Remote Sensing for Geological Mapping and Mineral Exploration - Spectral Analysis (Part 1 of 4) - Auracle Geospatial Science, Inc.

QUALICUM BEACH, BC, CANADA, September 19, 2011

Researchers and scientists use remote sensing to obtain information about land, water, or other parts of the environment without actually touching what is being studied. Today, remote sensing is usually carried out by satellite sensors or airplanes. Remote sensing is used to measure and map the Earth, and to ensure that the Earth's resources are being used responsibly.

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Understanding how the Earth is changing over time can be very difficult because the Earth is so large. Satellites allow scientists to look at large parts of the world at one time, and to compare newer pictures to older ones. Satellites can also "see" with sensors the things that we normally couldn't see, like infrared and ultraviolet radiation. Satellite remote sensing is a very reliable way to get information about the Earth's oceans and landforms, as well as its atmosphere and weather. By using the data that they collect from space, scientists can figure out how environmental change will affect people, animals and plants.

Part 1

Spectral Analysis

Electromagnetic (EM) Spectrum

The word 'spectrum' (the plural of which is 'spectra') is used today to mean 'a display of electromagnetic radiation as a function of wavelength.

Satellite sensors record reflected and emitted energy from Earth in various wavelengths of the electromagnetic spectrum. The electromagnetic (EM) spectrum is the continuous range of electromagnetic radiation, extending from gamma rays (highest frequency & shortest wavelength) to radio waves (lowest frequency & longest wavelength) and including visible light. The EM spectrum can be divided into seven different regions; gamma rays, X-rays, ultraviolet, visible light, infrared, microwaves and radio waves; we can see color, or reflected light, ranging from violet to red.

The reflectance of radiation from one type of surface material, such as soil, varies over the range of wavelengths in the EM spectrum. This is known as the spectral signature of the material. All Earth surface features, including minerals, vegetation, dry soil, water, and snow, have unique spectral reflectance signatures.

The property used to quantify these spectral signatures is called spectral reflectance: the ratio of reflected energy to incident energy as a function of wavelength. The spectral reflectance of different materials can be measured in the laboratory or in the field, providing reference data that can be used to interpret images.

Spectral Reflectance

In the first instance, a satellite sensor's spectral resolution specifies the number of spectral bands in which the sensor can collect reflected radiance. But the number of bands is not the only important aspect of spectral resolution. The position of bands in the electromagnetic spectrum is important, too.

High spectral resolution: - 220 bands
Medium spectral resolution: 3 - 15 bands
Low spectral resolution: - 3 bands

Electromagnetic (EM) Spectrum

Pixels, Images and colors

In displaying a color composite image, three primary colors (red, green and blue) are used. When these three colors are combined in various proportions, they produce different colors in the visible spectrum. Associating each spectral band (not necessarily a visible band) to a separate primary color, results in a color composite image. Many colors can be formed by combining the three primary colors (Red, Green, Blue) in various proportions for detecting certain objects in the image.

Multispectral remote sensors such as the Landsat TM, ASTER and SPOT produce images with a few relatively broad wavelength bands. Hyperspectral remote sensors, on the other hand, collect image data simultaneously in dozens or hundreds of narrow, adjacent spectral bands. These measurements make it possible to derive a continuous spectrum for each image cell. After adjustments for sensor, atmospheric, and terrain effects are applied, these image spectra can be compared with field or laboratory reflectance spectra in order to recognize and map surface materials such as particular types of vegetation or diagnostic minerals associated with ore deposits.

Hyperspectral Cube
The hyperspectral data cube: the high number of very narrow spectral bands results in a continuous spectral curve for each image pixel. The front of the cube shows a false colour image using the infrared spectral bands 1721nm, 2306nm and 1565nm in RGB.

Hyperspectral images contain a wealth of data, but interpreting them requires an understanding of exactly what properties of ground materials we are trying to measure, and how they relate to the measurements actually made by the hyperspectral sensor.

Hyperspectral images can be used in mineral exploration to locate minerals that are exposed or weathered in areas of residual soil. To detect mineral anomalies, the hyperspectral image is processed so that new bands are derived showing the distribution of spectrally distinct materials. The band that maps the target spectrum is then selected and further processed to highlight anomalous occurrences of the target being sought.

High resolution spectral imaging (hyperspectral) is an advanced remote sensing technology that can be applied to applications in mineral exploration, geological mapping and environmental monitoring and various other applications.

Spectral analysis procedures are used to analyze:

Land cover classification and mapping

Extraction of culture data

Geological and Mineral Exploration

Lithological classification and mapping

Change detection

Environmental monitoring

Auracle Geospatial Science Inc (AGS), is a geospatial company and offers a comprehensive range of geospatial information services that include Geographic Information Systems GIS, satellite image processing, geospatial analysis and geospatial modeling. Our global clients from mineral exploration, oil exploration, forestry management, and the agricultural industry have come to rely on our expertise to acquire, analyze, and visualize their geographic information.

We have years of experience in remote sensing technologies including image interpretation and analysis, data integration, data fusion and data visualization.