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ee.Algorithms.TemporalSegmentation.Verdet
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Vegetation Regeneration and Disturbance Estimates through Time，森林变化检测算法。该算法可根据卫星图像生成年度晴空合成图像，计算该合成图像中每个像素的光谱植被指数，将植被指数图像在空间上分割为多个图块，将时间序列在时间上分割为不同斜率的多个分段，然后将这些分段标记为受扰动、稳定或再生。空间和时间步长的分割均使用全变差正则化进行。
输出由每个像素的 1D 数组组成，其中包含拟合趋势线的斜率。负值表示干扰，正值表示再生。
请参阅：Hughes, M.J.，Kaylor, S.D. 和 Hayes, D.J.，2017 年。基于 Landsat 时间序列的基于块的森林变化检测。Forests，8(5)，第 166 页。
| 用法 | 返回 |
|---|
ee.Algorithms.TemporalSegmentation.Verdet(timeSeries, tolerance, alpha, nRuns) | 图片 |
| 参数 | 类型 | 详细信息 |
|---|
timeSeries | ImageCollection | 要从中提取 VeRDET 分数的集合。此集合应包含每年 1 张图片，并按时间顺序排序。 |
tolerance | 浮点数，默认值：0.0001 | 收敛容差。 |
alpha | 浮点数，默认值：0.03333333333333333 | 用于细分的正则化形参。 |
nRuns | 整数，默认值：100 | 收敛运行次数上限。 |
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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\u003eThe algorithm detects forest change by analyzing yearly satellite imagery and identifying areas of disturbance and regeneration.\u003c/p\u003e\n"],["\u003cp\u003eIt uses a spectral vegetation index and segments the imagery spatially and temporally to track vegetation changes.\u003c/p\u003e\n"],["\u003cp\u003eDisturbance is indicated by negative trend slopes, while regeneration is indicated by positive slopes in the output array.\u003c/p\u003e\n"],["\u003cp\u003eThe algorithm utilizes total variation regularization for both spatial and temporal segmentation to enhance accuracy.\u003c/p\u003e\n"],["\u003cp\u003eUsers can adjust parameters like tolerance, regularization, and maximum runs for customized analysis using the provided function.\u003c/p\u003e\n"]]],[],null,["# ee.Algorithms.TemporalSegmentation.Verdet\n\nVegetation Regeneration and Disturbance Estimates through Time, forest change detection algorithm. This algorithm generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization.\n\n\u003cbr /\u003e\n\nThe output consists of a 1D array per pixel containing the slope of fitted trend lines. Negative values indicate disturbance and positive values regeneration.\n\nSee: Hughes, M.J., Kaylor, S.D. and Hayes, D.J., 2017. Patch-based forest change detection from Landsat time series. Forests, 8(5), p.166.\n\n| Usage | Returns |\n|------------------------------------------------------------------------------------------------|---------|\n| `ee.Algorithms.TemporalSegmentation.Verdet(timeSeries, `*tolerance* `, `*alpha* `, `*nRuns*`)` | Image |\n\n| Argument | Type | Details |\n|--------------|-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| `timeSeries` | ImageCollection | Collection from which to extract VeRDET scores. This collection is expected to contain 1 image for each year, sorted temporally. |\n| `tolerance` | Float, default: 0.0001 | Convergence tolerance. |\n| `alpha` | Float, default: 0.03333333333333333 | Regularization parameter for segmentation. |\n| `nRuns` | Integer, default: 100 | Maximum number of runs for convergence. |"]]
