Histogram Analysis and Statistics

Washington D.C. Image Histograms and Statistics
For Washington D.C. I collected 7 histograms, one for each band of the image.

As we can see from these histograms, the data tends to trend in the area of being a low intensity image, meaning the image is darker or contains several dark areas. Although there are two histograms that are trending in the mid-tonal intensity range. In order to improve this and make the image more easy to analyze and distinguish different feature, would be to apply a contrast stretch. This stretches the image data along the x axis allowing for more even tones.

For the image statistics I chose to use scatter plot graphs.

From these scatter plats we can see that the areas where the blue line is more concentrated is in the lower intensity range. Again, if a contrast stretch is done then the image would not be as dark and features could be distinguished slightly better.

Canmore, Alberta Image Histograms and Statistics

The histogram trends are similar to Washington, but in this case there is a lot more concentration at the low intensity area of the graph, meaning there is quite a lot of darkness in the image. In this case again, applying a contrast stretch would allow those low intensity areas to be stretched out over the x-axis and stretches the values along the full range of pixel values. This was the resulted image is less harsh and the values are not stretched too far where the image would not long be able to distinguish features.

I would chose a contrast stretch for both of these images, rather than a histogram equalization that only apply a linear scaling function to the pixel values, but provides a harsh image as a result.

Scatter Plots

Once again these scatter plats are showing, from the concentration of the blue line, that these are low level intensity images and again, a contrast stretch would help to distribute the pixel values along the x-axis.

These images are interesting because they show the differences between an older well established city, and a city that is experiencing growth in a extremely difficult environment, that being in a valley of the rocky mountains. More importantly I wanted to focus on the actual satellite imagery and the fact that TM images don't allow one to look at the fine details of cities, much like I had anticipated but was disappointingly disproved. I learned that if I were to be looking at vegetation or needed to measure mountain ranges then this imagery would be useful, but for looking closely at cities, especially if I were to be tracking rate of development and growth, then I would need finer and more powerful satellite imagery.  

References:

[All satellite imagery retrieved from]

Tm-earthsat-orthorectified. (6/14/06, 7/29/02). Retrieved from http://glcfapp.glcf.umd.edu:8080/esdi/ftp?id=12736 & ftp://ftp.glcf.umd.edu/glcf/Landsat/WRS2/p015/r033/p015r33_5t870516.TM-EarthSat-Orthorectified/ 

[information concerning histogram analysis retrieved from]

Fisher, R., Perkins, S., Walker , A., & Wolfart, E. (2004).Intensity histograms. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/histgram.htm

Lillesand, T.M., R.W. Kiefer, and J.W. Chipman. 2008. Remote sensing and image

interpretation (6th edition). Hoboken, NJ: Wiley. 756 p.

[Google Earth Images retrieved from]

Source; "Banff National Park/Canmore." 51°05’00.65”N, 115°20’47.49”W. Google Earth. 2012. January 18, 2012.

Final Maps and Analysis

Two Views of Washington D.C.
For the map analysis I composed two views of the city of Washington D.C.

Original Image

As you can see, details in natural elements such ad water and vegetation have been more enhanced and easier to differentiate from build up regions, like roads and buildings.

For my next view I added a pseudocolour layer to all seven bands to compare which band applying this view to would help with visual analysis of the satellite image and what features were better highlighted as a result. 

As you can tell from each addition of  pseudcolour layer, it is not always as clear or useful in every band and some bands can offer more information that others in terms of what can actually me analysed on the ground, especially if enhancements and classifications were performed on these images. But the addition of all layers allow us to see the multiple aspects of each layer and let us determine what features are on the ground in terms of water, or vegetation in developed cities like Washington DC.

Two Views of Banff National Park from the view of Canmore

For these views I used zoom features as well as enhancement features.

For the zoom features I used an overview map and a zoomed image. The zoomed image was used to illustrate the lack of detail in TM images in terms of analyzing small pockets of an image like Canmore. The zoomed image was as close as I could get to viewing the actual city before the image became too pixelated. The overview map was used to illustrate the idea that TM images can be used for large areas like the region of Banff that is featured in the image and to zoom in to specific attributes like lakes and large vegetated regions for analysis in general regions. In terms of detailed analysis, it would be very difficult with these types of TM images. 

For the next view I wanted to compare images after they had been enhanced to determine whether certain enhancements are more suitable for certain analysis.

This is a linear enhancement of the region.

Not a great choice of enhancement though, as it made the image extremely dark and not easier to interpret the data.

Comparing the Linear enhancement with a Root Enhancement.

This enhancement would benefit interpretation of the satellite data especially because I am looking at finding details of the city of Canmore and the better I can see detail the more it will benefit my interpretation.

The Study Area and Metadata

Washington D.C. DC, United States 
This image was aquired from the Global Land Cover Facilities Database, from the Landsat Programs Thematic Mapper (TM) system, which is a satellite image where the data is sensed in seven spectral bands. The Spatial Resolution of TM is 30m; 120m in band 6, the thermal band. The spectral range is between 0.45-12.5 µm. The Temporal resolution is 16 days, and has a 185km swath width. The data was acquired May 16, 1987.
                                       Washington DC in terms of focus area and relation to States.

Washington DC in relation to the Region of North America that it is located in.

Metadata

Path: 15
Row: 33
File Format: GEOTIFF
Data Format: Byte
Row Count: 7682
Column Count: 8022
Row Start:  1
Column Start: 2
Projection: UTM18
Datum: WGS84
Coordinates: 38°53’46.18”N
                    77°02’09.61”W

Banff National Park, Alberta

Banff National Park, Canada: This image was acquired from the Global Land Cover Facilities database, from the Landsat Program’s Thematic Mapper (TM) system. The spatial resolution is 30m; 120m in bad 6. The spectral range in between 0.45-12.5 µm. Temporal resolution is 16 days with a 185km swath width.

View of Banff National Park in relation to west coast provinces of Canada. 

View of the city of Canmore, Alberta in relation to its place within Banff and distance from closest largest cities.

Metadata:
Data Acquired: July 26, 1985
Path: 42
Row: 25
File Format: GEOTIFF
Data Format: Byte
Row count: 7574
Column Count: 7921
Row Start: 1
Column Start: 1
Platform: Landsat5
Projection: UTM11
Datum: WGS84
Units: Meters

Assignment 1 for GEOG 4440, or Remote Sensing Remote Sensing and Image Processing for Geographical Analysis and Environmental Monitoring... was an interesting one. In ths assignment I have pulled up two different satellite images of two different cities, Washngton D.C., DC and Canmore, Alberta, to compare and contrast what cities built on flat and managabe terrain look like and what cities built in mountainous and difficult terrain look like. I am also going to take a look at limitations of growth when the natural barriers, like the Rocky Mountains, is a huge factor.


To obtain the maps I used the Global Land Cover Facility database for my map sources. I searched for Washington D.C., DC in the United States. I then chose to use Landsat Imagery, TM because the files would be smaller and much more convenient to store; I was also more familiar with working with this form of imagery. I then picked a file to download and extracted the zipped file, in order for it to be used in PCI Geomatica. This is the resulted image of Washington D.C. I repeated the same process, and chose Banff National Park as my area of focus.

Image 1) I will use Washington DC as my example for the process of obtainign data. On the Global Land Cover Facility database, I used the “place” tab in order to do a queary and get results specifically for the city of Washington in the Distrct of Columbia. I chose Thematic Mapper as my satellite source because the files were relatively easy to download and upload and access metadata from.

Image 2) This is the data and as is shown, one of the layer files is highlighted for download, and was repeated for all of the bands.

Image 3) Once the file was downloaded I used a universal extracter to unzip the file so it would actually be usable on PCI Geomatica.

Image 4) The band file was uploaded in Pci Geomatica and works!

Image 5) Just to show an uploaded band image of Banff national Park after one of the band files was downloaded.

Image 6) Eporting image to Google Earth in order to make a Global Locator Map.

Image 7) The Results.

Image 8) Performing a Linear enhancement on Band 1 of the image of Washington D.C.

Image 9) Add pseudocolour layer to each band to see what sort of results will occur.

Image 10) The results of all seven layers, which will be analyzed later on.

Image 11) Performing a Linear enhancement on Band 1 of the image of Banff National.

Image 12) Performing a Root Enhancement on image of Banff to compare with the Linear enhancement done previously

Image 13) Finding histograms of bands in image of Washington D.C. Performing histogram inquiry on band 1.Repeated for all layers. Repeated for image of Banff.