This class holds landsat data. Such are available at several websites (e.g. [1]). Although the various functions may work for other satellites, the discussion here focusses on Landsat 8 and Landsat 7.
dataAs with all oce objects, the data slot
for landsat objects is a
list containing the main data for the object.
metadataAs with all oce objects, the metadata slot
for landsat objects is a list containing
information about the data or about the object itself.
processingLogAs with all oce objects, the processingLog slot
for landsat objects is a
list with entries describing the creation and evolution
of the object. The contents are updated by various oce functions to
keep a record of processing steps. Object summaries and
processingLogShow both display the log.
Although the [[<- operator may permit modification of the contents
of landsat objects (see [[<-,landsat-method),
it is better to use oceSetData
and oceSetMetadata, because that will save an entry
in the processingLog to describe the change.
The full contents of the data and metadata slots of a landsat
object named landsat may be retrieved in the standard R way. For example,
slot(landsat, "data") and slot(landsat, "metadata") return
the data and metadata slots, respectively. The
[[,landsat-method operator can also be used to access slots,
with landsat[["data"]] and landsat[["metadata"]], respectively.
Furthermore, [[,landsat-method can be used to retrieve
named items (and potentially some derived items) within the
metadata and data slots, the former taking precedence
over the latter in the lookup. It is also possible
to find items more directly, using oceGetData and
oceGetMetadata, but this cannot retrieve derived items.
The data are stored with 16-bit resolution. Oce
breaks these 16 bits up into most-significant and least-significant bytes.
For example, the aerosol band of a Landsat object named x are
contained within x@data$aerosol$msb and x@data$aerosol$lsb,
each of which is a matrix of raw values. The results may be combined as e.g.
256L*as.integer(x@data[[i]]$msb) + as.integer(x@data[[i]]$lsb)
and this is what is returned by executing x[["aerosol"]].
Landsat data files typically occupy approximately a
gigabyte of storage. That means that corresponding Oce objects are about
the same size, and this can pose significant problems on computers with
less than 8GB of memory. It is sensible to specify bands of interest when
reading data with read.landsat, and also to use
landsatTrim to isolate geographical regions that need
processing.
Experts may need to get direct access to the data, and this is easy because
all Landsat objects (regardless of satellite) use a similar storage form.
Band information is stored in byte form, to conserve space. Two bytes are
used for each pixel in Landsat-8 objects, with just one for other objects.
For example, if a Landsat-8 object named L contains the tirs1
band, the most- and least-significant bytes will be stored in matrices
L@data$tirs1$msb and L@data$tirs1$lsb. A similar Landsat-7
object would have the same items, but msb would be just the value
0x00.
Derived bands, which may be added to a landsat object with
landsatAdd, are not stored in byte matrices. Instead they
are stored in numerical matrices, which means that they use 4X more storage
space for Landsat-8 images, and 8X more storage space for other satellites.
A computer needs at least 8GB of RAM to work with such data.
The Landsat 8 satellite has 11 frequency bands, listed below (see [2]).
.------------------------------------------------------------------------------. | Band | Band | Band | Wavelength | Resolution | | No. | Contents | Name | (micrometers) | (meters) | |------+---------------------------+--------------+---------------+------------| | 1 | Coastal aerosol | aerosol | 0.43 - 0.45 | 30 | | 2 | Blue | blue | 0.45 - 0.51 | 30 | | 3 | Green | green | 0.53 - 0.59 | 30 | | 4 | Red | red | 0.64 - 0.67 | 30 | | 5 | Near Infrared (NIR) | nir | 0.85 - 0.88 | 30 | | 6 | SWIR 1 | swir1 | 1.57 - 1.65 | 30 | | 7 | SWIR 2 | swir2 | 2.11 - 2.29 | 30 | | 8 | Panchromatic | panchromatic | 0.50 - 0.68 | 15 | | 9 | Cirrus | cirrus | 1.36 - 1.38 | 30 | | 10 | Thermal Infrared (TIRS) 1 | tirs1 | 10.60 - 11.19 | 100 | | 11 | Thermal Infrared (TIRS) 2 | tirs2 | 11.50 - 12.51 | 100 | .------------------------------------------------------------------------------.
In addition to the above, setting band="terralook" may be used as
an abbreviation for band=c("red", "green", "nir").
Band 8 is panchromatic, and has the highest resolution. For convenience of
programming, read.landsat subsamples the tirs1 and
tirs2 bands to the 30m resolution of the other bands. See Reference
[3] for information about the evolution of Landsat 8 calibration
coefficients, which as of summer 2014 are still subject to change.
Band information is as follows (from [8]). The names are not official, but
are set up to roughly correspond with Landsat-8 names, according to wavelength.
An exception is the Landsat-7 bands named tirs1 and tirs2, which
are at two different gain settings, with identical wavelength span for
each, which roughly matches the range of the Landsat-8 bands tirs1
and tirs2 combined. This may seem confusing, but it lets code like
plot(im, band="tirs1") to work with both Landsat-8 and Landsat-7.
.------------------------------------------------------------------------------. | Band | Band | Band | Wavelength | Resolution | | No. | Contents | Name | (micrometers) | (meters) | |------+---------------------------+--------------+---------------+------------| | 1 | Blue | blue | 0.45 - 0.52 | 30 | | 2 | Green | green | 0.52 - 0.60 | 30 | | 3 | Red | red | 0.63 - 0.69 | 30 | | 4 | Near IR | nir | 0.77 - 0.90 | 30 | | 5 | SWIR | swir1 | 1.55 - 1.75 | 30 | | 6 | Thermal IR | tirs1 | 10.4 - 12.50 | 30 | | 7 | Thermal IR | tirs2 | 10.4 - 12.50 | 30 | | 8 | SWIR | swir2 | 2.09 - 2.35 | 30 | | 9 | Panchromatic | panchromatic | 0.52 - 0.90 | 15 | .------------------------------------------------------------------------------.
1. See the USGS "glovis" web site.
2. see landsat.gsfc.nasa.gov/?page_id=5377
3. see landsat.usgs.gov/calibration_notices.php
4. http://dankelley.github.io/r/2014/07/01/landsat.html
5. http://scienceofdoom.com/2010/12/27/emissivity-of-the-ocean/
6. see landsat.usgs.gov/Landsat8_Using_Product.php
7. see landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf
8. see landsat.usgs.gov/band_designations_landsat_satellites.php
9. Yu, X. X. Guo and Z. Wu., 2014. Land Surface Temperature Retrieval from Landsat 8 TIRS-Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method, Remote Sensing, 6, 9829-9652. http://www.mdpi.com/2072-4292/6/10/9829
10. Rajeshwari, A., and N. D. Mani, 2014. Estimation of land surface
temperature of Dindigul district using Landsat 8 data. International
Journal of Research in Engineering and Technology, 3(5), 122-126.
http://www.academia.edu/7655089/ESTIMATION_OF_LAND_SURFACE_TEMPERATURE_OF_DINDIGUL_DISTRICT_USING_LANDSAT_8_DATA
11. Konda, M. Imasato N., Nishi, K., and T. Toda, 1994. Measurement of the Sea Surface Emissivity. Journal of Oceanography, 50, 17:30. http://www.terrapub.co.jp/journals/JO/pdf/5001/50010017.pdf
Data from AMSR satellites are handled with amsr-class.
A file containing Landsat data may be read with read.landsat or
read.oce, and one such file is provided by the ocedata
package as a dataset named landsat.
Plots may be made with plot,landsat-method. Since plotting can be quite
slow, decimation is available both in the plotting function and as the separate
function decimate. Images may be subsetted with
landsatTrim.
landsat-class for handling data from the Landsat-8 satellite.
Other things related to landsat data: [[,landsat-method,
[[<-,landsat-method,
landsatAdd, landsatTrim,
landsat, plot,landsat-method,
read.landsat,
summary,landsat-method