The Pixel Time Series Change Explorer allows you to identify changes in a single pixel value over time using the Continuous Change Detection and Classification (CCDC) method or the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method. This allows you to refine model parameters to focus on specific change events before processing the data using the Analyze Changes Using CCDC or Analyze Changes Using LandTrendr tools for an entire dataset.

## Data settings

Define the data settings for your chart on the Data tab in the Chart Properties pane. The input data can be any a multidimensional raster type, but a multidimensional raster in CRF format with transpose built will have better performance. See Multidimensional Cloud Raster Format for more details.

### Time series

There are two options for generating a pixel time series change chart. The two options available in the Time series drop-down menu will fit a model curve to the time series using the selected method. The following options are available for a pixel time series change chart:

- CCDC—Evaluates changes in a pixel value over time and fits a curve using the Continuous Change Detection and Classification method.
- LandTrendr—Evaluates changes in a pixel value over time and fits a curve using the Landsat-based detection of trends in disturbance and recovery method.

## Explore pixel change using CCDC

Use the CCDC algorithm to explore changes in a pixel value over time. Because CCDC analysis performs recursive harmonic regression, it is recommended that the input multidimensional raster have dense temporal resolution that can best represent the change cycle. For example, an ideal dataset would have monthly data over several years for one location. For more information on this method, see Analyze Changes Using CCDC.

### Define a pixel location

You can use the Point tool to enter a pixel location to model change, and the point will display as a graph in the map display. The chart will be created when you click the Fit and create chart button. You can change the symbol size of the points in the chart, and you can turn off the chart by clicking the check box of the corresponding point.

### Select the Bands table

You can define the bands you want to use for modeling the change. The first band is selected by default, and you can add or remove bands. Define the line color and line thickness for the chart.

The Bands table also allows you to specify the width of the line in the chart, and the label that appears in the chart legend and hover tips. If you plot multiple bands in a single chart, you can also specify the color of the line for each variable or band. If you plot a single band, the color of the line will match the color of the location marker in the Define a pixel location table.

### Define model parameters

The Bands for Temporal Masking parameter defines the band IDs to be used in the temporal mask (Tmask). It is recommended that you use the green band and the SWIR band. If no band IDs are provided, no masking will occur.

The Chi-square Threshold for Detecting Changes parameter is the chi-square statistic change probability threshold. If an observation has a calculated change probability that is above this threshold, it is flagged as an anomaly, which is a potential change event. The default value is 0.99.

The Minimum Consecutive Anomaly Observations parameter defines the minimum number of consecutive anomaly observations that must occur before an event is considered a change. A pixel must be flagged as an anomaly for the specified number of consecutive time slices before it is considered a true change. The default value is 6.

The Updating Fitting Frequency (in years) parameter defines the frequency, in years, at which to update the time series model with new observations. The default value is 1.

### Fit and create chart

Click the Fit and create chart button to generate the chart, which includes the original points and the fitted curves. You can change the symbol and color of the fitted curves. One fitted curve indicates that the pixel location does not change over time, and multiple curves mean that the pixel location changes over time. The legends show the time periods of the changes.

### Data labels

Specify whether you want to label the data points in your chart.

## Explore pixel change using LandTrendr

Use the LandTrendr method to explore pixel change in a single variable or band. The LandTrendr method models yearly change over time. It is recommended that you choose data from the same season across multiple years. If you have multiple images for each year, the change explorer will find the images close to a common date in the change detection process. For more information on this method, see Analyze Changes Using LandTrendr.

### Define a pixel location

Use the Point tool to enter one or multiple pixel locations to model change. Each point will be plotted as a graph in the change explorer chart. The chart is created when you click the Fit and create chart button. You can change the symbol size of the plot, and turn off the chart by clicking the check box of the corresponding point.

### Select Bands table

When generating a chart for a single band, you can select the band to graph using the Select Bands table.

LandTrendr works with only one band. Select a band, and set the symbol color and width of the plot line for the chart. The chart will be created after clicking the Fit and create chart button.

### Define Model Parameters

The Snapping Date parameter will identify a slice for each year in the dataset based on the date closest to that specified. The default is 06-30, or June 30.

The Maximum Number of Segments parameter defines the maximum number of segments to be fitted to the time series for each pixel. The default is 5.

The Vertex Count Overshoot Threshold parameter defines the number of additional vertices beyond Maximum Number of 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 Maximum Number of Segments +1. The default is 2.

The Spike Threshold parameter defines 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.

The Recovery Threshold parameter defines 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.

Use the Prevent One Year Recovery option to specify whether segments that exhibit a one-year recovery will be excluded.

Use the Recovery has increasing trend option to specify whether the recovery has an increasing (positive) trend.

The Minimum Number of Observations parameter defines 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.

The Best Model Proportion parameter defines 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.

The P-Value Threshold parameter defines the 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 optimizing 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.

### Fit and create chart

Click the Fit and create chart button to generate the chart, which includes the original points and the fitted curves. You can change the symbol and color of the fitted curves. One fitted curve indicates that the pixel location does not change over time, and multiple curves mean that the pixel location changes over time. The legends show the time periods of the changes.

### Data labels

Specify whether you want to label the data points in your chart.

## Axes

Define the axes settings for your chart on the Axes tab in the Chart Properties pane.

### X-axis

The temporal profile x-axis values are date or time values. The default date and time format displayed on the chart can be modified according to your preference. The date must always be displayed, but time can be removed from the x-axis display.

### Y-axis

Default minimum and maximum y-axis bounds are set based on the range of data values represented on the axis. These values can be customized by entering a new y-axis bound value. Clicking the reset icon returns the y-axis bound to the default value.

By default, line chart axes are displayed on a linear scale. Numeric (nondate) axes can be displayed on a logarithmic scale by checking the Log axis check box.

You can format the way an axis will display numeric values by specifying a number format category or by defining a custom format string. For example, $#,### can be used as a custom format string to display currency values.