Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?

By Conghe Song, Curtis E. Woodcock, Karen C. Seto, M. Lenney, S. Macomber. 2001

1,863 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/1f1654e2b557f1c488cb125e40f36f2ebd1c2f4b

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Citations in the corpus

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, background): “Furthermore, radiometric and atmospheric calibration of remote sensing data is needed for biomass estimation (Song et al. 2001).”
Remote sensing imagery in vegetation mapping: a review (2008, background): “…correction is aimed toward extracting the absolute reflectance of scene objects at the surface of the earth, requiring the input of simultaneous atmospheric properties and sensor calibration, which are difficult to acquire in many cases (Chen et al. 2005; Du et al. 2002; Song et al. 2001).”
Forest change detection by statistical object-based method (2006, background): “Heterogeneous atmospheric effects may require a preliminary stratification of the region or indeed, corrections of these effects (Song et al., 2001).”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, background): “Radiometric normalization was not performed, since this step is not required when images are analyzed simultaneously rather than individually (Song et al., 2001).”
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery (2014, background): “For classification on a single date image, there is no need to do atmospheric correction if the sky is clear, which is the case in this study [7,11].”
Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges (2012, neutral): “Song et al. (2001) showed how use of surface reflectance can improve the change detection using Landsat data.”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, methodology): “Removal of path radiance, which is assumed to be 1% of the surface reflectance for dark objects (Moran et al. 1992; Song et al. 2001), was carried out as recommended by Vincini and Frazzi (2003) and Soenen, Peddle, and Coburn (2005) before implementing topographic effect suppression.”
A survival guide to Landsat preprocessing. (2017, methodology): “To help answer this question, we start with the guiding principle that only the preprocessing steps necessary for a given analysis should be applied because each step may risk introducing artifacts and/or error into the data (Song et al. 2001, Riaño et al. 2003, Kennedy et al. 2009).”
Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica (2008, background): “Atmospheric correction was not considered useful, because only a single Landsat TM scene was used (Song et al., 2001) and no information on variation of atmospheric conditions was available.”
Balancing food production and nature conservation in the Neotropical dry forests of northern Argentina (2008, methodology): “…the ETM 1 and MSS sensors comparable, the values of bands 2 (green), 3 (red), and 4 (near infrared) of MSS were transformed into equivalents of reflectance for the year 2002 using a method of cross calibration assuming a linear relationship between sensors (Song et al., 2001; Chen et al., 2005).”
Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors (2005, methodology): “A typical example is the application of atmospheric correction to reduce temporal or spatial variability in vegetation indices (Carlson & Ripley, 1997; Song et al., 2001).”
Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon (2006, methodology): “DOS3 was selected for use in this study in lieu of other DOS methods based on its ability to create a consistent common scale as evaluated by change detection and classification accuracy for part of the Landsat scene under investigation (Song et al., 2001).”