LiDAR technology is a remote sensing method used in structural geology to measure geological structures. It helps monitor and describe rock formations. This method is often used to collect detailed information about rock shapes and changes, which helps identify risks from geological hazards, such as predicting rockfalls or studying signs of ground movement before earthquakes.

Geological structures are formed by tectonic forces that shape the Earth's surface. These structures include folds, fault lines, and features like the size, length, and number of rock breaks in an area. These breaks can affect the stability of slopes, leading to landslides or splitting rock into separate pieces that may fall (rockfalls). Some rock pieces found along faults can indicate past earthquakes.

Traditionally, geotechnical engineers studied rock breaks by hand. However, after geological events like rockfalls, areas where rocks fall from are often dangerous and hard to reach, making it difficult to measure structures or calculate volumes needed for safety studies. LiDAR allows scientists to study geological structures from a distance, creating 3-D models of slopes and virtual rock surfaces for analysis.

LiDAR (Light Detection and Ranging) is a remote sensing tool that gathers accurate 3-D data and distance measurements. It works by measuring the time it takes for a laser pulse to travel to a surface and return. LiDAR creates topographic maps and is helpful for studying the natural environment.

Importance of measuring geological structures by LiDAR

Geological structures give rock masses unique physical properties. Discontinuous features and forces from plate tectonics can change rock masses and their shapes. These structures include joints, fractures, bedding planes, shear zones, mechanical breaks, or other features ranging from very small (less than 1 cm, like foliation from metamorphism) to very large (more than 100 meters, like mid-oceanic ridges).

Geological structures are often long and straight, and their direction is described as "strike." If a rock body is tilted significantly, considering its slope resistivity, it may be more likely to cause rockfalls. LiDAR technology helps measure landform features from small outcrops to large areas. Examples of geological structures and their importance include:

Rock plane orientations describe how rock layers naturally tilt. Examples include bedding planes and fault planes. These orientations are measured using dip and dip direction with a clinometer and compass. Dip is the maximum angle a plane tilts from horizontal, while dip direction is the direction where a horizontal line meets the tilted plane. A stereonet can show how dip and dip direction are distributed to study slope movement.

Kinematics describes how a rock body moves without external forces causing it to shift. This analysis focuses on sliding failures along planes, though other failures, like wedge or toppling failures, may also occur.

Fault behavior helps measure sediment movement rates and predict earthquakes. Earthquakes can create fault scraps, where one side of a block moves upward, causing vertical shifts. By studying fault scraps, scientists can estimate their age and calculate how long it took to form these features.

Earthquakes begin with slow slips, which are small movements along fault sides. These slips are too small for seismometers to detect (up to 5 mm per day). When slipping blocks reach a critical speed, faults accelerate and eventually cause a full earthquake. The size of fault displacement is related to the initial speed of the slip.

By comparing LiDAR data before and after an earthquake, scientists can create 3-D digital terrain models to measure displacement and deformation. This helps predict future earthquake sizes by analyzing fault and slip characteristics, as well as the size of affected areas. Short-term earthquake predictions may also be possible.

During geological mapping, aerial photos and satellite images are often used, but forests make it hard to see landforms like ridges and valleys. Topographic maps are then created manually using collected data.

LiDAR uses a full-waveform system that allows laser pulses to pass through vegetation, capturing ground-level geological data. This method helped Webster et al. discover new craters in Northern Canada using LiDAR and digital terrain models. Digital terrain models are needed to measure structural features like tilt angles and river depths. With precise LiDAR mapping of bedrock and surface layers, scientists can study surface processes.

Traditional methods for measuring rock plane orientations only examine exposed rock manually. Engineers previously studied rock discontinuities with limited samples, which might not represent the entire area. This can lead to biased results.

Geotechnical studies also examine other factors, such as how long discontinuities extend, block size, and spacing between rock joints.

LiDAR technology

LiDAR (Light Detection and Ranging) is a fast method used to gather 3-D information by sending out and receiving laser pulses. By using different light wavelengths, LiDAR can create detailed topographic maps. These maps are useful in fields like geology, geomorphology, and surveying. The creation of these maps depends on tools such as the Inertial Measuring Unit and the Global Positioning System. LiDAR is also helpful for studying steep slopes and rock cliffs.

For LiDAR data to be accurately placed on a map, it must be geo-referenced, meaning it is matched to specific local or global coordinates. This allows LiDAR data to be combined with old aerial photographs to compare changes in topography over time.

LiDAR systems use both pulsed and continuous-wave lasers to collect 3-D data. The laser scanner is the main part of the LiDAR system. Ground-based systems, such as handheld or terrestrial scanners, use lasers with wavelengths between 550-600 nm. Airborne systems use lasers with wavelengths between 1000-1600 nm.

The distance a laser pulse travels is calculated using this formula:
R = ½ × c × t
– R represents the range in meters
– c is the speed of light in meters per second
– t is the time the laser pulse takes to travel in seconds

LiDAR collects data in two ways: discrete return and full-waveform return. Full-waveform is often used in airborne LiDAR for studying forests because it can capture information about vegetation at different heights. Discrete return is used in ground-based systems and only captures data from the surface.

LiDAR data is usually stored in a format called point cloud (.las). This format records X, Y, and Z coordinates for each data point. Each point represents a specific location in space and can show detailed 3-D images of rock faces in hard-to-reach areas.

LiDAR data includes the following details:
– Coordinates (X, Y, Z) in a local reference system
– Color information (red, green, blue)
– The intensity of light reflected from a surface
– Photographs taken during scanning that can be overlaid onto point clouds
– Additional data such as hyperspectral information

These data help analyze rock features, including the shape and surface details of natural or man-made rock slopes.

Before 2003, LiDAR data was stored in ASCII format, which had problems:
1. Slow to read and interpret
2. Data loss during processing
3. Not standardized

Since 2003, the American Society for Photogrammetry and Remote Sensing (ASPRS) has standardized LiDAR data in a binary format (.las) that includes point cloud records.

Geo-referencing means matching coordinates on an aerial photo or digital map to a global or regional geographic system. This allows users to locate data points on Earth’s surface. The Global Positioning System often uses the World Geodetic System of 1984 (WGS84) and stores data in formats like GeoTIFF or GeoPDF. In some cases, users may need elevation data above sea level, such as when studying changes in sea levels using hydrological data.

Geo-referencing can be done by placing control points at the base of a slope. Using at least three known points, point cloud data can be adjusted to match a precise coordinate system. This helps measure distances, volumes, and areas accurately.

Types of LiDAR

LiDAR data can be collected using ground-based, airborne, and mobile systems. Examples include Airborne LiDAR Scanning (ALS), Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanning (TLS), and Handheld Laser Scanning (HLS). The tables below will compare these data collection systems based on:

1) The methods used to collect data
2) The technique for finding the exact location of a point (georeferencing)
3) The benefits and drawbacks of each system

Digital terrain modelling

A digital terrain model (DTM) is a tool that shows Earth's visible land surfaces in three dimensions. It uses data points from special sensors, like LiDAR, to create a smooth 3-D surface. These points are connected to form flat planes, which help scientists study the shape and structure of the land. This method is also used to make models of other planets and has other uses.

DTMs are grouped based on the shapes used to build them, such as triangles or squares. Three main types are used:

• Point-based
• Triangular-based
• Grid-based

Triangular and grid-based models are most commonly used.

A point-based model creates a surface by linking individual data points to form small, flat sections. Each section is a flat plane connected to one point. This method can create regular or irregular patterns, depending on how the areas around each point are divided. Regular patterns, like hexagons or squares, are often used for easier calculations. The height of each flat section matches the height of the point it is connected to.

A triangular-based model builds a more detailed surface by connecting points into triangles. This is the main way to create complex DTMs. Triangles are flexible and can be used to break down other shapes, like squares or rectangles, into smaller triangles. A network of connected triangles can include lines that help create curved surfaces. To make a triangle, three nearby points are grouped together using a method called Delaunay triangulation, which ensures triangles do not overlap.

This method works well even if some data points are missing or added, as only the affected triangle needs to be changed, not the whole model. Two settings affect how the surface is formed:

• The maximum angle between connected triangles
• The smallest size allowed for each section

A grid-based model is less useful for areas with sharp changes or steep slopes. It requires at least four data points to create a grid, which connects shapes like squares or rectangles to form a smooth surface. This method is helpful because data can be arranged into evenly spaced squares, making it easier for some software to process. Some programs can convert scattered data points into a grid format for better organization.

Approach for digital terrain modelling

The data collected are in the form of a point cloud, which means it consists of many 3D points. To create a digital terrain model, two main steps are needed:

1) Classifying the point cloud and filtering the ground points.
2) Reconstructing the ground surface by connecting the scattered laser points through interpolation.

Classifying the data and removing noise helps create an unbiased representation of the slope. When airborne laser scanning (ALS) collects data with multiple laser returns, this method can separate objects into different groups. Software like TerraScan and TerraModel, developed by TerraSolid, can automatically classify point cloud data. However, some manual checks are needed to ensure the classifications are correct.

Algorithms used in this process can identify major landform features. These algorithms assume that surfaces with large differences are not part of the ground. To create a surface model of a rock slope, only the classified ground data is needed. Ground and non-ground surfaces are divided into the following categories:

Data filtering helps separate the Earth's surface from other objects by removing unnecessary points or noise. For rock slope studies, outcrop mapping, and topography, only information about the rock slope itself is needed. Therefore, filtering separates point clouds into ground and non-ground features. Objects like bugs, plants, or man-made structures are considered non-ground features.

To filter 3D laser points, methods from open-source software can be used to separate ground points from the rest of the area being studied.

The purpose of a ground verification survey is to check the accuracy of LiDAR data. LiDAR collects data by sending laser pulses at different angles and receiving the signals that return. These signals may have errors caused by the atmosphere absorbing certain wavelengths. A ground-truth survey ensures the collected data matches the local coordinate system. For example, horizontal accuracy is tested by comparing data collected using different methods. Corrections can also be made by using multiple control points with known coordinates.

Structural orientation analysis

DTMs can find the shape and features of geological structures, such as the tilt of a fold limb. For example, the angle and direction of rock surfaces can be measured using a DTM. The process follows these steps:

1) Collect LiDAR data from a specific slope.
2) Remove unwanted or incorrect data points.
3) Use math to convert the data points into a DTM.
4) Use software like Coltop 3D to automatically calculate the shape of rock surfaces or joints.
5) Identify rock surfaces by using colors or statistical methods to group points into single or separate planes.
6) Save the orientation data in computer files and display the angles and directions of different planes on a stereonet.

Data partitioning divides unevenly spread data into cubes to make it easier to analyze. Point cloud data has different densities because of differences in collection tools and settings. The number of points in each cube determines the cube size, with at least 4 points required per cube. Points that do not fit the pattern are removed. Then, the normal direction of each cube is calculated.

Software like CloudCompare and Geomagic can use Octree Partitioning to divide data. Rock masses in different areas have varying roughness, so users must set cube sizes manually for the best results. Each cube usually has 15 to 30 points, with spacing between 4mm and 7mm.

Rock discontinuities clustering groups parts of a slope into the same set of cracks or joints. These cracks may have wavy or uneven surfaces. Points in the same group have similar angles. The method checks if points lie on the main layer of rock or on surfaces that run parallel to each other. Clustering uses normal vectors from each face to sort different joint sets based on their similar directions.

Challenges for LiDAR Technology

LiDAR is good at collecting data quickly over large areas. However, some challenges remain in processing the data and creating accurate results.

Vegetation may not be completely removed during data filtering. This can affect the smoothness and grouping of rock surfaces. Most 3-D models are created using a method called triangulation. This process can sometimes create sharp, needle-like shapes called spikes. These spikes can make rock surfaces appear uneven and cause mistakes when calculating the direction of rock planes. Spikes form when data points have similar X and Y coordinates but very different Z coordinates, leading to uneven surfaces.

Filtering settings for grouping surfaces often depend on the user's past experience. For example, a setting called the octree parameter can be adjusted based on how dense the data points are. Users must decide how the final Digital Terrain Model (DTM) looks, which means testing is often needed to create a smooth surface.

Point cloud density refers to how closely data points are spaced in LiDAR data. This affects the accuracy of measuring rock slopes. It is an important factor to consider during data processing, including steps like filtering, classifying data, identifying features, and recognizing objects. Point cloud density depends on several factors:

• How much the laser scans overlap
• How reflective the object being scanned is
• The speed at which the laser scans

Although LiDAR is effective for collecting data on rock slopes, its high cost makes it less practical. When the area being studied is very small, the usefulness of Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) is limited. This is because using ALS requires an aircraft, a trained pilot, a planned flight path, and approval from local aviation authorities.

Using an unmanned aerial vehicle (UAV) can solve this problem. UAVs can collect data in hard-to-reach areas at a lower cost for small-scale projects. They are portable and lightweight, making them easier to use than traditional methods.

Other LiDAR applications

Laser scanning is a precise and efficient method for collecting data. It can be used in many areas beyond measuring structures:

• Checking for emergencies and mapping landslides
• Managing projects that require 3-D models or topographic data
• Tracking site progress and measuring excavation or filling volumes
• Calculating volumes
• Creating topographic maps
• Building and highway construction
• Assessing damage from natural disasters
• Collecting data from hard-to-reach areas
• Precision farming
• Recording completed construction details
• Detecting unauthorized work (like recording the actual site conditions)
• Monitoring surface changes using repeated LiDAR scans
• Archaeology and reconstructing historical sites
• Studying earthquakes
• Forestry work
• Research in atmospheric physics
• Exploring space and other planets