Available with Image Server
Evaluates changes in pixel values over time using the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method and generates a change analysis raster containing the model results.
For information about the LandTrendr algorithm, see How Analyze Changes Using LandTrendr works.
Note:
This raster function is only supported in conjunction with the Detect Change Using Change Analysis function. Use the output layer of the LandTrendr Analysis function as input to the Detect Change Using Change Analysis function. To produce a raster dataset output, connect the LandTrendr Analysis function with the Detect Change Using Change Analysis function using the Function Editor, save this as a raster function template, and use it as the input to the Generate Raster From Raster Function geoprocessing tool.
Notes
This raster function can only be used as input to the Detect Change Using Change Analysis raster function. To generate a raster output, connect the LandTrendr Analysis function to the Detect Change Using Change Analysis function in a raster function template, and use the template as input in the Generate Raster From Raster Function geoprocessing tool. The result is a raster containing information regarding the time at which pixel values changed.
This tool extracts changes in an observed feature, so the ideal input multidimensional imagery should capture a consistent observation throughout time and should not include atmospheric or sensor interference, clouds, or cloud shadow. The best practice is to use data that has been normalized and can be masked using a QA band—for example, Landsat Collection 1 Surface Reflectance products with a cloud mask.
The tool performs analysis on one image per year, and the number of yearly slices must be equal to or greater than the value specified in the Minimum Number of Observations parameter. It is recommended that you have at least six years of data.
If you have monthly, weekly, or daily data, it is recommended that you select several images from each year (preferably from the same season), remove clouds and cloud shadow, and combine the images to generate a single image that captures the observation well. If monthly, weekly, or daily data is provided as the input multidimensional raster, the tool will identify one slice for analysis based on the date closest to that provided in the Snapping Date parameter.
A feature in a landscape will often take time to recover from a nonpermanent change such as a forest fire or insect infestation. To control the rate of recovery recognized by the model, set the Recovery Threshold parameter. A distinct segment cannot have a recovery rate that is faster than 1/recovery threshold.
The recovery from a change in landscape can occur in the positive or negative direction. For example, when a landscape experiences forest loss, a time series of vegetation index values shows a drop in index values, and the recovery shows a gradual increase in vegetation index values, or a positive recovery trend. Specify the direction of recovery trend with the Recovery Has Increasing Trend parameter.
Parameters
| Parameter | Description |
|---|---|
Raster | The input Landsat multidimensional raster layer. |
Processing Band Name | The image band name to use for segmenting the pixel value trajectories over time. Choose the band name that will best capture the changes in the feature you want to observe. |
Snapping Date | The date used to identify a slice for each year in the input multidimensional dataset. The slice with the date closest to the snapping date will be used. This parameter is required if the input dataset contains sub-yearly data. |
Maximum Number of Segments | The maximum number of segments to be fitted to the time series for each pixel. The default is 5. |
Vertex Count Overshoot | The number of additional vertices beyond max_num_segments + 1 that can be used to fit the model during the initial stage of identifying vertices. Later in the modeling process, the number of additional vertices will be reduced to max_num_segments + 1. The default is 2. |
Spike Threshold | The threshold to use for dampening spikes or anomalies in the pixel value trajectory. The value must range between 0 and 1 in which 1 means no dampening. The default is 0.9. |
Recovery Threshold | The recovery threshold value in years. If a segment has a recovery rate that is faster than 1/recovery threshold, the segment is discarded and not included in the time series model. The value must range between 0 and 1. The default is 0.25. |
Minimum Number of Observations | The minimum number of valid observations required to perform fitting. The number of years in the input multidimensional dataset must be equal to or greater than this value. The default is 6. |
P-Value Threshold | The p-value threshold for a model to be selected. After the vertices are detected in the initial stage of the model fitting, the tool will fit each segment and calculate the p-value to determine the significance of the model. On the next iteration, the model will decrease the number of segments by one and recalculate the p-value. This will continue and, if the p-value is smaller than the value specified in this parameter, the model will be selected and the tool will stop searching for a better model. If no such model is selected, the tool will select a model with a p-value smaller than the lowest p-value × best model proportion value. The default is 0.01. |
Best Model Proportion | The best model proportion value. During the model selection process, the tool will calculate the p-value for each model and identify a model that has the most vertices while maintaining the smallest (most significant) p-value based on this proportion value. A value of 1 means the model has the lowest p-value but may not have a high number of vertices. The default is 1.25. |
Prevent One Year Recovery | Specifies whether segments that exhibit a one year recovery will be excluded.
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Recovery Has Increase Trend | Specifies whether the recovery has an increasing (positive) trend.
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Output Other Bands | Specifies whether other bands will be included in the results.
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