Stephen Teet

Lab 3

1. During a drought, the spectral signatures for a) water will likely get smaller (less reflectance) because of a decrease in water depth, b) the vegetation spectral signature will decrease because soil will become drier and c)the soil spectral signature might actually decrease just off shore because of the larger wetted perimeter as the water level falls.

2. Band 4, the NIR spectrum that ranges from about 0.7 to 3 µm wavelength, is appropriate for looking at changes in water level at Lake Powell because differences in both soil wetness and vegetation can be seen in this region.

3. You can see a decrease in lake surface area from 2000 to 2005 just by switching between the two images viewed in true color. This is seen as narrower channels, larger islands and islands becoming connected to the shore.

4. ENVI seems to be defaulted to deal with wavelengths in µm and the image is represented in nm so the class thresholds need to be changed by an order of magnitude of 3.

5. Higher positive value changes (+2) are bright red (255,0,0), darker red (192,63,63) is the intermediate positive value changes (+1), there are sparse grey pixels (127,127,127) that represent no change, blue (63,63,192) represents the intermediate negative change (-1) and there are no high negative value changes (-2).

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7. The most change in NIR reflectance took place along the shore, particularly on the Western part of the lake. This is seen because of newly revealed soil/rocks where there was water before. In question 3, I noted that one way to see the difference in water level was the expanding shoreline (larger islands, etc.).

8. Over a large portion of the map, NIR went down, likely due to drier plants and soil. The largest changes, however, were increases around the lake where it went from shallow water to bare soil. This is not what I expected to happen, I thought there would be much more of a decrease in NIR reflectance overall.

9. The mean value is 3.020482, this point is meaningful; it is at approximately the lowest point (frequency) between two high peaks in frequency.

10. The percent of difference for each class of the image are: class 1 = 3.7832%, class 2 = 38.8057%, class 3 = 8.9999%, class 4 = 48.4022%, and class 5 = 0.009%.

11.

12. The water level increased between 2005 and 2010. This can be seen by the dark blue (0,0,255) border around most of the lake representing a large decrease in NIR reflectance (change = -2). This is due to the existence of shallow water where there was previously bare soil.

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14. The difference in red spectra reflectance shows a slightly larger area of high increase , but significantly less overall increase and much more area of decrease in reflectance. It also shows less area of no change.

15. Changes in NDVI could be predicted simply by looking at areas of large change in NIR and Red reflectance, particularly areas where red reflectance had a larger difference.

16. The time of the year (location and incidence angle of the sun) could also change Red or NIR reflectance. Other things like difference in cloud cover could also lead to differing results.

17.

Figure 1. NDVI for Lake Powell, UT 2005.

Figure 2. NDVI for Lake Powell, UT 2010.

18. 2005: Min = -0.971831 Max = 0.598086

2010: Min = -0.900000 Max = 0.467290

19.

Figure 3. Change in NDVI at Lake Powell between 2000 and 2005.

20. From 2000 to 2005, in the area immediately surrounding Lake Powell where new shore has been exposed, there is a large (positive) change in NDVI (assuming higher values = brighter pixels). The surrounding area shown in the image seems to show more of a decrease in .

21. If the image were changed using the Change Detection tool with default thresholds, less change would be seen since larger values of NDVI would not have been used. Since NDVI is a normalized, the ideal threshold values would be 1 and -1.

22. The change detections can contribute in not only showing changes in water level, but also vegetation cover and health as well as soil moisture. This can give a clearer overall view of how drought and water use from the lake is changing the region than just water levels. The change detection could be better if land cover classification (plant, soil, water) could be pulled out so that percent cover could

be seen as well as the moisture values in plant and soil cover. This coupled with NDVI might also be used in some way to show which plants are more drought resistant or possibly even plant water usage.