Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon

By Todd A. Schroeder, Warren B. Cohen, Conghe Song, Morton John Canty, Zhiqiang Yang. 2006

409 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/aac0f966dc81ac8c8480650dcbc5171efc801991

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Citations in the corpus

Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery (2014, methodology): “Radiometric calibration and atmospheric correction were conducted according to [34].”
Land cover 2.0 (2018, methodology): “Previously, relative normalization of re ﬂ ectance or radiance from di ﬀ erent image was often undertaken, and based typically on statistical calibration among images at pseudo-invariant features identi ﬁ ed in spatially overlapping acquisitions (Schott, Salvaggio, and Volchock 1988; Schroeder et al”
Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions (2013, background): “For studies such as ours that cover a large geographical area that may have been collected over several years, radiometric correction is essential to minimize the noise not related to ground conditions (Song & Woodcock, 2003; Schroeder et al., 2006).”
Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, background): “When analysing a multitemporal data set, some level of radiometric correction is considered necessary to differentiate real change from noise (Schroeder et al. 2006).”
Remote sensing imagery in vegetation mapping: a review (2008, methodology): “For example, nearly 20-year continuous Landsat TM/ETM+ image datasets (19 images) covering western Oregon were used to detect and characterize continuous changes in early forest succession (Schroeder et al. 2006).”
Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms (2010, methodology): “Following Schroeder et al. (2006), we used the cosine–Theta (COST) correction of Chavez (1996) to remove most atmospheric effects for a single reference image in a given LTS. Dark object values for use in the COST calculations were based on visual assessment by a trained interpreter following the…”
Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation (2010, background): “The few studies that have evaluated annual or near-annual datasets have mostly involved only one Landsat scene (Kaufmann & Seto, 2001; Healey et al., 2006; Schroeder et al., 2006).”
Trajectory-based change detection for automated characterization of forest disturbance dynamics (2007, methodology): “Landsat TM images were acquired for Path 46 Row 29 (WRS II grid system) for the years between 1984 and 2004 (Table 1; see also Schroeder et al. (2006)).”
A survival guide to Landsat preprocessing. (2017, background): “Further, these corrections can introduce additional errors (Schroeder et al. 2006).”
Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation (2008, result): “satisfactory regression for radiometric normalization (Schroeder et al., 2006).”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, background): “Generally, there are two categories of atmospheric correction approaches: (1) relative normalization (Schroeder et al., 2006) and absolute correction (Chávez, 1996; Song and Woodcock, 2003).”