Advantages of using lidar in GIS
Lidar provides a wealth of information for various applications and offers several advantages over traditional methods for aerial mapping. Lidar is changing the paradigm of terrain mapping and gaining popularity in many applications such as forest inventory, floodplain mapping, hydrology, geomorphology, urban planning, landscape ecology, coastal engineering, survey assessments, and volumetric calculations. All these applications can take advantage of combining lidar and GIS to conduct analysis and manage, visualize, and disseminate lidar data.
A few key advantages of lidar include the following:
- Data can be collected quickly with very high accuracy.
- Surface data has a higher sample density. The high sample density improves results for certain applications such as floodplain delineation.
- Collect elevation data in a dense forest, where photogrammetry fails to reveal the accurate terrain surface due to dense canopy cover.
- Lidar uses an active illumination sensor and can be collected day or night when compared to traditional photogrammetric techniques.
- Lidar does not have any geometric distortions like a side-looking radar.
- Lidar can be integrated with other data sources.
ArcGIS provides three container types for working with lidar LAS files in ArcGIS: the LAS dataset, the terrain dataset, and the mosaic dataset.
Lidar project considerations
There are a few important aspects that need investigation when looking into obtaining lidar data. Focus on what the project needs are and how much funding you have for a lidar survey.
Question the following: What will the data be used for? How will the data be used? What level of data processing is appropriate? What resources will be needed? Are derived data layers important?
The cost of the lidar project depends on project scope, location, boundary shape, point density, deliverables, time frame, weather patterns, and so on. Remember, all lidar systems are not the same—although some components are similar—but each of them is optimized to provide datasets that can differ greatly. Do not be under the impression that more points are always better, as a high density dataset is not required for every project. Also, lidar datasets are very large, and to process them, you will need substantial resources.