Unsupervised Classification

Unsupervised Classification of the UWF Campus

This week in Remote Sensing we used an aerial image of the UWF campus to try some unsupervised classification techniques.  Unsupervised classification is when we rely on the computer to group like pixels together and make assumptions that these represent similar features.  In the image above, we started by using ERDAS Imagine tools to create 50 unsupervised classes.  Then I manually assigned these 50 classes to 5 categories (Buildings/Roads, Trees, Grass, Shadows, and Mixed).  This process actually is pretty easy and goes quickly with the ERDAS tools.  This was one of the first assignments where I felt like the ERDAS tools were easier to use for this task than a similar task in ArcMap.  From the 5 new classifications, I was able to create an estimate of the permeable versus impermeable surfaces that make up the UWF campus.

GIS Internship - Article Review

Review of Mapping Benthic Habitats; the Marine GIS Challenge by Joe Breman

ArcUser Online, Spring 2005

http://www.esri.com/news/arcnews/spring05articles/mapping-benthic.html 

In Mapping Benthic Habitats; the Marine GIS Challenge, the author offers an outline of the challenges of both modeling the ocean floor and then making important conservation decisions with that model.  I chose this topic as it covers several areas of interest to me personally.  I am a recent UWF Maritime Studies graduate, so I have a strong interest in the ocean and all things maritime.  I am also a current UWF GIS Certificate student and this topic covers the use of various GIS tool in the process of mapping the ocean floor.   Finally, I am an avid SCUBA diver and have a strong interest in conservation of ocean resources and wildlife and the end result of this study is a better understanding of the areas we need to conserve.

The article primarily discusses two types of GIS tools.  The author notes that there is an extensive database of benthic sensor data.  We have been taking sonar readings and scans of many parts of the US coast for decades and this provides a rich set of data to be utilized.  Most of the data is simple x, y, and z point data and is easily converted to surfaces using tools readily available in ArcMap such as  the Inverse Distance Weighting, Spline or Terrain Dataset tools.  Once the surface is created, 3D Analyst can be used to visualize the ocean floor and assign a “bottom type” to each cell in the model.  These types are then used in mapping out habitat regions that need to be conserved for different species.  Making this process all the more complicated is that some species spend different portions of their lives (i.e., juvenile and adult) in differing habitat types.

Overall the report was informative, albeit rather brief.  I am especially interested in 3D modeling and wish that we had an entire separate class on it as part of the GIS Certificate.  I would like to continue on in my own studies and expand my skill set using 3D modeling.  I am also glad to see GIS tools being used effectively for conservation and, hopefully, improving or minimizing our human impact on some of these areas that are often “out of sight, out of mind” for most of the general public.

Scythian Mounds, Final Report

The final lab for the Scythian mounds project involved creating the final predictive results based on our previous work.

Using these surfaces, we created a feature class of random points (minimum 30 m spacing) throughout the study area and merged these points with the possible mounds identified from an aerial image.  This feature class was then expanded with fields and filled with the values from the reclassified surfaces (elevation, aspect, and slope).  These points were then subjected to an Ordinary Least Squares linear regression method based on these three surfaces (top map above).

The results of the OLS give the following results:

               Adjusted R-Squared:   0.760202

	Coefficient	Probability
Intercept	-1.770434	0.000000
Aspect	0.115368	0.000001
Slope	0.113834	0.000001
Elevation	0.630913	0.000000

The Adjusted R-Squared value of 0.760 suggests that the three surface variables can explain 76.0% of the sites in the predictive model.

Since the coefficient values are positive (with the exception of Intercept) and not very close to zero, it appears that each surface property is making a positive contributing to the model results (with our reclassified weightings).  The probability values < 0.05 also support this conclusion (near 100% confidence level for each coefficient).

We also subjected our OLS results to a spatial autocorrelation test, returning the following results:

z-score: 11.43385

p-value: 0

The very low (zero) p value indicates that it is very unlikely that the data is distributed randomly.  The high Z-score (in conjunction with the low p value) indicates that the data is normally distributed and spatially autocorrelated.

Based on a visual review of the OLS residuals and hot spot analysis, it does appear that many of the predicted areas are within valley bottoms.  It may be beneficial to add an additional variable that accounts for this spatial distribution, such as access to water in the form of rivers and streams.

It does appear that we have established that the variables in use are helpful for use in a predictive model. Per the lab, it would probably be a good idea to test these variables further with a larger point set and additional regression models (such as GWR).  Additionally the variables should be analyzed further to ensure the relationships are truly linear.  Adding additional variables (such as access to water) may also expose additional relationships.  Finally, any model should be subjected to some ground truthing to understand if it is truly useful.

Thermal Imagery and Analysis

Thermal Imagery using ETM+ Thermal and TM True Color

This week in Remote Sensing, we focused on Thermal Imagery.  Thermal imagery has all sorts of uses, from military to environmental.  I chose an environmental perspective for this lab project.

I started by creating ETMcomposite.img, a combination of 8 ETM images using ArcMap’s Composite Bands tool.  I wanted to work directly with the thermal band, so I started out with just band 6 of ETMcomposite selected.  I selected a color ramp that intuitively matches what we expect for cool to warm colors (blue to red).  By adjusting the Symbology/ Histogram breakpoints, I was able to accentuate the range of values in the overall image, really increasing the contrast between warm and cool areas.  This is really when I noticed Santay Island for the first time.  It was so cool (as in cold) in the middle of this large urban area and really not very far away physically.  It seemed to make a good ecological point that these small refuges can exist within urban areas if we take care of them.  Unfortunately, it looks like someone is already clear cutting in the middle of the island and that can be seen clearly in the thermal image.

Multispectral Analysis

This week in Remote Sensing we experimented with multispectral analysis.  Using a LandSat image of western Washington state (my home state!), we attempted to determine what features were causing various spikes on histogram representations of the image.  In the first image, there was a prominent spike at the dark side of Band 4, indicating to me that this must represent all the water in the arms of Puget Sound.  I used TM False Natural Color to best present the water feature.

In the second image, we had smaller spikes in the bright end of the histogram.  This appears to be the snow on the peaks of the Olympic range.  True Color seemed the best representation for white snow.

Finally, some bodies of water appeared lighter and brighter than others. I captured this in the bottom map of Grays Harbor.  We can see that either a lot of sediment or algae is making this image appear lighter.  TM False Color IR really helps this brighter feature stand out in this image.

Scythian Landscape - Analysis

This week we began our analysis of the landscape around the Scythian mounds at Tuekta.  This consisted primarily of running reclassification operations on various landscape attributes.  The first map (top left) is a simple contour map of the study area.  This gives us a general idea of the elevation where the mounds are found.  The second map (top right) is a reclassification of the slope.  Using this map, we can determine (not at this scale) that the mound areas generally have a slope in the range of 0 - 9.3° (in dark green). This will provide a good guide in the future on areas that might have undiscovered mounds.  The next map (middle left) is a reclassification of the elevation.  From this map, we can determine area that are in a similar elevation band as the known mounds.  The middle right map shows reclassified aspect.  This map shows the directional exposure of the area.  The areas that have a mostly southern exposure (towards sunlight) are highlighted in light blue.  Finally, the bottom image shows my guesses at mounds (some obvious, others not so much) based on an aerial view of the Tuekta area.  These are areas that we may want to explore further.

Spatial Enhancements

This week we experimented with spatial enhancements on some imagery.  The original image was a panchromatic layer that exhibited pretty distinct banding.  Through the use of a Fourier transform and some experimentation with different convolutions, I was able to produce the image above.  This image attempts to minimize banding while finding a happy medium between being too generalized (low pass convolution) and too "edgy" (high pass convolution).  In the end, the banding is generalized so it is not distracting and the urban features remain relatively sharp and distinct.

Modeling Scythian Landscapes - Part 1

This week we were largely in preparation mode as we start into a module on Modeling Scythian Landscapes.  I am excited for this module as I am particularly interested in landscape archaeology and have been looking forward to attempting to model landscape concepts in GIS.  In the map above, we've created a mosaic of ASTER images as the DEM background for our study area.  In the inset is an aerial view of the mounds that has been georectified to the basemap.  This was all a pretty straightforward process, so we should be good to go for Parts 2 and 3.