When displaying the results of the raster ‘hm.tif’ I find that in the inner city areas the heat mitigation exactly overlaps the polygons of the LULC, as if there were no temperature mixing effects. Denser urban areas correspond to a lower hm value, as if the green areas had no influence.
I divided my area of interest into a grid of 100x100m pixels for more accurate visualisation but in the vector ‘UHI_effect’, there is no hm value in the urban area.
Thanks for writing in with your issues interpreting results of the InVEST Urban Cooling model.
Please grant read access to the Google Drive folder you linked so that we may download and examine your input data. As of now, the Drive is private so I am unable to access it.
In your biophysical table, only LULC classes 1, 2, 3, 4, 10, and 11 are parameterized as Green_area. There are few pixels designated as one of these classes in the inner city. The ones that are there do NOT appear to total >2 ha. Thus, values in ‘hm.tif’ are equal to those from ‘cc.tif’ because the heat mitigation index is equal to cooling capacity when the pixel is unaffected by green spaces of >2 ha. The maximum green area cooling distance was set to 450m. Look at the margins of the inner city. There, the blending effects of large (>2 ha) neighboring green areas (mostly “Agriculture”) are visible in the ‘hm.tif’ result when those spaces are within 450m.
This is an issue with symbolization and interpretation. There are certainly HM values in the urban area. First, there is no file named ‘UHI_effect’ in your data nor generated by InVEST. But, instead you shared a screenshot showing ‘uhi_results.shp’. I can see in your Layers pane in QGIS that it is symbolized improperly. No values below 0.4250679493 are symbolized. Change the lower bound of the first bin from nan to 0 in order to view those values. You can also click around with the “Identify tool” to view values of individual pixels in ‘hm.tif’. The HM values in the inner city range from ~0.253417 to ~0.749383 when these inputs are run with v3.14.2. In fact, the minimum value in that raster result is 0.0774297; there are no values of 0.
This is again an issue of symbolization and interpretation. Use the “Identify tool” in QGIS to click around the inner city with the ‘T_air.tif’ raster turned on and you will see that there is indeed variation between pixels, not a single value. Relying on a visual interpretation based on perceived colors of a raster symbolized in a GIS is a problematic approach when attempting to detect variations.
These are the results I would expect given the model inputs.
But if the ones I have named as Green_area are indeed the only ones present, how do I affect the heat mitigation index? In general, how do I get more ‘nuanced’ results in the urban area?
Putting 0 as the minimum value gives values even within the urban area.
Clear, but how can I visualise the slightest variations of T_air within the urban area? Because now, visually, different values look the same. Should I narrow down the area of interest?
In general, how do I eventually identify the areas that are more prone to UHI and those that are less?
