In current usage, the termremote sensing generally refers to the use ofsatellite- or airborne-basedsensor technologies to detect and classify objects on Earth. It includes the surface and theatmosphere andoceans, based onpropagated signals (e.g.electromagnetic radiation). It may be split into "active" remote sensing (when a signal is emitted by a sensor mounted on 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][5]
This video is about howLandsat was used to identify areas of conservation in theDemocratic Republic of the Congo, and how it was used to help map an area calledMLW in the north.
Remote sensing can be divided into two types of methods:Passive remote sensing andActive remote sensing. Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas. Reflectedsunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include filmphotography,infrared,charge-coupled devices, andradiometers. 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 andLiDAR 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 monitoringdeforestation in areas such as theAmazon Basin,glacial features in Arctic and Antarctic regions, anddepth sounding of coastal and ocean depths. Military collection during theCold 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 theelectromagnetic spectrum, which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such asEl Niño and other natural long and short term phenomena. Other uses include different areas of theearth sciences such asnatural resource management, agricultural fields such as land usage and conservation,[6][7]greenhouse gas monitoring,[8] oil spill detection and monitoring,[9] and national security and overhead, ground-based and stand-off collection on border areas.[10]
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.
Conventional radar is mostly associated withair traffic control, early warning, and certain large-scale meteorological data.Doppler radar is used by local law enforcements' monitoring of speed limits and in enhancedmeteorological collection such as wind speed and direction within weather systems in addition to precipitation location and intensity. Other types of active collection includesplasmas in theionosphere.Interferometric synthetic aperture radar is used to produce precisedigital elevation models of large scale terrain (SeeRADARSAT,TerraSAR-X,Magellan). Laser andradaraltimeters 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 are used to measure sea level, tides and wave direction in coastal and offshore tide gauges.
Light detection and ranging (LiDAR) is used for weapon ranging, laser illuminated homing of projectiles, and 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 radar technology. LiDAR can be used to detect ground surface changes typically by creating Digital Surface Models (DSMs) or Digital Elevation Models (DEMs).[11] Vegetation remote sensing is a principal application of LIDAR.[12]
The most common instruments in use areradiometers andphotometers, which collect reflected and emitted radiation in a wide range of frequencies. The most prevalent of these frequencies are visible and infrared sensors, followed by microwave, gamma-ray, and rarely, ultraviolet. They may also be used to detect theemission spectra of various chemicals, providing data on chemical concentrations in the atmosphere. Radiometers are also used at night, asartificial light emissions are a key signature of human activity.[13] Applications include remote sensing of population, GDP, and damage to infrastructure from war or disasters. Radiometers and radar onboard of satellites can be also used to monitor volcanic eruptions[14][15]
Examples of remote sensing equipment deployed by or interfaced with oceanographicresearch vessels.[16]
Spectropolarimetric Imaging has been reported to be useful for target tracking purposes by researchers at theU.S. Army Research Laboratory. They determined that manmade items possess polarimetric signatures that are not found in natural objects. These conclusions were drawn from the imaging of military trucks, like theHumvee, and trailers with theiracousto-optic tunable filter dualhyperspectral and spectropolarimetric VNIR Spectropolarimetric Imager.[17][18]
Simultaneous multi-spectral platforms such as Landsat have been in use since the early 1970s. These thematic mappers take images in multiple wavelengths and are usually found onEarth observation satellites, including (for example) theLandsat program or theIKONOS satellite. Maps ofland cover andland use from thematic mapping can be used to prospect for minerals, detect or monitor land usage, detect invasive vegetation, deforestation, and examine the health of indigenous plants and crops (satellite crop monitoring), including entire farming regions or forests.[22] Prominent scientists using remote sensing for this purpose includeJanet Franklin andRuth DeFries. Landsat images are used by regulatory agencies such as KYDOW to indicate water quality parameters including Secchi depth, chlorophyll density, and total phosphorus content.Weather satellites are used in meteorology and climatology.
Hyperspectral imaging produces image cubes where each pixel has full spectral information with imaging narrow spectral bands over a contiguous spectral range. Hyperspectral imagers are used in various applications including mineralogy, biology, defence, and environmental measurements. Within the scope of the combat againstdesertification, remote sensing allows researchers to follow up and monitor risk areas in the long term, to determine desertification factors, to support decision-makers in defining relevant measures of environmental management, and to assess their impacts.[23] Remotely sensed multi- and hyperspectral images can be used for assessing biodiversity at different spatial 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.[24][25][26] Remote sensing has also 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.[27] Remote sensing is often utilized in the collection of agricultural and environmental statistics, usually combining classified satellite images withground truth data collected on a sample selected on anarea sampling frame[28]
Geodetic remote sensing can begravimetric or geometric. Overhead gravity data collection was first used in aerial submarine detection. This data revealed minute perturbations in the Earth'sgravitational field that may be used to determine changes in the mass distribution of the Earth, which in turn may be used for geophysical studies, as inGRACE. Geometric remote sensing includes position and deformationimaging usingInSAR, LIDAR, etc.[29]
Three main types of acoustic and near-acoustic remote sensing exist: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 measureearthquakes (after they occur) by comparing the relative intensity and precise timings.Ultrasound acoustic sensing is made up of 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 fromsatellite 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 requiregyroscopic-aided orientation, periodically realigned by different methods including navigation from stars or known benchmarks.
The size of apixel that is recorded in araster 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).
The bandwidth of the different frequency bands recorded – usually, this is related to the number of frequency bands recorded by the platform. CurrentLandsat collection is that of seven bands, including several in theinfrared 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.
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 instrumentnoise.
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. 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 calledgeoreferencing 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.[30] 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 ofquantitative remote sensing application.
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 alight 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 GI-Science 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 withlossless compression. The difficulty with this approach is that the data is fragile, the format may be archaic, and the data may be easy to falsify. One of the best systems for archiving data series is as computer-generated machine-readableultrafiche, usually in typefonts such asOCR-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 theinverse problem: while the object or phenomenon of interest (thestate) may not be directly measured, there exists some other variable that can be detected and measured (theobservation) 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 viathermodynamics to the temperature in that region.
To facilitate the discussion of data processing in practice, several processing "levels" were first defined in 1986 by NASA as part of itsEarth Observing System[31] and steadily adopted since then, both internally at NASA (e. g.,[32]) and elsewhere (e. g.,[33]); 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 formost 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.
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,NOAA and theUSDA in 1974–77.[34][35] Many other application projects on crop area estimation have followed, including the Italian AGRIT project and the MARS project of theJoint Research Centre (JRC) of theEuropean Commission.[36] Forest area and deforestation estimation have also been a frequent target of remote sensing projects,[37][38] the same as land cover and land use[39]
Ground truth or reference data to train and validate image classification require a field survey if we are targetingannual crops or individual forest species, but may be substituted byphotointerpretation if we look at wider classes that can be reliably identified onaerial 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.[40][41][42] Additional care is recommended to ensure that training and validation datasets are not spatially correlated.[43]
We suppose now that we have classified images or aland 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 thatestimator is that it is generallybiased becausecommission and omission errors in aconfusion matrix do not compensate each other[44][45][46]
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 aprobabilistic sample selected on anarea sampling frame. Traditionalsurvey methodology provides different methods to combine accurate information on a sample with less accurate, but exhaustive, data for a covariable orproxy 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).[47][48]
If we target other variables, such ascrop yield orleaf area, we may need different indicators to be computed from images, such as theNDVI, a good proxy tochlorophyll activity.[28]
TheTR-1 reconnaissance/surveillance aircraftThe2001 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 (aliasNadar) made photographs of Paris from his balloon in 1858.[51] 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.
Systematicaerial photography was developed for military surveillance and reconnaissance purposes beginning inWorld War I.[52] After WWI, remote sensing technology was quickly adapted to civilian applications.[53] 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."
The development of remote sensing technology reached a climax during theCold War with the use of modified combat aircraft such as theP-51,P-38,RB-66 and theF-4C, or specifically designed collection platforms such as theU2/TR-1,SR-71,A-5 and theOV-1 series both in overhead and stand-off collection.[54] 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.[55]
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.[56] Instrumentation aboard various Earth observing and weather satellites such asLandsat, theNimbus and more recent missions such asRADARSAT andUARS provided global measurements of various data for civil, research, and military purposes.Space probes to other planets have also provided the opportunity to conduct remote sensing studies in extraterrestrial environments, synthetic aperture radar aboard theMagellan spacecraft provided detailed topographic maps ofVenus, while instruments aboardSOHO allowed studies to be performed on theSun and thesolar wind, just to name a few examples.[57][58]
Recent developments include, beginning in the 1960s and 1970s, the development ofimage processing ofsatellite imagery. The use of the term "remote sensing" began in the early 1960s whenEvelyn 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.[59][60] With assistance from her fellow staff member at the Office of Naval Research, Walter Bailey, she coined the term "remote sensing".[61][62] Several research groups inSilicon Valley includingNASA Ames Research Center,GTE, andESL Inc. developedFourier 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.[63]
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.TerraSAR-X andRapidEye are developed constantly and the demand for skilled labour is increasing steadily. Furthermore, remote sensing exceedingly influences everyday life, ranging fromweather forecasts to reports onclimate change ornatural disasters. As an example, 80% of the German students use the services ofGoogle 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.[64] There exists a hugeknowledge 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.[65] 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 ascompetences 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.[66] In order to integrate remote sensing in a sustainable manner organizations like theEGU orDigital Earth[67] encourage the development oflearning modules andlearning portals. Examples include:FIS – Remote Sensing in School Lessons,[68]Geospektiv,[69]Ychange,[70] or Spatial Discovery,[71] to promote media and method qualifications as well as independent learning.
Remote sensing data are processed and analyzed with computer software, known as aremote sensing application. A large number of proprietary and open source applications exist to process remote sensing data.
There are applications ofgamma rays to mineral exploration through remote sensing. In 1972 more than $2 million were spent on remote sensing applications with gamma rays to mineral exploration. Gamma rays are used to search for deposits ofuranium. 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.[72]
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.[73] Sputnik 1 sent back radio signals, which scientists used to study theionosphere.[74]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'sVan Allen radiation belts.[75] TheTIROS-1 spacecraft, launched on April 1, 1960, as part of NASA'sTelevision Infrared Observation Satellite (TIROS) program, sent back the first television footage of weather patterns to be taken from space.[73]
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.[73] By 2021, that total had grown to over 950, with the largest number of satellites operated by US-based companyPlanet Labs.[76]
MostEarth observation satellites carry instruments that should be operated at a relatively low altitude. Most orbit at altitudes above 500 to 600 kilometers (310 to 370 mi). Lower orbits have significantair-drag, which makes frequent orbitreboost maneuvers necessary. The Earth observation satellitesERS-1, ERS-2 andEnvisat ofEuropean Space Agency as well as theMetOp spacecraft ofEUMETSAT are all operated at altitudes of about 800 km (500 mi). TheProba-1,Proba-2 andSMOS 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 inLow Earth orbits (LEO) orbits and providingsatellite imagery of various parts of the Earth.[77][78]
To get global coverage with a low orbit, apolar orbit is used. A low orbit will have an orbital period of about 100 minutes and the Earth will rotate around its polar axis about 25° between successive orbits. Theground track moves towards the west 25° each orbit, allowing a different section of the globe to be scanned with each orbit. Most are inSun-synchronous orbits.
Ageostationary 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 formeteorological satellites.
^Howard, A.; et al. (19 August 2015). "Remote sensing and habitat mapping for bearded capuchin monkeys (Sapajus libidinosus): landscapes for the use of stone tools".Journal of Applied Remote Sensing.9 (1) 096020.doi:10.1117/1.JRS.9.096020.S2CID120031016.
^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 athttp://hdl.handle.net/2060/19860021622Archived 27 October 2021 at theWayback Machine
^Houston, A.H. "Use of satellite data in agricultural surveys".Communications in Statistics – Theory and Methods (23):2857–2880.
^Allen, J.D. "A Look at the Remote Sensing Applications Program of the National Agricultural Statistics Service".Journal of Official Statistics.6 (4):393–409.
^Taylor, J (1997).Regional Crop Inventories in Europe Assisted by Remote Sensing: 1988–1993. Synthesis Report. Luxembourg: Office for Publications of the EC.
^Zhen, Z (2013). "Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification".International Journal of Remote Sensing.34 (19):6914–6930.Bibcode:2013IJRS...34.6914Z.doi:10.1080/01431161.2013.810822.
^Czaplewski, R.L. "Misclassification bias in areal estimates".Photogrammetric Engineering and Remote Sensing (39):189–192.
^Bauer, M.E. (1978). "Area estimation of crops by digital analysis of Landsat data".Photogrammetric Engineering and Remote Sensing (44):1033–1043.
^Sannier, C (2014). "Using the regression estimator with landsat data to estimate proportion forest cover and net proportion deforestation in gabon".Remote Sensing of Environment.151 (151):138–148.Bibcode:2014RSEnv.151..138S.doi:10.1016/j.rse.2013.09.015.
^The Indian Society of International Law – Newsletter: VOL. 15, No. 4, October – December 2016 (Report). Brill. 2018.doi:10.1163/2210-7975_hrd-9920-2016004.
^"In Depth | Magellan".Solar System Exploration: NASA Science.Archived from the original on 19 October 2021. Retrieved19 February 2019.
^Campbell, James B.; Wynne, Randolph H. (21 June 2011).Introduction to Remote Sensing (5th ed.). New York London: The Guilford Press.ISBN978-1-60918-176-5.
^Fussell, Jay; Rundquist, Donald; Harrington, John A. (September 1986)."On defining remote sensing"(PDF).Photogrammetric Engineering and Remote Sensing.52 (9):1507–1511. Archived fromthe original(PDF) on 4 October 2021.
^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
^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
^Bednarz, S.W.; Whisenant, S.E. (July 2000). "Mission Geography: Linking national geography standards, innovative technologies and NASA".IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120). Vol. 6. pp. 2780–2782 vol.6.doi:10.1109/IGARSS.2000.859713.ISBN0-7803-6359-0.S2CID62414447.
^Kuznetsov, V.D.; Sinelnikov, V.M.; Alpert, S.N. (June 2015). "Yakov Alpert: Sputnik-1 and the first satellite ionospheric experiment".Advances in Space Research.55 (12):2833–2839.Bibcode:2015AdSpR..55.2833K.doi:10.1016/j.asr.2015.02.033.
^"James A. Van Allen".nmspacemuseum.org. New Mexico Museum of Space History. Retrieved14 May 2018.
