Final Project

For our final project assignment we were given two basic choices: either apply what we've learned to a pre-fab project, or do the same to one we've created on our own.  I went with the latter option mainly because I was curious as to how the 2013 Rim Fire has affected the overall forest health in the area.  Given that the state of California is currently in a severe drought I suspected that these effects would also be highly visible on Landsat imagery.

Unsupervised classification of my study area.  MMU was 1:100,000 so take this with a grain of salt.

To start, I'd like to mention that I'm not a botanist, so a lot of the plant terminology and spectral analysis discussion mentioned throughout the course was something I've only recently picked up.  Before this class I had no idea what a NDVI was, or even how to tell one tree from another when viewing various multi-spectral data (I still can't, unless I've been given pointers on what to look for first!).  So in the spirit of applying all that I'd learned over this course I focused on a project that required the classification of healthy vs. non-healthy vegetation.

The first image shows a very rough classification of healthy vs. non-healthy vegetation.  The MMU was at 1:100,000, which at first I had done for time saving purposes (mostly to not go crazy during the analysis portion).  I now feel that I made the right call, since the study area is rather large, and I was trying to quantify general trends.  For something more in-depth, perhaps a 1:50,000 or larger scale should be used.

Comparison view of three years shown across two different band combinations.

The comparison of the multi-spectral imagery was perhaps the most exciting part of this project, and also the most enlightening.  For example, on the bottom left of the image above you'll see a map inset with a lot of bright green - that's healthy (fast-growing) vegetation.  The image in the middle was taken during the Rim Fire, and one can see how dark the remaining vegetation had become.  Part of this could be due to the change of Landsat satellites used - 2010 marked the wane of the Landsat TM 5, and since 2012 Landsat 8 has been generating imagery for the United States.  With the change of satellites marked a change in the number of bands, which is why I've listed two band combinations on the map.

The 2015 imagery had smoke cover... but the website said no cloud cover!  Which I suppose is technically true.  The smoke made interpretation of the image very difficult, although if nothing else the image shows how pollutants in the air can be visualized.


*Originally published December 9, 2015.  Updated on 2/27/2017 to repair image links.

Lab 10 - Supervised Image Classification

For this lab we used a supervised image classification method to create a thematic land use/land cover (LULC) map of Germantown, Maryland.

This method uses what are called 'training areas' to guide the computer in assigning a LULC value per pixel.  These training areas are selected prior to running the automated classification method, which does imply that the thematic map creator knows quite a bit about what to expect land cover-wise prior to beginning the process.

The LULC classes as created with supervised classification.

The map above was created with several training classes provided for most categories - the idea here is that more than one example is better for the program when it assigns classes to the various pixels.  The pixel assignments are neighborhood based and used a maximum likelihood assignment method.  This means that pixel assignments are based on those having the highest probability of matching the spectral values as provided in the training class(es). 

As can be seen in the map above, there are quite a few acres devoted to roads... and that is not technically correct.  Quite a bit of those roads seem to represent urban areas, or possibly even grasses.  Some tweaks are needed for the roads training classes (I had used two).  Unfortunately the spectral signatures for roads is very similar to that of the urban areas, so there will always be some error on the map no matter how much those training classes get altered.

A spectral euclidean distance map is also shown above as an inset.  As I understand it, this map represents the amount of error on my thematic map - and is displayed as bright pixels.  Since my inset map is quite bright, that means there happens to be quite a bit of error on my map... and most of those errors seem to follow along my roads class.  It seems that this process requires a lot of trial and error before a final product can be presented.

*Originally published on November 10, 2015.  Updated on 2/27/2017 to repair image links.

Lab 9 - Unsupervised Classification

This week's lab focused on using an automated method to classify aerial imagery: the unsupervised classification.  This method isn't exactly hands free - it's just called unsupervised because the program that completes the process does so without a training data set.  If it had training data to guide it, then the process would be considered supervised.

With unsupervised classification the computer program iterates through the image using whatever algorithms and input parameters were assigned at the start of the process.  When the program creates the classes it does so by grouping similar brightness values together.  Once the process is complete it is necessary to review the results and then manually classify (or re-classify) as needed.

Map depicts an image that had been re-classed into 5 land use/land cover categories using unsupervised classification.

The above image represents an unsupervised classification that was run using the ERDAS Imagine program.  An ISODATA classification was used; specified input parameters included the choice of 50 classes to be created, setting the maximum number of iterations to 25, and setting the convergence threshold to 0.950.  All other options were left at their defaults. 

After the image was re-classed with 50 (!) classes, I then manually pared this down to 5 based on very general land use/land cover types (grass, trees, urban areas, mixed, and shadows).  The shadow category was something of a surprise, but given the time of day the image was taken there were quite a few shadows!  The mixed category represents those pixels that actually could be assigned to more than one land use/land cover category.

*Originally published on November 4, 2015.  Updated on 2/27/2017 to reset image links.

Lab 8 - Thermal & Multispectral Analysis

This week's lab focused on interpreting thermal imagery using ERDAS Imagine and ArcGIS.  To this end we each had to select a unique feature on a multispectral composite image of the Pensacola, Florida and analyze how it appears in various wavelengths.

Comparison of how a sandbar is viewed using various views of multispectral imagery.

The wavy appearance of the sandbars along the northern shorelines caught my eye, so I'd decided to focus on how these appear within various wavelengths.  While the sandbars are visible in just about all of the combined multispectral imagery, when viewed within separate bands it was almost impossible to see.  The contrast, or brightness values, had to be altered in most cases.  The only individual bands that the sandbar was semi-visible in was within Band 4 (a near infrared band) and Band 6 (a thermal infrared band).

Lab 7 - Multispectral Analysis

This week's lab focused on identification of features using various bands of satellite imagery.  The maps below show the results of a seek-and-find type exercise, using spikes in pixel values between image bands as our guide to find the required features.

Map 1.  The darkness of the pixel values representing open water were offset by using false natural color.

Map 2.  The brightness of the snow pack is offset by the surrounding landscape, shown in false color infra-red.

Map 3.  The variations within the shallow water are visible by setting the color bands to a TM Bathymetry setting.

Lab 6 - Spatial Enhancements

View of the spatially enhanced image.

This week we utilized our newly acquired ERDAS Imagine skills to apply visual enhancements to raster imagery.  The image above originally had these thick black diagonal lines crossing through it - with the spatial enhancement measures applied these lines are not quite as noticeable.

The enhancements were applied using both the ERDAS Imagine software and ArcGIS.  The processing began with ERDAS Imagine by applying a Fourier transformation with a low pass filter.  Then the image was further processed by applying a 3x3 sharpening effect (the 3x3 refers to the number of cells used as the kernel for the enhancements).

The image was then opened in ArcGIS, since this program has a Focal Statistics tool (and ERDAS Imagine doesn't quite have these statistics in its arsenal of enhancements).  After setting the Focal Statistics tool to run a rectangular 3x3 mean pass the image was considered to be ready for the final map layout.

While the diagonal lines haven't disappeared completely at least it is now possible to figure out what the features in the image are.  Apparently it is possible to remove those diagonal lines, but that requires some additional (advanced) techniques... trying to get the Fourier transformation correct for this exercise was difficult as it was.

Lab 5a - Intro to ERDAS Imagine and Digital Data 1

This week's lab was mostly a tutorial on how to navigate within ERDAS Imagine.

Detail view of land cover data set within Washington State.

The functionality of ERDAS Imagine is great - it makes sense on the user's end, and one doesn't really need to drill down into various properties just to change one little thing.  Being able to mess with each color band was neat, and helped to make certain aspects of remotely sensed imagery processing a little less abstract.  However some drawbacks to the program do seem to be its map making capabilities (there are known bug issues).  The above map was finished using ArcGIS.

The view above is an inset of a much larger classed raster image.  It took some tries to get the correct view extent to transfer over to ArcGIS... even though this step was spelled out in the directions I suppose I still needed to do my own trial and error to learn this part! 

Module 4 - Ground Truthing & Accuracy Assessment

This week was all about 'ground truthing' classification maps and evaluating the overall accuracy of the classes assigned.  Using the Land Use / Land Cover map created for Module 3 we 'ground truthed' our own classifications.  Overall my map from last week was 67% accurate... ouch!

The above map had been 'ground truthed' for accuracy - overall accuracy was found to be 67%.

Since physically visiting Pascagoula, Mississippi was not an option for me (or most of my classmates) we used Google Maps as our higher grade dataset, then visually compared pre-selected sample locations against Google Maps. 

The sample locations were derived using a stratified random scheme, with at least 2 points per class type.  Most classes had 3 points, and two classes (which did not have very large representation on the map) had only 1 point.  The largest class contained 4 sample points.  Sample locations were plotted using a program called the Sampling Design Tool.  The program is available as a download through ESRI and was created by NOAA's Biogeography Branch.

After checking Google Earth against the sample point locations on my original LULC map, I have some basic observations:

1. My original cropland designation apparently was just grass mowed with a distinctive pattern... I'm still not buying it, and believe instead that the land use has changed over the years.
2. My beach was apparently someone's home - they live right on the water and seem to have little to no landscaping.  That was not readily apparent at my MMU of 1:4,000.
3. I should have set 3 sample points per class, and then manually messed with their location.  Many of the sample points were bunched up for some reason.  Also, classifications that did not cover as much map space should have had at least 1 sample point taken away.  But the largest classes didn't really need to absorb those 'excess' sample points.

Lab 3 - Land Use / Land Cover Classification Mapping

This week we got to try our hand at classifying an aerial photograph.  Using a Land Use Land Cover classification system, which was developed in 1976 by Anderson et al. for the USGS, we classed a single aerial photo to the second level.  My minimum mapping unit (MMU) was at 1:4,000. 

Aerial map showing Level II Land Use and Land Cover Classification.

What does all this mean, exactly?  To start, land use is a bit different from land cover.  Land use shows human-based uses of the landscape (urban areas, agriculture), and land cover shows primarily natural settings (forests, water).  To classify a map one first needs to differentiate between farmland, urban areas, forested areas, etc.  That is a primary classification level.  To map something at the second classification level is to specify if, for example, the areas within an urban area are for residential use versus an industrial use.

The map above shows various classifications within a small section of Pascagoula, Mississippi.  For consistency I had digitized my classification polygons at the 1:4,000 level only.  While I may have zoomed in or out to double check on a classification type or my overall location on the aerial photo, when I digitized an area it was always at 1:4,000 (also known as my MMU).

Completing this map was a bit rough at times - occasionally I felt like I was adding to much detail, and other times like I wasn't adding enough.  Since a Level II classification is meant to be a bit coarse perhaps my biggest lesson was in learning to let go of the details!  For example, a high-tension wire crosses through the lower left of the aerial photo.  This was not mapped in mainly because to do so would have been very difficult given the level of detail that it would require... if my MMU was a bit larger then perhaps it would have been possible, however I would also probably still be working on this map!

Reference:
Anderson, J.R., and E. E. Hardy, J.T. Roach, R. E. Witmer
1976     A Land Use and Land Cover Classification System for Use with Remote Sensor Data.  Geological Survey Professional Paper 964.  United States Government Printing Office, Washington D. C.

Lab 2 - Visual Interpretation

This week’s lab focused on the elements of aerial photo interpretation.  To accomplish this we viewed two separate photos, then identified various elements within each. 

Map 1 - Comparing tone and texture.

The first map is a study in texture and tones.  We were to identify various tones (ranging from very dark to very light) and various textures (ranging from very fine to very coarse).  I found the tones to be a bit of a challenge, as to me the gradation of very dark to dark was a bit subjective.  Never fear, I was able to tell the basic difference between light and dark!

 

Map 2 - Picking out features based on specific criteria.

The second map required that we identify features within the photo that correspond to a specific criteria: association, shadows, shape and size, and pattern.  It was interesting to see just how many findings based on association I was able to make… I didn’t map all of them in as the assignment only called for two examples, but there are quite a bit as can be seen above.