Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach

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A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “For 300 example, Schneider (2012) was able to successfully mine a time series of 50 Landsat images from 1988 to 2010 301 for changes in urban extent using supervised support vector machine (SVM) and decision tree classifiers.”
Current status of Landsat program, science, and applications (2019, background): “More broadly, dense time series of Landsat data are eminently suitable for detection of land cover change, including for urban areas (Schneider, 2012), agricultural regions (Hurni et al.”
Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, background): “For instance, dense time-series data contain the temporal information of objects on the land surface, which are extremely helpful for discriminating different land cover types [1], monitoring vegetation seasonality [2], modeling the carbon sequestration [3], estimating crop yields [4], exploring the”
Expansion and growth in Chinese cities, 1978–2010 (2014, methodology): “To quantify the uncertainty in the maps, we estimated multiple measures (see Schneider 2012).”