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Landsat-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.
See: 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.
Yearly time-series from which to extract breakpoints. The first band is usedto find breakpoints, and all subsequent bands are fitted using those breakpoints.
maxSegments
Integer
Maximum number of segments to be fitted on the time series.
spikeThreshold
Float, default: 0.9
Threshold for dampening the spikes (1.0 means no dampening).
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.
preventOneYearRecovery
Boolean, default: false
Prevent segments that represent one year recoveries.
recoveryThreshold
Float, default: 0.25
If a segment has a recovery rate faster than 1/recoveryThreshold (in years), then the segment is disallowed.
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.
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.
minObservationsNeeded
Integer, default: 6
Min observations needed to perform output fitting.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\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. |"]]
