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As the ability to rapidly produce classifications of satellite images grows, it will be increasingly important to have algorithms that can shift through them to separate the signal from inevitable classification noise. The purpose of this chapter is to explore how to update classification time series by blending information from multiple classifica...
Knowledge of forest change type and timing is required for forest management, reporting, and science. Time series of historic satellite data (e.g. Landsat) have resulted in an invaluable record of changes in forest conditions. Natural resource management and reporting typically operate at an annual time step, yet the recent addition of data streams...
The ongoing march toward freely available, highly pre-processed satellite imagery has given both researchers and the public unprecedented access to a vast and varied data stream teeming with potential. Among many sources, the multi-decade Landsat archive is certainly the best known, but legacy and current data from other sensors is available as wel...
With the opening of the Landsat archive, researchers have a vast new data source teeming with imagery and potential. Beyond Landsat, data from other sensors is newly available as well: these include ALOS/PALSAR, Sentinel-1 and -2, MERIS, and many more. Google Earth Engine, developed to organize and provide analysis tools for these immense data sets...