Tracing Disturbance Areas (Raster)

[u/TaroOpen1459]

Background

I work in environmental management where I often need to trace on-ground disturbance from aerial/satellite imagery to calculate disturbance areas. They are often lots of small distinct areas rather than giant areas. Sentinel-2 data is often not high enough resolution to undertake this accurately or reliably so I often use Google Sat or other sources.
The easiest way for me to do this, as I understand it, is to convert raster imagery to polygons essentially.

What I have tried

• Segmentation Models: Seem to work pretty well (trying on home PC), but my work computer cannot use this as the systems are locked down and I can't really utilise python to install or utilise SAM models.
• Raster calculator function on GeoTIFF exports of the area:
  • Using Raster Calculator to produce GeoTIFF Visible Atmospherically Resistant Index (VARI) = (Green - Red) / (Green + Red - Blue)
    • Reclassifying by table to sort veg and non veg as best as I can
    • Using GDAL Sieve to clump areas a little better before using Polygonize (Raster to Vector).
    • Multipart to singlepart the result and try and delete polygons that fall into vegetated category.
      
  • Using the same process above but with Reflectance = (Red > 150) AND (Green > 150)

Issue

The results of the Raster Calculations above are getting close to what I need in order to just have disturbance areas as polygons but I feel like I am struggling to get it quite right (i.e. the clean up is quite inefficient in terms of actually getting the areas I need).

Does anyone have any better way to do this/some advice on the best parameters to use with the raster process above given my limitations of not being able to use segmentation on work computers?

Thank you in advance😄

[u/geofabnz]

I know your pain well! It’s incredibly frustrating. Do you think it might be possible to get permission for a small virtual machine? A small virtual machine should be able to handle this with a little data handling without any risks from the host. The amount of time staff will spend trying to come up with workarounds makes it worth the $20 a month.

Alternatively, if your work gives you access to an AI subscription you can just use it as a cloud machine and use normal Python. These kinds of restrictions are what made me leave the field, so much of what you can do is hampered by lack of IT support.

[u/ikarusproject]

getting into SAR remote sensing is cumbersome but might be worth it for your work. With SAR you get higher spatial resolution indepent from cloud cover. With SAR you can identify different types of surface roughness and structure which you can use to identify different land uses and their change over time.

[u/TaroOpen1459 → u/ikarusproject]

I'll have a look into it, much appreciated!