More Than A Trend

With lidar progressing quickly into the mapping mainstream, it pays to understand its background and latest advancements.
By Bill Emison

Anyone can see that light detection and ranging (lidar) has quickly evolved since its development in the 1960s. And as with all technologies today, it continues to advance in effectiveness, accuracy, and applications, enhancing the new world it provides the user. In recent years, lidar has come to have a huge impact in the surveying and mapping world, from both aerial and terrestrial platforms.

Where did it come from? The National Aeronautic and Space Administration (NASA) developed the first lidar sensor technology to accurately measure distances between spacecrafts. The 1960s saw development of the first ground-based lidar sensors, followed by the first flown sensors on small aircraft in 1969. By the late 1970s, sensors were being flown on large aircraft capable of long-range measurements.

In the 1980s and 90s, scientists expanded their traditional use of lidar to include atmospheric analysis, volumetric determinations, and analysis of material composition. Remote sensors and their use for developing topographic maps came into being as well. The new century led to the fusion of multiple sensors and simultaneous collection of data and real-time data processing.

Lidar data are collected via three methods. Airborne lidar is collected via fixed-wing aircraft or helicopter. A stationary sensor collects terrestrial lidar, and mobile lidar is collected from a moving truck, train, or other vehicle. Over the years, airborne lidar has become the prevalent method of data collection, with terrestrial the second-most used and mobile lidar the third.

The advances in the use of lidar and its applications have grown quickly. Today, lidar users are experiencing increased capacity and accuracy in sensors, expansion of the uses and applications of lidar data, more robust systems to support the processing of data, and the new world of three dimensions versus the traditional two dimensions.

Lidar sensors are rapidly advancing due to expanding technology. The volume of points that can be collected at one time is increasing along with a subsequent increase in accuracy due to the density of the point collection. In the early days of airborne lidar, the sensor’s pulse rate ran between 10 and 20 MHz, which means that 10,000 to 20,000 points were collected per second. Today, commercial sensor vendors are actively developing airborne sensors with pulse rates in excess of 400MHz. Furthermore, short-range lidar sensors used for transportation and engineering applications are surpassing pulse rates of 1 GHz, one million points per second!

As volume and accuracy increase, software tools are required to process the collected data points and convert those to useful information for the end user. Various software applications have been developed to provide efficient data processing tools, including Merrick’s Advanced Remote Sensing (MARS) software. The software has been in use since the late 1990s, with users including the U.S. Geological Survey, Conoco-Phillips, the U.S. Army Corps of Engineers, and the National Geospatial-Intelligence Agency. A MARS software upgrade has been recently released that provides advancements including support for 64-bit operating systems, automated topographic data generation functions, quality control tools, and a robust data classification toolset.

Applications of Lidar Data Grow

In its first generation of use, lidar typically provided data for flood plain management, dam and levee certification, general mapping, and topographic mapping. Now those applications have expanded. Utility companies increasingly engage lidar service providers to define new transmission line routing, manage the expansion of capacity for existing power lines, and manage encroaching vegetation on existing power lines. Oil and gas companies are using lidar to define new transmission corridors and for exploration. The military is using lidar for defense/intelligence activities and warfighter training. For example, lidar data can be collected within a specific area where the warfighter will be deployed. This data documents, in three dimensions, the topography, vegetation, and structures within the area of interest. This data is then imported into simulators that allow the warfighter to practice in the exact environment in which they’ll be deployed.

Expanding uses of lidar also include commercial forest management to plan for tree harvesting, planting, and planning for future crops. In the natural resources management field, mining companies are using lidar data to monitor excavation activities and for compliance with environmental regulations. Other uses range from atmospheric research and meteorology to the detecting pipeline gas leaks, mapping forest canopies, measuring snow pack, and managing vehicle speed. The applications begin to seem endless.

As sensors have advanced in technology, the supporting PC hardware has become more powerful. This power provides more efficient and faster processing of the massive volumes of data that come from lidar data acquisition. Processing software, such as the MARS suite, is keeping pace with this PC power by providing the capabilities to accommodate 64-bit processing. The new 64-bit operating systems, such as Microsoft’s Windows 7, allow users to spread large data processing tasks over multiple CPUs, which significantly improves overall performance and shortens the time for generating data products and distributing them to project members. In addition, video card graphical processing units (GPUs) are becoming more powerful and can improve 3D visualization and rendering performance. Between the CPUs and GPUs, the days of being able to process and visualize huge data sets of information are here.

Seeing the World in a New Way

The world of surveying and mapping has traditionally been two-dimensional with documentation of roads, buildings, infrastructure, and topography on the flat surface of paper. With the rapid expansion of information technologies and incorporation of lidar data, our world is quickly becoming three-dimensional. Now, lidar provides a 3D view of the environment being worked on and gives viewers the opportunity to see the details of features within this environment.

Beyond just seeing the world in 3D, they can manipulate it through the use of various software applications. The MARS software allows you to intelligently reclassify lidar points to represent real-world features like electrical transmission lines, buildings, bridges, and overpasses. Furthermore, lidar data derivatives can be used to populate GIS and CAD systems, which commonly provide traditional mapping and analysis activities. For example, lidar is allowing planners to better understand, visualize, and plan complex urban areas that contain dozens of infrastructure features and elements.

Taking the three-dimensionality a step further, this new 3D spatial reality becomes a medium for content, from factual information to marketing and advertising opportunities. For example, technologies, including lidar, can be combined and converted to support applications for an iPhone. Imagine walking through a shopping mall or down a street in London, viewing a 3D,
real-time version of the mall or street on your iPhone, shown from your exact, current location. As you walk, advertisements for current sales show up on your iPhone from the stores you are walking by, or information about historic buildings pops up on your iPhone for the buildings you see. It’s real-time information in your hand, and those opportunities are coming for associating 3D content with new, emerging technologies.

To handle the massive lidar datasets currently collected in the commercial and military markets, organizations frequently turn to relational database management systems (RDBMS) to store, manage, and serve point cloud data to users within an enterprise. Both Oracle and Microsoft (SQL Server) offer spatial feature support within their database products, providing unprecedented performance gains over the traditional binary file formats commonly used today.

Another emerging trend is the use of unmanned airborne vehicles (UAVs) for data acquisition. The on-going war effort in  Iraq and Afghanistan is heavily subsidizing the development of these data collection technologies, which allow for persistent surveillance and direct support for the warfighter within a complex urban terrain. While not yet available in the United States, UAVs represent an efficient and economical platform for future data collection work.

The next generation of remote sensing technologies will aim to seamlessly integrate multiple sensors within a data collection platform, allowing users to simultaneously collect spatial, optical, and spectral information. This approach directly supports data fusion exploitation, where multiple, coincident signals can significantly improve our ability to detect and identify relevant features within a complex and noisy 3D landscape.

However, it will be the post-acquisition phases of the workflow that will experience the most advances and benefits for the end user. Major enhancements in automated and knowledge-based calibration, orientation, filtering, and 3D feature extraction are already coming from specialized software providers. Data from several different sensor types are being fused, providing improved scientific and analytical value.

Sharing, publishing, and disseminating all or selected slices of data will occur with ease over the internet, allowing colleagues to easily collaborate. In the end, the many advancements forthcoming will greatly assist end users to exploit and comprehend many different types of data, from a host of sensors, to solve complex real-world problems and create a better place to live.

Bill Emison is MARS product manager for Merrick & Company, an engineering firm in Aurora, CO. MARS supports Merrick’s activities in collecting, processing, and applying lidar data. He has an undergraduate degree in business administration from the University of Louisville and a Master’s degree in GIS (city planning) from the Georgia Institute of Technology.

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