Skip to main content

LiDaR Principles and applications

LIDAR, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser light to measure distances and create detailed three-dimensional representations of objects and environments. Here are the fundamental principles of LIDAR remote sensing:


1. Laser Emission: LIDAR systems emit laser pulses towards the target area. These laser pulses are typically in the form of short, intense bursts of light.


2. Propagation of Light: The emitted laser pulses travel through the atmosphere, where they may interact with particles or molecules, experiencing scattering and absorption. The laser light continues until it strikes an object or a surface.


3. Reflection or Scattering: When the laser pulse encounters an object or surface, a portion of the light is reflected back towards the LIDAR sensor. The time it takes for the laser pulse to travel to the target and return is measured.


4. Time-of-Flight Measurement: LIDAR calculates the distance to the target by precisely measuring the time it takes for the laser pulse to travel to the target and back. This is done using the speed of light as a constant.


5. Multiple Measurements: LIDAR systems typically emit thousands or even millions of laser pulses per second and record the return time and intensity of each pulse. This results in a dense cloud of points, often referred to as a "point cloud."


6. Data Processing: The collected data is processed to generate a detailed three-dimensional representation of the target area. This point cloud can be used to create digital elevation models, maps, or 3D models of objects and terrain.


7. Applications: LIDAR remote sensing is used in various applications, including topographic mapping, forestry management, urban planning, archaeology, autonomous vehicles, and more. Its ability to provide precise elevation and object information makes it invaluable for many industries.


LIDAR technology can be implemented in various ways, such as airborne LIDAR using aircraft or UAVs, terrestrial LIDAR for ground-based scanning, and even spaceborne LIDAR for planetary exploration. It has revolutionized the way we collect detailed geospatial information and has numerous practical applications in science, engineering, and environmental monitoring.



1. ICESat-2 (Ice, Cloud, and land Elevation Satellite-2): Launched in 2018 by NASA, ICESat-2 is designed for Earth science research, specifically to measure changes in ice sheet thickness and sea ice freeboard. It uses a LIDAR system called the Advanced Topographic Laser Altimeter System (ATLAS) to collect elevation data.


2. GEDI (Global Ecosystem Dynamics Investigation): Launched as part of the International Space Station (ISS) payload, GEDI is a LIDAR instrument that measures the three-dimensional structure of forests and ecosystems. It provides valuable data for understanding the Earth's carbon cycle.


3. LRO (Lunar Reconnaissance Orbiter): While primarily designed for lunar exploration, NASA's LRO carries a LIDAR instrument called the Lunar Orbiter Laser Altimeter (LOLA). LOLA measures the surface topography of the Moon with high precision.


4. TanDEM-X: This is a German radar satellite mission operated in conjunction with TerraSAR-X. Although it primarily uses radar technology, it also features a bistatic mode that, when combined with TerraSAR-X, can produce a global digital elevation model (DEM) with unprecedented accuracy.


5. ISAT (Indian Satellite for Antarctic Observation): ISAT-1, an Indian remote sensing satellite, was equipped with a LIDAR altimeter. It was used for monitoring ice sheet dynamics and elevation changes in the polar regions.


6. ATLAS (Advanced Topographic Laser Altimeter System): ATLAS is a LIDAR instrument onboard the Earth, Science, and Climate Pathfinder satellite, which is part of NASA's Earth System Science Pathfinder Program. It's used to monitor ice sheet elevation changes.





Comments

Popular posts from this blog

RADIOMETRIC CORRECTION

  Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...