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000052478 084__ $$2WoS$$aEnvironmental Sciences
000052478 084__ $$2WoS$$aRemote Sensing
000052478 084__ $$2WoS$$aImaging Science & Photographic Technology
000052478 1001_ $$0P:(DE-Juel1)VDB49697$$aSchroeder, T.$$b0$$uFZJ
000052478 245__ $$aRadiometric correction of Multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon
000052478 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2006
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000052478 440_0 $$012722$$aRemote Sensing of Environment$$v103$$x0034-4257
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000052478 520__ $$aDetecting and characterizing continuous changes in early forest succession using multi-temporal satellite imagery requires atmospheric correction procedures that are both operationally reliable, and that result in comparable units (e.g., surface reflectance). This paper presents a comparison of five atmospheric correction methods (2 relative, 3 absolute) used to correct a nearly continuous 20-year Landsat TM/ETM+ image data set (19-images) covering western Oregon (path/row 46/29). In theory, full absolute correction of individual images in a time-series should effectively minimize atmospheric effects resulting in a series of images that appears more similar in spectral response than the same set of uncorrected images. Contradicting this theory, evidence is presented that demonstrates how absolute correction methods such as Second Simulation of the Satellite Signal in the Solar Spectrum (6 s), Modified Dense Dark Vegetation (MDDV), and Dark Object Subtraction (DOS) actually make images in a time-series somewhat less spectrally similar to one another. Since the development of meaningful spectral reflectance trajectories is more dependant on consistent measurement of surface reflectance rather than on accurate estimation of true surface reflectance, correction using image pairs is also tested. The relative methods tested are variants of an approach referred to as "absolute-normalization", which matches images in a time-series to an atmospherically corrected reference image using pseudo-invariant features and reduced major axis (RMA) regression. An advantage of "absolute-normalization" is that all images in the time-series are converted to units of surface reflectance while simultaneously being corrected for atmospheric effects. Of the two relative correction methods used for "absolute-normalization", the first employed an automated ordination algorithm called multivariate alteration detection (MAD) to statistically locate pseudo-invariant pixels between each subject and reference image, while the second used analyst selected pseudo-invariant features (PIF) common to the entire image set. Overall, relative correction employed in the "absolute-normalization" context produced the most consistent temporal reflectance response, with the automated MAD algorithm performing equally as well as the handpicked PIFs. Although both relative methods performed nearly equally in terms of observed errors, several reasons emerged for preferring the MAD algorithm. The paper concludes by demonstrating how "absolute-normalization" improves (i.e., reduces scatter in) spectral reflectance trajectory models used for characterizing patterns of early forest succession. (c) 2006 Elsevier Inc. All rights reserved.
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000052478 65320 $$2Author$$aatmospheric correction
000052478 65320 $$2Author$$arelative normalization
000052478 65320 $$2Author$$amultivariate alteration detection (MAD)
000052478 65320 $$2Author$$alandsat time-series
000052478 65320 $$2Author$$aforest succession
000052478 7001_ $$0P:(DE-Juel1)VDB62789$$aCohen, W.$$b1$$uFZJ
000052478 7001_ $$0P:(DE-Juel1)VDB62790$$aSong, C.$$b2$$uFZJ
000052478 7001_ $$0P:(DE-Juel1)VDB4989$$aCanty, M. J.$$b3$$uFZJ
000052478 7001_ $$0P:(DE-Juel1)VDB62791$$aYang, Z.$$b4$$uFZJ
000052478 773__ $$0PERI:(DE-600)1498713-2$$a10.1016/j.rse.2006.03.008$$gVol. 103, p. 16 - 26$$p16 - 26$$q103<16 - 26$$tRemote sensing of environment$$v103$$x0034-4257$$y2006
000052478 8567_ $$uhttp://dx.doi.org/10.1016/j.rse.2006.03.008
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