Fixing ValueError: NaN DEMs In ArcticDEM Processing

Encountering a ValueError: 'dem_to_be_aligned' had only NaNs error when running fetch_and_coregister.py from the ArcticDEM scripts can be frustrating. This article provides a comprehensive guide to understanding and resolving this issue, ensuring a smooth workflow for your DEM processing tasks. We will delve into the causes of this error, offer step-by-step solutions, and provide best practices to prevent it from occurring in the future.

Understanding the Error

The error message ValueError: 'dem_to_be_aligned' had only NaNs indicates that the coregistration process is failing because the Digital Elevation Model (DEM) intended for alignment contains only Not-a-Number (NaN) values. In simpler terms, the DEM is completely masked, meaning there are no valid elevation data points within the specified area. This typically happens during the fetch step, where the script retrieves DEM tiles, and one or more of these tiles end up being entirely masked due to various reasons such as cloud cover, water bodies, or other data anomalies.

The fetch_and_coregister.py script is designed to automate the process of fetching ArcticDEM tiles and coregistering them to create a seamless and accurate elevation model. However, if the fetch step returns a DEM that is entirely masked, the subsequent coregistration step will fail, as it cannot align a raster with no valid data. Understanding this fundamental issue is the first step towards effectively resolving the error.

The root cause often lies in the data itself. ArcticDEM data, while generally high-quality, can sometimes contain areas with significant data gaps due to the challenges of remote sensing in polar regions. These gaps are typically filled with NaN values to indicate missing or invalid data. When an entire DEM tile falls within such a data gap, it results in a completely masked raster, triggering the ValueError during coregistration. Therefore, identifying and handling these masked DEMs is crucial for a successful processing workflow.

Reproduction of the Error

To reproduce the error, you can use the following command, executed from the repository root. Note that you may need to adjust the local paths to match your specific environment:

python scripts/fetch_and_coregister.py \
  --bounds 161.18581265 56.60286466 161.19244308 56.60878054 \
  --fetch_output_dir /tmp/fetch_arcticdem/fetch \
  --coreg_output_dir /tmp/fetch_arcticdem/coregistered \
  --date_range 2015-10-01 2025-10-17

This command specifies a bounding box and a date range for fetching ArcticDEM tiles. The --fetch_output_dir and --coreg_output_dir options define the directories where the fetched DEMs and the coregistered output will be stored, respectively. When this command encounters a fully masked DEM during the fetch step, it will raise the ValueError: 'dem_to_be_aligned' had only NaNs during the coregistration process.

By running this command, you can simulate the error and test the solutions provided in this article. This hands-on approach will help you gain a deeper understanding of the issue and how to effectively address it. Remember to check the output directories for any fetched DEMs that might be causing the error. Identifying these problematic DEMs is essential for implementing the necessary corrective actions.

Solutions and Workarounds

1. Inspecting the Fetched DEMs

The first step in resolving this issue is to inspect the DEMs that were fetched by the fetch script. Navigate to the --fetch_output_dir you specified in the command. Use a GIS software like QGIS or GDAL tools to open and examine the DEM files. Look for DEMs that appear completely black or transparent, indicating that they are fully masked (filled with NaN values). These are the culprits causing the error.

By visually inspecting the DEMs, you can quickly identify the problematic files. GIS software allows you to view the raster data and check for any anomalies or missing data. You can also use GDAL tools like gdalinfo to get detailed information about the DEM, including its dimensions, data type, and the presence of NaN values. This information can help you confirm whether a DEM is indeed fully masked and needs to be removed or replaced.

2. Removing or Replacing Invalid DEMs

Once you've identified the invalid DEMs, you have two options: remove them or replace them with valid data. If the area covered by the invalid DEM is not critical for your analysis, you can simply remove the file from the --fetch_output_dir. Alternatively, if the data is essential, you can try fetching DEMs from a different date range or a slightly different bounding box to see if you can obtain a valid DEM for that area. You might also consider using other data sources to fill the gap.

Removing the invalid DEMs will allow the coregister script to proceed without encountering the ValueError. However, keep in mind that this will leave a gap in your final elevation model. If data continuity is important, replacing the invalid DEMs with valid data is the preferred solution. This could involve fetching DEMs from different time periods or using interpolation techniques to fill the gaps. Choose the approach that best suits your specific needs and data requirements.

3. Running Fetch and Coregister Separately

A practical workaround is to run the fetch and coregister steps separately. First, run the fetch script to download the DEMs. Then, manually inspect the fetched DEMs as described above, removing any invalid files. Finally, run the coregister script on the remaining valid DEMs. This approach gives you more control over the process and allows you to handle invalid DEMs before they cause the coregistration to fail.

By decoupling the fetch and coregister steps, you gain the flexibility to intervene and correct any issues that arise during the data acquisition phase. This is particularly useful when dealing with large areas or time periods where the likelihood of encountering invalid DEMs is higher. Separating the steps also makes it easier to troubleshoot and debug the process, as you can isolate the source of the error more effectively.

To implement this workaround, first run the fetch script with the desired parameters. Then, manually inspect the output directory and remove any fully masked DEMs. Finally, run the coregister script, pointing it to the directory containing the filtered DEMs. This will ensure that the coregistration process only operates on valid data, preventing the ValueError from occurring.

4. Adjusting the Bounding Box and Date Range

Sometimes, the issue might be related to the specific bounding box or date range you've chosen. Try adjusting these parameters to see if you can avoid the areas with persistent data gaps. For example, you might slightly reduce the size of the bounding box or select a different date range with better data coverage. This approach can be particularly effective if the data gaps are due to seasonal effects or localized weather patterns.

By carefully selecting the bounding box and date range, you can minimize the chances of encountering fully masked DEMs. Consider the specific characteristics of the region you are studying and choose parameters that are likely to yield the best data coverage. For example, if you are working in an area with frequent cloud cover, you might select a date range during a drier season. Similarly, if you are interested in a specific feature, you might adjust the bounding box to focus on that area and exclude regions with known data gaps.

Preventing the Error in the Future

1. Implement Automated Checks

To prevent this error from occurring frequently, consider implementing automated checks in your workflow. You can write a script that automatically inspects the fetched DEMs and flags or removes any files that are fully masked. This script can be integrated into your fetch process to ensure that only valid DEMs are passed on to the coregister script. The script can use GDAL to check for NaN values.

Automating the process of checking for invalid DEMs can significantly reduce the amount of manual intervention required. By integrating this check into your workflow, you can ensure that only valid data is used for coregistration, preventing the ValueError from occurring. The script can be designed to run automatically after the fetch step, flagging or removing any DEMs that are fully masked. This will save you time and effort in the long run.

2. Use Metadata to Filter DEMs

ArcticDEM data comes with metadata that can provide valuable information about the quality and coverage of each DEM tile. Use this metadata to filter out DEMs that are likely to be invalid. For example, you might exclude DEMs with a high percentage of masked pixels or those acquired during periods of poor weather conditions. This proactive approach can help you avoid the ValueError by preventing invalid DEMs from being used in the first place.

The metadata associated with ArcticDEM data can provide valuable insights into the quality and reliability of each DEM tile. By leveraging this information, you can filter out DEMs that are likely to be problematic, such as those with a high percentage of masked pixels or those acquired during periods of poor weather conditions. This proactive approach can help you avoid the ValueError and ensure that only high-quality data is used in your analysis.

3. Monitor Data Coverage and Quality

Regularly monitor the data coverage and quality of ArcticDEM in your area of interest. This will help you identify any persistent data gaps or areas with consistently poor data quality. By staying informed about the data availability, you can adjust your workflow accordingly and avoid encountering the ValueError.

Regular monitoring of data coverage and quality is essential for maintaining a smooth and efficient workflow. By staying informed about the availability and reliability of ArcticDEM data in your area of interest, you can proactively address any potential issues and avoid encountering the ValueError. This might involve adjusting your processing parameters, selecting alternative data sources, or implementing specific data cleaning techniques.

Conclusion

Encountering the ValueError: 'dem_to_be_aligned' had only NaNs error during ArcticDEM processing can be a roadblock, but by understanding its causes and implementing the solutions outlined in this article, you can overcome this challenge. Remember to inspect your fetched DEMs, remove or replace invalid data, and consider running the fetch and coregister steps separately for more control. By implementing automated checks and monitoring data quality, you can prevent this error from disrupting your workflow in the future. With these strategies in hand, you'll be well-equipped to process ArcticDEM data effectively and efficiently.

For more information on ArcticDEM and related tools, consider exploring resources like the Polar Geospatial Center. This website provides access to ArcticDEM data, documentation, and other valuable resources for working with polar geospatial data.