Every day any number of aircraft, satellites and various forms of recording equipment scan the Earth’s surface producing a wealth of data. Collecting the data in this way is known as remote sensing, and has been used for many years.
Remote sensing is defined as the recording, observing and/or perceiving of objects from a distance and is usually done by satellite or high-flying aircraft (1). This information can be obtained through the use of active and passive sensors. Active sensors use internal stimuli to acquire data e.g. LiDAR (Light Detection and Ranging) measures the two-way travel time (the time to travel to a surface and return) of a laser-beam which can then be used to ascertain the topography of the Earth’s surface. Conversely, a passive sensor acquires data from what is naturally emitted from the object being observed, such as the electromagnetic (EM) radiation reflected/emitted from the Earth’s surface e.g. light (2).
With such a wealth of data being collected in the process of scanning the Earth’s surface, there is at least one question to ask – how do we use it? Remote sensing has found uses across a wide range of fields but finds itself being used most commonly to highlight differences in land uses i.e. urban, grassland, woodland, etc. and is based on the differences in frequencies, or bands, in the EM spectrum that are reflected back e.g. visible light, infrared, near infrared, ultraviolet, etc. It is also commonly used in the creation of height maps for use in computer models.
Each band is set to measure a certain frequency of the EM spectrum which corresponds to a particular colour in the visible part of the spectrum or a specific part of the non-visible spectrum, these are then combined to make either true or false colour images.
But what is a true or false colour image? Humans view the world based on three main colours – red, green and blue – and it is the combination of these colours that provide us with a true colour image i.e. the world as we perceive it. However, in order to make full use of remote sensing, false colour images are created to highlight the features we are interested in by substituting the standard red, green or blue with ratios between different bands instead e.g. the ratio between red reflectance and ultraviolet (3).
One of my favourite examples of how remote sensing is used, and the spectacular maps it can create, is in mineral exploration, in particular the Goldfield Mining District in south-central Nevada. This area birthed the use of remote sensing to identify hydrothermally altered rocks to indicate the presence of ore deposits (4). By comparing the differences in the rock’s reflectance of different spectra it was possible to accurately identify areas of alteration, making the process of identifying suitable mining sites much quicker. Here is an example of one of the amazing maps that can be produced when identifying areas of hydrothermally altered rock:
The first image is a true colour image of the area, with the second being a false colour image highlighting areas of hydrothermally altered rocks, which are located in the west, north east and north west of the image.
Groundsure’s Japanese Knotweed report (5) is an example of passive remote sensing, which makes use of the EM radiation absorbed and reflected by Japanese Knotweed to distinguish it from other types of vegetation. An example of active remote sensing that is used within Groundsure reports would be JBA’s surface water (pluvial) flooding data (6,7). This dataset makes use of LiDAR and photogrammetry to produce a height map of the Earth’s surface, and is subsequently used in JBA’s model to highlight areas where water is likely to flow to and collect during heavy rainfall events.
1. CRISP – Centre for Remote Imaging, Sensing and Processing (2001). What is Remote Sensing? (online). Singapore, http://www.crisp.nus.edu.sg/~research/tutorial/intro.htm, accessed 18th May 2015.
2. NOAA – National Oceanic and Atmospheric Administration (2014). What is remote sensing? (online). USA, http://oceanservice.noaa.gov/facts/remotesensing.html, accessed 18th May 2015.
3. SEOS – Science education through Earth Observation for high Schools (2015). Introduction to Remote Sensing (online). European Union, http://www.seos-project.eu/modules/remotesensing/remotesensing-c03-p01.html, accessed 18th May 2015.
4. Sabins, F.F. (1999). Remote Sensing for mineral exploration. Ore Geology Reviews 14: 157-183.
This article was submitted to be published by Groundsure Ltd. as part of their advertising agreement with Today’s Conveyancer. The views expressed in this article are those of the submitter and not those of Today’s Conveyancer.