Continuous change detection and classification of land cover using all available Landsat data

By Zhe Zhu, Curtis E. Woodcock. 2014

1,647 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/b31b31714e49a21471764c6dae6d5bc58e0a4ea9

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Citations in the corpus

How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms (2017, methodology): “The others focused on disturbance across all land cover types, but targeted a more limited range of magnitudes (MIICA, Multi-index Integrated Change Analysis; CCDC, Continuous Change Detection and Classification).”
Analysis Ready Data: Enabling Analysis of the Landsat Archive (2018, methodology): “The monitoring element of LCMAP will be executed through the continuous change detection and classification (CCDC) algorithm [58] that is straightforward to implement using the ARD as an input.”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “Univariate time series are widely used, and only a few of them use multiple spectral bands of Landsat time series (Fu and Weng, 2016; Goodwin et al., 2013; Goodwin and Collett, 2014; Zhu et al., 2016a; Zhu and Woodcock, 2014a, 2014b).”
Land cover 2.0 (2018, methodology): “…as part of the land cover classi ﬁ cation process (Hansen et al. 2008; Lindquist et al. 2008) or removed prior to classi ﬁ cation using cloud masking algorithms that are labelled directly in the input data (Frey et al. 2008; Foga et al. 2017; Zhu and Woodcock 2014a; Zhu, Wang, and Woodcock 2015).”
Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review (2020, background): “purposes, several functions, such as continuous change detection and classification (CCDC) [30], exponentially weightedmoving”
Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change (2014, methodology): “…there are many kinds of land cover change occurring in this study area, including forest clearing, urbanization, and abandonment of agricultural ﬁ elds (Zhu & Woodcock, 2014); and 3) there are large areas of forest in this study area and in winter the detection of snow pixels is dif ﬁ cult for…”
Forest Types Classification Based on Multi-Source Data Fusion (2017, methodology): “[28] developed a new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data.”
The global Landsat archive: Status, consolidation, and direction (2016, background): “…unimaginable at that time that people would be doing global analyses with Landsat data (e.g., Gong et al., 2013, Hansen et al., 2013) or that analyses would involve such intensive mining of the archive (e.g., Hermosilla, Wulder, White, Coops, & Hobart, 2015, Roy et al., 2010, Zhu & Woodcock, 2014).”
Self-Attention for Raw Optical Satellite Time Series Classification (2019, methodology): “While we concentrated on one type of preprocessed data in this work, we should emphasize that a comparison to models that include distinct model-specific preprocessing, as used in CCDC [27], BFAST [32], LandTrendr [33], or TimeSat [22], will be neces-”