Lab 8

The purpose of this lab is to gain experience collecting and analyzing spectral signatures. This was done by collecting 12 spectral signatures of different surface features by developing AOIs with the polygon drawing tool in Erdas 2013 and graphing their curves.

Methods: Landsat ETM+ image of Eau Claire, WI in 2000 was used in this project. AOIs were collected from the drawing tool and then included on a graph using a supervised signature editor in Raster options. 12 spectral signature curves were collected and reported on one graph. These included: Standing Water, moving water, vegetation, riparian vegetation, crops, urban grass, dry soil, moist soil, rock, asphalt highway, airport runway, and a concrete parking lot. Each curve was then looked at to determine the most and least amount of spectral reflectance in bands 1-6 and then compared against each other to determine which surfaces were most similar to each other and which ones were least alike.

Results: 

Spectral Reflectance Curve graph created with a
Supervised Spectral Signature Editor.              

Spectral Signatures- Highest/Lowest Reflectance:
Standing water: Highest: band 1 (blue)= ~77um, Lowest: Band 4 (NIR)=~20um
Moving water: High: Band 1 (Blue)=79um Low: Band 6 (MIR2)=26um
Vegetation: High: Band 4 (NIR)= 123umLow: Band 3 (red) and 6 (MIR2) are both ~32um
Riparian vegetation: High: Band 4 (NIR)=109um Low: Band 6 (MIR2)= 36um (close tie with band 3 (red)=37um)
Crops: High: band 4(NIR)= 76um Low: Band 6 (MIR2)= 26um
 Urban Grass: High: Bands 4 (NIR) and 5 (MIR1)  =~116um each. Low: Band 6 (MIR2)= 61um
 Dry soil (uncultivated): High: Band 5 (MIR1)=128um Low: Band 3 (Red)= 81um

Moist soil (uncultivated): High: Band 1 (blue)= 70um Low: band 6 (MIR2)=37um
Rock: High: Band 1(blue)=84um Low: Band 6 (MIR2)= 50um

Asphalt highway: High: band 1 (blue)= 106um Low: Band 4 (NIR)= 63um

Airport runway: High: Band 3(red) and band 5(MIR1)= 153um Low: Band 4 (NIR)= 82um

Concrete surface (parking lot): High: Band 3 (red)= 114um Low: Band 4 (NIR) = 61um

Standing water and the airport runway differ the most in spectral signature curves. Water reflected the most light in band 1 while the airport runway reflected the most light in band 3 and 5. The mean differences between their reflectance values in any given band had a wider range than most other signature curves. This is due to the nature of the absorbency of light in water and concrete surfaces.

Riparian vegetation and vegetation spectral signature curves were the most similar. Both curves reflect the most light in band 4 and reflects the least in bands 6 and 3. Riparian vegetation reflected more light than vegetation in bands 1,2,3, and 6 while regular vegetation reflected more in bands 4 and 5. I expected to see riparian vegetation absorbing more (consequently reflecting less) than regular vegetation across all bands because of the nearby water feeding the plants that would therefore have more water within their systems. However, this was not the case. Riparian vegetation may then not have any more water within their structures than other vegetation and the differences between the two vegetative areas may simply be do to chance and slight differences in health and photosynthetic processes. Both vegetative regions consisted of trees and thus are subject to a fair and similar amount of diffuse reflection and given similar amount of nutrients (not necessarily water as this may not be the limiting nutrient), and if displayed similar species composition, both would exhibit similar reflectance curves. The differences may lie in these stated conditions as well but may only deviate slightly.

Sources:
Wilson, Cyril. (2014). Geog 338: Remote Sensing of the Environment Lab 7 Photogrammetry. Personal collection of  Cyril Wilson, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin.

Lab 7

The goal of this lab was to become familiar with stereoscopy and orthorectification tasks on satellite and aerial photographs. This included creating an anaglyph to observe elevation features in the Eau Claire, WI region and collecting GCPs to create tie points.

Stereoscopy:

Methods: To become more familiar with stereoscopy, I created an anaglyph image of the city of Eau Claire, WI. I did so by running a digital elevation model (DEM) of the region through the Anaglyph Generation tool in Erdas Imagine 2013. I then observed elevation features.

Results:

The anaglyph showed regions of high and low elevations such as valleys near the Chippewa River or Halfmoon Lake and areas of high elevation on the UWEC campus hill. All elevation shifts seemed to be more dramatic than in real life.





Anaglyph of Eau Claire, WI. 


Orthorectification:

Methods: I orthorectified SPOT satellite images by collecting GCPs (with the classic point measurement tool) on two reference images using the LPS tool. 9 GCPs were collected using the first horizontal reference image and 2 GCPs were collected from the second horizontal reference image. Vertical reference information was collected by using a digital elevation model image and updating the Z values on the collected GCPs. More GCPs were collected on another reference image according to the GCPs already collected after running the DEM and Z values. A triangulation was then run on the two satellite images being orthorectified.

Results:

 The resulting photograph was an accurate spatial boundary between the two images with very little skewing.







Orthorectified SPOT satellite images

Sources:
Wilson, Cyril. (2014). Geog 338: Remote Sensing of the Environment Lab 7 Photogrammetry. Personal collection of  Cyril Wilson, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin.