字幕列表 影片播放 列印英文字幕 (gentle intro music) - My name's Tristan Goulden. I'm a remote sensing scientist with the Airborne Observation Platform and I'm going to do a talk this afternoon on Introductin to LiDAR. Primarily on discrete LiDAR because Keith Cross is going to be following me talking about the waveform LiDAR. LiDAR is a active remote sensing system which means that it has its own energy source. And so the main subsystem of a LiDAR is the laser and we'll use the laser for is to generate a pulse of energy that comes out of the sensor which is pointed out the bottom of the aircraft, travels down to the ground, reflects off targets on the ground, and then returns back to the aircraft. And so, we're actually able to measure the time it takes for that laser pulse to go down, reflect, and come back. Based on that two way travel time we can calculate a range from the aircraft down to the ground. Also have a GPS sensor on the roof of the aircraft to get the aircraft position, an inertia measurement unit inside the aircraft to get orientation, roll, pitch, and yaw off the aircraft and then a scanning mirror which directs the laser pulse within a squad beneath the aircraft. When you combine all of these subsystems together you can actually coordinate points on the ground based on all the observations from these subsystems. What makes LiDAR really unique is that it is able to achieve a really accurate and dense raw sample of the terrane. It's able to do that mostly from the Laser Ranger which today's rangers can operate at about 500 kHz. This means that the system is capable of sending out 500,000 pulses per second. Each pulse is capable of getting multiple returns. It's possible that some of the energy is going to reflect off the top of vegetation then, energy will continue down through the vegetation and might reflect off the understory, then hopefully will make it down to the ground reflect off the ground and we can get multiple returns from each pulse. That means we're able to get, for sending out 500,000 pulses per second, a multiple of that many points every second. It's an incredible amount of data. I just want to briefly introduce the difference between discrete and full waveform. I won't get into a lot of details cause Keith is going to talk about that in a minute. But basically there's kind of two flavors of the observations we get from the LiDAR. The discrete gives us points only. When we get that reflection off of an object, we that return range and we calculate just a single coordinate. From multiple returns we could get three or four individual three dimensional coordinates. With the Optech Gemini when that signal actually comes back its split and part of the signal goes to a waveform digitizer and that records the entire return energy signature. This energy signature will include the outgoing impulse here and then some time will pass and then as we're going through vegetation you're getting this humps here where we're getting additional energy returns from the object. And so in the discrete LiDAR we're going to cut off the timing at each one of these humps, here, here, and here and give us three individual points. But with the waveform LiDAR we get this full return signal and you're able to do more advance analysis on the structure of the vegetation with that signal. The NEON LiDAR currently we are operating two Optech Gemini systems which are slightly older technology. We purchased these in 2011 and 2012. In the future we will be also doing surveys with the Riegl Q780 which is a more contemporary system. Right now we run our obtech at a Pulse Repetition Frequency of 100 kHz. We chose this frequency because it's the highest we can go and still maintain the accuracy that we want. If we go any higher than that, it's capable of going up to a 166 kHz, but there is a large degradation in accuracy. We fly at 1,000 meters. We have 30% overlap in our flight lines. That gives us 2-4 pulse per squared meter. In the overlapped areas we get 4 pulse per squared meter but in the non overlapped areas we get 2. It's capable of recording up to 4 overturns per pulse on the discrete LiDAR. And so that's theoretically we could achieve 400,000 points per second but generally you'll never get 4 returns on every pulse. In order to position all of the LiDAR data we also have to determine a trajectory. So this is the information that the GPS IMU collects. As part of that we set up GPS base stations in the local vicinity of our survey areas. I think somebody asked about this yesterday. Generally we try to exploit the CORS Network as much as we can. And these are GPS base stations that are set out across the United States and are run by the local governments or the federal government. We use those CORS stations. There's kind of stationary GPS sites with really accurate coordinates and we use those to differentially correct the GPS trajectory. We go to the site and there's COR station and we see that the distribution of COR stations doesn't give us sort of less than a 20 kilometer baseline between that reference station and the aircraft then we go ahead and we set up our own GPS base station to correct the airborne trajectory. For the most part, unless we're transiting from the airport to the site, we'll never have base stations that are more than 20 kilometers from the aircraft. We do this because we're aiming for errors in the GPS trajectory to be between 5 cm and 8 cm. In order to do that we really need those local GPS base stations that are close to the airborne trajectory. We try to achieve errors of .005 degrees in pitch and roll and .008 degrees in yaw. This is just a picture of the IMU that's located inside the aircraft to get the roll, pitch, and yaw as well as the GPS antenna. One of the reasons we're able to get these really accurate trajectories is because GPS IMU are really complimentary technologies. The IMU is able to achieve really fast positioning but it's prone to drift over time. Where GPS gives us really good position every second or so but we can't get a position in between those two GPS observations. The GPS operates at 1 hertz. The plane's traveling at 50 meters per second. That means we're only getting 1 GPS observation every 50 meters. A lot could happen to the plane in 50 meters. That's where the IMU takes over. It's operating at 200 hertz. It takes care of the positioning in between those two GPS observations. We get a good position 200 times per second. I should mention that as you're going in between the two GPS observations the IMU is prone to drift but it gets corrected every time you get to a new GPS observation. So really it only needs to do its positioning for one second. And this is just an example of some results of a trajectory. This was done at Smithsonian Environmental Research Center. This is the upper left hand side you can see the software that we use to process the trajectory and then the results of the trajectory in Google Earth where you can really see each one of the flight lines that we flew going up and down the site. We actually also worked up statistics for all of our trajectories from our 2016 flights and we'll probably do the same for the 2017 flights. Just so we can get an impression of the type of quality we're getting on those trajectories. You can see generally we try to keep our roll below 20 degrees so that we maintain locked to all the satellites. You can see that for the most part our roll was always between 20 and -20. Generally we always had above 6 satellites but more like 8 or 9. Our PDOP was generally below 4 which is quite good. This is the distance to the nearest base station. You can see that for most of the time we're below 20 kilometers. There is sometimes where we get up a little bit higher but that's generally during transits between the site and the local airport. Once we have that trajectory we're able to mix that with the range and scanning information that's collected by the LiDAR sensor to produce the point cloud. This is an example of our L1 product which is point clouds produced in LAS format. LAS is a standard binary format for exchange of LiDAR point clouds. This is an example from the San Joaquin Experimental Range You can see all the individual points that were collected by the LiDAR and even the structure of the vegetation you can make on those individual points. That's just our L1 product. All the L3 products that we produce are rasters opposed to point clouds Instead of all those individual coordinates we have a grid of points. We actually have to convert those points into a raster product. You can imagine if we observe this area of land with the LiDAR we might get a sampling like this of all the LiDAR points. But what we really want to create our raster product is the elevation at each one of these grid points. Basically all of the points overlaying where we want those grid notes. What we do is we look in the area surrounding a particular gride note and then we use an interpolation method to calculate what the elevation of that grid note might be. And of course we can create that at any size And here at NEON we create these rasters at 1 meter resolution. There's lots of different interpolation methods that you could use. I encourage you to go out there and research the different ones that are available. At NEON we use what's called a Triangular Irregular Network. Which basically means we're just creating linear connections between all the points and forming triangles between all the points. If you think about it you can kind of lay that grid underneath this Triangular Irregular Network that's connecting all those points. Just interpolate the elevation from the plane of the triangle that overlaps the raster cell and pull that elevation down and assign it to that raster cell. That's how we get the elevation from the LiDAR Point Cloud. All the points here are LiDAR observations and we interpolate in between them, pull the elevation from the triangular plane, and assign that elevation to the raster grid. One the advantages of this is that obviously it honors the location of the true data point. You're never interpolating down or filtering a lot of observations in creating a new elevation from what you observed, and it's computationally efficient. This is the main reason that we want to use it. Because when we're producing a lot of data in an automated fashion we want a really computationally efficient algorithm. The main downfall of the Triangular Irregular Network is it doesn't exploit redundancy in the LiDAR data to improve your accuracy.