R: Class to Store landsat Data

landsat-class {oce} | R Documentation

## Class to Store landsat Data

### Description

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.

### Slots

`data`

  As with all `oce` objects, the `data` slot for `landsat` objects is a `list` containing the main data for the object.

`metadata`

  As with all `oce` objects, the `metadata` slot for `landsat` objects is a `list` containing information about the `data` or about the object itself.

`processingLog`

  As 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.

### Modifying slot contents

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.

### Retrieving slot contents

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.

### Data storage

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.

### Landsat 8

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.

### Landsat 7

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 |
    .------------------------------------------------------------------------------.

### Author(s)

Dan Kelley and Clark Richards

### References

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

### See Also

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`

[Package oce version 1.1-1 Index]