hyperspectral imaging lidar hyperspectral imaging lidar: forest canopy heights and gaps for...

Hyperspectral Imaging LiDAR

Hyperspectral Imaging Lidar: Forest canopy heights and gaps for

modelling the global carbon cycle

Jan-Peter MullerMullard Space Sciences Laboratory

University College [email protected]


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, [email protected] – Steve Hancock, Philip Lewis, Mathias Disney, UCL Geography,

[email protected] – Mike Foster, Lidar Technologies Limited, [email protected]


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.


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


• 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:Ec energy from canopy Eg energy from groundc fractional canopy cover c effective canopy reflectancec effective ground reflectance

which can be calculated by plotting Eg against Ec and solving:

cE cc .= )1.( cE gg −=

gcc

gg EE

+−=


• 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 50o.

Simulated signal with material information allows precise quantification of error


• 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


• 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


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

Parameter Value #1 Value #2

532 nm 1064

Spacecraft altitude 350 km 350 km

Laser pulse energy 25 mJ 15 mJ

Number of shots 1 1

Telescope diameter 1 m 1 m

Filter bandwidth 1 nm 1 nm

Receiver transmission

0.8 0.8

Transmitter transmission

0.9 0.9

Filter transmission 0.8 0.8

Detector QE 0.7 0.75

Detector Fill Factor 1 1

Lidar reflectivity 0.1 0.075

Atmospheric transmission

0.8 0.8

Number of returned photons

10880 10497


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


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



• Laser < stereoscopic camera

• DPSSL • Laser mass <

15kg• APD used



• Mono-static design

• Telescope dominant component

• Instrument mounted on the base of the

telescope


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


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

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.


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


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


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


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


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.


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

http://www.rsimd.com/raman.htm


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

hyperspectral imaging lidar hyperspectral imaging lidar: forest canopy heights and gaps for...

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