Introduction: LIDAR Remote Sensing
Lidar remote sensing is the modernist form of remote sensing (The Basic Concept of Remote Sensing). It stands for “light detection and ranging,” which is similar to radar (The Concept of Microwave Remote Sensing, its Geometry, and its Applications) imaging in that both families of sensors are intended to broadcast energy in a limited range of frequencies and then receive the backscattered energy to generate an image of the surface of the earth. Both the lidar and radar technologies are examples of active remote sensing, they can generate their source of energy Which simply means they don’t depend on solar energy for gathering information about the surface of the earth.
They may compare the properties of the transmitted and returned energy—the pulse timing, wavelengths, and angles—to analyze not only the brightness of the backscatter but also its angular location, frequency variations, and the timing of reflected pulses. Knowing these properties implies that lidar data, like data gathered by active microwave sensors, may be processed to derive information characterizing the structure of terrain and vegetation features that ordinary optical sensors do not represent.
The main principles of lidar are based on a laser application, they employ a type of coherent light—light consisting of a very small range of wavelengths—that is particularly “pure” in terms of color.
The laser, which stands for “light amplification by stimulated emission of radiation,” is a device that delivers a powerful electrical current to a “leasable” substance, often crystals or gases such as rubies, carbon dioxide, helium-neon, argon, and many less common materials. as we discussed that the emitted light of lidar system is a coherent beam, Each and every substance provides a specific wavelength characteristic to a single laser.
Historic Development and Components of Lidar Remote Sensing
In the late 1950s, lasers were invented. They were first utilized for scientific research and industrial purposes. The early lidar uses were mostly for atmospheric (Structure & Composition of the Atmosphere) profiling: static lasers may be installed to gaze upward into the sky to measure atmospheric aerosols. A portion of the laser beam is directed back to the ground by solid particles suspended in the atmosphere, where it is analyzed to show the number of atmospheric particles. Because lasers can measure the time delay of backscatter, they can assess the purity of the atmosphere across many kilometers in-depth providing data on the elevations of the layers they detect.
The use of lidar Remote Sensing for precise terrain elevation estimation began in the late 1970s. The first systems were profiling sensors that only collected elevation data directly beneath an aircraft’s path. These early laser terrain systems were sophisticated and not always well adapted for cost-effective terrain data collecting across broad regions, therefore their use was limited.
Lidar, which takes photos of the Earth’s surface, was just recently classified as remote sensing equipment. By the late 1980s, various technologies had evolved and converged to establish the context for the creation of precision scanning lidar systems that we currently know.
One of the most successful early uses of lidar was the precise measurement of water depths. In this case, the initial reflected return records the water surface, followed by a weaker return from the water body’s bottom. The depth of the water may then be determined using the pulse returns’ differential travel times (figure a).
Modern lidar acquisition begins with an aircraft equipped with high-precision GPS (Global Position System: Different Segments of GPS, its working Principle, Popular Substitute of GPS), an IMU (for measuring the angular orientation of the sensor with respect to the ground), a rapidly pulsing (10,000 to 100,000 pulses/sec) laser, a highly accurate clock, significant onboard computer support, reliable electronics, and robust data storage in Lidar Remote Sensing.
In addition to quick pulsing, current systems of lidar may capture five or more returns every pulse, allowing them to distinguish not only characteristics like a forest canopy and bare ground but also surfaces in between (such as the intermediate forest structure and understory).
Advantages of Lidar Land Survey
- High survey accuracy (+– Vertical accuracy 5-15 cm and Horizontal accuracy 30-50 cm).
- Lidar Remote Sensing technology is capable to generate three dimensional (3D) model of the surveyed land.
- It plays an important role in the utilization and evaluation of land resources.
- It can be used to build a land use and land cover database at very high accuracy.
- It can be used for gathering information and monitoring the land in real time.
- The lidar technology is very helpful to monitor the potential geological hazards in real-time and minimizes the damage caused by geological disasters.
- It is useful for height estimation of man-made as well as non-man-made objects.
- Traditional survey methods can take days or weeks to collect data from the field but in the case of lidar drone survey work will spend less time gathering information.
- With the help of a lidar drone, the surveyor can easily survey those areas which are difficult or impossible to undertake by other means such areas as cliffs and valleys with inaccessible steep slopes, and hazardous terrain.
- The DGPS systems require a couple of GCPs (ground control points) to reference the receiver but in the case of a lidar land survey, it is not needed to reference any system with GCPs.
- Drones fly at very low elevations which makes it possible to generate data at centimeter spatial resolution.
- Lidar Remote Sensing technology can penetrate vegetation to reach the ground below which makes highly precise highly detailed DSMs and DTMs.
- Data collection is independent of sun inclination and at night and slightly bad weather.
- Lidar generates data in point cloud density Up to 167,000 pulses per second. More than 24 points per m2 can be measured
- Lidar also observes the amplitude of backscatter energy thus recording a reflectance value for each data point. This data, though poor spectrally, can be used for classification, as at the wavelength used some features may be discriminated accurately