Retaining Metadata When Clipping Rasters In Qgis

Preserving Raster Metadata during Clipping Operations

When working with raster datasets in QGIS, it is often necessary to clip or subsample large raster files for focused analysis. However, clipping and subsetting operations can sometimes remove important metadata from the original raster if care is not taken to preserve it.

Raster metadata provides key information about the source, structure, processing history, accuracy, and intended usage of raster files. Losing this metadata during clipping makes the clipped rasters less usable for analysis since critical spatial referencing, scaling, measurement units, and other aspects become unclear.

Fortunately, with proper parameter configuration, raster clipping and subsetting in QGIS can retain nearly all original metadata. This article provides guidance on identifying at-risk metadata, setting clip parameters to maintain metadata, troubleshooting missing metadata, and automating robust clipping workflows.

Table of Contents

Raster Metadata Components at Risk

The following raster metadata components are most vulnerable during clipping operations in QGIS:

Georeferencing information
Projection parameters
Scale and units of measurement
Data encoding and compression settings
Lineage and processing history logs
Accuracy and quality indicators
Null value definitions

If any of these metadata components are missing after clipping, the interpretation and analysis suitability of the derived raster can be severely impacted. For example, losing projection parameters makes overlay analysis with other geospatial data layers unreliable or even impossible.

Clipping Algorithm Limitations

By default, the clipping algorithms in QGIS conduct a simplified extraction of raster pixels within the clip geometry, without necessarily retaining all original metadata. The assumed priority is processing speed and efficiency.

Exacerbating this, different underlying GDAL raster subsetting drivers vary in their ability to preserve metadata based on implementation differences. For example, the standard gdalwarp driver strips away quite a few metadata fields.

Thankfully the gdal_translate utility and some other GDAL drivers are designed to retain metadata if correctly parameterized. So utilizing the right drivers with the appropriate configuration is key.

Backup Original Raster Metadata First

Before clipping any important raster dataset, first ensure the original metadata is backed up. This provides a reference checkpoint and allows reconstruction if anything gets unintentionally stripped away.

Use the QGIS layer pane context menu to export a copy of the layer metadata from the original unclipped raster. XML and HTML exports are ideal for fully capturing all metadata components in an easily inspectable format.

Setting Clip Parameters to Retain Metadata

When executing raster clipping operations in QGIS, including through the Clipper tool algorithm or GDAL subprocess, set the parameters below to maximize metadata retention:

Set Copy all subdatasets of this file to true to clone all metadata
Enable Override projection: same as input to automatically propagating source projection and spatial referencing info
Check Place each output file in a separate directory to minimize metadata overwrites
Use the gdal_translate creation option to leverage its metadata preservation logic

Combined together through carefully configured workflows, these parameter settings will ensure clipped raster output layers retain the vast majority of original metadata content from the source datasets.

Example Code for Clipping with Full Metadata Retention

The Python code snippet below demonstrates invoking the gdalwarp clipper algorithm with the above best practice parameters set to maximize metadata retention:

import processing

input_raster = "large_source_raster.tif" 
clip_vector = "study_area_polygon.shp"
output_dir = "clip_output"

params = {
  "INPUT": input_raster,
  "CLIP": clip_vector,  
  "COPY_SUBDATASET" : True, 
  "PROJ_DIFFERENCE" : True,
  "OUTPUT_DIRECTORY" : output_dir,     
  "EXTRA" : "-of GTiff -co TILED=YES -co COPY_SRC_OVERVIEWS=YES",
  "DATA_TYPE" : 0, 
  "OUTPUT" : "clip.tif" 
}

processing.run("gdal:cliprasterbymasklayer", params)

The key parameters enabling full metadata retention here are COPY_SUBDATASETS, PROJ_DIFFERENCE, and the gdal_translate-based OUTPUT format specification.

Verifying Metadata Preservation in Clipped Output

After completing any raster clipping operations, carefully inspect the metadata components of the derived layers to validate completeness relative to the original source. This may include:

Checking coordinate system and georeferencing tags
Verifying projection, datum, and parameter continuity
Confirming grid measurement units and scales match source
Ensuring null values and encoding adherence
Validating lineage entries reflect clipping processing steps

Compare the clipped raster metadata exports to backed up pre-clipping metadata for the most thorough validation. Any discrepancies in content can then be traced back to configuration oversights or other issues.

Troubleshooting Missing Metadata Issues

If raster clipping outputs are missing critical portions of the source metadata content, the standard troubleshooting workflow includes:

Double checking all clipping parameters match the recommendations above
Trying alternative clipping utility algorithms (gdalwarp vs gdal_translate, etc)
Testing more robust underlying drivers (GTiff instead of IMG, etc)
Manually reconstructing any missing metadata based on the pre-clip backups

Tracing runtime logs during clipping execution can also help pinpoint any driver-level metadata stripping issues. GDAL and QGIS log folders contain valuable debugging troubleshooting during anomalous clipping workflows.

Automating Clipping Workflows to Maintain Metadata

Once raster clipping parameters that reliably retain metadata are identified, automating the clipping workflows will minimize manual oversight and quality assurance burden.

Python scripts, Processing algorithm jobs, batch Runtime Commands, and other forms of automation eliminate variability while enforcing metadata preservation policies run after run. CLI flags and environment variables can also lock in key parameters.

Some best practices for automated clipping workflows include:

Standardizing flag configurations in reuseable recipe scripts
Freezing gdalwarp driver versions for stability
Parameterizing outputs to isolate derived layers
Using metadata compliance reports for QA checks
Logging run outputs including metadata exports

Combined together, these automation strategies make retaining metadata through extensive raster clipping workflows more robust and repeatable.