Remote Sensing Techniques

From Open Energy Information

Exploration Technique: Remote Sensing Techniques

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Exploration Technique Information
Exploration Group: Remote Sensing Techniques
Exploration Sub Group: None
Parent Exploration Technique: Exploration Techniques
Information Provided by Technique
Lithology:
Stratigraphic/Structural:
Hydrological:
Thermal:
Dictionary.png
Remote Sensing Techniques:
Remote sensing utilizes satellite and/or airborne based sensors to collect information about a given object or area. Remote sensing data collection methods can be passive or active. Passive sensors (e.g., spectral imagers) detect natural radiation that is emitted or reflected by the object or area being observed. In active remote sensing (e.g., radar) energy is emitted and the resultant signal that is reflected back is measured.
Other definitions:Wikipedia Reegle


 
Introduction
"<div style="page-break-inside: avoid;">

<table border-color:="null" class="wikitable openei-infobox" style="text-align: center"> <tr> <th/> <th style="text-align: center">Ultra<br/>Violet</th> <th style="text-align: center">Visible</th> <th style="text-align: center">Near<br/>Infrared</th> <th style="text-align: center">Water<br/>Absorption</th> <th style="text-align: center">SWIR</th> <th style="text-align: center">Water<br/>Absorption</th> <th style="text-align: center">MWIR</th> <th colspan="2" style="text-align: center">LWIR</th> <th style="text-align: center">Micro-<br/>waves</th> <th style="text-align: center">Radio<br/>Waves</th></tr> <tr> <th style="text-align: left">Wavelength<br/>(nanometers) </th> <td></td> <td>390-<br/>750</td> <td>750-<br/>1400</td> <td style="background-color: #ADD8E6">1450</td> <td>1500-<br/>2800</td> <td style="background-color: #ADD8E6">2900</td> <td>3,000-<br/>8,000</td> <td colspan="2">8,000-<br/>14,000</td> <td></td> <td>1 mm-<br/>100 km</td></tr> <tr> <th colspan="12" style="background-color: #e27c00; text-align: left">Passive Sensors</th></tr> <tr> <th rowspan="3" style="text-align: left">Types</th> <td rowspan="3" /> <td style="background-color: #FFE4C4; text-align: center">Aerial<br>Photography</td> <td style="background-color: #FFE4C4; text-align: center">FLIR</td> <td style="background-color: #ADD8E6" /> <td style="background-color: #FFE4C4; text-align: center">SWIR</td> <td style="background-color: #ADD8E6" /> <td></td> <td>TIR-1</td> <td>TIR-2</td> <td colspan="2" rowspan="3" /></tr> <tr> <td colspan="8" style="background-color: #FFE4C4; text-align: center">Hyperspectral Imaging</td></tr> <tr> <td colspan="8" style="background-color: #FFE4C4; text-align: center">Multispectral Imaging</td></tr> <tr> <th colspan="12" style="background-color: #e27c00; text-align: left">Active Sensors</th></tr> <tr> <th style="text-align: left">Types</th> <td colspan="3" style="background-color: #FFE4C4; text-align: center">LiDAR</td> <td style="background-color: #ADD8E6" /> <td></td> <td style="background-color: #ADD8E6" /> <td colspan="4" />

<td style="background-color: #FFE4C4; text-align: center">Radar</td></tr></table></div>" cannot be used as a page name in this wiki.
Ultra
Violet
Visible Near
Infrared
Water
Absorption
SWIR Water
Absorption
MWIR LWIR Micro-
waves
Radio
Waves
Wavelength
(nanometers)
390-
750
750-
1400
1450 1500-
2800
2900 3,000-
8,000
8,000-
14,000
1 mm-
100 km
Passive Sensors
Types Aerial
Photography
FLIR SWIR TIR-1 TIR-2
Hyperspectral Imaging
Multispectral Imaging
Active Sensors
Types LiDAR Radar
 
Use in Geothermal Exploration
"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)." cannot be used as a page name in this wiki.
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).


 
Data Access and Acquisition

  • 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.




 
References


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