lab 6A
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School
University of Waterloo *
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Course
271
Subject
Geography
Date
Dec 6, 2023
Type
docx
Pages
5
Uploaded by MegaRhinoceros3530
GEOG 271
March 16, Winter 2022
Lab 6
1)
# ----- raster calculations -----
print("")
print("Starting raster calculations...")
# Digital Number to Radiance Conversion
file = inputfile
### enter the ML and AL values in the below line
ML = 3.3420E-04 # TIR_1 Band 10 RADIANCE_MULT_BAND_10
AL = 0.10000
# RADIANCE_ADD_BAND_10
radiance = f"%11 = {ML} * %9 + {AL}"
model(file, radiance, [])
# Radiance to brightness temperature conversion
file = inputfile
### enter the K2 and K1 values below, as well as the same ML and AL values from the radiance
before
### this basically includes the radiance calculation into the Brightness Temperature calculation
### enter the K2 and K1 values in the below line, as well as the ML and AL values from the radiance
K1 = 774.89 # K1_CONSTANT_BAND_10
K2 = 1321.08 # K2_CONSTANT_BAND_10
Tkelvin = f"%12 = {K2} / Ln({K1} / ({ML} * %9 + {AL}) + 1)"
model(file, Tkelvin, [])
# NDSI
file = inputfile
ndsi = "%13 = (%3 - %6) / (%3 + %6)"
model(file, ndsi, [])
# NDVI
file = inputfile
ndvi = "%14 = (%5 -%4)/(%5 + %4)" # NIR-R/NIR+R
model(file, ndvi, [])
2)
Examples of band combinations that distinguish between water, snow, ice, and clouds
Bands: 4, 6, 7
Bands: 11, 5, 4
Bands: 6, 4, 13
3)
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The three main peaks are around NDSI values of -0.15, 0.23, and 0.50
Which depicts the estimation of ice/snow, and soil percentage within the pixel. Pixels within the first and largest peak that has around -0.15 values are land features with lots of visible soil. The
next peak at around values of 0.23 is in the water at areas where water is mixed with slush of snow and ice. Finally, pixels on the large ice sheet on the waterbody has values in the highest 0.50 peak.
4) True Color Composite
TIR_1 Band By resampling the spatial resolution, the thermal signatures (temperature readings of the surface) are lowered in quality as seen in the screenshots above where small features' temperature values are hard to distinguish because their values have been affected by neighboring values during the resampling calculation process. This makes surface temperature
values less accurate than what is wanted from a 100m spatial resolution.
5)
Brighter values correspond to places without the presence of snow or surfaces with high reflectivity (high albedo) such as dark soil, dirt, mud. (usually the brown and dark gray areas in a
true color composite)
Darker values correspond to places and features with high reflectivity such as fresh snow, glaciers, and clouds and then grayish areas where there is ice and slush.
Examples of pixel values:
Ice/Snow: 271 - 273
Open water: 278 - 284
Cloud: 258 - 265
Land soil: 293 - 297