What is the role of LiDAR in GIS data collection?

LiDAR is pivotal in GIS data collection by providing accurate and detailed 3D data about the Earth's surface. Mounted on aerial or ground vehicles, LiDAR sensors measure the distance to objects by analyzing the reflected light. This technology enables the creation of exact elevation models, capturing terrain topography and surface features with exceptional detail. It enables photographic rendering models when coupled with an optical band that is photos acquired simultaneously. In GIS, LiDAR data enhances mapping, urban planning, forestry, and environmental monitoring. Its ability to penetrate vegetation and depict terrain intricacies makes LiDAR indispensable for creating comprehensive and reliable geospatial datasets.

LiDAR, using laser pulses, captures precise 3D data of the Earth's surface. It's key in GIS for high-resolution elevation models, aiding flood mapping, urban planning (identifying buildings, roads), and vegetation analysis (canopy height, density). It supports disaster response by assessing terrain changes. This technology offers crucial data for infrastructure design, environmental monitoring, and resource management, enhancing GIS with detailed surface information for multiple applications.

Geospatial professionals often over indent their data specifications. They request for 5cm accuracy levels, when 10 or 15cm accuracies will suffice for the project. Similarly, LiDAR is also misquoted. LiDAR gives a point cloud with millions of points with 3D coords with millimetric accuracy. But, the usage of this kind of data is needed only in high end jobs like Dam Deformation Studies etc., LiDAR is like an X ray which should be used in Orthopedic applications. Being an expensive one, LiDAR should be judicially used. Over enthusiasm leads to unnecessary expenses.

In GIS data collection, LiDAR provides detailed three-dimensional surface information. It maps terrain accurately, aids in land cover classification, urban planning, forestry management, and flood risk assessment. Its versatility extends to infrastructure projects, making it a cornerstone technology for diverse GIS applications.

The full potential of LIDAR is yet to be realised in India. Though the first mapping mission was undertaken in Delhi at the turn of century, it did not catch up. One of the major reasons is lack of skilled manpower. Except for the efforts by IIT Kanpur under Dr Bharat Lohani, no institution offered training programmes. Today, with the fast changing scenario, the requirement is LIDAR sensors deployed on Drones. It is time to invest in.capacity building in LIDAR drones.

The ability of LiDAR to 'penetrate' vegetation and map areas below the tree canopy is just one of it's superpowers! By selecting different returns you can produce a variety of datasets such as Digital Terrain Models (DTM) or Digital Surface Models (DSM). You can then take this further and try and calculate the average canopy height of an area by subtracting the DTM from the DSM. This will give you an output that is a raster where the values are the approximate canopy height. Experiment on an area you are familiar with to see what sort of results you can achieve. Another advantage of LiDAR is the fantastic visualizations you can create. Using 3D mode within a GIS package mean you can use the data to create intuitive visualizations.

There are many advantages to using LiDAR for spatial data collection some of them are, -Penetration, LiDAR sensor is known to have the ability to penetrate vegetation canopy thus helping to acquire data below (ground or vegetation stem) -Survey duration, to acquire data with the same output specification, surveying with LiDAR requires less time than a ground survey (topography) -processing duration, compared to photogrammetry for large areas and/or detailed output, LiDAR data can be processed faster.

This helps to collect the height data of the earth's surface and the objects on the surface which can later on be deployed to generate 3d models including digital surface models. If these models are incorporated with other available spatial data lot of GIS matrices can be calculated such as available total rooftop area in a metropolitan city, average building heights, proximity of high-rise buildings etc.

LiDAR offers several advantages in mapping: Accuracy: LiDAR provides highly accurate distance measurements, allowing for precise mapping of terrain, structures, and objects. Penetration through Vegetation: LiDAR can penetrate through vegetation, making it effective for mapping forested areas and obtaining ground elevation data beneath the canopy. High Resolution: LiDAR can capture data at high spatial resolutions, resulting in detailed and fine-grained maps. Speed of Data Collection: LiDAR systems can quickly collect data over large areas, making it efficient for mapping extensive regions. Night and Day Operation: LiDAR is not dependent on ambient light, allowing for mapping in various lighting conditions, including nighttime.

Le LiDAR capte le réel, il s’appuie sur le concret, le terrain. Utilisé pour suivre l’évolution de zones urbaines, il pourrait permettre de détecter ce que les données officielles ne font pas appara?tre (Les constructions illégales par exemple).

Like any dataset derived from remote sensing techniques, LiDAR can be subject to artefacts and anomalies which can be related to things such as the surface being measured, weather effects and obstructions between the sensor and the target area. One funny example I experienced (during a live demo) was a flock of birds causing a ‘mountain range’ to appear in the south of England after creating a DSM. As with all data analysis make sure to thoroughly check the data for any issues before and during processing. Apart from these rare issues the biggest challenge analysts often face is the file size which can translate into long processing times. Tip: Use the most powerful PC you can find and clip the data to the smallest area possible.

Although LiDAR perceives to have high accuracy, error still could be happened due to weather, noise or obstruction. Therefore, fieldwork measurement need to be done as a control for LiDAR Imageries, sampling point for LiDAR accuracy assessment need to be carefully designed for this.

Limited Reflectivity in Certain Materials: Some surfaces, such as glass and water, have low reflectivity, making it challenging for LiDAR to accurately capture data from these materials.

In my experience using UAV-based LiDAR, water can be a problem in LiDAR survey. Whether it is in the form of fog, rain (objects covered by rain), or wetland. The data usually have fewer points and returns, which can be void sometimes.

There are certain restrictions with aerial Lidar surveys that could compromise data accuracy. It becomes difficult to gather the points in places with high reflectivity, like black-topped roads, lake surfaces, or any other black surface where laser light penetration and ranging are not achievable.

LiDAR or Light Detection and Ranging, is an optical remote sensing technology that uses laser light to accurately measure the Earth's surface in three coordinates. This has become a favorite method of data collection for Geographic Information Systems (GIS) due to its cost-effectiveness compared to other means of surveying. One example of such a GIS system would be ArcGIS Pro or ArcGIS desktop, where you be able to upload the LiDAR and process it. Also, you can import LiDAR data directly, if you are using the open-source software QGIS.

The evolving concept and technology of LiDAR applications, particularly with the increasing prominence of Advanced Driver Assistance Systems (ADAS), mark a significant development. Achieving precise localization with an accuracy of up to 2cm is made possible through the integration of LiDAR, SONAR, GPS, and IMU within ADAS systems. This convergence of technologies, complemented by dynamic data acquisition and processing in GIS, collectively empowers ADAS systems. This enables autonomous cars to receive guidance, facilitating informed decision-making during navigation and manoeuvring. This integration not only elevates the precision of localization but also contributes to the overall efficiency and safety of autonomous vehicle operations.

LiDAR is renowned for its ability to produce high-resolution, three-dimensional maps of the Earth's surface. It's particularly effective in capturing the topography of the land, even in areas where vegetation is dense. This capability is crucial for various applications, including flood modeling, landslide risk assessment, and urban planning.

One of the Unique use of LIDAR data is creating a temporal data for Terrain modelling. Generally Elevation models are created from data obtained from topographic sources and LIDAR. The creation and availability of a Multidate / Temporal Elevation Database like Remotely sensed database (managed by space agencies) can be a turning point for many 3 Dimensional applications. some of the examples are analysis for flood patterns, soil degradation, seismic hazards, man made risks, Geological analysis, Species migration etc.

Lidar reveals hidden cities by detecting thin linear elevations beneath the forest canopy. It sends out more than 160,000 air-to-ground laser pulses every second, making it an excellent sampling tool for the exploration of archaeological sites. LiDAR data assesses forest health, monitor tree, estimate density and biomass . It helps with forest certification and forest management planning. flood It provides important information for water resources management. It helps create accurate 3D city models.

Among various applications, LiDAR is very useful to monitor and assess structural or architectural health to insure public safety, environmental impact and protect the significance of particular architecture by providing measures of conservation.