FAQ#
This page records useful questions and answers raised by users of pdemtools, on the GitHub issues page and elsewhere. If you have any further questions, do not hesitate to raise them via GitHub issue!
Q: How large an area is appropriate to work with?#
To an extent, ‘how big of an area can I work with’ is basically a question of how much RAM you have available, your internet speed, and your patience - pdemtools won’t try and stop you from downloading a 2 m mosaic of the whole of Antarctica!
However, the tool - as it currently stands - is designed around the challenge of downloading time-series stacks of specific study sites for change analysis (e.g. batch download and coregistration to the same datum). These sites are generally smaller than the size of strip data (generally, ArcticDEM and REMA strips are of the order of ~10 km wide and ~100 km long). This design philosophy won’t necessarily make things more convenient for other tasks. For example, if you wanted to download every REMA strip over the Larsen C ice shelf, (i) the aoi_frac variable in search() probably won’t be very convenient, as all individual strips will be much smaller than your AOI; (ii) once they’re downloaded, many strips will have no stable ground to coregister against (although ICESat-2 coregistration is planned as a forthcoming feature); (iii) given the very large AOI, the single contiguous images output by workflows such as the batch_download_and_coregister.py script will be (for mosaics) inordinately large and (for strips) largely NaN data. As such, the workflows presented in the pdemtools readthedocs are best tuned to the needs of users who are looking at contexts similar to those in the examples: study sites on the order of 10s kms in size. These notebooks, in our experience, seem to run succesfully on a range of laptops/desktops outside an HPC environment.
pdemtools will still be useful in some ways for these larger-scale tasks. For instance, the dataframe returned by the search tool can providing you a list of download urls for strip tarballs (accessed via the dataframe’s 'fileurl' column) that can then be retrieved via wget or similar. Or the mosaic() function can return mosaics on a custom tile grid suited to your own requirements if the standard PGC tiles aren’t useful for your needs.
We would encourage users who are interested in larger-scale analysis to get in touch with their needs if they find pdemtools doesn’t quite suit. We have the goal of making pdemtools a one-stop-shop for ArcticDEM/REMA DEM analysis, and other perspectives and requirements are an essential part of this.
Q: I have downloaded a strip scene, but the strip appears to have no data within the area. Why?#
Strips can appear empty (i.e. NaN values) when photogrammetry fails (due to cloud, water, etc) or it is masked by the PGC bitmask. Data previewed by the pdt.load.preview() function is masked according to this bitmask, and data loaded by the pdt.load.from_search() function is also masked by default (this can be disabled by using the bitmask = False option).
As a result, it is entirely possible that the search() function can return a valid datastrip that covers a sufficient proportion of the AOI to meet the min_aoi_frac requirements, but it will appear empty (i.e. all-NaN) when viewed. The preview() function will help you identify these ‘empty’ scenes, but there may still be (poor-quality) data present if it is downloaded using the load.from_search() function with bitmask = False.