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############################################################################
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# AUTHOR(S): Agustin Lobo, Markus Neteler
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# Converted to Python by Glynn Clements
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# Code improvements by Leonardo Perathoner
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# PURPOSE: At-satellite reflectance based tasseled cap transformation.
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# COPYRIGHT: (C) 1997-2014 by the GRASS Development Team
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# This program is free software under the GNU General Public
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# License (>=v2). Read the file COPYING that comes with GRASS
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#############################################################################
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# LANDSAT-4/LANDSAT-5:
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# script based on i.tasscap.tm4 from Dr. Agustin Lobo - alobo@ija.csic.es
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# TC-factor changed to CRIST et al. 1986, p.1467 (Markus Neteler 1/99)
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# TASSCAP factors cited from:
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# DERIVATION OF A TASSELED CAP TRANSFORMATION BASED ON LANDSAT 7 AT-SATELLITE REFLECTANCE
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# Chengquan Huang, Bruce Wylie, Limin Yang, Collin Homer and Gregory Zylstra Raytheon ITSS,
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# USGS EROS Data Center Sioux Falls, SD 57198, USA
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# http://landcover.usgs.gov/pdf/tasseled.pdf
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# This is published as well in INT. J. OF RS, 2002, VOL 23, NO. 8, 1741-1748.
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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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# 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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# 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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# expressed through transformed MODIS data.
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# International Journal of Remote Sensing, Volume 28(22), Table 3
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#############################################################################
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#% description: Performs Tasseled Cap (Kauth Thomas) transformation.
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#% keyword: transformation
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#% keyword: Tasseled Cap transformation
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#%option G_OPT_R_INPUTS
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#% description: For Landsat4-7: bands 1, 2, 3, 4, 5, 7; for Landsat8: bands 2, 3, 4, 5, 6, 7; for MODIS: bands 1, 2, 3, 4, 5, 6, 7
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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: Satellite sensor
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#% options: landsat4_tm,landsat5_tm,landsat7_etm,landsat8_oli,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_oli;Use transformation rules for Landsat 8 OLI;modis;Use transformation rules for MODIS
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import grass.script as grass
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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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( 0.1509, 0.1973, 0.3279, 0.3406,-0.7112,-0.4572)],
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[( 0.2909, 0.2493, 0.4806, 0.5568, 0.4438, 0.1706, 10.3695), # Landsat TM5
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(-0.2728,-0.2174,-0.5508, 0.7221, 0.0733,-0.1648, -0.7310),
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( 0.1446, 0.1761, 0.3322, 0.3396,-0.6210,-0.4186, -3.3828),
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( 0.8461,-0.0731,-0.4640,-0.0032,-0.0492,-0.0119, 0.7879)],
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[( 0.3561, 0.3972, 0.3904, 0.6966, 0.2286, 0.1596), # Landsat TM7
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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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#satellite information
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satellites = ["landsat4_tm", 'landsat5_tm', 'landsat7_etm', 'landsat8_oli', 'modis']
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used_bands = [6,6,6,6,7]
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#components information
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ordinals = ["first", "second", "third", "fourth"]
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names = ["Brightness", "Greenness", "Wetness", "Haze"]
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def calc1bands6(out, bands, k1, k2, k3, k4, k5, k6, k0 = 0):
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grass.mapcalc("$out = $k1 * $in1band + $k2 * $in2band + $k3 * $in3band + $k4 * $in4band + $k5 * $in5band + $k6 * $in6band + $k0",
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out = out, k1 = k1, k2 = k2, k3 = k3, k4 = k4, k5 = k5, k6 = k6, k0 = k0, **bands)
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def calc1bands7(out, bands, k1, k2, k3, k4, k5, k6, k7):
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grass.mapcalc("$out = $k1 * $in1band + $k2 * $in2band + $k3 * $in3band + $k4 * $in4band + $k5 * $in5band + $k6 * $in6band + $k7 * $in7band",
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out = out, k1 = k1, k2 = k2, k3 = k3, k4 = k4, k5 = k5, k6 = k6, k7 = k7, **bands)
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def calcN(outpre, bands, satel):
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i=satellites.index(satel)
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grass.message(_("Satellite %s...") % satel)
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for j, p in enumerate(parms[i]):
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out = "%s.%d" % (outpre, j + 1)
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name = " (%s)" % names[j]
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grass.message(_("Calculating %s TC component %s%s ...") % (ord, out, name))
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bands_num=used_bands[i]
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eval("calc1bands%d(out, bands, *p)" % bands_num) #use combination function suitable for used number of bands
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grass.run_command('r.colors', map = out, color = 'grey', quiet=True)
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options, flags = grass.parser()
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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 = used_bands[satellites.index(satellite)]
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if len(inputs) != num_of_bands:
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grass.fatal(_("The number of input raster maps (bands) should be %s") % num_of_bands)
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for i, band in enumerate(inputs):
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bands['in' + str(band_num) + 'band'] = band
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grass.debug(1, bands)
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calcN(output_basename, bands, satellite) #core tasseled cap components computation
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#assign "Data Description" field in all four component maps
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for i, comp in enumerate(names):
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grass.run_command('r.support', map = "%s.%d" % (output_basename, i+1), description = "Tasseled Cap %d: %s" % (i+1, comp))
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grass.message(_("Tasseled Cap components calculated"))
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if __name__ == "__main__":