I´m using the Urban Cooling Model to evaluate the ecosystem service provisioning from urban and periurban agriculture in São Paulo metropolis (Brazil). After understanding and running many times the UCM, I have some doubts about the values for shade. As for many of them I could not find local data, my reference is the biophysical table provided on sample data and the articles in reference.
The guide says: “the shade factor (‘shade’) represents the proportion of tree canopy (for trees >2m) associated with each land use/land cover (LULC) category. Its value is comprised between 0 and 1”. Anyway in the sample data we can find that agriculture and pasture (which are not covered by trees >2m) the considered value for shade equals 1.
Would it make sense to consider agriculture and pasture areas as shade = 0 or some value beneath 1?
How to consider hybrid forms between agriculture and forest, as in the study we are also considering agroforestry systems?
Hi @jayamstel,
The sample data is intended to illustrate the correct CSV format, not to be a reference for biophysical values. You’re right that it doesn’t make sense for agriculture or pasture to have a shade value of 1. Many of the other values also don’t make sense. So I would assume that none of the values are legitimate.
To get a general idea of shade values, you could try looking at satellite imagery (even Google Maps) and estimate the proportion of tree cover. For more precise data, you could use a tree cover/tree canopy map like this: Global 30m Landsat Tree Canopy Version 4 Released | Landsat Science and average it over each of your LULC areas.
Note that that one is for trees >5 meters tall and the model calls for trees >2 meters. I’m not sure how much that would affect the result.
Thanks for your prompt answer. What you explain to me about the values on the biophysical table is very important. I´m not sure if the user guide let this so clearly, anyway it reinforces the necessity for systematic reviews on local values.
For the alternatives you presented to me, it seems that would have some discompass with the requirements of the model if I use a dataset with trees taller than 5m, there would be a range of trees that I would not consider (from 2 up to 4.9 m). The Google Maps alternative seems on one hand to have more imprecisions, but on other hand is the most accessible way.
As an exercise, I downloaded GFCC Canopy Tree Map and crossed it with my LULC, utilizing the zonal statistics for raster layers on QGIS (was this your suggestion, isn´t?). I obtained meas values of canopy tree cover according to my land use classes. They make sense for me, please have a look on them:
But still it is underestimating smaller trees. This is something we can assume as a limitation on available datasets? I mean by defining the shade parameter would have some kind of arbitrariness, and I am not sure about how to weight those two alternatives.
I would recommend searching the literature for methods for calculating tree canopy from satellite data. The Global Landsat Tree Canopy dataset cites a paper: Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., Townshend, J.R.G. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS Vegetation Continuous Fields with lidar-based estimates of error. International Journal of Digital Earth, 130321031236007. doi:10.1080/17538947.2013.786146.
If the tree height is a big concern, you could look into adapting their methods for >2m rather than >5m. Or if you have knowledge of the area, you could estimate what proportion of shade comes from trees in the 2-5 meter range and decide if that’s significant or not.