This Weeks Lab
- Topographic Effect
- Assignment 2
The Topographic Effect
Feature extraction and classification are complicated in mountainous areas by topography, as aspect influences the DNs for similar habitats in varying slopes relative to the solar illumination at time of image capture (~10.30am local time = from the SE). In Geog 432 you looked at a few approaches to try to deal with issues of topography. You performed ratios and indexes for instance. We are going to look at another method for trying to reduce the effects of topography in this lab.
You may recall Roger and Scott speaking about what we are doing when we perform operations such as classification in Remote Sensing. We are really working through multivariate statistics in hopes of recognizing patterns in the data that can help us make decisions about the type of features existing in the landscape. We are looking to see what qualities are present within a series of Digital Numbers, but we also have some knowledge of he landscape itself.
Data for the lab
For this lab, we will be using a small subset from an older Bowron subset. You should make an appropriate directory in your home workspace and copy /home/labs/geog457/bowron_data/bowron.pix into it. Why are we making a copy of the data?
Open the dataaset once you have the file in your directory,.
A quick Classification – not a surprise..
Perform an unsupervised classification by selecting bands 3,4,5 and using K-means with defaults values (16 classes). You should notice that the shadow areas are included in the water class.
If you feel like trying a ratio to help offset the shadow effects – use the raster calculator to generate a 4/3 ratio and save it in a 32-bit channel. Try the same classification again, but this time use channels 3,4,5 and the 4/3 ratio.
How does it look now? Is there still some confusion around North West slopes? Can you think of why some terrain is affected more that others – and why North west?
There are several approaches to trying to offset this including the use of multiple diverse training areas for each class in a supervised approach, incorporating DEM layers especially slope and incidence as input to classification, and using topographically reduced channels such as ratios, NDVI, Tassel Cap or Principal Components (as we tried last week). Here we will try the approach used by Kuzera, Rogan and Eastman (2005) in extracting a measure of topography from the image channels, by modelling the hill shading component from the raw imagery.
Check through the file in focus to determine more about the bowron.pix file (sensor type, date, resolution, projection…). Do you find all the answers? Can you tell who originally created the file and on what day the file was created?
Open the bowron.meta – metadata text file in the bowron_data directory – in a text editor and look to see what date the imagery was acquired (among other things).
The date is ideal for a mid-latitude mountain area with most snow gone, and still reasonable sun angle, found in the metadata file. The azimuth should be fairly constant for BC scenes as it won’t change with time of year, but the angle will decrease further away from the summer solstice, and also the further north. The area seems to be a good test ground with ridges running mostly perpendicular to the sun direction. To estimate the effect, check some typical DNs for similar vegetation areas, e,g, conifers, alpine meadow, avalanche chutes on opposite slopes facing NW and SE in bands 3,4, and 5.
Using Elevation Data to lower the topographic effect
Steps in the Kuzera process:
1.) There is a DEM layer (that includes a shaded relief layer to be added to your file in the bowron_dem_28.5.pix file in the bowron_data directory.
2.) Using the REL algorithm create hillshading using the sun azimuth and angle (height) values given in the metadata.
Look in the DEM sub-directory to see if you can guess how the DEM for the layer was assembled.
Perform a Google search to see if you can find the suns location and angle of incidence through a web site here are a couple examples:
3.) Using layer ->scatterplot, regress the hillshading (X) against each band 1-5, 7 (Y) to generate an equation with slope and intercept unique to each band. Record this equation for each band you are interested in adding the effect to..
Note that this should be restricted to land covers well represented in all topographic aspects – this should exclude water. How would you achieve this?
4.) Create up to 12 new 8 bit channels, and from the equation under the scatterplot y = ax +b apply this to the hillshaded image DNs for each of the bands to yield 6 new ‘estimated illumination effects’ – these should be 8 bit for simplicity.
use EASI modeling to accomplish this – e.g. if the new empty channel is 9 and the hillshading is chanel 8: %9 = .05*%8 + 53.6
.. and so on for the other new channels (10-14) and matching values of a and b
5.) Use layer -> histogram to find the mean for each of the new 6 bands, record this rounding the decimal to the nearest integer.
6.) Then subtract the result for each band in step 4. above from each of the 6 ETM bands while at the same time adding the mean value of the bands gathered in step 5. Input these into the same modeling equation. The result will be mostly to cancel out the large subtraction but include local differences. Again ignore or round up decimal values for the mean.
e.g. %15 = %1-%9 +51
This should now create new channels with minimal topography. How do they look (RGB or greyscale)
7.) Run tassel first on the original bands (input = all six reflective bands) and then on these new six channels and compare for content. The first component (Brightness) should show much less topographic component.
8.) You could now run a classification on this image using the new channels and/or the Tassel results.
Summary of the Kuzera et al process:
- generate hillshading with the sun azimuth and angle as given for the satellite data
- regress hillshading against the 6 Landsat bands one at a time
- Apply this regression equation to create 6 illumination effect channels
- Subtract this channel from its original band for each 6 bands, and add back in the mean for the original band
- Run Tassel cap to transform the data into components for change analysis
This is a two piece assignment
Using your (or Scott’s) Sentinel-2 20 metre resampled dataset created in last weeks lab, clip out a scene that has the same geographical bounded area (the same size of extent), to form a similar dataset from OLI data that overlaps approximately 25% of your (Scott’s) Sentinel-2 datatset. Once this is clipped out, atmospherically correct this OLI data using the Top Of the Atmosphere method. Describe how well the two different sensor datasets compare side by side (overlapping). Write out the steps you took to complete the work, and make sure you use illustrations or screen shots. Finally, create PCA Eigenchannels 1,2 and 3 using only the Visual, NIR and MIR bands (channels).
The data for this is in the /home/labs/geog457/terrace_sentinel_oli folder (including the necessary meta data and Scott’s clipped 20 metre layers
Using the remotepixel.ca site, duplicate the work performed in last weeks lab to create a Sentinel-2 dataset at a 20 metre resolution matching the date and extend of the Sentinel dataset used in Partt 1. Once that is created – perform a PCA on this dataset, but only use the same input band combination as you did in Part 1 for the Landsat 8 data. Load the PCA data from both Part 1 and Part2 into focus and compare the results of the two sensors.