Let's tackle the second question first.
2.) The higher the value of a cell the higher the cost for a panda to travel through this cell from one habitat to the next. Is that correct? |
A cost distance raster is a raster where each cell contain the accumulated cost to get there from the origen using the most optimized route (lowest cost). So yes, the higher the value the more difficult it is for a panda to reach that cell.
1.) Why do I get so many NoData cells? Is that because the cost there to connect the two sources would be too great? (but too great compared to what? The maximum dispersal limit that I specified when using Cost Distance?) --> I am actually quite happy that there is so much NoData, because IF it really means travel for pandas is too "costly" there, then I have the area for possible corridor establishment narrowed down pretty neatly. I just want to make sure I am not drawing incorrect conclusions. |
If you have two origins and generated a cost distance from each point separately, you should process each separately and apply a threshold to define what the potential habitat from that origin is. Once you have the areas you can combine them. I notice that according to the analysis pandas from the southern habitat can reach the northern part of the highway and vice versa. Is that true? In case it is not, the highway should be configured and a part that cannot be crossed and the habitat should be restricted to the same side of the highway. The end will be two habitats, one for each group of pandas.
In case pandas can pass from one side to the other, then the individual results are correct. I don't think it makes sense, based on what I'm understanding, to determine the maximum of the two rasters. You should look at the minimum value, since this represents the cost of the panda that can reach that part with the least "cost".