Brief Introduction to Remote Sensing (3/3): Landsat image conversion

This is the third part of some basic definitions of remote sensing that are already in the user manual of the Semi-Automatic Classification Plugin.

This post provides information about the Landsat conversion to reflectance implemented in SCP Landsat.

Landsat images downloaded from http://earthexplorer.usgs.gov or through the SCP tool Download Landsat are composed of several bands and a metadata file (MTL) which contains useful information about image data.

Radiance at the Sensor’s Aperture

Radiance is the “flux of energy (primarily irradiant or incident energy) per solid angle leaving a unit surface area in a given direction”, “Radiance is what is measured at the sensor and is somewhat dependent on reflectance” (NASA, 2011, p. 47).
The Spectral Radiance at the sensor’s aperture (Lλ) is measured in [watts/(meter squared * ster * μm)] and for Landsat images it is given by (https://landsat.usgs.gov/Landsat8_Using_Product.php):

Lλ=ML∗Qcal+AL

where:

• ML = Band-specific multiplicative rescaling factor from Landsat metadata (RADIANCE_MULT_BAND_x, where x is the band number)
• AL = Band-specific additive rescaling factor from Landsat metadata (RADIANCE_ADD_BAND_x, where x is the band number)
• Qcal = Quantized and calibrated standard product pixel values (DN)

Top Of Atmosphere (TOA) Reflectance

“For relatively clear Landsat scenes, a reduction in between-scene variability can be achieved through a normalization for solar irradiance by converting spectral radiance, as calculated above, to planetary reflectance or albedo. This combined surface and atmospheric reflectance of the Earth is computed with the following formula” (NASA, 2011, p. 119):

Brief Introduction to Remote Sensing (2/3): Supervised Classification Definitions

This is the second part of some basic definitions about remote sensing that are already in the user manual of the Semi-Automatic Classification Plugin.

This post provides basic definitions about supervised classifications.

Land Cover

Land cover is the material at the ground, such as soil, vegetation, water, asphalt, etc. (Fisher and Unwin, 2005). Depending on the sensor resolutions, the number and kind of land cover classes that can be identified in the image can vary significantly.

Supervised Classification

A semi-automatic classification (also supervised classification) is an image processing technique that allows for the identification of materials in an image, according to their spectral signatures. There are several kinds of classification algorithms, but the general purpose is to produce a thematic map of the land cover.
Image processing and GIS spatial analyses require specific software such as the Semi-Automatic Classification Plugin for QGIS.

Training Areas

Usually, supervised classifications require the user to select one or more Regions of Interest (ROIs, also Training Areas) for each land cover class identified in the image. ROIs are polygons drawn over homogeneous areas of the image that overlay pixels belonging to the same land cover class.

Classes and Macroclasses

Land cover classes are identified with an arbitrary ID code (i.e. Identifier). SCP allows for the definition of Macroclass ID (i.e. MC ID) and Class ID (i.e. C ID), which are the identification codes of land cover classes. A Macroclass is a group of ROIs having different Class ID, which is useful when one needs to classify materials that have different spectral signatures in the same land cover class. For instance, one can identify grass (e.g. ID class = 1 and Macroclass ID = 1 ) and trees (e.g. ID class = 2 and Macroclass ID = 1 ) as vegetation class (e.g. Macroclass ID = 1 ). Multiple Class IDs can be assigned to the same Macroclass ID, but the same Class ID cannot be assigned to multiple Macroclass IDs, as shown in the following table.

Macroclass name	Macroclass ID	Class name	Class ID
Vegetation	1	Grass	1
Vegetation	1	Trees	2
Built-up	2	Road	3

Therefore, Classes are subsets of a Macroclass as illustrated in Figure Macroclass example.

Macroclass example

If the use of Macroclass is not required for the study purpose, then the same Macroclass ID can be defined for all the ROIs (e.g. Macroclass ID = 1) and Macroclass values are ignored in the classification process.

Brief Introduction to Remote Sensing (1/3): Basic Definitions

I am still working on the new tutorials about the Semi-Automatic Classification Plugin. In the meantime, I think it is useful to write some posts about remote sensing basics that are already in the user manual of the plugin. 

GIS definition

There are several definitions of GIS (Geographic Information Systems), which is not simply a program. In general, GIS are systems that allow for the use of geographic information (data have spatial coordinates). In particular, GIS allow for the view, query, calculation and analysis of spatial data, which are mainly distinguished in raster or vector data structures. Vector is made of objects that can be points, lines or polygons, and each object can have one ore more attribute values; a raster is a grid (or image) where each cell has an attribute value (Fisher and Unwin, 2005). Several GIS applications use raster images that are derived from remote sensing.

Remote Sensing definition

A general definition of Remote Sensing is “the science and technology by which the characteristics of objects of interest can be identified, measured or analyzed the characteristics without direct contact” (JARS, 1993).
Usually, remote sensing is the measurement of the energy that is emanated from the Earth’s surface. If the source of the measured energy is the sun, then it is called passive remote sensing, and the result of this measurement can be a digital image (Richards and Jia, 2006). If the measured energy is not emitted by the Sun but from the sensor platform then it is defined as active remote sensing, such as radar sensors which work in the microwave range (Richards and Jia, 2006).
The electromagnetic spectrum is “the system that classifies, according to wavelength, all energy (from short cosmic to long radio) that moves, harmonically, at the constant velocity of light” (NASA, 2013). Passive sensors measure energy from the optical regions of the electromagnetic spectrum: visible, near infrared (i.e. IR), short-wave IR, and thermal IR (see Figure Electromagnetic-Spectrum).

Electromagnetic-Spectrum

by Victor Blacus (SVG version of File:Electromagnetic-Spectrum.png)
[CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)]
via Wikimedia Commons
http://commons.wikimedia.org/wiki/File%3AElectromagnetic-Spectrum.svg