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Russell Large April 28, 2015 Remote Sensing and Air. Interp. 374

Final ProjectReport Summary

The purpose of this project is to select a moderate resolution multispectral dataset of our choosing, create unsupervised/supervised classifications, and conduct an Error Assesment. Conducting an unsupervised classification can be used as a tool via ArcMap called “Iso Cluster Unsupervised Classification”. This feature allows the user to choose the amount of output classes he/she wishes to use for the area of interest. This unsupervised classification, however, may not be quite as accurate as the supervised classification. This is why the unsupervised classification is used manly as giving an estimate of how many classifications to use for a given area.

The supervised classification is then used as a more precise tool to more accurately create polygonal shapes of each classification. This method is called “Training Samples”. For each classification, I zoomed to areas around my area of interest and created polygonal training samples. After I had completed this task at least 15 times, I then “merged” the training samples into one classification. I did this for each of my classifications (Forest, Water, Residential Housing, Bare Soil, Commercial, Sparse vegetation, and Roads) as well as change the color and label. The next step was to run the “Maximum Likelihood Classification” and allow the tool to use my training samples. This supervised classification was then used to conduct the Project error Assesment with Random Points.

While creating an Error Assesment with the random points, I noticed my classification had issues misinterpreting sparse vegetation for forest areas. This did effect my Error Assesment quite a bit, having my sparse vegetation count for 18 where it should have been forest. Although I did have 24 sparse vegetations correctly classified. This example shows why my Khat Coefficient of Agreement was about 56%. This is an indication that an improvement can be made in my supervised classification. The largest needed room for improvement within my classification was the sparse vegetation, although residential housing and roads could also be improved. What may have happeend is I had not completed a thorough enough training sample for those classifications. What is important when doing so is making sure the samples done are from all over the map, not just in certain locations. Overall, I believe my Project Error Assesment could have been improved knowing exactly where improvements can be made.

The NDVI image showed in white indicate areas of healthy vegetation (reflectiveness) and dark areas indicate where red and nearinfrared are reflected poorly. An example of low value is the lakes/riviers/streams because they show darker colors in the NDVI image. Any area where higher values are indicate Where healthy vegetation, such as the river form th North and flows down towards the South East.

Table 1. M.L. Classification and Reference Sample Point Data.

Sample Point

M.L. Classification

Reference Data

Sample Point

M.L. Classification

Reference Data

1 Residential Housing

Forest 50 Sparse vegetation

Forest

2 Sparse vegetation

Sparse vegetation

51 Sparse vegetation

Sparse vegetation

3 Sparse vegetation

Sparse vegetation


Sparse vegetation

4 Sparse vegetation

Forest 53 Forest Forest

5 Forest Forest 54 Sparse vegetation

Sparse vegetation


Sparse vegetation

7 Forest Forest 56 Residential Housing

Residential housing

8 Bare soil Bare soil 57 Forest Forest


Roads 58 Forest Forest


Residential housing


Forest


Sparse vegetation


Residential housing


Sparse vegetation


Sparse vegetation


Forest

14 Roads Forest 63 Sparse vegetation

Sparse vegetation

15 Bare soil Bare soil 64 Sparse vegetation

Forest


Forest 65 Residential Housing

Residential housing


Sparse vegetation


Forest


Sparse vegetation


Sparse vegetation

19 Forest Forest 68 Forest Forest


21 Forest Forest 70 Bare soil Bare soil




Forest 72 Commercial Commercial

24 Forest Forest 73 Roads Residential housing


Sparse vegetation


Sparse vegetation

26 Roads Roads 75 Forest Forest

27 Forest Forest 76 Water Water



Forest


Sparse vegetation


Sparse vegetation



31 Water Forest 80 Sparse vegetation

Forest


Sparse vegetation


34 Forest Forest 83 Sparse Forest

vegetation35 Roads Roads 84 Sparse

vegetationSparse vegetation


Sparse vegetation


Bare soil



Forest

38 Bare soil Bare soil 87 Sparse vegetation

Sparse vegetation


Sparse vegetation


Sparse vegetation


Sparse vegetation


Residential housing



Forest

43 Forest Forest 92 Commercial Forest

44 Forest Forest 93 Residential Housing

Forest


Sparse vegetation

94 Roads Bare soil





Residential housing


Sparse vegetation

97 Forest Forest



Forest


Sparse vegetation

100 Bare soil Bare soil

Table 2. M.L. Classification and Reference Data Matrix

Reference data

ML Classification Water Roads Res. Hous. Comm. Bare soil

Forest Sp. veg.

Total

Water 3 0 0 0 0 1 0 4Roads 0 2 1 0 1 1 0 5Residential Housing

0 1 5 0 1 5 1 13

Commercial 0 0 0 2 0 1 0 3Bare soil 0 0 0 0 5 0 0 5Forest 0 0 0 0 0 27 0 27Sparse vegetation 0 0 1 0 0 18 24 43Total 3 3 7 2 7 53 25 100

Khat Coefficient of Agreement 0.56391

Producer's AccuracyWater 3/3=0Roads 2/3=.33Residential Housing 5/7=.29Commercial 2/2=0Bare soil 5/7=.29Forest 27/53=50Sparse vegetation 24/25=.04

User's Accuracy Water 3/4=.25Roads 2/5=0.6Residential Housing

5/13=0.62

Commercial 2/3=.33Bare soil 5/5=0Forest 27/27=0Sparse vegetation 24/43=.45

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