Analyze Changes Using LandTrendr

inputMultidimensionalRaster

(Required)

The portal item ID, image service URL, cloud multidimensional raster dataset or shared multidimensional raster dataset. At least one type of input needs to be provided in the JSON object. If multiple inputs are given, the itemid takes priority.

Syntax: A JSON object describes the input multidimensional raster.

Example:

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//Portal Item ID
inputMultidimensionalRaster={"itemId": <portal item id>}

//Image Service URL
inputMultidimensionalRaster={“url”: <image service url>}

//Cloud Raster URI or Shared Data Path
inputMultidimensionalRaster={“uri”: <cloud raster uri or shared data path>}

outputName

(Required)

Output hosted image service properties. If the hosted image service is already created, the portal item ID or service URL can be provided, and the output path of the multidimensional raster dataset generated in the raster store will be used to update the existing service definition. The service tool can also generate a new hosted image service with the given service properties.

Syntax: String

Example:

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//Portal Item ID
outputName={"itemId": <portal item id>}

//Image Service URL
outputName={“url”: <image service url>}

//Cloud Raster URI or Shared Data Path
outputName={“uri”: <cloud raster uri or shared data path>}

//Service properties
outputName={
  "serviceProperties": {
    "name": "Landsat_LandTrendr",
    "capabilities": "image, metadata"
  }
}

processingBand

(Optional)

The name of the band to use for segmenting pixel value trajectories over time. Choose the band that will best capture the changes in the feature you want to observe. If no band name is specified and the input is multiband imagery, the first band from the input multiband imagery will be used.

Syntax: String

Example:

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processingBand="Band_4"

snappingDate

(Optional)

The date used to select a slice for each year in the input multidimensional dataset. The slice with the date closest to the snapping date will be selected. This parameter is required if the input dataset contains sub-yearly data, since this tool only processes one slice per year.

The default is 06-30, or June 30.

Syntax: String, in the format of MM-DD.

Example:

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snappingDate="12-31"

maxNumSegments

(Optional)

The maximum number of segments to be fitted to the time series for each pixel. The default is 5.

Syntax: Long integer.

Example:

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maxNumSegments=6

vertextCountOvershoot

(Optional)

The number of additional vertices beyond maxNumSegments + 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 maxNumSegments + 1. The default value is 2.

Syntax: Long integer.

Example:

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vertexCountOvershoot=2

spikeThreshold

(Optional)

The threshold to use for dampening anomalies in the pixel value trajectory. The value must range between 0 and 1, where 1 means no dampening. The default is 0.9.

Syntax: Double

Example:

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spikeThreshold=0.8

recoveryThreshold

(Optional)

The recovery threshold value in years. If a segment has a recovery rate that is faster than 1 / recoveryThreshold, 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.

Syntax: Double

Example:

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recoveryThreshold=0.3

preventOneYearRecovery

(Optional)

Specifies whether segments that exhibit a one-year recovery will be excluded. When set to false, segments that exhibit a one-year recovery will be counted as segments. When set to true, segments that exhibit a one-year recovery will not be counted and will be excluded from the model results. This is the default.

Values: true | false

increasingRecoveryTrend

(Optional)

Specifies whether recovery from change is represented by an increasing (positive) trend. When set to false, recovery from change is represented by a decreasing (negative) trend. When set to true, recovery from change is represented by an increasing trend. This is the default.

Values: true | false

minNumObservations

(Optional)

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.

Syntax: Long

Example:

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minNumObservations=6

bestModelProportion

(Optional)

The best model proportion value. During the model selection process, the tool will calculate the p-value for each model and select 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.

Syntax: Double

Example:

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bestModelProportion=1.0

pvalueThreshold

(Optional)

The p-value threshold to be reached 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.

Syntax: Double

Example:

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pvalueThreshold=0.01

outputOtherBands

(Optional)

Specifies whether other bands will be included in the segmentation process. When set to true, the segmentation and vertices information from the initial segmentation band specified in the processingBand parameter will also be fitted to the remaining bands in the multiband images. The model results will include the segmentation band first, and then the remaining bands. When set to false, only the band specified in the processingBand parameter will be segmented and generated in the model results.

Values: true | false

context

Contains additional settings that affect task execution. This task has the following settings:

• Extent (extent)—A bounding box that defines the analysis area.
• Output Spatial Reference (outSR)—The output raster will be projected into the output spatial reference.
• Snap Raster (snapRaster)—The output raster will have its cells aligned with the specified snap raster.
• Cell Size (cellSize)—The output raster will have the resolution specified by cell size.
• Parallel Processing Factor (parallelProcessingFactor)—The specified number or percentage of processes will be used for the analysis.

Example:

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context={"cellSize": "20", "parallelProcessingFactor": "4"}

f

The response format. The default response format is html.

Values: html | json

outputMultidimensionalRaster

The output multidimensional raster itemId and URL

Example:

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{"url": "http://<raster analysis url>/AnalyzeChangesUsingLandTrendr/jobs/<jobId>/results/outputLandTrendrAnalysisRaster"}

The result has properties for parameter name, data type, and value. The content of the value is always the output raster dataset's itemId and image service URL.

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{
  "paramName": "outputLandTrendrAnalysisRaster",
  "dataType": "GPString",
  "value": {
    "itemId": "c267610d0feb4370bf38cc6e2c4ac261",
    "url": "https://<server name>/arcgis/rest/services/Hosted/<service name>/ImageServer"
  }
}