Hyperspectral Imaging

Technically, hyperspectral sensors (or imaging spectrometers as they are also known) image the earth in many hundreds of narrow bands (typically over a hundred) while multi-spectral sensors image in an average of only ten, wide bands. The most common hyperspectral sensors image in the Visible, Near-Infrared, and Shortwave Infrared wavelength range (~0.35 – 2.5 microns). Hyperspectral sensors measure the electromagnetic spectrum continuously, rather than piecemeal like their multi-spectral cousins.

Thematically, hyperspectral sensors are capable of absolute surface material identification while multi-spectral sensors are only capable of relative material delineation. As an example, historical, multi-spectral images from NASA’s LANDSAT satellite (collecting 7-bands of data over the visible, near IR and shortwave IR spectrum) can be used to create maps of the surface, delineating clay from iron-oxides. With today’s hyperspectral imagers, hundreds of bands allow unique identification of minerals such as kaolinite vs. alunite or hematite vs. goethite.

Spectral fidelity comes at a price; hyperspectral datasets are large and computationally intensive to work with (imagine 224 pieces of information—one for each band—stored for each pixel in an image). However recent processing advances and the ever-increasing speed of computers in the last five years, means that data is interpreted into usable mineral or other material maps in very short time periods (weeks vs. months). Though many in the industry still appreciate the 7-band LANDSAT images or the 14-band ASTER images (public-sector multi-spectral imagers producing data at low, government-subsidized prices), the four common thematic remote sensing-based maps created from hyperspectral data for use in geothermal exploration, (including mineral maps, cultural maps, vegetation maps, and high-resolution digital photographs), are categorically more accurate, more precise and richer in information than multi-spectral datasets.

Spectral data have been used in geothermal exploration to detect and map soil-mineral, shallow subsurface thermal, and vegetation anomalies.

Mineral Maps

Mineral maps can be used to show the presence of hydrothermal minerals and mineral assemblages. Specifically the presence of a mineral in conjunction with other minerals may indicate the presence of an active geothermal system through the presence of a hydrothermal mineral assemblage. For example, the detection of kaolinite might not indicate the presence of a hydrothermal system (since kaolinite can form from weathering, not only hydrothermal alteration). However, the co-location of kaolinite, alunite, and opal (amorphous silica) could indicate active or fossil hydrothermal alteration.

Vegetation Maps

Most of the time vegetation maps are only mildly interesting to geothermal exploration, but sometimes geothermal activity can cause vegetation stress – this is common along faults, because it’s hot or because changes in groundwater pH or presence of gasses such as CO2 and H2S can lead to plant stress.

Hyperspectral data collection requires sunny days - the images are not as clear when collected on cloudy days.

 

• Hyperspectral datasets are very large and cumbersome to work with (imagine 224 pieces of information—one for each band—stored for each pixel in an image). Data can be interpreted into maps so that the information can be shared in much more manageable datasets. Though many in the industry still appreciate the 7-band LANDSAT images (which can be made through data aggregation of hyperspectral images), there are four common types of maps that are created from hyperspectral data for use in geothermal exploration, including mineral maps, cultural maps, vegetation maps, and high-resolution photographs.

• There are many commercial vendors that provide data products from calibrated radiance to surface reflectance to derived mineral maps. Since processing techniques vary widely in creating surface mineral maps it's a good practice for derived products to be reviewed by geologists for quality control.

• Hyperspectral mapping of hydrothermal minerals has been complicated in the past by the difficulty in displaying millions of categorized pixels in a way that is meaningful to geologists and fits with geophysical and geological mapping norms. Such issues have been resolved in the last five years with the advent of ‘targeting’ maps that plot mineral assemblages of interest in a myriad of ways including as density maps (that look similar to geophysical gradient maps) and as small, but easily accessible digital files compatible with not only standard software, but also web-based portals such as Google Earth. Mineral assemblage maps are a useful way for presenting and understanding both airborne and satellite spectral images. They ultimately provide a way to rapidly map vast areas of land (tens of thousands of acres), targeting areas with prospective hydrothermal mineral assemblages for more in-depth geothermal prospecting (i.e. high resolution geophysics and field mapping).