R: Class to Store Landsat Satellite Data

landsat-class {oce}

R Documentation

Class to Store Landsat Satellite Data

Description

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.

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 those functions save an entry in the processingLog that describes the change.

Retrieving slot contents

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.

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 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.

Landsat 7

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

Author(s)

Dan Kelley and Clark Richards

References

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. doi:10.1007/BF02233853