Geoscience Reference
In-Depth Information
More recently, Unmanned Airborne Vehicles (UAVs) have provided a cost-
effective way of acquiring very high resolution (sub-meter) imagery using com-
mercially available cameras. Their main advantage is that they are highly flexible in
terms of when and where they can be flown and have proved their value for rapid
damage assessment following natural disasters.
In terms of active remote sensing, multi-waveform LiDARs have generated con-
siderable interest from the research and operational communities. The future of
LiDAR systems is likely to continue to develop with new types of sensors such as
dual-wavelength (or multi-spectral) LiDAR systems as well as spaceborne LiDAR
systems, similar to IceSAT. Similarly, photon counting LiDARs are considered by
some to be the next major development within the LiDAR community. These highly
sensitive instruments can detect and measure individual photons that are scattered
back to the instrument.
One set of EO systems that is not addressed in this topic are RADAR (SAR)
sensors, which can be either spaceborne, airborne or terrestrial. Like LiDAR instru-
ments, RADAR sensors are active sensors that measure the time a signal is transmitted
from the sensor's antennae to a feature on the ground and its return time; the sensors
also have the capability to detect frequency and polarization shifts. RADAR data are
used for a range of applications from land-monitoring, landslide monitoring, moni-
toring weather systems and for aviation and transport systems. However, the methods
and techniques used to analyze these data differ substantially to optical data, relying
principally on synthetic aperture radar.
Finally, one of the most significant changes in this field that has occured over
the last decade is the increasing accessibility of remote sensing data and derived
information to ordinary people. This has largely been facilitated by Internet based
Web mapping systems like Google Earth, Yahoo! Maps and OpenStreetMap, which
can be accessed from an array of devices. This trend will likely continue as will the
demand for higher spatial and temporal resolution data that will be achieved through
the development of sensors and Web-based technologies. This in turn will increase
people's requirements to analyze and visualize these data, which strengthens the
value and role of open source geospatial utilities.
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