Light detection and ranging (LiDAR), aka airborne laser scanning (ALS), is a widely used remote sensing technology with promising potential to assist in mapping, monitoring, and assessing geographical landscapes.
LiDAR has several advantages over traditional analog or digital passive optical remote sensing, including nearly perfect spatial data registration and the ability to penetrate the vertical profile of a forest canopy and quantify its structure.
LiDAR has been successfully used in many parts of the world to assess the height and size of individual trees or estimate canopy closure, volume, and biomass of forest stands at the stand level; to assess wildlife habitat and quantify stand susceptibility to fire. A clear advantage of LiDAR is that it is stable and consistent – it can produce products such as DTMs that are comparable and can be used across time and space.
In this post, we highlight some of the strengths and weaknesses of LiDAR.
Strengths of LiDAR
Most accurate 3D information: LiDAR is an airborne sensing technology that makes data collection fast and precise. LiDAR provides the most accurate and quick data on the 3D structure of any remote sensing technique. LiDAR gives a much higher surface density than other data collection methods such as photogrammetry, with low-pulse density LiDAR typically exhibiting sub-meter accuracy. LiDAR can also collect elevation data in a dense forest and can be used day and night. It is not affected by any geometry distortions such as angular landscapes or light variations such as darkness and light. LiDAR can be used to map inaccessible featureless areas such as high mountains and thick snow areas. Besides, it is not affected by extreme weather. This means that data can be collected under any conditions and sent for analysis.
Versatile data: LiDAR technology is a versatile technology that can be used integrated with other data sources to produce digital models of terrain and canopy and customized for diverse needs to suit the research questions at hand. It makes it easier to analyze complex data automatically with minimum human dependence, especially during the data collection and data analysis phase. LiDAR provides highly complementary data that can be used in conjunction with satellite and aerial imagery to gain insights that neither imagery nor LiDAR can reach alone.
Ability to cover large areas cheaply and quickly: It has quicker turnaround, lower costs than photogrammetric methods, and is less labor-intensive. It can also collect data in steep terrain and shadows and produce digital elevation models (DEM) and digital surface models (DSM). LiDAR is a cheaper remote sensing method in several applications, especially when dealing with vast land areas considering that it is fast and highly accurate.
Weaknesses of LiDAR
High cost and difficulties of LiDAR data collection: Although LiDAR is inexpensive when used in large applications, it can be costly when used to collect data in smaller areas. LiDAR costs decrease per unit area as the total area surveyed increases, but this can be substantial and depend on the cost of fuel, pilots, airplane rental, all of which depend on geography and weather. It also involves technically complex processing, analyzing, and interpretations, which can be time-consuming and technically challenging, that may require expertise in GIS and remote sensing to use and interpret correctly.
Limited spatial and temporal availability: LiDAR currently largely lacks multispectral data limits its utility to non-spectral analyses. Freely available LiDAR datasets are available for a small fraction of the world and are frequently limited to data collected at a single point in time.
LiDAR cannot penetrate thick canopies: Dense tropical forests are problematic due to a lack of ground hits. In some cases, where the forest canopy is dense, the LiDAR pulses may not penetrate the canopy, resulting in incomplete data. When collecting data, LiDAR pulses may not penetrate thick vegetation, resulting in inaccurate data. LiDAR cannot work on altitudes higher than 2000 meters because the pulses will not be effective at these heights.
Influences from the weather: LiDAR can be ineffective during heavy rain or low-hanging clouds. Because the laser pulses are based on reflection, LiDAR does not work well in areas or situations with high sun angles or large reflections. It may not return accurate data when used on water surfaces or where the surface is not uniform because high water depth will affect the reflection of the pulses.
Enormous datasets are difficult to interpret: LiDAR collects extremely large datasets that require extensive analysis and interpretation. As a result, analyzing the data could take a long time. Because of the enormous data sets and the complexity of the data being collected, it may require skilled techniques to analyze the information, which adds to the overall cost.
No International protocols: The Laser beams used by LiDAR pulses are usually powerful in some instances, affecting the human eye. Notably, when using LiDAR technology, there are no strict international protocols that guide data collection and analysis; as a result, it is done haphazardly.
