Hi everyone,
I am applying the InVEST Habitat Quality model to forested lands across the United States. One of the input layers represents forest disturbances, including wildfire, disease, and clear-cutting, etc., and is provided as a raster. My question is how the raster values should be encoded. Should disturbance types be represented by categorical labels, or should the raster contain numeric values?
Hi @hkarimi ,
Thanks for your question about the Habitat Quality model. Please always refer to the User Guide for details on InVEST model data.
It states:
All values in [the land cover rasters] must have corresponding entries in the Sensitivity Table.
and
Each code must be a unique integer.
The rasters should have numeric values for each land cover class, but you’re also free to add additional fields with human-readable class/category descriptions.
-Jesse
@hkarimi if you are intending to use that disturbance raster as a “threat raster”, then it should have values ranging from 0 - 1, representing the relative intensity of the threat. Perhaps all the pixels could be coded as 1 if all the categories represent an equal threat to habitat.
Hi Jesse,
Thank you for your reply. It is not lad use layer, and as Dave mentioned it is threat layer that I think it should have value from 0-1.
Thank you, Dave. Yes, I understand now.
Hazhir
Hi Dave and Jesse, @jesseG @dave
First, thank you very much for your previous advice, it was very helpful. I have a couple of questions regarding accessibility and protected areas.
I am working at the U.S. scale and have downloaded the Protected Areas layer (PAD-US) for my study area. This layer includes four GAP status classes (1–4), where the level of protection decreases from class 1 to class 4. In the context of the InVEST Habitat Quality model, this suggests that these classes should be assigned different accessibility or threat-related weights.
I was wondering if you have recommendations for appropriate weights for GAP Status 1 and 2, which represent fully or strongly protected areas. For example, would values such as 0.1 (or similarly low values) be reasonable, or would you suggest a different approach?
Additionally, once the Habitat Quality model is run and produces values ranging from 0 to 1, what range would you recommend classifying as highly suitable habitat if I want to categorize the output map (e.g., low, moderate, high quality)?
Hazhir
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