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Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems Public Deposited

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https://ir.library.oregonstate.edu/concern/articles/v979v471j

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  • Automated cloud and cloud shadow identification algorithms designed for Landsat Thematic Mapper (TM) and Thematic Mapper Plus (ETM+) satellite images have greatly expanded the use of these Earth observation data by providing a means of including only clear-view pixels in image analysis and efficient cloud-free compositing. In an effort to extend these capabilities to Landsat Multispectal Scanner (MSS) imagery, we introduce MSS clear-view-mask (MSScvm), an automated cloud and shadow identification algorithm for MSS imagery. The algorithm is specific to the unique spectral characteristics of MSS data, relying on a simple, rule-based approach. Clouds are identified based on green band brightness and the normalized difference between the green and red bands, while cloud shadows are identified by near infrared band darkness and cloud projection. A digital elevation model is incorporated to correct for topography-induced illumination variation and aid in identifying water. Based on an accuracy assessment of 1981 points stratified by land cover and algorithm mask class for 12 images throughout the United States, MSScvm achieved an overall accuracy of 84.0%. Omission of thin clouds and bright cloud shadows constituted much of the error. Perennial ice and snow, misidentified as cloud, also contributed disproportionally to algorithm error. Comparison against a corresponding assessment of the Fmask algorithm, applied to coincident TM imagery, showed similar error patterns and a general reduction in accuracy commensurate with differences in the radiometric and spectral richness of the two sensors. MSScvm provides a suitable automated method for creating cloud and cloud shadow masks for MSS imagery required for time series analyses in temperate ecosystems.
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  • Braaten, J. D., Cohen, W. B., & Yang, Z. (2015). Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sensing of Environment, 169, 128-138. doi:10.1016/j.rse.2015.08.006
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  • 169
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  • The development and accuracy assessment of MSScvm were made possible by the support of the Landscape Change Monitoring System (LCMS) project funded by the USDA Forest Service and by NASA's Carbon Monitoring System program (NNH13AW62I).
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