Understanding the Terra Function Classify for Large File Compression

As a technical blogger, I often receive questions from users who are struggling with data compression and classification. In this article, we will delve into the world of terra functions, specifically the classify function, to understand how it can be used to compress large files.

Introduction to Terra Functions and Classification

Terra is a popular R package for working with satellite imagery and geospatial data. The classify function in terra allows users to reclassify raster data based on a set of rules defined in a text file. This function is particularly useful when working with large datasets that need to be simplified or reclassified.

Background on File Compression

When working with large files, it’s essential to understand the concept of compression. Compression reduces the size of a file by eliminating redundant data and representing the data more efficiently. There are various compression algorithms available, including LZW (Lempel-Ziv-Welch), DEFLATE, and others.

Working with Terra Raster Data

In terra, raster data is represented as a matrix of values, where each value represents a pixel on the map. The classify function takes in two inputs: the raster data itself and the reclassification rules defined in a text file.

Reclassification Rules

The reclassification rules are defined in a text file, where each line specifies a new class ID and its corresponding old class IDs. For example:

ID  = 10
OldClassIDs = c(1,2)
NewClassIDs = c(5,6)

This rule states that pixels with old class IDs 1 and 2 should be reclassified as new class IDs 5 and 6.

The classify Function

The classify function takes in the following inputs:

• raster: The input raster data.
• reclass_table: A text file containing the reclassification rules.
• othersNA=TRUE: Specifies whether pixels with no match should be classified as NA (Not Available).
• datatype: The data type of the output raster. For example, “INT1U” represents a byte value between 0 and 254.

Optimizing File Size

When working with large files, it’s essential to optimize file size to reduce storage requirements. Here are some tips to minimize file size:

Specifying Datatype

By specifying the datatype argument, users can take advantage of more efficient compression algorithms. For example, using “INT1U” instead of the default “FLT4S” can result in a 4 times smaller file.

writeRaster(habitat_simple, "reclass_hab.tif", 
        wopt=list(datatype="INT1U", gdal="COMPRESS=LZW"))

Using Compression

Using compression algorithms like LZW can further reduce file size. However, it’s essential to note that not all compression algorithms work well with terra raster data.

habitat_simple <- classify(raster, reclass_table, othersNA=TRUE, 
         datatype="INT1U", gdal="COMPRESS=LZW")

Conclusion

The classify function in terra is a powerful tool for reclassifying large datasets. By specifying the correct data type and using compression algorithms, users can optimize file size to reduce storage requirements.

Best Practices

When working with terra raster data, here are some best practices to keep in mind:

• Specify the correct data type to take advantage of more efficient compression algorithms.
• Use compression algorithms like LZW to further reduce file size.
• Consider using the gdal argument to specify compression algorithms that work well with terra raster data.

Example Use Cases

The classify function has numerous applications in satellite imagery and geospatial analysis. Here are some example use cases:

• Land Cover Classification: Use the classify function to reclassify land cover data based on a set of rules defined in a text file.
• Disaster Response Analysis: Use the classify function to analyze satellite imagery of disaster-affected areas and identify affected regions.

By following these best practices and using the classify function effectively, users can optimize their geospatial analysis workflows and reduce storage requirements for large datasets.

Last modified on 2023-09-27