Hi,
I am using the topographic position index (TPI), which is derived from high-resolution DEM. This index shows the difference between a central point and the mean elevation in the neighbourhood around the point. I have this value for different radiuses, so for example for 100m, 200m etc till 5000m. I made a composite file (multiple band raster file) from the summation of TPI of several radiuses, to obtain TPI values ranging from 0 to 100. Thus every band represents the value of the TPI of a certain radius. In attachment I added a picture of the TPI of the meso-scale (100-1000m). The lower TPI values (in blue-green) show areas that are lower situated than the higher TPI values (in orange-red).
Is there a way to find/detect/identify patterns in this multiple band raster file?
I am interested particularly in depressions which actually show wetlands, as these TPI index shows infiltration-seepage patterns. Is there a way to detect these depressions (perhaps based on a threshold value, just thinking out loud)?
And how can you assign a 'signature' on such a kind of pattern, for example based on the values of the different bands? As you see in attachment the graph shows the TPI values of 1 pixel for different bands (radiuses). It can be interpreted as a reaction of the groundwater level on a rainfall event. Of course this is different for every pixel, but it would be nice to have something that 'characterises' a wetland/depression/the pattern that were found.
(I am using ArcGIS.)
Many thanks!
As someone who didn't specialize in this, it was difficult for me to understand. My suggestion is to link the blue and green areas with the rivers and waterways. So the blue and green areas that are directly linked to the waterways are most likely wetlands.
I hope this will be useful.
Good luck Annelies Broeckx
to get a signature you have to do some statistical analysis (descriptive) . For this you have to extract the wetland polygons and compare the values of TPI for each. You can classify or group the wetlands based on elevation which can help you.
We combined several elements in the early days before we obtained LiDAR coverage, which enabled a much higher resolution DEM. Since many of the early DEMs were based on the topographic maps, many things like the location of a stream in a heavily forested valley were basically guesses by the Cartographer. To help identify wetlands at the landscape scales, we used coverage of hydric soils, bottomland hardwoods, habitats with hydrophytic species such as Titi, and infrared photos. Some wetlands in the coastal plain especially are difficult to identify by topographic depression only. Some are hydric flatwoods dominated by species such as loblolly pine. The US Fish and Wildlife used the wetland and deep water habitat classification described by Cowardin et al to try to map wetlands using remote sensing plus ground truthing.
In addition, some rivers develop a natural response to high sand loading by building a levee along the floodplain margin. This levee is some instances with high rainfall is enough to detain and retain water sufficient for hydric conditions to develop. Note also that some riparian areas with bottomland hardwoods would not classify out as wetlands as per US COE Wetland Delineation Manual (1987). Having the LIDAR also helped identify historic drainage ditches or in some instances channel patterns, locations where channel was moved for farming, etc. The LIDAR pulses are also absorbed by water, so can help identify areas of shallow or deep standing water.
Hi everyone, I reformulated my question. I hope my question is more clear now!
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