Difference between revisions of "Optical remote sensing"
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==Restrictions== | ==Restrictions== | ||
| − | Restrictions apply | + | Restrictions apply whenever the object-specific signals are masked or interfered with in any ambiguous manner, with regards to both the sensor resolution and retrieval methods. Therefore only a few restrictions can be identified in general and most restrictions can only be determined with site-specific observations, sensors used, or algorithms applied. |
| − | ===Examples | + | ===Examples of general restrictions=== |
| − | + | Examples of general restrictions are clouds masking optical signals reflected from the earth surface or geometric recording conditions from the water surface that add intense sun glitter <ref> Sun glitter is defined as (spatial usually very variable) contribution of direct sunlight, that is reflected at the water surface and increasing substantially the intensity of radiance measured at the sensor. It appears at specific geometric recording conditions between sun, the water surface and the sensor and is affecting approximate 30-70% of all earth observation imageries. </ref> | |
| − | <ref> Sun glitter is defined as (spatial usually very variable) contribution of direct sunlight, that is reflected at the water surface and increasing substantially the intensity of radiance measured at the sensor. It appears at specific geometric recording conditions between sun, the water surface and the sensor and is affecting approximate 30-70% of all earth observation imageries. </ref> | + | contributions to the signal. |
| − | contributions to the signal | ||
===Examples of case-dependent restrictions=== | ===Examples of case-dependent restrictions=== | ||
| − | a) Intermediate sun glitter conditions can be treated | + | a) Intermediate sun glitter conditions can be treated only if the radiometric and spectral (and depending on the algorithm, maybe also spatial) resolution of the sensor is sufficient and the processing approach supports such a correction or consideration of this effect (> example). |
| − | + | b) The spatial resolution of the sensor must be better in most cases than the spatial heterogeneity of the target areas to be mapped. Sub-pixel classification of surfaces is only possible for a very restricted number of spectral endmembers. At the very least, every contribution of members not implemented into the model will create errors. | |
| − | b) The spatial resolution of the sensor in most cases | + | c) The spectral behaviour of optical classes must differ significantly from each other with respect to the sensor’s spectral resolution. e.g. Chlorophyll and Gelbstoff cannot be estimated independently if the sensor’s spectral and radiometric resolution is insufficient and/or the ecosystem specific absorption spectra are too similar for an independent, quantitative estimate. |
| − | |||
| − | c) The spectral behaviour of optical classes must | ||
==References== | ==References== | ||
<references/> | <references/> | ||
Revision as of 22:45, 27 March 2007
Definition of Passive optical remote sensing:
Passive optical sensors detect natural energy (radiation) that is emitted or reflected by the object or scene being observed. Reflected sunlight is the source of radiation measured by passive optical sensors.
[1] [1].
This is the common definition for Passive optical remote sensing, other definitions can be discussed in the article
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Remote sensing using satellite and airborne sensors is a powerful, often-preferred tool for monitoring coastal zones, as these detection instruments can provide accurate, large scale, synoptic environmental data essential for understanding and managing marine ecosystems. Optical multi- or hyperspectral sensors enable the detection of in-water properties, such as suspended matter and phytoplankton concentration, and provide information on benthic substrate type, vegetation composition, and bathymetry in optically-shallow waters (see “Applications”).
Contents
See also
Optical remote sensing: habitat mapping, SPM maps, chlorophyll maps, bathymetry
...
Principles
Optical remote sensing using passive satellite or airborne sensors is the spatially resolved detection and utilization of sunlight which has been transmitted through the atmosphere and reflected back from the earth’s surface to the sensor.
- ↑ WIKIPEDIA (From Wikipedia, the free encyclopedia)
In marine and aquatic environments, the sunlight spectrum is modified on its way through the atmosphere, water surface, and water body. Materials in each of these boundary layers absorbs, scatters, and reflects the different light wavelengths in a specific manner. As a result, the reflected light carries spectral information about the composition of these substances.
Sensors
This spectral information is detected differently, in terms of accuracy, for temporal, spatial, spectral, and radiometric resolution, and is characteristic for each different sensor. Multi- or hyperspectral detectors on space or airborne platforms may be chosen depending upon specific requirements, such as cost, area size, or availability.
Data processing
Different remote sensing data processing methods are available, and each evaluates or interprets the spectral information in a slightly different manner. To best retrieve the content information with regards to space and time, the output from a number of different algorithms can be compared against ground-based observational data.
Physics-based retrieval algorithms
Physics-based retrieval algorithms can generally be applied world wide and are very flexible under all conditions covered by the optical models upon which they were based. Once set up, they do not rely on manual adaptations in order to generate map products and are frequently completely independent of input from ground truth measurements.
Empirical algorithms
Empirical algorithms are powerful analytical tools and are often able to exploit the most information from remote sensing data. They usually rely on accompanied ground truth measurements and thus are typically not transferable between different types of aquatic and marine ecosystems.
Generic processing systems
Generic processing systems cover a wide range of applications and can be applied to new sites, applications, and sensors due to a systematic, modular approach and easy adaptations for sensor- and site-specific properties.
Geo-rectification
Geo-rectification procedures are applied to remote sensing data in order to assign each spatially-resolved pixel with a geographic coordinate. Accurate, geo-coded maps can then be produced and incorporated into Geographical Information System (GIS) software. The trigonometric principles and procedures for geo-referencing are essentially developed and operational, therefore, sub-pixel accuracies can be achieved given adequate navigation data were available during collection. In practice, the spatial accuracy of operational geo-coded products depends on the accuracy of the satellite or airborne platform‘s navigation system. Therefore, for high-precision tasks, many satellite or airborne images have to be refined using manual geo-rectification approaches or automatic matching algorithms.
Restrictions
Restrictions apply whenever the object-specific signals are masked or interfered with in any ambiguous manner, with regards to both the sensor resolution and retrieval methods. Therefore only a few restrictions can be identified in general and most restrictions can only be determined with site-specific observations, sensors used, or algorithms applied.
Examples of general restrictions
Examples of general restrictions are clouds masking optical signals reflected from the earth surface or geometric recording conditions from the water surface that add intense sun glitter [1] contributions to the signal.
Examples of case-dependent restrictions
a) Intermediate sun glitter conditions can be treated only if the radiometric and spectral (and depending on the algorithm, maybe also spatial) resolution of the sensor is sufficient and the processing approach supports such a correction or consideration of this effect (> example). b) The spatial resolution of the sensor must be better in most cases than the spatial heterogeneity of the target areas to be mapped. Sub-pixel classification of surfaces is only possible for a very restricted number of spectral endmembers. At the very least, every contribution of members not implemented into the model will create errors. c) The spectral behaviour of optical classes must differ significantly from each other with respect to the sensor’s spectral resolution. e.g. Chlorophyll and Gelbstoff cannot be estimated independently if the sensor’s spectral and radiometric resolution is insufficient and/or the ecosystem specific absorption spectra are too similar for an independent, quantitative estimate.
References
- ↑ Sun glitter is defined as (spatial usually very variable) contribution of direct sunlight, that is reflected at the water surface and increasing substantially the intensity of radiance measured at the sensor. It appears at specific geometric recording conditions between sun, the water surface and the sensor and is affecting approximate 30-70% of all earth observation imageries.
