Bare Soil Line

Description

Finds Bare Soil Line (BSL) and maximum vegetation point.

Usage

BSL(band3, band4, method = "quantile", ulimit = 0.99, llimit = 0.005, maxval = 255)

Arguments

Details

Finding the BSL requires identifying the lowest NIR values for each level of red. The quantile method takes the lowest set of points, those with a NIR/red ratio less than the llimit-th quantile. The minimum value method takes the lowest NIR value for each level of red. However they are found, these points with low NIR for their red values are used in a major axis regression to find the Bare Soil Line. This function also identifies the full canopy point (maximum vegetation), by using the ulimit to identify the top points, with NIR/red ratio greater than the ulimit-th quantile, and with high NIR values. Red or NIR values of 255 (saturated sensor) are omitted when calculating the BSL.

Value

Author(s)

Sarah Goslee

References

Maas, S. J. & Rajan, N. 2010. Normalizing and converting image DC data using scatter plot matching. Remote Sensing 2:1644-1661.

Examples

	data(nov3)
	data(nov4)
	nov.bsl <- BSL(nov3, nov4)
	plot(as.vector(as.matrix(nov3)), as.vector(as.matrix(nov4)))
	abline(nov.bsl$BSL, col="red")
	points(nov.bsl$top[1], nov.bsl$top[2], col="green", cex=2, pch=16)

Dark Object Subtraction

Description

Calculates calibration value for the Dark Object Subtraction (DOS) method of radiometric correction.

Usage

DOS(sat = 5, scattering.coef = c(-4, -2, -1, -0.7, -0.5), SHV, SHV.band, gain,
	offset, Grescale, Brescale, sunelev, edist, Esun = c(198.3, 179.6, 153.6, 
	103.1, 22, 8.34), blackadjust = 0.01)

Arguments

Details

The Dark Object Subtraction method assumes that the darkest parts of an image (water, artificial structures) should be black if not for the effects of atmospheric scatter. Corrections to make it possible to use the black value from one band to correct the remaining bands.

Value

Author(s)

Sarah Goslee

References

Chavez, Jr., P. S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24:459-479.

Chavez, Jr., P. S. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing 55: 1285-1294.

See Also

radiocorr

Examples

	data(july1)
	data(july3)

	# One approach to choosing a Starting Haze Value is to take
	# the lowest DN value with a frequency greater than some 
	# predetermined threshold, in this case 1000 pixels. 
	SHV <- table(july1@data[,1])
	SHV <- min(as.numeric(names(SHV)[SHV > 1000]))
	# this is used as Lhaze in the radiocorr function

	# G_rescale, B_rescale, sun elevation comes from metadata for the SHV band
	july.DOS <- DOS(sat=7, SHV=SHV, SHV.band=1, Grescale=0.77569, 
		Brescale=-6.20000, sunelev=61.4, 
		edist=ESdist("2002-07-20"))$DNfinal.mean

	# DOS() returns results for the complete set of scattering coefficients
	# need to choose the appropriate one based on general atmospheric conditions
		###	-4.0: Very Clear	SHV <= 55
		###	-2.0: Clear		SHV 56-75
		### 	-1.0: Moderate		SHV 76-95
		###	-0.7: Hazy		SHV 96-115
		###	-0.5: Very Hazy		SHV >115
	# for july, SHV == 70, so use -2.0: Clear
	july.DOS <- july.DOS[ , 2]

	# Use DOS value as Lhaze in radiocorr() for DOS correction to reflectance
	july3.DOSrefl <- radiocorr(july3, Grescale=0.77569, Brescale=-6.20000, 
		sunelev=61.4, edist=ESdist("2002-07-20"), Esun=1533, 
		Lhaze=july.DOS[3], method="DOS")

Earth-Sun distance for a given date.

Description

Calculates the estimated Earth-Sun distance in Astronomical Units (AU) for a given date.

Usage

ESdist(adate)

Arguments

Value

Returns estimated Earth-Sun distance in AU.

Author(s)

Sarah Goslee

Examples

	ESdist("2010-08-30")

Pseudo-Invariant Features

Description

Pseudo-invariant features identification for relative radiometric normalization.

Usage

PIF(band3, band4, band7, level = 0.99)

Arguments

Details

Pseudo-invariant features (PIFs) are areas such as artificial structures that can reasonably be expected to have a constant reflectance over time, rather than varying seasonally as vegetation does. Differences in PIF reflectance between dates can be assumed to be due to varying atmospheric conditions.

Value

Returns a PIF mask in the same format as the input files, with 1 for pseudo-invariant features and 0 for background data.

Author(s)

Sarah Goslee

References

Schott, J. R.; Salvaggio, C. & Volchok, W. J. 1988. Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment 26:1-16.

See Also

RCS

Examples

	# identify pseudo-invariant feature
	data(july3)
	data(july4)
	data(july7)
	july.pif <- PIF(july3, july4, july7)

	# use PIFs to related nov to july Landsat data for band 3
	# properly, would also remove cloudy areas first
	data(nov3)
	# use major axis regression: error in both x and y
	nov.correction <- lmodel2:::lmodel2(july3@data[july.pif@data[,1] == 1, 1] ~ 
	nov3@data[july.pif@data[,1] == 1, 1])$regression.results[2, 2:3]
	nov3.corrected <- nov3
	nov3.corrected@data[,1] <- nov3@data[,1] * nov.correction[2] + nov.correction[1]

Radiometric Control Sets

Description

The Radiometric Control Sets method of relative radiometric correction for Landsat data.

Usage

RCS(data.tc, level = 0.01)

Arguments

Details

Radiometric Control Sets (RCSs) are areas such as artificial structures and large bodies of water that can reasonably be expected to have a constant reflectance over time, rather than varying seasonally as vegetation does. Differences in RCS reflectance between dates can be assumed to be due to varying atmospheric conditions. Pixels with low greenness and either high or low brightness are identified.

Value

Returns an RCS mask file in the format of the original data (vector, matrix, data frame or SpatialGridDataFrame, as preseved by tasscap()) with 1 for RCS pixels and 0 for background.

Author(s)

Sarah Goslee

References

Hall, F.; Strebel, D.; Nickeson, J. & Goetz, S. 1991. Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment 35:11-27.

See Also

PIF, tasscap

Examples

	# identify radiometric control set
	data(july1)
	data(july2)
	data(july3)
	data(july4)
	data(july5)
	data(july7)
	july.tc <- tasscap("july", 7)
	july.rcs <- RCS(july.tc)

	# use RCS to relate nov to july Landsat data for band 3
	# properly, would also remove cloudy areas first
	data(nov3)
	# use major axis regression: error in both x and y
	nov.correction <- lmodel2:::lmodel2(july3@data[july.rcs@data[,1] == 1, 1] ~ 
		nov3@data[july.rcs@data[,1] == 1, 1])$regression.results[2, 2:3]
	nov3.corrected <- nov3
	nov3.corrected@data[,1] <- nov3@data[,1] * nov.correction[2] + nov.correction[1]

Create a cloud mask from Landsat bands 1 and 6.

Description

Uses Landsat band 1 and band 6 to identify clouds and create a cloud mask.

Usage

clouds(band1, band6, level = 0.0014, buffer=5)

Arguments

Details

Clouds are reflective (high) in band 1 and cold (low) in band 6, so the ratio of the two bands is high over clouds. The ratio must be adjusted for data type, whether reflectance, radiance, or DN.

Value

Returns a cloud mask in the same format as band1. Clouds are 1; noncloud areas are NA. Cloud areas are expanded by buffer pixels to ensure that cloud edges are captured.

Author(s)

Sarah Goslee

References

This function is loosely based on: Martinuzzi, S., Gould, W.A., Ramos Gonzales, O.M. 2007. Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal. USDA Forest Service General Technical Report IITF-GTR-32.

Examples

data(july1)
data(july61)
july.cloud <- clouds(july1, july61)

par(mfrow=c(1,2))
image(july1)
image(july.cloud)

Decimal Date

Description

Convert a vector containing year, month, day or individual year, month, day arguments into a decimal date in years.

Usage

	ddate(year, month, day)

Arguments

Details

ddate() will accept a vector with the three date components so that it can be conveniently used with apply() on a data frame containing columns for year, month and day.

Value

The decimal date in years.

Author(s)

Sarah Goslee

Examples

	ddate(2001, 5, 15)

Digital Elevation Model

Description

A 30-meter resolution elevation model in SpatialGridDataFrame format that matches the Landsat images nov and july.

Usage

data(dem)

Details

Elevations are in meters.

Source

Digital Elevation Models for the United States are available from the United States Geologic Survey, http://www.usgs.gov

Examples

	data(dem)
	dem.slopeasp <- slopeasp(dem)

	par(mfrow=c(1,3))
	image(dem)
	image(dem.slopeasp$slope)
	image(dem.slopeasp$aspect)

Simple image-matching georeferencing function.

Description

Finds best fit between target image and tofix image by minimizing RMSE between the two. The tofix image is moved one pixel at a time horizontally or vertically. Simple automated georeferencing is adequate for some image-processing tasks.

Usage

georef(target, tofix, maxdist = 1000, startx = 0, starty = 0)

Arguments

Details

This function offers a simplistic approach to georeferencing using an iterative algorithm that at each step moves the tofix image one pixel in the direction that produces the greatest reduction in RMSE. When RMSE no longer decreases or maxdist is reached, the algorithm stops, assuming that the tofix image now matches the reference target image. This algorithm can produce local minima. Results should always be checked visually.

Note: this algorithm is only effective with images larger than the samples included with this package. The July and November images are already georectified, but this function will show them as needing considerable adjustment. Images of at least 1000x1000 pixels are necessary for adequate results.

Value

Author(s)

Sarah Goslee

See Also

geoshift

Examples

# to use for georeferencing
data(nov3)
data(july3)
july.shift <- georef(nov3, july3, maxdist=50) # match july to november
july3.corr <- geoshift(july3, padx=50, pady=50, july.shift$shiftx, july.shift$shifty)

# only need to run georef once for a particular date
# use the same correction for all bands
data(july4)
july4.corr <- geoshift(july4, padx=50, pady=50, july.shift$shiftx, july.shift$shifty)

Shift and pad an image

Description

Shifts an image vertically or horizontally and adds a padded border.

Usage

geoshift(mat, padx, pady, shiftx, shifty, nodata = NA)

Arguments

Details

This function can be used to correct spatially-referenced images that are off by a few pixels in the x or y directions. It does not warp an image, only slide it. Adding padding to the outside edge makes it possible to match several images even if they are not stored with georeferecing information. geoshift() can be used in conjunction with georef() to automatically match up geospatial images. Note: directions are relative to the image as displayed by the image() command, and not the underlying matrix representation.

Value

Returns data in the same format as the function was given: matrix, data frame, or SpatialGridDataFrame.

Author(s)

Sarah Goslee

See Also

georef

Examples

testmat <- matrix(1:9, 3, 3)
geoshift(testmat, 5, 10, 0, 0)
geoshift(testmat, 5, 10, 2, 2)

# to use for georeferencing
data(nov3)
data(july3)
july.shift <- georef(nov3, july3, maxdist=50) # match july to november
july3.corr <- geoshift(july3, padx=50, pady=50, july.shift$shiftx, july.shift$shifty)

# only need to run georef once for a particular date
# use the same correction for all bands
data(july4)
july4.corr <- geoshift(july4, padx=50, pady=50, july.shift$shiftx, july.shift$shifty)

Histogram matching of an image

Description

Force image x to match target image by matching their histograms.

Usage

histmatch(master, tofix, mask, minval = 0, maxval = 255, by = 1)

Arguments

Details

The histogram of the tofix image will be forced to match that of the target image.

Value

Author(s)

Sarah Goslee

See Also

relnorm

Examples

## Not run: 
	data(nov3)
	data(july3)
	par(mfrow=c(2,2))
	image(nov3)
	image(july3)

	nov3.newR <- relnorm(master=july3, tofix=nov3)
	image(nov3.newR$newimage)

	nov3.newH <- histmatch(master=july3, tofix=nov3)
	image(nov3.newH$newimage)

## End(Not run)

Sample Landsat ETM+ data

Description

SpatialGridDataFrame containing a 300 x 300 pixel subset (1500 x 1500 m) of the Landsat ETM+ image for path 15, row 32, obtained on 20 July 2002. Each band, including both thermal bands, is contained in a separate file.

Usage

data(july1)

Format

Images are in SpatialGridDataFrame format. More information is available in the documentation for the sp package.

Details

Date: 2002-07-20
Satellite: Landsat ETM+ (7)
Sun elevation: 61.4
Sun azimuth: 125.8


——–
band Grescale Brescale
1 0.77569 -6.20
2 0.79569 -6.40
3 0.61922 -5.00
4 0.63725 -5.10
5 0.12573 -1.00
7 0.04373 -0.35

Source

Landsat images can be obtained from the United States Geological Survey at http://landsat.usgs.gov

Examples

	data(july3)
	image(july3)

Subset a geotiff image.

Description

Uses GDAL tools to reproject (optional) and subset a geotiff given the center point and the desired size.

Usage

lssub(filename, outname, centerx, centery, centerepsg, widthx, widthy)

Arguments

Details

The new image will be a subset of size (widthx, widthy) with center point (centerx, centery), with the same pixel size. If the center point coordinates are in a different projection than the original image, they will be reprojected.

Value

The new image is exported as a geotiff. Nothing is returned within R.

Note

Requires gdalinfo and gdaltransform to be available to the operating system. Only known to work on linux. This function was written to speed processing of multiple files for a specific project, and may be dropped in future releases of the landsat package. On my computer, lssub() is over an order of magnitude faster than reading the image into R, subsetting it, and writing out the result.

Author(s)

Sarah Goslee

Examples

	## Not run: lssub("/data/gis/testimage.tif", "/data/gis/subimage.tif", centerx = 260485, 
	centery = 4527220, centerepsg = 26918, widthx = 50, widthy = 50)
## End(Not run)

Whole-image and pixel-based Minnaert topographic correction of remote sensing data.

Description

Adds several modified Minnaert corrections to the capabilities of topocorr().

Usage

minnaert(x, slope, aspect, sunelev, sunazimuth, na.value = NA, GRASS.aspect=FALSE, 
	IL.epsilon=0.000001, slopeclass = c(1, 5, 10, 15, 20, 25, 30, 45), coverclass)

Arguments

Details

Calculates the Minnaert k coefficients for the whole image and for the individual slope classes.

Value

Author(s)

Sarah Goslee

References

Lu, D., Ge, H., He, S., Xu, A., Zhou, G., and Du, H. 2008. Pixel-based Minnaert correction method for reducing topographic effects on a Landsat 7 ETM+ image. Photogrammetric Engineering and Remote Sensing 74:1343-1350.

See Also

topocorr

Examples

	# require slope and aspect for topographic correction
	data(dem)
	dem.slopeasp <- slopeasp(dem)

	# use cosine method of topographic correction
	data(july4)
	july4.minpix <- minnaert(july4, dem.slopeasp$slope, dem.slopeasp$aspect, 
		sunelev=61.4, sunazimuth=125.8, slopeclass=c(1, 5, 10, 15, 50))
	july4.minpix$classcoef # all coefficients

Simple moving window function.

Description

Very simple function to apply a kernel to a matrix across a moving window.

Usage

movingwindow(x, kernel, na.rm=TRUE)

Arguments

Details

This function is used in the calculation of slope and aspect by slopeasp().

Value

Returns the transformed matrix.

Note

Should be rewritten in C for greater efficiency.

Author(s)

Sarah Goslee

See Also

slopeasp

Examples

	data(dem)
	dem.smoothed <- movingwindow(dem, matrix(c(1,1,1,1,0,1,1,1,1), 3, 3)/8)

	par(mfrow=c(1,2))
	image(dem)
	image(dem.smoothed)

Sample Landsat ETM+ data

Description

SpatialGridDataFrame containing a 300 x 300 pixel subset (1500 x 1500 m) of the Landsat ETM+ image for path 15, row 32, obtained on 25 November 2002. Each band, including both thermal bands, is contained in a separate file.

Usage

data(nov1)

Format

Images are in SpatialGridDataFrame format. More information is available in the documentation for the sp package.

Details

Date: 2002-11-25
Satellite: Landsat ETM+ (7)
Sun elevation: 26.2
Sun azimuth: 159.5


——–

band Grescale Brescale
1 0.77569 -6.20
2 0.79569 -6.40
3 0.61922 -5.00
4 0.63725 -5.10
5 0.12573 -1.00
7 0.04373 -0.35

Source

Landsat images can be obtained from the United States Geological Survey at http://landsat.usgs.gov

Examples

	data(nov3)
	image(nov3)

Radiometric correction of Landsat data

Description

Implements several different methods for absolute radiometric correction of satellite data.

Usage

radiocorr(x, gain, offset, Grescale, Brescale, sunelev, satzenith = 0, edist,
	Esun, Lhaze, method = "apparentreflectance")

Arguments

Details

Uses one of four image-based radiometric correction methods to adjust a satellite image to compensate for atmospheric conditions.

Value

Returns a radiometrically-corrected image in the same format as x.

Author(s)

Sarah Goslee

References

Chavez, Jr., P. S. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing 55:1285-1294.

Chavez, Jr., P. S. 1996. Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62:1025-1036.

Song, C.; Woodcock, C. E.; Seto, K. C.; Lenney, M. P. & Macomber, S. A. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment 75:230-244.

See Also

DOS

Examples

	data(july1)
	data(july3)

	# One approach to choosing a Starting Haze Value is to take the lowest DN value
	# with a frequency greater than some predetermined threshold, in this case 1000 pixels. 
	SHV <- table(july1@data[,1])
	SHV <- min(as.numeric(names(SHV)[SHV > 1000]))
	# this is used as Lhaze in the radiocorr function

	# Grescale, Brescale, sun elevation comes from metadata for the SHV band
	july.DOS <- DOS(sat=7, SHV=SHV, SHV.band=1, Grescale=0.77569, Brescale=-6.20000,
		sunelev=61.4, edist=ESdist("2002-07-20"))$DNfinal.mean

	# DOS() returns results for the complete set of scattering coefficients
	# need to choose the appropriate one based on general atmospheric conditions
		###	-4.0: Very Clear	SHV <= 55
		###	-2.0: Clear		SHV 56-75
		### 	-1.0: Moderate		SHV 76-95
		###	-0.7: Hazy		SHV 96-115
		###	-0.5: Very Hazy		SHV >115
	# for july, SHV == 70, so use -2.0: Clear
	july.DOS <- july.DOS[ , 2]

	# Use DOS value as Lhaze in radiocorr() for DOS correction to reflectance
	july3.DOSrefl <- radiocorr(july3, Grescale=0.77569, Brescale=-6.20000, 
		sunelev=61.4, edist=ESdist("2002-07-20"), Esun=1533, 
		Lhaze=july.DOS[3], method="DOS")

Relative normalization of an image

Description

Use regression methods to adjust distribution of values in image tofix to match those in the master image.

Usage

relnorm(master, tofix, mask, method = "MA", nperm = 1000)

Arguments

Details

The regression coefficients from tofix ~ master will be used to match the distribution of values of tofix to those in the master image.

Value

Author(s)

Sarah Goslee

See Also

histmatch

Examples

## Not run: 
	data(nov3)
	data(july3)
	par(mfrow=c(2,2))
	image(nov3)
	image(july3)

	nov3.newR <- relnorm(master=july3, tofix=nov3)
	image(nov3.newR$newimage)

	nov3.newH <- histmatch(master=july3, tofix=nov3)
	image(nov3.newH$newimage)

## End(Not run)

Calculate slope and aspect from elevation data.

Description

Uses gridded elevation data to calculate slope and aspect, by default using a 3x3 region. The horizontal resolution and vertical resolution must be in the same units.

Usage

slopeasp(x, EWres, NSres, EWkernel, NSkernel, smoothing=1)

Arguments

Details

By default, a 3x3 Sobel filter is used (as is standard in many GIS packages). A larger Sobel filter or a different filter will give varying results. This filter provides the third-order finite difference weighted by reciprocal of distance method proposed by Unwin (1981).

Value

Author(s)

Sarah Goslee

References

Unwin, D. 1981. Introductory Spatial Analysis. London: Methuen. Clarke, K.C.and Lee, S.J. 2007. Spatial resolution and algorithm choice as modifiers of downslope flow computed from Digital Elevation Models. Cartography and Geographic Information Science 34:215-230.

See Also

movingwindow, topocorr

Examples

	data(dem)
	dem.slopeasp <- slopeasp(dem)

	par(mfrow=c(1,3))
	image(dem)
	image(dem.slopeasp$slope)
	image(dem.slopeasp$aspect)

Tasseled Cap for Landsat data

Description

Tasseled cap transformation for Landsat TM, ETM+, or OLI.

Usage

tasscap(basename, sat = 7)

Arguments

Details

For Landsat TM, the coefficients are to be applied to "reflectance factors", which appear to be the DN. For ETM+ and OLI, the coefficients are for top-of-atmosphere reflectance. For both TM and ETM+, the bands to be provided are 1, 2, 3, 4, 5, and 7. For OLI, the bands needed are 2 through 7. Future updates will allow use of a raster stack rather than separate objects.

Value

If the input files are matrices or data frames, returns a data frame with three columns, one for each component. If the input files are SpatialGridDataFrames, returns a list with one element for each component. In either case three components are returned: Brightness, Greenness, Wetness.

Author(s)

Sarah Goslee

References

Original papers:

Baig, M. H. A., Zhang, L., Shuai, T. & Tong, Q. 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters 5:423-431.

Crist, E. P. 1985. A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17:301-306.

Crist, E. & Kauth, R. 1986. The tasseled cap de-mystified. Photogrammetric Engineering and Remote Sensing 52:81-86.

Huang, C., Wylie, B., Yang, L., Homer, C. & Zylstra, G. 2002. Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing 23:1741-1748.

Examples

data(july1)
data(july2)
data(july3)
data(july4)
data(july5)
data(july7)
july.tc <- tasscap("july", 7)

Thermal band to temperature conversion.

Description

Converts Landsat thermal band DN (TM or ETM+ band 6-1 and 6-2) to temperature using default coefficients from Chander et al. 2009.

Usage

thermalband(x, band)

Arguments

Value

Returns a temperature image in the same format as x.

Author(s)

Sarah Goslee

References

Coefficients from Chander, G., Markham, B.L., Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113:893-903.

Examples

	data(nov61)
	nov.temp1 <- thermalband(nov61, 61)
	image(nov.temp1)

Topographic correction of remote sensing data.

Description

Implements several different methods for topographic correction of remote sensing data.

Usage

topocorr(x, slope, aspect, sunelev, sunazimuth, method = "cosine", na.value = NA,
	GRASS.aspect=FALSE, IL.epsilon=0.000001)

Arguments

Details

Uses one of the available topographic correction methods to compensate for the effects of slope and aspect on reflectance from the land surface.

Value

Returns a topographically-corrected image in the same format as x.

Author(s)

Sarah Goslee

References

Gao, Y. & Zhang, W. 2009. LULC classification and topographic correction of Landsat-7 ETM+ imagery in the Yangjia River Watershed: the influence of DEM resolution. Sensors 9:1980-1995.

Gu, D. & Gillespie, A. 1998. Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry. Remote Sensing of Environment 64:166-175.

Riano, D., Chuvieco, E., Salas, J. & Aguado, I. 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE Transactions on Geoscience and Remote Sensing 41:1056-1061.

Richter, R., Kellenberger, T. & Kaufmann, H. 2009. Comparison of topographic correction methods. Remote Sensing 1:184-196.

See Also

slopeasp

Examples

	# require slope and aspect for topographic correction
	data(dem)
	dem.slopeasp <- slopeasp(dem)

	# use cosine method of topographic correction
	data(july3)
	july3.topo <- topocorr(july3, dem.slopeasp$slope, dem.slopeasp$aspect, 
		sunelev=61.4, sunazimuth=125.8)