn L. |date=1979 |title=The Office of Naval Research and Geography |url=https://www.jstor.org/stable/2569553 |journal=Annals of the Association of American Geographers |volume=69 |issue=1 |pages=103–108 |doi=10.1111/j.1467-8306.1979.tb01235.x |jstor=2569553 |issn=0004-5608}}</ref> Several research groups in [[Silicon Valley]] including [[NASA Ames Research Center]], [[GTE]], and [[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.<ref>{{Cite web|url=http://www.nasa.gov/centers/ames/about/overview.html|title=Ames Research Center Overview|last=Colen|first=Jerry|date=2015-04-08|website=NASA|access-date=2019-02-19|archive-date=28 September 2021|archive-url=https://web.archive.org/web/20210928042152/https://www.nasa.gov/centers/ames/about/overview.html|url-status=live}}</ref>
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== Further reading ==
* {{cite book | last=Campbell | first=J. B. | date=2002 | title=Introduction to remote sensing | edition=3rd | publisher=The Guilford Press | isbn=978-1-57230-640-0}}
* {{cite journal | author=Datla, R.U. | author2=Rice, J.P. | author3=Lykke, K.R. | author4=Johnson, B.C. | author5=Butler, J.J. | author6=Xiong, X. | title=Best practice guidelines for pre-launch characterization and calibration of instruments for passive optical remote sensing | date=March–April 2011 | journal=Journal of Research of the National Institute of Standards and Technology | issue=2 | volume=116 | pages=612–646 | doi=10.6028/jres.116.009| pmid=26989588 | pmc=4550341 }}▼
* {{cite journal |doi=10.3390/rs12071087 |doi-access=free |title=How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review |date=2020 |last1=Dupuis |first1=Chloé |last2=Lejeune |first2=Philippe |last3=Michez |first3=Adrien |last4=Fayolle |first4=Adeline |journal=Remote Sensing |volume=12 |issue=7 |page=1087 |bibcode=2020RemS...12.1087D }}
* {{cite book | last=Jensen | first=J. R. | date=2007 | title=Remote sensing of the environment: an Earth resource perspective | edition=2nd | publisher=Prentice Hall | isbn=978-0-13-188950-7}}
* {{cite book | last=Jensen | first=J. R. | date=2005 | title=Digital Image Processing: a Remote Sensing Perspective | edition=3rd | publisher=Prentice Hall}}
* {{cite journal | author=Lentile, Leigh B. | author2=Holden, Zachary A. | author3=Smith, Alistair M. S. | author4=Falkowski, Michael J. | author5=Hudak, Andrew T. | author6=Morgan, Penelope | author7=Lewis, Sarah A. | author8=Gessler, Paul E. | author9=Benson, Nate C. | title=Remote sensing techniques to assess active fire characteristics and post-fire effects | url=http://www.treesearch.fs.fed.us/pubs/24613 | date=2006 | journal=International Journal of Wildland Fire | issue=15 | volume=3 | pages=319–345 | doi=10.1071/WF05097 | s2cid=724358 | access-date=4 February 2010 | archive-date=12 August 2014 | archive-url=https://web.archive.org/web/20140812022744/http://www.treesearch.fs.fed.us/pubs/24613 | url-status=dead }}▼
* {{cite book | last=Lillesand | first=T. M. |author2=R. W. Kiefer |author3=J. W. Chipman | date=2003 | title=Remote sensing and image interpretation | edition=5th | publisher=Wiley | isbn=978-0-471-15227-9}}▼
* {{cite book | last=Richards | first=J. A. |author2=X. Jia | date=2006 | title=Remote sensing digital image analysis: an introduction | edition=4th | publisher=Springer | isbn=978-3-540-25128-6}}▼
▲* {{cite journal | author=Datla, R.U. | author2=Rice, J.P. | author3=Lykke, K.R. | author4=Johnson, B.C. | author5=Butler, J.J. | author6=Xiong, X. | title=Best practice guidelines for pre-launch characterization and calibration of instruments for passive optical remote sensing | date=March–April 2011 | journal=Journal of Research of the National Institute of Standards and Technology | issue=2 | volume=116 | pages=612–646 | doi=10.6028/jres.116.009| pmid=26989588 | pmc=4550341 }}
* KUENZER, C. ZHANG, J., TETZLAFF, A., and S. DECH, 2013: Thermal Infrared Remote Sensing of Surface and underground Coal Fires. In (eds.) Kuenzer, C. and S. Dech 2013: Thermal Infrared Remote Sensing – Sensors, Methods, Applications. Remote Sensing and Digital Image Processing Series, Volume 17, 572 pp., {{ISBN|978-94-007-6638-9}}, pp. 429–451
* Kuenzer, C. and S. Dech 2013: Thermal Infrared Remote Sensing – Sensors, Methods, Applications. Remote Sensing and Digital Image Processing Series, Volume 17, 572 pp., {{ISBN|978-94-007-6638-9}}
* Lasaponara, R. and [[Nicola Masini|Masini N.]] 2012: Satellite Remote Sensing - A new tool for Archaeology. Remote Sensing and Digital Image Processing Series, Volume 16, 364 pp., {{ISBN|978-90-481-8801-7}}.
▲* {{cite journal | author=Lentile, Leigh B. | author2=Holden, Zachary A. | author3=Smith, Alistair M. S. | author4=Falkowski, Michael J. | author5=Hudak, Andrew T. | author6=Morgan, Penelope | author7=Lewis, Sarah A. | author8=Gessler, Paul E. | author9=Benson, Nate C. | title=Remote sensing techniques to assess active fire characteristics and post-fire effects | url=http://www.treesearch.fs.fed.us/pubs/24613 | date=2006 | journal=International Journal of Wildland Fire | issue=15 | volume=3 | pages=319–345 | doi=10.1071/WF05097 | s2cid=724358 | access-date=4 February 2010 | archive-date=12 August 2014 | archive-url=https://web.archive.org/web/20140812022744/http://www.treesearch.fs.fed.us/pubs/24613 | url-status=dead }}
▲* {{cite book | last=Lillesand | first=T. M. |author2=R. W. Kiefer |author3=J. W. Chipman | date=2003 | title=Remote sensing and image interpretation | edition=5th | publisher=Wiley | isbn=978-0-471-15227-9}}
▲* {{cite book | last=Richards | first=J. A. |author2=X. Jia | date=2006 | title=Remote sensing digital image analysis: an introduction | edition=4th | publisher=Springer | isbn=978-3-540-25128-6}}
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