Hello, I have query regarding the habitat degradation values. I have run the model for 10 years with current LULC. I have not used accessibility vector as it was optional. I got same degradation values for all the years. But the Habitat quality values slightly differ for each year. How do I infer the Habitat degradation values for all the 10 years. It would be very helpful if someone can help.
Thanks and Regards.
Hi @Prathibha,
Thanks for your post. We’ll need some more information to try and get at a solution to what you’re seeing. It sounds like you’re doing a scenario analysis over 10 years by updating the LULC layer for each year and run of the model. When you first ran the model, did you update the half saturation constant k? From the User Guide:
The default value is 0.05. In general, you want to set to half of the highest grid cell degradation value on the landscape. To perform this model calibration you will have to the run the model once to find the highest degradation value and set for your landscape.
Setting this value accordingly helps the scaling of the output to have more contrast. Another important note about running scenarios related to the half saturation and the LULC can also be found in the User Guide and is clpped below:
If you are doing scenario analyses, whatever value you chose for
for the first landscape you ran the model on, that same must be used for all alternative scenarios on the same landscape. Similarly, whatever spatial resolution you chose the first time you ran the model on a landscape use the same value for all additional model runs on the same landscape. If you want to change your choice of or the spatial resolution for any model run then you have to change the parameters for all model runs, if you are comparing multiple scenarios on the same landscape.
If it sounds like you’re following the above best practices for looking at scenario’s with this model we can investigate further. Let us know!
Hello @dcdenu4 thanks for replying to the query. I want to answer your questions. Yes I updated k value after default it was half the highest pixel value of degradation output obtained. The sensitivity and threats weight values all remain same for all the LULC scenarios. The spatial resolution of all the LULC scenarios is 30m. I have 4 LULC classes for all the scenarios. Yet the value of degradation is same for all the 10 years it is 0.646656. If anything else is required to solve the query please do let me know I would be happy to provide the details. Looking forward for your response.
Thanks and Regards.
Thanks @Prathibha for that information. Since you only have 4 LULC classes, I’m assuming 1 LULC class is the habitat. Are the other 3 LULC classes considered threats or degradation sources? What is the decay of the threats over the landscape / raster? If they’re set to decay linearly and over enough distance, this could be why you’d see the same degradation score throughout.
Cheers,
Doug
Hello @dcdenu4 thanks for the reply. The four classes are Lakes, Commercial+Residential , Industrial and Forest. The habitat is Lake. The threats are Commercial+Residential and Industrial. The decay for Commercial+Residential is linear and for Industrial is exponential. What do I do to see variations then? Should I change the linear decay to exponential? Please do let me know. Looking forward for your reply.
Thanks and Regards
Hi @Prathibha -
Just to clarify for my understanding: Does every pixel in the degradation raster have the same value of 0.646656 for every scenario? Or is there variation in the pixel values within the degradation raster?
~ Stacie
Hi @swolny thank you for replying to the query. The pixel values vary from 0.161664 to 0.646656 for the habitat chosen. But this variation in the pixel is same for all the scenarios that is for LULC variations of 10 years. If anything else is required to solve the query please do let me know I would be happy to provide the details. Looking forward for your response.
Thanks and Regards.
Hi @Prathibha -
It might be most efficient if you can create datastacks for at least two of your scenario runs and send them to me, so I can see the inputs and outputs. To create these datastacks, please follow the instructions in this post about using the Save as feature in Workbench. That way I will have the exact inputs that you are using. You can upload the datastacks to any cloud service that you use where I can download them. Thank you.
~ Stacie
Hi @swolny thank you for the reply. I tried doing data stacks but I am unable to save them. I don’t know the issue even after following the steps mentioned in the post. So I am attaching the Log files for two scenarios. I hope it may help. Looking forward for your response. Please find the attachments.
InVEST-natcap.invest.habitat_quality-log-2025-04-28–22_06_20.txt (7.6 KB)
InVEST-natcap.invest.habitat_quality-log-2025-04-28–22_30_03.txt (7.6 KB)
Thanks and Regards.
Thanks for the log files @Prathibha, and I also need the data itself to see what’s going on. What happened when you tried to save the datastacks? Did it give an error?
If saving the datastacks doesn’t work, you can manually zip all of the input data up and provide it that way. Along with the log files you posted here I can recreate your inputs.
~ Stacie
Hi @swolny thank you for the replies. I did not get any error message but when I stacked the data the created file was empty. Anyways I am attaching the data and the outputs I have got for two scenarios so that you can help me with the issue. Looking forward for your response. Please find the attachment. Please ask for the access to download the data.
Thanks and Regards.
Thanks for sending the data @Prathibha. There is actually quite a bit of difference between the 2023 and 2015 degradation rasters that you provided. You can see the difference in this screenshot, where I just subtracted the 2015 degradation raster from the 2023 degradation raster. In it, gray areas have no change, blue areas change in a positive direction, and brown areas change in a negative direction:
The range of values is the same for both degradation rasters (0 - 0.646656), but the patterns of values are different, and, as you can see from the map, there is definitely change between them. We need to look not only at the legend, but look closely at the map data itself, zooming around, turning layers on and off and comparing patterns and pixel values, to understand the outputs and how they’re different between scenarios.
~ Stacie
Hi @swolny Thank you for reply. This insight is very helpful for me. I was in a notion that maybe my data preparation was not right but now I know the variations how they are. Thank you for solving the query.
Thanks and Regards.
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