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UNIVERSITY OF NOVA GORICA SCHOOL OF APPLIED SCIENCES LIDAR MEASUREMENTS REPORT FOR PHYSICAL LABORATORY III Anže Peternel Mentor: prof. dr. Samo Stanič Nova Gorica, 9. 4. 2013 1 CONTENTS 1 Contents .............................................................................................................................. 2 2 What is remote sensing and LIDAR .................................................................................. 3 3 Mobile LIDAR at UNG ...................................................................................................... 7 4 Experiment ......................................................................................................................... 9 5 Questions .......................................................................................................................... 11 5.1 What are the killers of the LIDAR signal? ............................................................... 11 5.2 Which parameters are related to the atmospheric optical properties from the LIDAR equation, and what do they mean? ....................................................................................... 11 5.3 Analyze the sources of noise and what is your way to eliminate the noise from return signal? ........................................................................................................................ 11 5.4 Why do we do range-corrected and take the natural logarithm to the return signal? 11 6 Conclusion ........................................................................................................................ 12 7 References ........................................................................................................................ 13 2 2 WHAT IS REMOTE SENSING AND LIDAR Remote sensing is defined as the measurement of object properties on the Earth's surface using data acquired from aircraft and satellites. It is therefore an attempt to measure something at a distance, rather than in situation. Since we are not in direct contact with the object of interest, we must rely on propagated signals of some sort, for example optical, acoustical or microwave. There are two logical types of remote sensing. First one is passive remote sensing, which depends on natural source, such as radiation of observed object or reflection of radiation which comes from third object, which we cannot control. Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include film photography, infrared, charge coupled devices and radiometers. The second type is more flexible and is called active remote sensing. This type depends on an artificial source, which is controlled by those who are doing the observation. Source of signal can be located near or far from the sensor and can be either electro-magnetic waves or sound waves. Typical examples of active remote sensing are SODAR, SONAR, RADAR and LiDAR. SODAR stands for SOnic Detection And Ranging and which is an active remote sensing device, used to remotely measure the vertical turbulence structure and the wind profile of the lower layer of atmosphere. For detection it uses sound waves. Figure 1: Application of SODAR SONAR stands for SOund Navigation And Ranging and is similar to SODAR. This technique also uses propagating sound waves, usually underwater, to navigate, communicate or detect objects on or under the surface of the water, such as other vessels. SONAR is essentially used in navy. It can be both active and passive. Passive is essentially only listening for the 3 sound, made by other vessels, active, on the other hand, is emitting pulses of sounds and listening for echoes. Figure 2: SONAR used in navy RADAR stands for RAdiowave Detection And Ranging. It is an active remote sensing device which uses electromagnetic specter from radio waves to microwaves. The RADAR dish or antenna transmits those electromagnetic pulses which bounce off any object in their path. The object returns a tiny part of the wave’s energy to a dish or antenna which is usually located at the same site as transmitter. The uses of RADAR are highly diverse, including air traffic control, air-defense systems, antimissile systems, astronomy, ocean research, outer space surveillance, meteorological monitoring and geological observations. Figure 3: NEXRAD weather RADAR 4 LIDAR works quite similar as RADAR, except that instead of radio waves it uses ultraviolet, visible or near infrared light. LIDAR stands for LIght Detection And Ranging or Laser Imaging Detection And Ranging. LIDAR devices can be applied in even more fields than RADAR. It has been used extensively for atmospheric research and meteorology. Downwardlooking LIDAR instruments fitted to aircraft and satellites are used for surveying and mapping. It is also used for environmental research, space research and for ocean research. It can also be found in military where it is used for defense; it is perfect for detecting biological weapon attack. Figure 4: Sondrestrom Rayleigh Lidar Figure 5: Experimental Advanced Airborne Research Lidar (EAARL) The LIDAR is made of few different components. These include a transmitter, receiver and a detection system with a controller. From transmitter we shoot short pulses of light that has wavelength as close as possible to the radius of particles we want to observe. LIDAR uses ultraviolet, visible or near infrared light to image objects and can be used with a wide range of targets, including non-metallic objects, rocks, rain, chemical compounds, aerosols, clouds and even single molecules. The wavelength of light pulse depends on laser that is installed in transmitter. Light then hits the particles and then some of the light reflects back to the receiver. This is called backscattering. There are different types of scattering that are used for different LIDAR applications. Most common are Reyleigh scattering, Mie scattering, Raman 5 scattering and fluorescence. Based on different kinds of backscattering, the LIDAR can be accordingly called Rayleigh LIDAR, Mie LIDAR, Raman LIDAR, Na/Fe/K Fluorescence LIDAR, and so on. Receiver is a set of mirrors which redirect light towards photodetector. Photodetector then converts signal into digital and sends it into computer. The data can then be analyzed. The delay in the received pulse tells us the distance from the instrument to the particle and the intensity of the reflected pulse tells us the density of the particles. Figure 6: Schematics of LIDAR measurement More about measuring and processing data can be found in section »experiment«. 6 3 MOBILE LIDAR AT UNG University of Nova Gorica has in 2007 developed a mobile LIDAR station which includes two elastic (Mie scattering) channels (at 266nm and 1064nm) and a fluorescence channel (at 296nm). The LIDAR can operate both in the daytime and nighttime conditions. Figure 7: Mobile LIDAR on a parking lot during measurements Transmitter is CFR400 by Quantel Big Sky pulse laser, which is capable of simultaneous emission of light at different wavelengths. CFR400 emits light at base wavelength of 1064 nm (IR), second harmonic (532 nm, green) and fourth harmonic (266nm, UV). As the attenuation of UV in air is much larger than attenuation of IR, IR light is used for regular Mie scattering operation and the UV light for the excitation of the Tryptophane fluorescence in organic materials. 12'' Dopsonian telescope by company Guan Sheng Optical, serves as the receiver. 302 mm parabolic primary mirror collects the backscattered light and the induced fluorescence to its focal length at 1520 mm, where another mirror is placed. Secondary mirror redirects light into 7 detecting system installed outside the telescope. Dichroic mirrors made by SLS Optics Limited were applied to divide elastic scattering (at 1064nm and 266nm) and induced fluorescence (UV). The first dichroic mirror in the receiver separates UV from IR. UV light is divided once more with the second dichroic mirror, where induced fluorescence is separated from elastic scattering. In order to separate laser backscattering signal from the background, interference filters by BARR Associates were installed. After the filters, the backscattered beam is focused onto the photomultiplier tubes Hamamatsu R7400-06 (266 and 296 nm) and an avalanche photodiode Si APD S8890-30 by Hamamatsu. These sensors convert the received light into measurable electrical signals. Amplitudes of the electrical signals are proportional to the power of received light. Digitalization of LIDAR measurements is performed by an analog/digital (AD) converter (Licel transient recorder) and read out by a Linux based computer for data acquisition and analysis. Figure 8: The CFR400 by Quantel Big Sky pulse laser 8 4 EXPERIMENT The experiment took place on 1st of March in 2013 at parking place behind Nova Gorica University at Rožna Dolina and it lasted from 11:26 am to 11:58 am. The weather was sunny with only few clouds. LIDAR angle was 49° from horizon. The data came in twelve ASCII files with two columns, where the first values represented distance and the second corresponding power. Each file had 2667 measurements. For data analysis and plotting, Wolfram Mathematica 8 software was used. Plotted raw data looks like this and it doesn’t represent much, except the fact that returned power decreases with distance. This plot was drawn using data of the first measurement which took place at 11:26 am. Figure 9: Raw data plot recorded at 11:26 am Next think in data analysis to do was to remove noise, which was caused by background light from airglow, starlight, and reflection of sunlight. To get rid of the noise, the average of the last 10 percent of the returned power signal was taken and was subtracted from returned power signal. Then for each plot the range correction had to be done. This is done by multiplying each value of returned power by corresponding square of range. For the plot to be clearer, the natural logarithm of range corrected signal was calculated. The plot of the first measurement looks like this. 9 Figure 10: Range corrected plot at 11:26 Experiment lasted for about thirty minutes and in that time twelve measurements were made. This is more than enough to put together all the data and to create time dependant density plot. The color scheme represents the amount of reflected energy. 10 5 QUESTIONS 5.1 What are the killers of the LIDAR signal? The signal attenuation happens when light hits a target for example rocks, rain, chemical compounds, aerosols, clouds or even single molecules. The attenuation is highly dependant on the wavelength of emitted light. For instance, the attenuation of UV light is much higher than that of IR light. 5.2 Which parameters are related to the atmospheric optical properties from the LIDAR equation, and what do they mean? This is single-scattering LIDAR equation for a monostatic single-wavelength pulsed laser: P( z ) P0 r (r ) c A 2 exp[2 (r ' )dr'] 2 r 0 The parameters related to the atmosphere are β(r) which is the volume backscatter of the atmosphere and σ(r) which is the attenuation coefficient of the atmosphere. 5.3 Analyze the sources of noise and what is your way to eliminate the noise from return signal? The source of noise is background light from airglow, starlight, and reflection of sunlight. The device that is sampling the return (LICEL) has a larger working range than the laser itself so the measurements beyond the working range of laser is noise. In order to get rid of this noise we can take the mean value of about 10 percent of that noise and subtract it from the measurements. 5.4 Why do we do range-corrected and take the natural logarithm to the return signal? Range correction is done in order to amplify returned power. Because the range corrected return signal falls exponentially with the distance, we take the natural logarithm of it in order to make plots clearer and more readable. 11 6 CONCLUSION The process of learning how LIDAR actually works took me a while. The first day when I was introduced to LIDAR made me confused a bit, because some things were not trivial to me. But I have tried to remember as much as possible so I will be able to completely understand how LIDAR works. For me this instrument seems very important because I was a bit shocked by the fact that LIDAR can be used in so many different fields in science. And I am also a bit shocked because for about a year ago I have not even heard of LIDAR before. Well, the last think changed and I can assure that I have learned quite a lot. Another think that took me quite some time to clarify was data analyzing. I had some difficulties with plotting in Mathematica 8 software and sometimes I was not sure whether the plots I made were correct or not. With help of my schoolmates Miha Gunde, Simon Lukman, Tine Bavdaž and Gregor Maver, I was able to overcome the problems I have encountered. I would also like to thank teaching assistang Tingyao He and Andrea Sušnik for all the help. 12 7 REFERENCES - http://books.google.si/books?id=KQXNaDH0XIC&pg=PA2&redir_esc=y#v=onepage&q&f=true - http://www.sodar.com/about_sodar.htm - http://en.wikipedia.org/wiki/Radar - Cracknell, Arthur P.; Hayes, Ladson (2007) [1991]. Introduction to Remote Sensing (2 ed.). London: Taylor and Francis. - James D. Klett; Stable analytical inversion solution for processing lidar returns - http://sabotin.ung.si/~sstanic/CRA/lidar/mobile/ 13 Fakulteta za aplikativno naravoslovje Fizikalni laboratorij 3 report Lidar Gorenje Nekovo, 15.4.2013 Author: Gregor Maver Mentor: Tingyao He Lecturer: Prof.dr. Samo Stanič Indexes table 1. Introduction ..................................................................................................................................... 3 2. Mobile Lidar Systems at UNG .......................................................................................................... 4 3. Results of experiment ..................................................................................................................... 5 4. Comments ..................................................................................................................................... 13 5. References ..................................................................................................................................... 13 2 1. Introduction What is lidar? Lidar stands for LIght Detection and Ranging. It is a remote sensing method that uses light in the form of a pulsed. These light pulses, combined with other data, generate precise, three-dimensional information about the shape of the observed object and its characteristics. Main components of a lidar are: transmitter – laser, receiver – telescope, detector and analysis part. LIDAR uses ultraviolet, visible, or near infrared light to image objects and can be used with a wide range of targets, including non-metallic objects, rocks, rain, chemical compounds, aerosols, clouds and even single molecules. A narrow laser beam can be used to map physical features with very high resolution. Picture 1: Lidar concept The lidar is used for many reasons; atmospheric, environmental, space, ocean, astronomy exploration and other industrially and military reasons. 3 2. Mobile Lidar Systems at UNG University of Nova Gorica has in 2007 developed a mobile lidar station which includes two elastic (Mie scattering) channels (at 266nm and 1064nm) and a fluorescence channel (at 296nm). The lidar can operate both in the daytime and nighttime conditions. UNG lidar uses CFR400 by Quantel Big Sky pulse laser for transmitter. It is a pulse laser capable of emitting multiple wavelengths at once, in our case 1064 nm IR light, the 532nm green light and 266nm UV. Picture 2: UNG lidar For receiver has been using 12'' Dopsonian telescope. 302 mm parabolic primary mirror collects the backscattered light and the induced fluorescence to its focal length at 1520 mm, where another mirror is placed. Secondary mirror redirects light into detecting system installed outside the telescope. The tube is rolled steel perfect to carry transmitter and detecting system. Diachronic mirrors made by SLS Optics Limited were applied to divide elastic scattering (at 1064nm and 266nm) and induced fluorescence (UV). The first diachronic mirror in the receiver separates UV from IR. Ultraviolet light is divided once more with the second diachronic mirror, where induced fluorescence is separated from elastic scattering. In order to separate laser backscattering signal from the background, interference filters by BARR Associates were installed. After the filters, the backscattered beam is focused onto photomultiplier tubes Hamamatsu R7400-06 (266 and 296 nm) and an avalanche photodiode Si APD S8890-30 by Hamamatsu. These sensors convert the received light into measurable electrical signals. Amplitudes of the electrical signals are proportional to the power of received light. 4 Digitalization of lidar measurements is performed by an analog/digital (AD) converter and read out by a Linux based computer for data acquisition and analysis. 3. Results of experiment We have measure with UNG lidar on March 1 for half of an hour from 11:26 to 11:58. Then we receive 12 text files for collecting data every 5 minutes. In this files was to columns; one representing height in meters and other was a returned power in watts. Every file contents 2667 measurements. The height goes up to 10km. 0 63.256 3.75 63.3071 7.5 63.2667 11.25 63.5643 15 61.1536 18.75 55.9333 22.5 72.5786 This is an example of our data. This is raw data, so we have to process it and make an analysis. I decided to use Mathematica for my processing software. First I have imported one file. From 12 available I have chosen the one, which was made at 11:46. Then I plot this raw data, just to see all this data represented in a graph. Graph 1: Raw plot 5 Graph 2: Raw plot all But these two graphs don’t show us the real picture. The data inside has a lot of noise and they are not properly range weighed. First I had to remove the noise. V (z) = P(z) – Pnoise I have decided that above m (2200 row) is all that we collected background noise. So I have calculated the average value of returned power above 8246m and then subtract it from my data. So I get graph looking like this: Graph 3: Noiseless plot 6 Now we have our noiseless plot, but couldn’t be the correct one. First, we could not get negative numbers. Returned power is always above zero. So we should fix this. Lets go to lidar equation for the answers. P(z): received power K: system constant C: light speed τ: pulse duration P0: transmitted power Ar: effective telescope area Y(z): overlap factor, 0≤Y(z) ≤1 σ(z) and β(z) are the main parameters of the atmosphere, σ(z) is the atmospheric optical thickness and visibility, β(z) is the density of aerosols and molecules. So σ(z) and β(z) are the main parameters of the atmosphere. Then I used Klett method for solving the lidar equation: ( ) ( ) Where k depends on lidar wavelength and properties of aerosol, 0.67≤k ≤1.3 For our measurements we chose k to be 1, so solution: ( ( ) ( ) ( ) ( ) ( ∫ 2 ( ) Where S(z) = ln[Z V(z)] 7 ) ( ) ) So I had to calculate ( ) ( ) or range correction. Graph 4: Graph before Log And then I had to logarithm this result and I get this: Graph 5: Range corrected graph He we can actually see, what was happening at 11:46 am on March 1. Some interesting things are happening around 2000m. There are some smaller jumps and falls in the graph. There are no bigger picks or falls in the graph, so it was more or less steady conditions. 8 But we have to see “bigger” picture. So I try to plot time-series graphs. Now I had to import all 12 files. I repeated whole process like plotting just one file, but this time plotting all 12 files together. Graph 6: Noiseless plot for all data Here we can see all 12 lines like on the noiseless graph with one. Now I had to correct it by range so I get this graph: Graph 7: Plot all 9 From this graph we can see, that from 3000m is almost steady power, so I have decided to give closer look just between 0 and 2700m. The range corrected graph: Graph 8: Range corrected Now I had all necessary for time-series plot. I used density plot to represent time and height changing conditions: (Note: I didn’t know how to change time scale on y-axis so I wrote time as 11.40. I know that this is wrong and it should be written as 11:40) 10 This is now 2D picture of a sky above our lidar when we were measuring. We can clearly see an aerosol layer on 1400m and one big below this. Unfortunately we didn’t measure for longer time period. We could see more layers. It is clearly seen, that the conditions in the sky didn’t change so much in this 30 min period. It was pretty steady. Then I went to calculate the atmospheric extinction coefficient σ(z). So we know the Klett method: ( ) ( ) And we know the expression for σ(z): ( ( ) ( ) ( ∫ ( ) ( ) ( ) ) ( ) ) Zc is the maximum detection range in our case 8246m. ( ) ( ) And according to the U.S Standard atmosphere model 1976: ( ) ( [ Where λ is wavelength. λ = 1064 nm 11 ) ]( ) I plot this extinction coefficient just for one measurement and I used that at 11:46. Here we can see almost identical picture as time-series plot. At 1400m we have one big pick or an aerosol layer and one thick below. One small pick is at 2000m and from that on is almost nothing. 12 4. Comments I think that the main killers for lidar signal are absorption by gases and aerosol scattering and of course anything that can block our light. There are two atmospheric optical properties in the lidar equation. σ(z) and β(z) are the main parameters of the atmosphere, σ(z) is the atmospheric optical thickness and visibility, β(z) is the density of aerosols and molecules. The noise is expected in our data. We were using laser in IR spectrum. This is very common in nature. And noise become from the absorption and scattering of our light. We had to range corrected our signal, because in logical that our signal will be losing strength with distance. So we have to multiply our signal with distance squared. And if we want to get our “real” signal out from it, we must use natural logarithm to the return signal. I thing that this experiment was very good. It is good example how to process data. You must do so much work to get form raw signal to the proper values to be able to plot some graphs to see what is happening in nature. 5. References http://www.ung.si/~htingyao/teaching/lidar/index.html http://sabotin.ung.si/~sstanic/CRA/lidar/mobile/index.html https://en.wikipedia.org/wiki/LIDAR http://www.lidar-uk.com/ 13 University of Nova Gorica Fakulteta za aplikativno naravoslovje LIDAR measurements Experimental report Miha Gunde Teaching assistant: Tingyao He Professor: Prof. dr. Samo Stanič Nova Gorica, 2013 CONTENTS 1. 2. 3. 4. 5. 6. Introduction Our system Results of experiment Conclusions Questions Sources 1. INTRODUCTION What is the lidar system? Lidar, Light-Detection-and-Raging, is a remote optical sensing technology, which exploits reflection of light to get information about the target it is pointed at. Lidar is somewhat similar to radar and sonar in that they all use the time delay of the returned signal to determine the range at which the signal scattered back. They differ, though, in the type of energy they emit. Radar uses an antenna to emit and receive electromagnetic energy (radio waves), while sonar emits acoustic energy (sound) through an electric-acoustic converter (speaker), and receives an echo of it through the headphones. Sonar Radar Lidar emits light via the laser, and receives the returned light through a telescope and a photon detector (photomultiplier). The laser emits light at wavelengths from 250nm to 10μm, which then gets scattered on small particles into all directions, some of it directly back into our receiver (telescope). We can use the time delay between the emitted and received signal to determine the range at which our signal scattered, and through the photomultiplier, we can also determine the exact power returned to the telescope. Lidar Different types of Lidar applications require the use of different types of scattering. Most common types of scattering used are Rayleigh scattering, Mie scattering and Raman scattering. The laser allows us to use a really narrow beam, which results in a very high resolution mapping of physical features, compared to radar and sonar technologies. Also, many chemical compounds interact strongly at wavelengths near the visible light, which results in a stronger image of those materials. Using a combination of lasers, allows mapping of contents by looking for wavelength-dependent changes in the intensity of the signal. We can use the lidar system pointed at just one point, or we can use a motor to move it around and scan a bigger portion of the sky. By attaching a mobile Lidar system to an airplane, one can successfully map out tree layouts in hardly-accessible forests and/or jungles, or create a very precise map of the landscape relief. But then, other methods of light reflection are to be used. 2. OUR SYSTEM At the University of Nova Gorica we have two lidar systems: one stationary, located at Otlica, and the other, mobile, located at the main university building. Our experiment was done on the mobile edition of it. This one can transmit two elastic channels with wavelengths 266nm and 1064nm, and one fluorescence channel at 296nm. Backscattered light is collected using a telescope with a parabolic mirror of 302mm radius, which focuses the light at the focal point, from where it gets reflected one more time into the spectrometer. The spectrometer filters received signal by the wavelengths, and sends the desired ones into the photomultipliers. There it gets digitalized and sent to a computer. 3. RESULTS OF EXPERIMENT Our experiment took place on a nice day with not many clouds on the sky. The motor which rotates the lidar system was not operational, so we pointed it into one direction under the angle 49°. Our data was collected from 11:26 to 11:58, in 4 or 5 minute intervals. Laser frequency was 10 hertz. The spatial resolution of the lidar is 3.75m. Data from the computer comes in .txt files, each containing two columns, first the range in meters, the second is the corresponding return power. Firstly, I multiplied range with sin(49°), so to get the true height. After that step, the plot of power as a function of range looks like this: Which makes sense, because the returned power decreases rapidly due to the noise we get from outside sources, such as sun, starts, etc. The theoretical range of the system is 10 kilometers, so we can easily say that the last 10 percent of the data is just noise. I've removed the noise by averaging the last 10% of the data and subtracting the value from all the other returned powers. The average noise through the data files seems to be around 64, which is a relatively small value. Next I did the range correction of the data. This is to get rid of the distance-dependent quantities, so only the atmospheric properties values remain. It follows from the lidar equation: Where P(z) is the returned power, K is the system constant, which depends on the lidar system, P0 is the transmitted power, c is the speed of light, τ is the pulse duration, Ar is the effective telescope area, z is the range at which light has been reflected, Y(z) is the overlap factor, 0<Y(z)≤1, β is the atmospheric back-scattering coefficient, in other words, “density” of the aerosols and molecules in the air. And σ is the atmospheric extinction coefficient, or the “thickness”. After the range correction, the power(range) plot look like this: In order to get a better look at what's actually going on, I did the logarithmic scale plot: And the same plot of some other data file: We can see that, clearly there wasn't much going on in the atmosphere. That is due to the nice weather we had during the experiment. The time difference between the two plots is about 25minutes. To get a better view at the time-dependent changes of the piece of atmosphere we were shooting our lidar at, I've made a contour plot with the clock on the x-axis, in the form (hhmm): 4. CONCLUSIONS From the time-dependent range contour plot, I can conclude that there was not much happening in the atmosphere at the time of our experiment. The darker patches on the plot represent the high reflected energy, while the white patches low. That means, there were a few layers of really thin clouds of particles, which didn't change much with time. They start to appear at around 3 kilometers height, and are quite evenly spaced all the way up to 10 kilometers. Some layers disappear, while new ones are also emerging. The darker line at the near-zero range is due to the signals not being totally overlapped and the random reflecting surfaces near our system, such as the trees and buildings, or other light not coming from our source. 5. QUESTIONS What are the killers of the lidar signal? The extinction of the lidar signal happens hits any target, for example clouds, aerosol particles, trees, rain, chemical compounds, or even single molecules. This depends highly on the wavelength of the emitted laser-light. Extinction of the shorter wavelength light is much higher than that of the longer wavelengths. So the extinction of UV-light is much higher than extinction of IR-light. Which parameters are related to the atmospheric optical properties from the lidar equation, and what do they mean? Parameters related to the atmospheric optical properties are the β(z), which is the atmospheric back-scattering coefficient or the “density”, the other atmospheric parameter is the σ(z), which is the atmospheric extinction coefficient or the “thickness”. Analyze the sources of noise and what is your way to eliminate the noise from the return signal? Sources of noise are the reflection of sunlight from random surfaces, the starlight and the airglow. The sampling device has a larger working range than the laser, so the data received beyond the laser range is noise. In order to eliminate the noise, I averaged the last 10 percent of the data and subtracted it from the measurements. Why do we range-correct and take natural logarithm of the return signal? We range correct the return signal in order to get rid of the distance-dependent quantities of the data, so only the atmospheric properties remain. We take the natural logarithm in order to get a better view of what is going on at larger changes of range. A logarithmic scale can represent smaller and bigger changes more easily than a linear function. In a logarithmic scale, the smaller changes get relatively amplified, while the bigger ones get relatively muted down. 6. SOURCES – – – – presentations from http://www.ung.si/~htingyao/teaching/lidar2013/index.html http://en.wikipedia.org/wiki/LIDAR http://sabotin.ung.si/~sstanic/teaching/physlab/Lidar/ James D. Klett; Stable analytical inversion solution for processing lidar returns University of Nova Gorica School of applied sciences Course: Physical laboratory 3 Teaching assistant:Dr. Tingyao He Lecturer: Prof.dr. Samo Stanič Experimental report LIDAR measurements 15.4.2013 Simon Lukman 1 Table of Contents What is LIDAR..........................................................................................................................................3 Lidar working principle.........................................................................................................................3 LIDAR at UNG..........................................................................................................................................4 Experiment data.........................................................................................................................................5 Data analysis..........................................................................................................................................6 Lidar equasion..................................................................................................................................6 Conclusion...............................................................................................................................................10 References................................................................................................................................................10 2 What is LIDAR LIDAR (LIght Detection and Ranging or Laser Imaging Detection and Ranging) is an optical remote sensing technology that can measure the distance to, or other properties of, targets by illuminating the target with laser light and analyzing the backscattered light. LIDAR technology has applications in geomatics, archaeology, geography, geology, geomorphology, seismology, forestry, remote sensing, atmospheric physics, airborne laser swath mapping (ALSM) and contour mapping. Lidar working principle The principle behind LIDAR is really quite simple. Shine a small light at a surface and measure the time it takes to return to its source. The LIDAR instrument fires rapid pulses of laser light at a surface, some at up to 150,000 pulses per second. A sensor on the instrument measures the amount of time it takes for each pulse to bounce back. Light moves at a constant and known speed so the LIDAR instrument can calculate the distance between itself and the target with high accuracy. By repeating this in quick succession the instrument builds up a complex 'map' of the surface it is measuring. 3 Generally there are two types of LIDAR detection methods. Direct energy detection, also known as incoherent, and Coherent detection. Coherent systems are best for Doppler or phase sensitive measurements and generally use Optical heterodyne detection. This allows them to operate at much lower power but has the expense of more complex transceiver requirements. In both types of LIDAR there are two main pulse models: micropulse and high-energy systems. Micropulse systems have developed as a result of more powerful computers with greater computational capabilities. High energy systems are more commonly used for atmospheric research where they are often used for measuring a variety of atmospheric parameters such as the height, layering and density of clouds, cloud particles properties, temperature, pressure, wind, humidity and trace gas concentration. LIDAR at UNG University of Nova Gorica has two LIDARs, one is stationed in Otlica and another one is a mobile LIDAR stored at university. Our experiment has been measured with mobile LIDAR, that transmits two elastic channels at wavelength 1064nm and 226nm and one fluorescence channel at wavelength 296nm. The laser used was the CFR400 by Quantel Big Sky pulse laser. With a mounted motor, it can vary both the azimuth and elevation angles, thus creating a 3D image. 4 Backscattered light is collected using parabolic mirror with radius 302mm which focuses all received light at the another mirror placed at it’s focal point, which is 1520mm away where light is reflected one more time into spectrometer. Spectrometer filters received signal separating light of different wavelengths and sending desired ones into photomultiplier. There signal is digitalized and send to computer where the data is stored. 5 Experiment data Our experiment took place on 27th February 2013, between 11:26 and 11:58. recording the signal in approximately 3 minute intervals. Output data was gathered in separate files, each for one time interval. Files consisted of two columns, one for altitude and another one for appropriate return power. LIDAR was positioned behind UNG on a parking lot. Weather was sunny with clear sky. Laser frequency was set to 10 pulses per second with laser inclination of 67 degrees. 6 Data analysis Lidar equasion P(z) represents return power, K is system constant dependent on setup of LIDAR, Po is transmitted power, c speed of light, τ pulse duration, Ar effective telescope area, z is distance at which the light has been reflected, Y(z) is total overlap factor (0<Y<1), σ(z) and β(z) are the main parameters of the atmosphere, σ(z) is the atmospheric optical thickness and visibility, β(z) is the density of aerosols and molecules. For data analysis and plotting I am using Python with matplotlib library with corresponding extensions. This is a plot of raw data recorded at 11:34 AM. We can see that return power is decreasing but after certain distance it stays the same. The reason why return power stays the same after some distance is the noise coming from the airglow, starlight etc. 7 Next step was getting rid of noise, by averaging the last ten percent of data and subtracting them from original return power. In order to amplify returned power, we do the range correction, which origins from the equation above. Each pulse of sent power has been scaled by the square of range at which it has reflected back. Multiplying the equation with square of z separates the distance dependent quantities from the ones, which are set only by atmosphere and LIDAR setup, so range correction makes clear how the atmospheric properties vary with distance. Final step is to take logarithmic scale. When subtracting noise some of the results turned zero or negative, in order for logarithm to take a definite value I used absolute value of return power. Final noiseless plot taken on 11:34 AM with range correction looks like this: 8 As our experiment took about 30 minutes, we can also plot 2D time plot from 11:26 AM to 11:58 AM. 9 Conclusion Our experiment took time in quite clear sky, so the results from plotting weren't that interesting as from a standpoint of detecting a cloud for example. But the whole process of analyzing and applying data was the most interesting part. In instructions for report were some questions which I believe I answered throughout report. Thanks to fur assistant Tingyao He for all the help with experiment. References - Wikipedia :http://en.wikipedia.org/wiki/LIDAR - UNG LIDAR page: http://sabotin.ung.si/~sstanic/teaching/physlab/ - http://www.lidar-uk.com/how-lidar-works/ - http://www.umass.edu/windenergy/research.topics.tools.hardware.lidar.php 10 University of Nova Gorica, School of Applied Sciences Physics Laboratory III, Experimental report: Lidar measurements By: Tine Bavdaž Teaching assistant: Tingyao He Lecturer: Prof. dr. Samo Stanič Contents: 1. Introduction to LIDAR ...........................................................................................................3 2. Mobile LIDAR system at UNG................................................................................................5 3. Data processing and experimental results............................................................................7 4. Conclusion...........................................................................................................................12 5. References...........................................................................................................................13 2 Introduction to LIDAR LIDAR has been used extensively for atmospheric research and meteorology. Downward-looking LIDAR instruments fitted to aircraft and satellites are used for surveying and mapping – a recent example being the NASA Experimental Advanced Research Lidar. In addition LIDAR has been identified by NASA as a key technology for enabling autonomous precision safe landing of future robotic and crewed lunar landing vehicles. LIDAR (Light Detection and Ranging) is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant target. The prevalent method to determine distance to an object or surface is to use laser pulses. Similar to radar technology, which uses radio waves instead of light, the range to an object is determined by measuring the time delay between transmission of a pulse and detection of the reflected signal. LIDAR uses ultraviolet, visible, or near infrared light to image objects and can be used with a wide range of targets, including non-metallic objects, rocks, rain, chemical compounds, aerosols, clouds and even single molecules. A narrow laser beam can be used to map physical features with very high resolution. 3 There are several major components to a LIDAR system: • Laser — 600–1000 nm lasers are most common for non-scientific applications. They are inexpensive but since they can be focused and easily absorbed by the eye the maximum power is limited by the need to make them eye-safe. Eye-safety is often a requirement for most applications. A common alternative 1550 nm lasers are eye-safe at much higher power levels since this wavelength is not focused by the eye, but the detector technology is less advanced and so these wavelengths are generally used at longer ranges and lower accuracies. They are also used for military applications as 1550 nm is not visible in night vision goggles unlike the shorter 1000 nm infrared laser. Airborne topographic mapping lidars generally use 1064 nm diode pumped YAG lasers, while bathymetric systems generally use 532 nm frequency doubled diode pumped YAG lasers because 532 nm penetrates water with much less attenuation than does 1064 nm. Laser settings include the laser repetition rate (which controls the data collection speed). Pulse length is generally an attribute of the laser cavity length, the number of passes required through the gain material (YAG, YLF, etc.), and Qswitch speed. Better target resolution is achieved with shorter pulses, provided the LIDAR receiver detectors and electronics have sufficient bandwidth • Scanner and optics — How fast images can be developed is also affected by the speed at which it can be scanned into the system. There are several options to scan the azimuth and elevation, including dual oscillating plane mirrors, a combination with a polygon mirror, a dual axis scanner. Optic choices affect the angular resolution and range that can be detected. A hole mirror or a beam splitter are options to collect a return signal. • Photodetector and receiver electronics — Two main photodetector technologies are used in lidars: solid state photodetectors, such as silicon avalanche photodiodes, or photomultipliers. The sensitivity of the receiver is another parameter that has to be balanced in a LIDAR design. • Position and navigation systems — LIDAR sensors that are mounted on mobile platforms such as airplanes or satellites require instrumentation to determine the absolute position and orientation of the sensor. Such devices generally include a Global Positioning System receiver and an Inertial Measurement Unit (IMU) 4 Mobile LIDAR system at UNG University of Nova Gorica has in 2007 developed a mobile lidar station which includes two elastic (Mie scattering) channels (at 266nm and 1064nm) and a fluorescence channel (at 296nm). The lidar can operate both in the daytime and nighttime conditions. Transmitter CFR400 by Quantel Big Sky pulse laser, which is capable of simultaneous emission of light at different wavelangths is being used as the transmitter. CFR400 emits light at base wavelength of 1064 nm (IR), second harmonic (532 nm, green, blocked in our case) and fourth harmonic (266nm, UV). As the attenuation of UV in air is much larger than attenuation of IR, IR light is used for regular Mie scattering operation and the UV light for for the excitation of the Tryptophane fluorescence in organic materials. Receiver 12'' Dopsonian telescope by Guan Sheng Optical company serves as the receiver. 302 mm parabolic primary mirror collects the backscattered light and the induced fluorescence to its focal lenght at 1520 mm, where another mirror is placed. Secondary mirror redirects light into detecting system installed outside the telescope. The tube is rolled steel perfect to carry transmitter and detecting system. Spectroscopic filters Dichroic mirrors made by SLS Optics Limited were applied to devide elastic scattering (at 1064nm and 266nm) and induced fluorescence (UV). The first dichroic mirror in the reciever separates UV from IR. Ultraviolet light is devided once more with the second dichronic mirror, where induced fluorescence is separated from elastic scattering. In order to separate laser backscattering signal from the background, interference filters by BARR Associates were installed. After the filters, the backscattered beam is focused onto the to the the photomultiplier tubes Hamamatsu R7400-06 (266 and 296 nm) and an avalanche photodiode Si APD S8890-30 by Hamamatsu. These sensors convert 5 the received light into measurable electrical signals. Amplitudes of the electrical signals are proportional to the power of received light. Data Acquisition Digitalization of lidar measurements is performed by an analog/digital (AD) converter (Licel transient recorder) and read out by a Linux based computer for data acquisition and analysis. From the time delay between the laser broadcasted light pulse and the received signal the distance to scatterer (the aerosol layer) is calculated, and from the intensity of backscattered light the density of aerosol layer is obtained. 6 Data processing and experimental results Killers for the lidar signal: The major killer for lidar signal is long range; this gives us weak signal (backscattered power decreases as 1/z 2 ). As we know lidar works in the spectrum of IR light, which is quite common in nature; this gives us background noise. Another problem is the absorbtion of light in the gases in the air; this shortens the lidar range. Lidar equation: P(z): received signal power K: lidar system constant P0 : transmitted power C: speed of light τ: pulse duration Ar : effective telescope area Y(z): overlap factor (0≤Y(z) ≤1) σ(z): atmospheric optical thickness and visibility β(z): density of aerosols and molecules 7 Recorded data: We got recorded data in twelve .txt files, each containing a single measurement. In .txt file there were two columns, one representing height and the other corresponding power. I have decided to use software Wolfram Mathematica 8 for data analysing and plotting. First, I had to import the data into Mathematica in the correct form. Since we have 12 measurements taken at different times and 2667 different heights, the easiest way for me to start analyzing was to create a matrix in which I have 12 columns and 2667 rows. Eliminating of noise: First of all, we have to know that when we are measuring something in the real world we will always get some errors, because of the limited precision of our measuring devices. 𝑉 𝑧 = 𝑃 𝑧 − 𝑃𝑛𝑜𝑖𝑠𝑒 𝑃𝑛𝑜𝑖𝑠𝑒 = 10000 𝑚 7500 𝑚 𝑃(𝑧) 𝑛 𝑃 𝑧 = received signal power (𝑅𝑎𝑤 𝑑𝑎𝑡𝑎) I have treated all measurements above 7500m as a background noise, because we have noise floor at around 63W. Then I have calculated the average power of all measurements from 7500m to 10000m and I have subtraced it from the received signal power. Noiseless data plot: 8 Range-corrected data: Why do we do range-corrected and take the natural logarithm to the return signal? We do range-corrected, because then the variation of atmospheric properties with respect to height can be seen much clearer. In fact, we normalize signal. To get range corrected data I have multiplied noiseless data with the square of the corresponding height and I have calculated the natural logarithm of it. 9 Height-Time plot: From this plot we can see that it was a sunny day with no clouds in the sky. 10 Extinction coefficient: According to U.S. Standard Atmosphere Model 1976: λ = wavelength (λ = 1064nm) We have to be careful, because the wavelength is in nanometers, but the height is in meters! Zc = maximum dettection range (7500m) S(z) = ln[𝑧 2 P(z)] σ(𝑧𝑐 ) = LR×β(𝑧𝑐 ) LR = lidar ratio (LR = 50) Klett method for solving the lidar equation tells us that β(z) and σ(z) are related: Where k depends on lidar wavelength and properties of aerosol (0.67≤k ≤1.3). Assistant told us to set k=1. 11 Conclusion I have decided to include answers to four questions from the instruction for this report into paragraph ''Data processing and experimental results'', rather than just writing down one by one answer. Athough I had some hard problems finding right software solutions and studying the experiment properties took me quite a lot of time, but I'm very happy that I went through it. I have learned a lot how to process and analyze data and most of all I have learned a lot about LIDAR system and how it works. Many thanks also to our teaching assistant Tingyao He for all the help. 12 References: http://www.usgs.gov/pubprod/aerial.html http://en.wikipedia.org/wiki/LIDAR http://www.fkaglobal.com/index.php/lidar-services http://www.ung.si/~htingyao/teaching/lidar2013/index.html 13