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ee.Algorithms.TemporalSegmentation.LandTrendr
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基于 Landsat 的干扰和恢复趋势检测：通过提取随时间变化的频谱轨迹，按时间对图像时间序列进行分段。每个图像的第一个波段用于查找断点，这些断点用于对所有后续波段执行拟合。断点以 4 行的二维矩阵形式返回，列数与图片数量相同。前两行是原始 X 值和 Y 值。第三行包含拟合到估计的细分上的 Y 值，第四行包含一个 1（如果相应的点用作细分顶点）或 0（如果相应的点不用作细分顶点）。所有其他拟合的频段都会作为行附加到输出中。断点拟合假设值越大表示干扰越大，值越小表示恢复越好。
参见：Kennedy, R.E.，Yang, Z. 和 Cohen, W.B.，2010 年。使用年度 Landsat 时序数据检测森林扰动和恢复趋势：1. LandTrendr - 时间分割算法。Remote Sensing of Environment, 114(12), pp.2897-2910.
| 用法 | 返回 |
|---|
ee.Algorithms.TemporalSegmentation.LandTrendr(timeSeries, maxSegments, spikeThreshold, vertexCountOvershoot, preventOneYearRecovery, recoveryThreshold, pvalThreshold, bestModelProportion, minObservationsNeeded) | 图片 |
| 参数 | 类型 | 详细信息 |
|---|
timeSeries | ImageCollection | 要从中提取断点的年时间序列。第一个频段用于查找断点，所有后续频段都使用这些断点进行拟合。 |
maxSegments | 整数 | 要拟合到时间序列的细分数量上限。 |
spikeThreshold | 浮点数，默认值：0.9 | 用于抑制峰值的阈值（1.0 表示不抑制）。 |
vertexCountOvershoot | 整数，默认值：3 | 初始模型可能会超出 maxSegments + 1 个顶点的数量。之后，它将被剪枝为 maxSegments + 1。 |
preventOneYearRecovery | 布尔值，默认值：false | 防止出现表示一年恢复期的细分受众群。 |
recoveryThreshold | 浮点数，默认值：0.25 | 如果某个细分的恢复率快于 1/recoveryThreshold（以年为单位），则不允许使用该细分。 |
pvalThreshold | 浮点数，默认值：0.1 | 如果拟合模型的 p 值超过此阈值，则舍弃当前模型，并使用 Levenberg-Marquardt 优化器拟合另一个模型。 |
bestModelProportion | 浮点数，默认值：0.75 | 如果具有更多顶点的模型的 p 值不高于最佳模型的 p 值的 (2 - bestModelProportion) 倍，则允许选择这些模型。 |
minObservationsNeeded | 整数，默认值：6 | 执行输出拟合所需的最少观测次数。 |
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最后更新时间 (UTC)：2025-07-26。
[null,null,["最后更新时间 (UTC)：2025-07-26。"],[[["\u003cp\u003eLandTrendr is a temporal segmentation algorithm designed to detect trends in disturbance and recovery within yearly Landsat time-series data.\u003c/p\u003e\n"],["\u003cp\u003eIt identifies breakpoints in spectral trajectories, using the first band of the image collection for initial detection and then fitting the breakpoints to all other bands.\u003c/p\u003e\n"],["\u003cp\u003eThese breakpoints, representing changes in land cover, are fitted to a model assuming increasing values indicate disturbance and decreasing values signify recovery.\u003c/p\u003e\n"],["\u003cp\u003eThe algorithm offers parameters for controlling spike dampening, segment recovery rates, model selection, and minimum data requirements to fine-tune the analysis.\u003c/p\u003e\n"],["\u003cp\u003eThe output is an image containing the original and fitted values, segment vertices, and optionally fitted values for additional bands.\u003c/p\u003e\n"]]],["The LandTrendr algorithm segments a time-series of images, using the first band to find breakpoints that identify spectral changes. These breakpoints are then applied to fit all other bands. Breakpoints are returned as a matrix, with the original data, fitted values, and segment vertex indicators. Increasing values suggest disturbance, while decreasing values suggest recovery. Users define parameters like maximum segments, spike dampening, and recovery rates to guide the fitting process. The algorithm outputs an image containing the results.\n"],null,["# ee.Algorithms.TemporalSegmentation.LandTrendr\n\nLandsat-based detection of Trends in Disturbance and Recovery: temporally segments a time-series of images by extracting the spectral trajectories of change over time. The first band of each image is used to find breakpoints, and those breakpoints are used to perform fitting on all subsequent bands. The breakpoints are returned as a 2-D matrix of 4 rows and as many columns as images. The first two rows are the original X and Y values. The third row contains the Y values fitted to the estimated segments, and the 4th row contains a 1 if the corresponding point was used as a segment vertex or 0 if not. Any additional fitted bands are appended as rows in the output. Breakpoint fitting assumes that increasing values represent disturbance and decreasing values represent recovery.\n\n\u003cbr /\u003e\n\nSee: Kennedy, R.E., Yang, Z. and Cohen, W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment, 114(12), pp.2897-2910.\n\n| Usage | Returns |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|\n| `ee.Algorithms.TemporalSegmentation.LandTrendr(timeSeries, maxSegments, `*spikeThreshold* `, `*vertexCountOvershoot* `, `*preventOneYearRecovery* `, `*recoveryThreshold* `, `*pvalThreshold* `, `*bestModelProportion* `, `*minObservationsNeeded*`)` | Image |\n\n| Argument | Type | Details |\n|--------------------------|-------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `timeSeries` | ImageCollection | Yearly time-series from which to extract breakpoints. The first band is usedto find breakpoints, and all subsequent bands are fitted using those breakpoints. |\n| `maxSegments` | Integer | Maximum number of segments to be fitted on the time series. |\n| `spikeThreshold` | Float, default: 0.9 | Threshold for dampening the spikes (1.0 means no dampening). |\n| `vertexCountOvershoot` | Integer, default: 3 | The initial model can overshoot the maxSegments + 1 vertices by this amount. Later, it will be pruned down to maxSegments + 1. |\n| `preventOneYearRecovery` | Boolean, default: false | Prevent segments that represent one year recoveries. |\n| `recoveryThreshold` | Float, default: 0.25 | If a segment has a recovery rate faster than 1/recoveryThreshold (in years), then the segment is disallowed. |\n| `pvalThreshold` | Float, default: 0.1 | If the p-value of the fitted model exceeds this threshold, then the current model is discarded and another one is fitted using the Levenberg-Marquardt optimizer. |\n| `bestModelProportion` | Float, default: 0.75 | Allows models with more vertices to be chosen if their p-value is no more than (2 - bestModelProportion) times the p-value of the best model. |\n| `minObservationsNeeded` | Integer, default: 6 | Min observations needed to perform output fitting. |"]]
