LANDSAT-4 MSS And Thematic Mapper Data Quality And Information Content Analysis

Anuta, P. E.; Bartolucci, L. A.; Dean, M. E.; Lozano, D.; Malaret, E.; McGillem, C.D.; Valdes, J.; Valenzuela, Carlos R.

doi:10.1109/tgrs.1984.350595

Cited by 51 publications

(16 citation statements)

References 2 publications

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“…The degree of optical blurring is conventionally measured by defining an effective instantaneous field of view for the sensor, which is taken to be limited by where the point spread function of the sensor falls to half its maximum value. Based on a variety of in-flight measuring criteria, the effective instantaneous field of view of the TM band 1-5 and band 7 optics has been variously reported in the range 33-46 m, and there is some evidence that its dimensions are greater in the scan direction than in the along-track direction [Anuta et al, 1984;Schowengerdt et al, 1985;Markham, 1985]. It is a reasonable approximation to assume that the area sampled by a TM "30 m" pixel is actually about 40 m across, which should be borne in mind when considering the information about hot volcanic features imparted by the TM data.…”

Section: Pixel Size and The Tm Point Spread Functionmentioning

confidence: 99%

“…It should be noted that a particularly hot anomaly could produce enough radiance to affect a pixel even when situated several meters beyond the edge of what is normally regarded as the effective instantaneous field of view; for example, the TM point spread function does not fall to 5% of its maximum value until about 30 m from the center of the pixel [Markham, 1985], whereas the edge of the effective instantaneous field of view, as usually defined is, only about 20 m from the center of the pixel. Errors would be greater for the dual-band method of temperature derivation using bands 4 and 5 together, due to the larger misregistration between bands 5 and 7 and the shorter-wavelength bands 1-4, which was reported to be as high as 0.6 of a 30-m pixel for early Landsat 4 TM data [Anuta et al, 1984;Wrigley et al, 1984]. In order to show how short-wavelength infrared data can be used in practice, we discuss below examples from several active volcanoes.…”

Section: Other Errorsmentioning

confidence: 99%

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Volcano monitoring using short wavelength infrared data from satellites

Rothery¹,

Francis²,

Wood³

1988

J. Geophys. Res.

282

183

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Data from remote sensing satellites operating in the short-wavelength infrared can be used to measure temperatures of about 150øC and above. The gain settings of the Landsat Thematic Mapper (TM) sensors are such that to record pixel-integrated temperatures from 150øC to over 1000øC requires the use of several spectral bands. If the radiant source occupies less than a whole pixel, as is the case for fumaroles or a crusted lava surface with incandescent cracks, then the pixel-integrated temperature is less than the actual surface temperature of the hot areas and appears to be different at each wavelength. By determining pixel-integrated temperature in two spectral bands we can estimate both the temperature and size of hot areas which occupy less than a complete pixel. In some cases our observations provide the only available data on the state of activity of a particular volcano; in others they support and refine field observations. Our Landsat TM data reveal otherwise unobserved precursor and subsequent activity related to the September 1986 eruption of Lascar volcano (Chile) and supplement other data on the March 1986 eruption of Augustine volcano (Alaska). They also indicate the continued presence of lava lakes at Erta 'Ale (Ethiopia) in the absence of any ground-based observations and document minor eruptive activity at Mount Erebus (Antarctica). Landsat Multispectral Scanner data have been used to measure the variation in temperature along a lava flow on Sierra Negra (Galapagos Islands) which occurred in 1979.

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Section: Pixel Size and The Tm Point Spread Functionmentioning

confidence: 99%

Section: Other Errorsmentioning

confidence: 99%

Volcano monitoring using short wavelength infrared data from satellites

Rothery¹,

Francis²,

Wood³

1988

J. Geophys. Res.

282

183

View full text Add to dashboard Cite

show abstract

“…Apart from change detection, (Byrne et al, 1980;Fung and LeDrew, 1987;Lodwick, 1979;Muchoney and Haack, 1994;Richards, 1984;Singh and Harrison, 1985) PCA has been applied to determine the underlying dimensions of remotely sensed data (Anuta et al, 1984;Ready and Wintz, 1973;Townshend et al, 1985), for image enhancement (Gillespie, 1980;Richards, 1986), image data compression (Gonzales and Wintz, 1977), merging multiresolution and multispectral data (Chavez et al, 1991), land-cover characterization (Hirosawa et al, 1996) and other research topics (Ahearn and Wee, 1991;Canas and Barnett, 1985;Conese et al, 1988;Dwivedi and Rao, 1992;Eastman and Fult, 1993;Ingerbritsen and Lyon, 1985;Jiaju, 1988;Loughlin, 1991;Siljeström and Moreno-López, 1995).…”

Section: Introductionmentioning

confidence: 99%

A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces

Koutsias

Mallinis

Karteris

2009

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The transfer function (or the point spread function) of the MSS and the TM have been experimentally determined by several authors for the entire imaging system (Anuta et al 1984), Malaret et al 1985), as well as the contribution of each component (optics, detector and filters) (Markham, 1985). In the present study, the transfer function for both MSS and TM was approximated as a separable Gaussian function, i.e.,…”

Section: Design Of the Interpolation-restoration Filter By Fir Windowmentioning

confidence: 86%

Combined interpolation—restoration of Landsat images through FIR filter design techniques

Fonseca

Prasad

Mascarenhas

1993

International Journal of Remote Sensing

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Abstract. In digital image processing for remote sensing there is often a need to interpolate an image. Examples occur in scale magnification, image registration, geometric correction, etc. On the other hand, this image can be subject to several sources of degradation and it would be interesting to compensate also for this degradation in the interpolation process. Therefore, this article addresses the problem of combining interpolation and restoration in a single operation, thereby reducing the computational effort. This is done by means of two-dimensional, separable, Finite Impulse Response (FIR) filters. The ideal low pass FIR filter for interpolation is modified to account for the restoration process. The Modified Inverse Filter (MIF) and the Wiener Filter (WF) are used for this purpose. The proposed methods are applied to the interpolation-restoration of Landsat-5 Thematic Mapper data. The later process takes into account the degradation due to optics, detector and electronic filtering. A comparison with the Parametric Cubic Convolution (PCC) technique is made. The experimental results consist of interpolation-restoration processes of Landsat-5 Thematic Mapper images from 30 m to 15 m (scale magnification) but they could also be generalized to include deblurring on more general interpolation problems, like geometric correction

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LANDSAT-4 MSS And Thematic Mapper Data Quality And Information Content Analysis

Cited by 51 publications

References 2 publications

Volcano monitoring using short wavelength infrared data from satellites

Volcano monitoring using short wavelength infrared data from satellites

A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces

Combined interpolation—restoration of Landsat images through FIR filter design techniques

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