Remote Sensing Techniques
Exploration Technique: Remote Sensing Techniques
|Exploration Technique Information|
|Exploration Group:||Remote Sensing Techniques|
|Exploration Sub Group:||None|
|Parent Exploration Technique:||Exploration Techniques|
|Information Provided by Technique|
Remote sensing applications are becoming more commonly used in geothermal exploration due the ease and speed of data collection for relatively large areas (100+ km2) and the lack of need for land access and few airspace restrictions. The primary applications of remote sensing to geothermal exploration include:
- identifying and distinguishing between different rock, mineral assemblage, or mineral types;
- identifying surface thermal and vegetation anomalies; and
- determination of structural features and their orientation (i.e., strike).
In general, remote sensing applications lack the ability to penetrate into the subsurface, although some sensors can penetrate to very shallow depths (i.e., < 1 m).
- Remote Sensing Techniques
- Active Sensors
- Passive Sensors
- The best time to acquire the majority of remote sensing data is in the summer (specifically, those months with the highest sun angles and longest days). Exceptions to summer data acquisition is the collection of both long-wave thermal data and active sensor data (eg. Radar, LiDAR). In thermal imaging where detectors are measuring heat, it is best to fly when the ground vs. air temperature gradient or contrast is highest. Cooler months are thus better for this type of imaging as are the several hours before dawn any time of year.
- There are additional considerations to keep in mind. Radar, for example, cannot image the bare-ground surface in thick snow cover; ditto with LiDAR. However, these active images are insensitive to light (or lack thereof) making them excellent choices for high latitude environments (as one example). Furthermore, both Radar and LiDAR are capable (depending on wavelengths used) of imaging beneath tree canopy making them useful in highly vegetated regions. In contrast, spectral data collection (both hyperspectral and multi-spectral) requires mostly sunny days; data collected in low-light conditions are typically low signal-to-noise making processing and interpretation more difficult And while hyperspectral data is capable of mapping and identifying vegetation ecosystems, it (and multi-spectral) are not capable of penetrating the tree canopy to measure the surface below.
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