Journal ArticleDOI
Object-based cloud and cloud shadow detection in Landsat imagery
Zhe Zhu,Curtis E. Woodcock +1 more
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The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.About:
This article is published in Remote Sensing of Environment.The article was published on 2012-03-15. It has received 1620 citations till now. The article focuses on the topics: Cloud top & Cloud fraction.read more
Citations
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Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984–2010 through PALSAR and time series Landsat images
Jie Wang,Xiangming Xiao,Xiangming Xiao,Yuanwei Qin,Jinwei Dong,George L. Geissler,Geli Zhang,Nicholas I. Cejda,Brian Alikhani,Russell Doughty +9 more
TL;DR: In this paper, a pixel and phenology-based mapping algorithm was developed to analyze PALSAR mosaic data in 2010 and all the available Landsat 5/7 data during 1984-2010 with the Google Earth Engine (GEE) platform.
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Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation
Zunyi Xie,Zunyi Xie,Stuart R. Phinn,Edward T. Game,David J. Pannell,Richard J. Hobbs,Peter R. Briggs,Eve McDonald-Madden +7 more
TL;DR: In this paper, an approach to map changes in vegetation cover amounts at the pixel level (30m) using Google Earth Engine (GEE) was presented, which can be used to identify degraded lands and their utility for achieving conservation endeavours.
Journal ArticleDOI
A generalization of spatial and temporal fusion methods for remotely sensed surface parameters
TL;DR: Non-linear methods, such as the sparse representation-based spatio-temporal reflectance fusion model, can successfully address land-cover changes in addition to phonological changes, thereby providing a promising option for STF problems in the future.
Journal ArticleDOI
The Landsat Burned Area algorithm and products for the conterminous United States
Todd J. Hawbaker,Melanie K. Vanderhoof,Gail L. Schmidt,Yen-Ju G. Beal,Joshua J. Picotte,Joshua D. Takacs,Jeff T. Falgout,John L. Dwyer +7 more
TL;DR: In this article, the authors developed the Landsat Burned Area (BA) algorithm, an update from the BAECV algorithm, which identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984 through 2018 using machine learning, thresholding, and image segmentation.
Journal ArticleDOI
Automatic boosted flood mapping from satellite data
TL;DR: The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input, allowing for a truly rapid and automatic response.
References
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Book
Morphological Image Analysis: Principles and Applications
TL;DR: This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
Journal ArticleDOI
A Landsat surface reflectance dataset for North America, 1990-2000
Jeffrey G. Masek,Eric Vermote,Nazmi Saleous,Robert E. Wolfe,Forrest G. Hall,Karl F. Huemmrich,Feng Gao,J. Kutler,Teng-Kui Lim +8 more
TL;DR: Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product.
Journal ArticleDOI
Discriminating clear sky from clouds with MODIS
Steven A. Ackerman,Kathleen I. Strabala,W. Paul Menzel,Richard A. Frey,Christopher C. Moeller,Liam E. Gumley +5 more
TL;DR: The MODIS cloud mask algorithm as discussed by the authors uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies, which is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options.
Journal ArticleDOI
Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data
TL;DR: Zhang et al. as discussed by the authors used a more advanced NASA Goddard Institute for Space Studies (GISS) radiative transfer model and improved ISCCP cloud climatology and ancillary data sets.
Journal ArticleDOI
Spectral signature of alpine snow cover from the Landsat Thematic Mapper.
Jeff Dozier,Jeff Dozier +1 more
TL;DR: In this article, the spectral signatures of the Landsat TM images of the Sierra Nevada were analyzed to distinguish several classes of snow from other surface covers, and a number of TM images were used for automatic analysis of alpine snow cover.