In order to simplify things, geospatial data is categorised into two main types - vector and raster. Vector data is a straightforward one which is separated by distinct geometry types - point, lines and polygons - while raster data is a slightly more complex one which is associated with terminology like wavelengths, spectral analysis and colour bands.
This post is an attempt to explain the different types of remote sensing and earth observation datasets which are, in the age of satellites, sensors and machine learning, becoming more and more relevant to the modern geospatialist.
Today is the Age of Geospatial Awareness.
Earth Observation and Remote Sensing are about applying ‘sensory’ and ‘observational’ techniques in order to gather deeper insights and wider knowledge about the planet. These techniques allow us to adopt a natural state of existence which is based on survival - present in the moment, un-distracted by thoughts or judgements, watching for threats and opportunities. Aware.
Today's techniques are about collecting and processing vast amounts of information about the planet which come from either active sensors (which transmit and record light sources) or passive sensors (which measure reflected or emitted energy). The following complex geospatial datasets each help to provide a distinct perspective on the natural and human worlds.
"Geospatial Awareness is about understanding the objects around us and the space which they occupy."
Topographic lidar is a technology that produces highly accurate 3D point cloud data which is used for above ground analyses and digital elevation modelling (DEM). In recent years, machine learning has been used by governments and companies around the world in order to further harness the potential of topographic lidar data outputs.
"Geospatial Awareness is about understanding what is both above and below the water's surface."
Topobathy lidar uses specialized airborne sensors which can penetrate the water's surface in order to map the features below. This technique is particularly useful for simultaneously mapping land and sea floors (too shallow for standard survey techniques) and it is relied upon by the marine navigation, coastal science and coastal management fields.
"Geospatial Awareness is about being able to sense and feel the temperature variations of the surrounding environment."
Thermal Imaging is a satellite remote sensing technique which uses infrared sensors in order to detect radiation emitted from land and sea surfaces across a range of regions and altitudes. It is an extremely valuable technique which can be used in the detection of forest fires, for measuring urban heat island effects, for locating sites with solar potential, and for monitoring infrastructure.
"Geospatial awareness is about being able to distinguish the different objects and materials on the planet's surface."
Hyperspectral Imaging is a technique which detects reflected solar energy across over 200 bands of light. It is commonly used in the precision agriculture, environmental monitoring, security and defense fields and is today captured by earth observation satellites such as the ESA's Sentinel‐3.
"Geospatial awareness is about being able to see the world more clearly and to notice features and patterns undetectable to the human eye.
Multispectral Imaging is a remote sensing technique for receiving, filtering and measuring bands of colour and light from the earth's surface. It is particularly valuable in the fields of atmospheric, land and ocean monitoring and today it informs climate change and food security policies and measures.
In the age of real time multi-faceted information and massive computer processing power - planetary scale awareness is now possible. Data can be visualised and analysed using geospatial tools in order to help build a more comprehensive understanding of the world. This presents an opportunity for humankind to become more aware of the earth, to become more connected to it, and to make better decisions regarding it.
Geospatial Awareness is not a means to an end - it is an end in itself.
The following excellent article by Will Fellers helped to inform this week's post.