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Hyperspectral Imaging LiDAR Hyperspectral Imaging Lidar: Forest canopy heights and gaps for modelling the global carbon cycle Jan-Peter Muller Mullard.

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Presentation on theme: "Hyperspectral Imaging LiDAR Hyperspectral Imaging Lidar: Forest canopy heights and gaps for modelling the global carbon cycle Jan-Peter Muller Mullard."— Presentation transcript:

1 Hyperspectral Imaging LiDAR Hyperspectral Imaging Lidar: Forest canopy heights and gaps for modelling the global carbon cycle Jan-Peter Muller Mullard Space Sciences Laboratory University College London

2 Hyperspectral Imaging LiDAR Overview/Team Mathematical model developed for LiDAR instrument simulation Includes Monte Carlo ray-tracer interfaced to LiDAR equation Simulator used to define design parameters for imaging LiDAR Emphasis on LiDAR echo waveform to measure tree height Hyperspectral measurement using Tunable Filters Revolutionary biofluorescent imager defined Spin-out to planetary science rover (ESA ExoMars) Team: –Andrew Griffiths, Peter Yuen, Bob Bentley, Jan-Peter Muller, UCL- MSSL, –Steve Hancock, Philip Lewis, Mathias Disney, UCL Geography, –Mike Foster, Lidar Technologies Limited,

3 Hyperspectral Imaging LiDAR Background Ecological models require accurate biophysical parameters to model the global Carbon cycle and predict future climate Two of the most important parameters for the Carbon cycle for vegetation are biomass and leaf area index (LAI) These are not directly measurable by lidar but are closely related to direct measurables. –Biomass can be estimated from tree height –LAI can be derived from canopy cover. Spaceborne instruments are needed for global coverage. –Traditional passive optical and current SAR instruments cannot measure tree height and signals saturate over moderately dense forests.

4 Hyperspectral Imaging LiDAR Project Objectives Develop and assess different designs for an Imaging LiDAR concept for retrieval of biophysical parameters Assess best design using scene simulation system interfaced to LiDAR instrument design syste Develop canopy-top height and canopy cover retrieval algorithm and assess how accurately retrievals can be performed using typical atmosphere, realistic trees within a forest model and different LiDAR characteristics Assess APD technology and its possible application to biophysical retrievals Assess use of Tunable filters (LCTF and AOTF) for making hyperspectral measurements

5 Hyperspectral Imaging LiDAR Tree height is the distance between signal start above noise and ground position in the absence of any surface slope Canopy cover can be calculated from the ratio of energy returned from the canopy and the ground –For both these parameters the ground and canopy returns must be distinguishable Simulated waveform where: E c energy from canopy E g energy from ground c fractional canopy cover  c effective canopy reflectance  c effective ground reflectance which can be calculated by plotting E g against E c and solving:

6 Hyperspectral Imaging LiDAR Controlled validation experiments are all but impossible in reality, where the “truth” is rarely known Monte-Carlo ray tracing over realistic geometric forest models provides a controlled alternative environment, allowing validation of inversion algorithms Computer model of Scots pine forest true colour from above at a zenith angle of 50 o. Simulated signal with material information allows precise quantification of error

7 Hyperspectral Imaging LiDAR Error is dominated by uncertainty in ground position –Even over flat ground Topography and understorey vegetation reduce the distinction between canopy and ground returns Height errors against signal level Topographic blurring

8 Hyperspectral Imaging LiDAR A lidar with two wavebands, each with different ratios of ground to canopy reflectance, offers the possibility to extract ground position: –The ratio of one band to the other will be different for canopy and ground returns. The sharpest change in this, found through iterative smoothing to remove noise, corresponds to the ground position –A second band provides more information to constrain the ground position Error in ground position against canopy cover for realistically noised signals Multi-spectral edge detection to overcome topographic blurring

9 Hyperspectral Imaging LiDAR Instrument Design objectives #1 Combined real instrument noise with UCL canopy LIDAR model Instrument must observe 10,000 photons Solve LIDAR equation to determine instrument configuration ParameterValue #1Value #2 532 nm1064 Spacecraft altitude350 km Laser pulse energy25 mJ15 mJ Number of shots11 Telescope diameter1 m Filter bandwidth1 nm Receiver transmission 0.8 Transmitter transmission 0.9 Filter transmission0.8 Detector QE0.70.75 Detector Fill Factor11 Lidar reflectivity0.10.075 Atmospheric transmission 0.8 Number of returned photons 1088010497

10 Hyperspectral Imaging LiDAR Instrument design objectives #2 Two wavelength operation 1 m diameter telescope –30 m spot Three detection channels –532 nm –1064 nm –Imaging system Must measure outgoing pulse shape Relative scattering at each wavelength must be measured –Calibration required description

11 Hyperspectral Imaging LiDAR Instrument Layout #1 Stereoscopic (2- or 3-line) camera provide imaging –Anchored with Canopy LIDAR Dichroic filters used to separate light Blackbody calibration A2D Read Amp Power supplies Laser Control Start trigger signal Laser 1 2 Detector (A and B) Stereoscopic Camera Fibre Optic Moveable mirror Attenuators Blackbody Dichroic filter

12 Hyperspectral Imaging LiDAR Instrument Layout #2 Laser < stereoscopic camera DPSSL Laser mass < 15kg APD used

13 Hyperspectral Imaging LiDAR Instrument Layout #3 Mono-static design Telescope dominant component Instrument mounted on the base of the telescope

14 Hyperspectral Imaging LiDAR ICESAT-GLAS Analysis 532nm laser only operated a short time before failing Atmospheric transmission at 532nm and 1064nm extracted Top-of-Canopy Reflectance only available at 1064nm Echo waveforms and reflectance useful for MCRT verification GLC2000 Land cover map showing ICESAT-GLAS returns from the surface for one week

15 Hyperspectral Imaging LiDAR ICESAT-GLAS reflectances Zonal histograms of top-of-canopy reflectances in 8-day time periods show.. Effects of snow and possibly of increased aerosols or cloudiness in N. hemisphere Atmospheric transmission shows typical values that can be expected from a future spaceborne imaging LiDAR

16 Hyperspectral Imaging LiDAR Follow-on Potential Explore greater range of forest types and introduce high resolution DEMs Simulate and assess waveforms from ICESAT-GLAS Develop breadboard system Assess potential for aircraft system including integration of INS/GPS and existing digital cameras Actively hunt for space launch opportunities, particularly with US or China or India Explore potential for bio-fluorescence using laboratory experiments, airborne system and fieldwork: Antarctica, Iceland, Greenland, Baltic Sea

17 Hyperspectral Imaging LiDAR Potential KE Applications #1: Global Climate Model Global Climate Models are beginnging to employ interactive Dynamic Vegetation Models The Sheffield Dynamic Vegetation Model (SDVGM) is one such model SDVGM does not use real obsrevations bu rather relies on the accuracy of theprocess model An alternative is to employ observations and data assimilation to guide the models Currently LAI from instruments such as the NASA MODIS are employed which as the DALEC simulation (courtesy of T. Quaife) shows have dramatic impacts on improving model accuracy In future imaging Lidar results could provide such results for data assimilation and time series could be used to improve model accuracy

18 Hyperspectral Imaging LiDAR Potential KE Applications #2: Cyanobacteria Ratio of cyanobacteria-to-Phytoplankton critical to understanding CO2 uptake potential by the oceans Also different species of cyanobacteria can be toxic to plankton and hence destroy the food chain (e.g. algal blooms) Multispectral laser can stimulate fluorescent signature at higher wavelengths Time delay history and spectral fluorescent signature indicate different CB species as well as be usd to invert biomass Example of CB areal estimate (in yellow on left) against Chl a derived from SeaWiFS for January, April, June, August, October 2001 Can also be applied to deserts and ice-sheets to assess the biological “health” of the planet Harel et al, Plant Physiology (2004) Fluorescent delay signature for cyanobacteria from Sahara desert

19 Hyperspectral Imaging LiDAR Potential KE Applications #3: Organics Oil seeps have characteristic signatures which differentiate them from other biofilms Could UV-VIS fluorescence measured using hyperspectral tunable filters be used to differentiate oil type from spaceborne platforms? © NPA 2008

20 Hyperspectral Imaging LiDAR Potential KE Applications #4: Extra-Terrestial Discovered that UV lasers/LED can be used to detect tiny amounts of organics (Storrie- Lombardi, Muller et al. GRL, 2008) Performed laboratory and field experiments to assess the limit of detectability of different PAH organics using Beagle2 filters Building breadboard for ExoMars PanCam and continuing experiments Could be applied to remote detection of astrochemicals and biology from space Evaluating fluorescence in regolith extruded by drill.

21 Hyperspectral Imaging LiDAR Potential KE Applications #5: Healthcare Fluorescent imaging using microscopes is a well- established technique for the identification of biological material at the cellular level Recently laboratory techniques have been developed to employ Raman lidar for the identification of different bacterial species Employing fluorescent imaging with an iPhone/PDA- sized device could address a major market, namely the detection of bacteria in hospitals so that these areas can be identified and sterilised

22 Hyperspectral Imaging LiDAR Concluding Remarks Imaging LiDAR simulator has been constructed for designing a spaceborne system for monitoring global biomass and canopy cover The imaging LiDAR system operating at bi-spectral wavelengths appears to be a low cost solution for improving our knowledge of the global Carbon Cycle When coupled with a hyperspectral tunable filter can be used for montiroing bio-fluorescence KE applications include –Global Climate Modelling; –monitoring cyanobacteria over the Earth’s oceans, deserts and ice- sheets; –monitoring organics/oils-seeps and –remote detection of extra-terrestrial astrochemcistry and even of life based on RNA/DNA

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