Courtesy of LiDAR News

This video provides valuable insight on the game changing use of unmanned aerial vehicles UAVs to cost effectively map potentially important archaeological sites.

Courtesy of GCN

Second of four parts, part one can be found here.

Sometimes it takes a crisis to spur innovation.

Such was the case with creation of the Red River Basin Decision Information Network (RRBDIN), and its LIDAR mapping of large swaths of North Dakota, Minnesota and central Canada, said Charles Fritz, director of the International Water Institute.

"In the Red River basin we had a major flood back in 1997. That's when Grand Forks went under," Fritz said. "My organization actually was one of the outcomes from that flood." In the wake of the flood, the Federal Emergency Management Agency took action that resulted in the creation of the RRBDIN, which is managed by the International Water Institute with support from the U.S. Army Corps of Engineers, North Dakota State University Extension and other partners.

"When the 1997 flood hit, we had a huge problem because the only things we had to work with were old [U.S. Geological Survey] maps," Fritz said. Even with the obvious need for better information, FEMA and state funding weren't enough to do the job. "At the time, in 2000, we knew LIDAR was available. We were looking at different technologies to acquire better elevation data, but the numbers were astounding. I mean, we were talking about $35 million."

LIDAR, or light detection and ranging, is a mapping technology that bounces light emitted from a laser source, typically aboard an aircraft, and captures the return to build a detailed map of an area. It’s been around for 50 years, but until recently was an expensive and cumbersome technology limited to specialized uses.

By 2005, however, "Costs were coming down, and the technology was improving," Fritz said. "We said OK, let's move on this."

In 2009, the RRBDIN finished its first pass, collecting LIDAR data covering 54,000 square miles.  The result was 8 terabytes of data that the team needed to make available in a useful form.
Since then, the team has been working to develop and deploy a series of online tools. 

"We did not want have a situation where everybody in the basin had to know ArcView if they want to use the LIDAR data, so we put together the online viewer," Fritz said. "There are some really cool tools in there."
 
RRBDIN's LIDAR viewer allows users to create and customize maps with elevations down to 2-foot contours or spot elevations. There's also a forecast display tool. When the National Weather Service generates a forecast of a flood in Fargo of, say, 38.5 feet, "What does that mean from an inundation and extent standpoint?” Fritz asked. “We can take the LIDAR data, combine it with that 38.5 feet forecast and produced an interactive map that shows the extent of the flooding."

Fritz said the LIDAR data can be combined with all sorts of other data. It can, for example, be used to determine where and to what extent nutrients will flow in irrigated fields.

"We're talking about efficiencies here, where we can get the most bang for the buck, whether it's a flood damage reduction project or water quality project or natural resource enhancement project," he said. "All of that is predicated on the LIDAR data."

When he started the project, Fritz felt like he was a voice in the wilderness. He spent a year and a half trying to convince people that the project was important to them. "Now," he said, "I can tell you that with no exceptions the people who have experience with the data that we have collected are all saying that it is the best thing since sliced bread. Even most local watershed districts will not start a meeting unless they have that LIDAR viewer open on the table."

While Fritz's team is focused on building more applications to use the data, they are also looking forward to a fresh collection of data. "Since we completed data collection in 2009," he said, "there's been a lot of work, especially in the major metropolitan areas. Fargo, Grand Forks. They put up flood dikes, etc., so now we are talking about how we're going to update the current LIDAR data set."

If flatness is a hydrological challenge in the Red River Basin, rugged terrain is a major challenge in Oregon. Feet-on-the-ground surveys of forest inventories, for example, are particularly time-consuming and, therefore, expensive and quickly outdated. 

Scanning from the air is much less costly. "It ends up being about offsetting costs," said John English, LIDAR data coordinator for Oregon's Department of Geology. "It is allowing people to save money on these long surveys."

With two specialists – English and one other scientist collecting and organizing the data – the state of Oregon covers between 5,000 and 7,000 square miles per year at a cost of $3 million to $4 million. So far, the team has collected LIDAR data on 26 percent of the state, focusing first on the more heavily populated western half. The state also has approximately 25 GIS analysts working with the data for a variety of agencies and purposes. 

The airborne LIDAR fires eight points per square meter, more than 100,000 pulses per second.  According to English, that's enough to ensure that even in dense forest, some of the points reach the ground. Comparing the distances of pulses reflected off the tree tops and those reflected off the ground allows the team to calculate the heights of trees very accurately. And not only that, the full array of returned pulses allows the team to survey undergrowth, too.
 
Oregon's LIDAR efforts, of course, aren't restricted to forest inventories. In fact, the first use of LIDAR was a joint effort of the state's Department of Geology and Mineral Industries and the U.S. Geological Survey to conduct a landslide study in the Portland area in 2004. They also use the data for habitat analysis assessment. 

"People are finding more uses for it," English said. "Municipal mapping of streets, measuring volumes for displaced sediment, flood mapping, hazard mapping. You can even detect wear and tear on roads. It can do a rough survey on everything in an entire city. If you have a house and you know its height, and there's a flood, we can infer how many houses are totaled according to FEMA. Right now we’re producing the most accurate flood-inundation maps ever made."

English says the plan is to scan the entire state. "But we're trying to do it in a methodical way. Primary areas of interest are places where work is being done and where humans interact with the environment,” he said. “So we've covered about 98 percent of the population of Oregon."

Courtesy of GCN

First of four parts.

It's faster than a speeding bullet. It can measure buildings in a single pulse. It can scan the ocean floor and peer through forest canopies to measure undergrowth. It's LIDAR – light detection and ranging.

A standard LIDAR system emits a beam of light from a laser source and then captures the returned light in sensors as it bounces back from a reflecting object, measuring the distance by calculating the time required for the round trip. While LIDAR systems were used by the federal government as early as the 1960 — primarily for atmospheric studies — it wasn't until after 2000 that a combination of factors resulted in a boom of LIDAR data-gathering projects that are now bearing fruit at federal, state and local government levels.

U.S. troops have used LIDAR to map the difficult terrain in Afghanistan and a Colorado State University scientist used it in creating the first forest height map to measure carbon cycles in ecosystems.

"It's being used by just about everybody who uses a map," said John English, LIDAR data coordinator for Oregon's Department of Geology. "Every municipality and county is using it. The Department of Land Management and the U.S. Forest Service use it for their forest inventory surveys." 

According to English, the agencies are increasingly turning to LIDAR because the technology has gotten both less expensive and more accurate, and, because surveys are generally done from aircraft, vast amounts of territory can be covered quickly. "It's been a huge timesaver," he said.  "The estimate of savings is incalculable."

Kirk Waters, a program manager at the National Oceanic and Atmospheric Administration's Coastal Services Center, agrees. "LIDAR is a way to get fairly accurate elevations over a broad area at a reasonable price," he said.

Waters pointed to a March 2012 report by the U.S. Geological Survey that found that a national program of collecting LIDAR data would result in net benefits of between $116 million and $620 million a year. According to the study — the National Enhanced Elevation Assessment — the biggest savings are to be realized in flood risk management, infrastructure and construction management, natural resources conservation, agriculture and water supply management.

Elevation data can tell city planners where to plan mitigation for floods. It can tell farmers where to expect irrigation runoff and where to plant crops that require the most expensive fertilizers. Cities are using LIDAR to build 3D maps.  

In all, "the study came up with 600 different uses,” Waters said. “There's just tons of applications."

"It's at the beginning stages," said Steve Snow, a mapping and LIDAR specialist with geospatial tech company Esri. "Everybody is learning about the technology." Esri, in fact, just added the ability to import native LIDAR directly into its industry-standard ArcGIS software.

In principle, the technology behind LIDAR is simple. By measuring the time it takes light to bounce off an object, and knowing the speed of light (186,000 miles per second), one can detect the distance of the object. The challenge has been in developing equipment that can fire rapid pulses of light — in some cases up to 150,000 pulses per second — and that can measure the returning light with accuracy.

LIDAR systems vary in the wavelengths of light and the power of the pulses employed. High-energy pulse systems, for example, typically are used for atmospheric research, while lower-powered micropulse systems are more often employed for downward scanning, since they are considered "eye safe." 

And although most airborne LIDAR systems use 1064-nanometer laser beams, bathymetric LIDAR systems — those used to penetrate water — employ a narrower 532-nm beam. Bathymetric LIDAR also transmits two light waves, one infrared and the other green. As a result, it can detect two returning signals, one off the water surface and the other from the seabed. 

Other critical elements in the development of LIDAR systems have been the enhancements in the production of higher-resolution and more flexible scanners, optics and photoreceptors. 

Finally, collecting LIDAR data from aircraft involves a few additional challenges. Because the LIDAR sensor is moving, the changes in location between the firing of the pulse of light and its return must be accounted for in making any measurement. That required the development of fast, high-resolution GPS devices and inertial measurement units that measure velocity and orientation. 

Coordinating of this, of course, is no mean feat, nor is digesting the massive amounts of data that are produced.   

According to Waters, NOAA's LIDAR scans are shooting between 100,000 and 200,000 points per second with about up to 10cm of error. "The rest is math," Waters said. "Lots of math, but it's still just math."

NEXT: LIDAR proves its worth in the floodplains of the Red River Basin and the Forests of Oregon.

Simultaneous localization and mapping, or SLAM for short, is the process of creating a map using a robot or unmanned vehicle that navigates that environment while using the map it generates. SLAM is technique behind robot mapping or robotic cartography.  The robot or vehicle plots a course in an area, but at the same time, it also has to figure out where its own self is located in the place. The process of SLAM uses a complex array of computations, algorithms and sensory inputs to navigate around a previously unknown environment or to revise a map of a previously known environment.  SLAM enables the remote creation of GIS data in situations where the environment is too dangerous or small for humans to map.

SLAM is similar to a person trying to find his or her way around an unknown place. First, the person looks around to find familiar markers or signs. Once the person recognizes a familiar landmark, he or she can figure out where they are in relation to it. If the person does not recognize landmarks, he or she will be labeled as lost. However, the more that person observes the environment, the more landmarks the person will recognize and begin to build a mental image, or map, of that place. The person may have to navigate this certain environment several times before becoming familiar with a previously unknown place.

In a related way, a SLAM robot tries to map an unknown environment while figuring out where it is at. The complexity comes from doing both these things at once. The robot needs to know its position before answering the question of what the environment looks like. The robot also has to figure out where it is at without the benefit of already having a map. Simultaneous localization and mapping, developed by Hugh Durrant-Whyte and John L. Leonard, is a way of solving this problem using specialized equipment and techniques.

The process of solving the problem begins with the robot or unmanned vehicle itself. The type of robot used must have an exceptional odometry performance. Odometry is the measure of how well the robot can estimate its own position. This is normally calculated by the robot using the position of its wheels. Something to keep in mind, however, is that there is normally a small margin of error with odometry readings. The robot might be off in its measurements by several centimeters. Consequently, the robot is not where it thinks it is in a given location. These errors must be taken into account in algorithms. Also, areas are often remapped to make up for this deficiency.

One requirement of SLAM is a range measurement device, the method for observing the environment around the robot. The most common form of measurement is a laser scanner such as LiDAR. Laser scanners are easy to use and very precise. However, they are also extremely expensive. There are other options, though. Sonar can be used, and this device is especially useful for mapping underwater environments. Imaging devices can also be used for SLAM. These optical readers can came in 2D or even 3D formats. The measurement device used depends on several variables, including preferences, costs, and availability.

Another key component in the SLAM process is acquiring data about the environmental surroundings of the robot. Just like a human, the robot uses landmarks to determine its location using its sensors, the laser, sonar, or whichever measuring device is used. A robot will use different landmarks for different environments. However, there are certain requirements for landmarks used in SLAM. First of all, landmarks should be stationary. A robot cannot determine its own location if a nearby landmark is constantly moving. Additionally, landmarks should be unique and distinguishable from the surrounding environment. Landmarks also need to be plentiful and should be able to be viewed from many different angles.

Once a robot has sensed a landmark, it can then determine its own location by extracting the sensory input and identifying the different landmarks. A method needs to be in place in order for the robot to do this. This landmark extraction can be done in a variety of ways from algorithms like Spike extraction to scan-matching. The important factor to remember is that the robot needs a way to identify a landmark. Robots can also use data from previously scanned landmarks and match them up with each other in order to determine its location.

SLAM is the mapping of an environment using the continual interplay between the mapping device, the robot, and the location it is in. As the robot interacts with the environment, it not only maps the area but also determines its own position simultaneously. Like other mapping technologies, SLAM is undergoing constant improvement as a tool for exploring the environments around us.

Robotic mapping using LiDAR. The robot was built by the Technische Universität Darmstadt in Germany.