This workflow demonstrates how to use imagery in a time series to create a multidimensional mosaic dataset.
Multidimensional mosaic dataset requirements
A multidimensional mosaic dataset must contain three fields:
- A string field with name of Variable.
- A string field with name of Dimensions, which stores the comma-separated dimension names.
- One or more fields that define the dimensions.
If your mosaic dataset has those three prerequisites, you can make a multidimensional mosaic dataset from a set of time series images.
Add the three fields, populate the values, then synchronize the mosaic dataset with the aggregating information option. For example, a set of TIFF images with the following file names WinterTemperature_2001_12.tif; WinterTemperature_2002_12.tif; WinterTemperature_2013_12.tif. You can deduce the variable and dimension information from the file names, as these will be the values in the mosaic dataset's Name field.
- Create a mosaic dataset using the Create Mosaic Dataset tool.
- Use the Add Rasters to Mosaic Dataset tool to add the time series data to the mosaic dataset using the Raster Dataset raster type.
- Add the mosaic dataset to the Map view or the Contents pane.
- In the Contents pane, right-click the Footprint sublayer, and click Open Attribute Table.
- Add a Field named Variable with a Type of text and a size of 20.
- Add a Field named Dimensions with a Type of text and a size of 10.
- Add a Field named StdTime with a Type of date.
- Save the changes and close the Field tab.
- Calculate the Variable field with the Python syntax Variable = !Name![0:17].
- Calculate the Dimension field with the string value Dimensions = “StdTime”.
- Calculate the StdTime field with the Python syntax StdTime = getDate( !Name!), where the function
getDate is defined in the code block as:
def getDate(name): year = name[18:22] month = name[23:25] return time.strftime("/".join(["1", month, year]))
- Run the Synchronize Mosaic Dataset tool, enabling the Refresh Aggregate Information option.
The mosaic dataset, as a capability, can be viewed as a container for raster data. It contains and manages complex datasets and their metadata, and can be time-enabled.