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@@ -30,6 +30,10 @@
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# Compare discussion:
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# http://adis.cesnet.cz/cgi-bin/lwgate/IMAGRS-L/archives/imagrs-l.log0211/date/article-14.html
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#
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+# Landsat8: Baig, M.H.A., Zhang, L., Shuai, T., Tong, Q., 2014. Derivation of a tasselled cap transformation
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+# based on Landsat 8 at-satellite reflectance. Remote Sensing Letters 5, 423-431.
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+# doi:10.1080/2150704X.2014.915434
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+#
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# MODIS Tasselled Cap coefficients
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# https://gis.stackexchange.com/questions/116107/tasseled-cap-transformation-on-modis-in-grass/116110
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# Ref: Lobser & Cohen (2007). MODIS tasselled cap: land cover characteristics
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@@ -49,10 +53,10 @@
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#% keywords: Tasseled Cap transformation
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#%end
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#%option G_OPT_R_INPUTS
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+#% description: For Landsat4-7: bands 1, 2, 3, 4, 5, and 7; for Landsat8: bands 2, 3, 4, 5, 6, and 7
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#%end
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#%option G_OPT_R_BASENAME_OUTPUT
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#% label: Name for output basename raster map(s)
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-#% description: For Landsat 4-7, the raster maps should be the bands 1, 2, 3, 4, 5, and 7.
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#%end
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#%option
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#% key: sensor
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@@ -60,15 +64,13 @@
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#% description: Satellite sensor
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#% required: yes
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#% multiple: no
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-#% options: landsat4_tm,landsat5_tm,landsat7_etm,modis
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-#% descriptions: landsat4_tm;Use transformation rules for Landsat 4 TM;landsat5_tm;Use transformation rules for Landsat 5 TM;landsat7_etm;Use transformation rules for Landsat 7 ETM;modis;Use transformation rules for MODIS
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+#% options: landsat4_tm,landsat5_tm,landsat7_etm,landsat8,modis
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+#% descriptions: landsat4_tm;Use transformation rules for Landsat 4 TM;landsat5_tm;Use transformation rules for Landsat 5 TM;landsat7_etm;Use transformation rules for Landsat 7 ETM;landsat8;Use transformation rules for Landsat 8;modis;Use transformation rules for MODIS
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#%end
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-import sys
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-import os
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import grass.script as grass
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-# weights for 6 Landsat bands: TM4, TM5, TM7
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+# weights for 6 Landsat bands: TM4, TM5, TM7, OLI
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# MODIS: Red, NIR1, Blue, Green, NIR2, SWIR1, SWIR2
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parms = [[( 0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863), # Landsat TM4
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(-0.2848,-0.2435,-0.5435, 0.7243, 0.0840,-0.1800),
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@@ -81,6 +83,10 @@ parms = [[( 0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863), # Landsat TM4
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(-0.3344,-0.3544,-0.4556, 0.6966,-0.0242,-0.2630),
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( 0.2626, 0.2141, 0.0926, 0.0656,-0.7629,-0.5388),
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( 0.0805,-0.0498, 0.1950,-0.1327, 0.5752,-0.7775)],
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+ [( 0.3029, 0.2786, 0.4733, 0.5599, 0.5080, 0.1872), # Landsat TM8
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+ (-0.2941,-0.2430,-0.5424, 0.7276, 0.0713,-0.1608),
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+ ( 0.1511, 0.1973, 0.3283, 0.3407,-0.7117,-0.4559),
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+ (-0.8239, 0.0849, 0.4396, -0.058, 0.2013,-0.2773)],
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[( 0.4395, 0.5945, 0.2460, 0.3918, 0.3506, 0.2136, 0.2678), # MODIS
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(-0.4064, 0.5129,-0.2744,-0.2893, 0.4882,-0.0036,-0.4169),
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( 0.1147, 0.2489, 0.2408, 0.3132,-0.3122,-0.6416,-0.5087)]]
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@@ -108,8 +114,8 @@ def calcN(outpre, bands, i, n):
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def main():
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options, flags = grass.parser()
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- satellite = options['satellite']
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- output_basename = options['basename']
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+ satellite = options['sensor']
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+ output_basename = options['output']
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inputs = options['input'].split(',')
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num_of_bands = 6
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if len(inputs) != num_of_bands:
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@@ -131,6 +137,8 @@ def main():
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calcN(output_basename, bands, 1, 5)
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elif satellite == 'landsat7_etm':
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calcN(output_basename, bands, 2, 7)
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+ elif satellite == 'landsat8':
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+ calcN(output_basename, bands, 2, 7)
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elif satellite == 'modis':
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calcN(output_basename, bands, 3, 7)
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else:
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Markus Neteler