This class holds landsat data. Such are available at several websites (e.g. reference 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 those functions save an entry in the processingLog
that describes the change.
The full contents of the data and metadata slots of a landsat
object may be retrieved in the standard R way using slot(). For
example slot(o,"data") returns the data slot of an object named o,
and similarly slot(o,"metadata") returns
the metadata slot.
The slots may also be obtained with the [[,landsat-method
operator, as e.g. o[["data"]] and o[["metadata"]], respectively.
The [[,landsat-method operator can also
be used to retrieve items from within the data and metadata slots.
For example, o[["temperature"]] can be used to retrieve temperature
from an object containing that quantity. The rule is that a named
quantity is sought first within the object's metadata slot,
with the data slot being checked only if metadata does not
contain the item. This [[ method can also be used to get
certain derived quantities, if the object contains sufficient
information to calculate them. For example, an object that holds
(practical) salinity, temperature and pressure, along with
longitude and latitude, has sufficient information to compute
Absolute Salinity, and so o[["SA"]] will yield the
calculated Absolute Salinity.
It is also possible to find items more directly, using oceGetData() and
oceGetMetadata(), but neither of these functions can
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 reference 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 reference 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 |
.------------------------------------------------------------------------------.
Dan Kelley and Clark Richards
See the USGS "glovis" web site.
see landsat.gsfc.nasa.gov/?page_id=5377
see landsat.usgs.gov/calibration_notices.php
https://dankelley.github.io/r/2014/07/01/landsat.html
https://scienceofdoom.com/2010/12/27/emissivity-of-the-ocean/
see landsat.usgs.gov/Landsat8_Using_Product.php
see landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf
see landsat.usgs.gov/band_designations_landsat_satellites.php
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. https://www.mdpi.com/2072-4292/6/10/9829
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
Konda, M. Imasato N., Nishi, K., and T. Toda, 1994. Measurement of the Sea Surface Emissivity. Journal of Oceanography, 50, 17:30. tools:::Rd_expr_doi("10.1007/BF02233853")
Data from AMSR satellites are handled with amsr.
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().
Other classes holding satellite data:
amsr-class,
g1sst-class,
satellite-class
Other things related to landsat data:
[[,landsat-method,
[[<-,landsat-method,
landsat,
landsatAdd(),
landsatTrim(),
plot,landsat-method,
read.landsat(),
summary,landsat-method