GEE：变化检测算法介绍，和代码（Landtrendr，Bfast，CCDC，CODED，多时间特征叠加的简图分类方法，CUSUM）遥感时间序列

本文主要是介绍GEE：变化检测算法介绍，和代码（Landtrendr，Bfast，CCDC，CODED，多时间特征叠加的简图分类方法，CUSUM）遥感时间序列，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

Landtrendr的原理图

APP（https://emaprlab.users.earthengine.app/view/lt-gee-pixel-time-series）
扰动年份和最大扰动幅度运算，源码：（https://blog.csdn.net/qq_35591253/article/details/115332724）
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Bfast原理

R语言代码（http://bfast.r-forge.r-project.org/）
APP（https://andreim.users.earthengine.app/view/bfastmonitor）
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Fitted seasonal, trend and remainder (i.e. estimated noise) components for a 16-day MODIS NDVI time series (data series) of a pine plantation in the northern part of the study area. Three abrupt changes are detected in the trend component of the time series. Time (- - -), corresponding conidence interval (red), direction and magnitude of abrupt change and slope of the gradual change are shown in the estimated trend component. The solid bars on the right hand side of the plot show the same data range, to aid comparisons.

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Simulated 16-day MODIS NDVI time series. The period from 2004 until mid-2010 (i.e., the time step just before the simulated break), is considered the history period and the period after the simulated break is the monitoring period (grey background). The monitoring period contains 6 observations. The result of the monitoring approach is shown: A stable history period is identified within the history period (i.e., 2007 until mid-2010) and used to model and predict the normal data variation (blue dashed line) to enable disturbance detection. Here, a disturbance is detected after 3 observations in the monitoring period (green vertical line)."
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