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Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas.

Earth Science Satellite Remote Sensing

Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include film photography , infrared , charge-coupled devices , and 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.

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 , 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. Other uses include different areas of the earth sciences such as natural resource management , agricultural fields such as land usage and conservation, [6] [7] and national security and overhead, ground-based and stand-off collection on border areas.

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. 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 is often provided within a degree or two with electronic compasses. Compasses can measure not just azimuth i.

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More exact orientations require gyroscopic-aided orientation , periodically realigned by different methods including navigation from stars or known benchmarks. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions. 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.

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  • Vol.1: Science and Instruments!
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As of the early s, most satellite images are sold fully georeferenced. Interpretation is the critical process of making sense of the data. Image Analysis is the recently developed automated computer-aided application which 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 lossless 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-readable 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.

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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.

To facilitate the discussion of data processing in practice, several processing "levels" were first defined in by NASA as part of its Earth Observing System [17] and steadily adopted since then, both internally at NASA e. A Level 1 data record is the most fundamental i. Level 2 is the first level that is directly usable for most scientific applications; its value is much greater than the lower levels.

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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. Our society can help.

Remote sensing - Wikipedia

We deal with the theory, concepts, and techniques of science and engineering as they apply to the remote sensing of the earth, oceans, atmosphere, and space, as well as the processing, interpretation and dissemination of this information. The new TC chairs for… Read more.

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  • Earth Science Satellite Remote Sensing: Vol.1: Science and Instruments.
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Technical committees provide a way for individuals with similar technical interests to network and participate in developing strong community ties based on common technical interests. Technical committees are actively involved in awards, conferences, publications, and educational activities and provide technical advice to the Society's leadership.

The mission of the Earth Science Informatics Technical Committee ESI TC is to advance the application of informatics to geosciences and remote sensing, to provide a venue for ESI professionals to exchange information and knowledge, and to give technology advice to major national and international ESI initiatives. The FARS committee mission is to provide technical assessments, guidance and recommendations regarding matters of frequency sharing and interference between remote sensing and other uses of the radiowave spectrum. The committee actively promotes and provides insight to institutions and industry on remote sensing instrument and technology development.

Exceptional customer service Get specialist help and advice. Satellite remote sensing for Earth science data has been rapidly expanding during the last decade. Both volumes are designed to give scientists and graduate students with limited remote sensing background a thorough introduction to current and future NASA, NOAA and other Earth science remote sensing missions.

Newsletter Google 4. Help pages. Prothero Michael J. Benton Richard Fortey View All. Go to British Wildlife. Conservation Land Management. Go to Conservation Land Management. Publisher: Springer Nature. Click to have a closer look.

Earth Science Satellite Remote Sensing Vol 2 Data Computational Processing and Tools

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