Retrieval of forest structural Retrieval of forest structural characteristics from lidar waveformscharacteristics from lidar waveforms::

new applications and methodsnew applications and methods

Ph.D. Dissertation Defense

by

Svetlana Y. Kotchenova

Dissertation Committee

Anthony B. Davis

Yuri Knyazikhin

Ranga B. Myneni

Nathan Phillips

Curtis Woodcock

Geography Department, Boston University October 4th, 2004

Lidar remote sensing technique

2

Lidar footprint

Lida

r w

avef

orm Data products:

- Canopy height

- Vertical canopy structure (vertical distribution of nadir- intercepted surfaces)

- Ground elevation

http://www.geog.umd.edu/vcl

8 km

1 km

Large-footprint waveform-recording laser altimeters:

- SLICER (air-borne)

- LVIS (air-borne)

- GLAS (space-borne)

Lidar = light detection and ranging

3

SLICER (Scanning Lidar Imager of Canopies by Echo Recovery)

Technical characteristics

Status previously-used

Platform air-borne

Wavelength 1064 nm

Pulse frequency 80 Hz

Pulse width 4 ns

Pulse form Raleigh

Footprint diameter 9-10 m

Transmit energy 0.7 mJ

Footprint distribution pattern

Data

flight path

telescope FOV

swat

h w

idth

Southern BOREAS study area, Saskatchewan, Canada. July 20-30, 1996

Smithsonian Environmental Research Center. Western shore of Chesapeake Bay, eastern Maryland. September 7, 1995.

Jack pine Black spruce

Mixed deciduous forest stands with

the overstory dominated by

tulip poplar

http://denali.gsfc.nasa.gov/research/laser/slicer/slicer.html

4

LVIS (Laser Vegetation Imaging Sensor)

Technical characteristics(advanced version of SLICER)

Status currently-used

Platform air-borne

Wavelength 1064 nm

Pulse frequency 500 Hz (320 Hz)

Pulse width 10 ns

Pulse form Gaussian

Footprint diameter 1-80 m (25 m)

Transmit energy 5 mJ

Footprint distribution pattern

Data

. . . . . . . . . . . . . . . . . . . . . . .

. . .

. . .

. . .

9 m 25 m

27 m

80 footprints

flight path

La Selva Biological Research Station, Costa Rica. March, 1998. (A 1546-ha area comprised of primary (73%) and secondary tropical rainforests and agroforestry plots.)

Harvard Forest, Massachusetts, USA. Summer 1999, summer 2003. Temperate deciduous broadleaf forest, dominant species include white pine, hemlock, spruce, oak, and maple.

5

GLAS (Geoscience Laser Altimeter System)

GLAS will provide continuous global measurements of the Earth’s land surface topography.

Technical characteristics

Status launched

Jan. 2003

Platform space-borne

Wavelength 1064 nm

(vegetation)

Pulse frequency 40 Hz

Pulse width 5 ns

Pulse form Gaussian

Footprint diameter 60-70 m

Transmit energy 5 mJ

Along-track

separation 170 m

Cross-track max 15 km

Cross-track min 2.5 km

Repeat cycle 183 days

Life-time 3 years

http://icesat.gsfc.nasa.gov

Current applications of lidar measurements (1)

))h(ercov1ln(CHPc

6

1. Retrieval of canopy height profiles (CHP),or the distributions of canopy material with height

Assumptions:

1) Uniform horizontal distribution of leaves

2) Absence of multiple scattering

3) Negligible influence of non-foliated surfaces

Current applications of lidar measurements (2)

7

2. Biomass estimation

Lidar recorded waveform

Canopy height

&CHP

Calculation of special indices

(mean, medium, quadratic mean

height)

Biomass, basal area, quadratic mean stem

diameter

Ground measurements to relate height indices

and biomass

Regression procedure

3. Other methods

- Canopy volume method (vegetation is treated as a number of 3-dim pixels, each of which is defined as containing canopy or not)

- Calculation of mean transmittance profiles (rate of attenuation, whole canopy transmittance, radiation effective height)

Shortcomings of the existing methods

8

Retrieval of canopy height profiles

Biomass estimation

Assumption of uniform horizontal

distribution of leaves

Foliage clumping is ignored

The method is not applicable for coniferous

forests

Assumption of single

scattering

Erroneous assignment of larger amounts of

foliage to the lower part of the waveform

Critical for photosynthesis and light transmission

studies

1.

2.

Requirement of extensive ground measurements (DBH of each tree within the footprint area, 25-30 footprint areas at least)

The method is hardly applicable

to GLAS data

Numerous other applications are possible but have not been explored yet.

Current applications*

*

*

Research plan

9

1. Evaluation of the contribution of multiple scattering

2. Development of a new application for lidar data

3. Improvement of the current algorithm for retrieval of CHPs

4. Development of a new algorithm for biomass estimation

*

*

*

Task 1: Contribution of multiple scattering

10

a) Development of time-dependent stochastic radiative transfer theory to

describe the radiation regime in heterogeneous vegetation canopies

b) Creation of a physical model describing the propagation of lidar signals

through vegetation on the basis of this theory

c) Validation of the model with field data

d) Evaluation of multiple scattering contribution through the comparison of

reflected signals formed with and without multiple scattering

Time-dependent stochastic RT theory

d),r,t(I)()r(),r,t(I)()r(),r,t(I

t

),r,t(I

c

1

4

S

11

0)( , d)(),H,r,t(I)(

),H,r,t(I

Hz0,0),r,0(I

0)( ,Sr, )()t(f),0,r,t(I

2

xysoil

xy

0fxy0xy

Boundary conditions:

Multiply scattered photons appear delayed in the waveform compared

to singly scattered photons

Expected results:

Enhancement of the lower part of the reflected signal

z

H

0

This function defines the probability that one finds a vegetated element at point , moving from point along direction , given that there is a vegetated element at .

Description of canopy structure

otherwise,0

ratleafaisthereif,1)r(

)z,y,x(M

)z(p

12

),,(M1 )z,y,x(M

zS

This function defines the probability of finding foliage elements in a horizontal plane at depth z.

zS

)z(pProbability function

),,z(K

Conditional probability function

Indicator function: Integration of the RT equation over the footprint area

),,z(K

)z,y,x(M

The integrated RT equation is solved numerically with the SOSA method (successive

orders of scattering approximations): ,

where , is the mean intensity of photons scattered k times.

Time-dependent stochastic RT model

z

z

S

S

dxdy)z,y,x(

dxdy),z,y,x,t(I)z,y,x(

),z,t(U

),r,t(J...),r,t(J),r,t(J),r,t(I n21nd

z

z

S

S

dxdy

dxdy),z,y,x,t(I

),z,t(I

13

Model inputs

(2) The mean intensity of radiation over a vegetated area at depth z

(1) Characteristicsof incoming radiation (direction, intensity and pulse duration)

(2) Canopy structural parameters (tree height, crown length, leaf area density, leaf normal orientation distribution, and statistical probability functions p and K)

(3) Optical properties of leaves and soils

(1) The mean intensity of radiation over a horizontal plane at depth z

Model outputs

1k )),,z,t(J(f),z,t(J 1kk

Model simulations for different types of forests

14

Averaged SLICER waveforms

Model-simulated waveforms

age 100-155 years height 10-11 m LAI 4.0 location BOREAS

age 99 years height 32-37 m LAI 5.16 location Maryland

Mature-aged forest

Tulip poplar association

SOBS forest

Southern Old Black Spruce

Simulations with and without multiple scattering

15

Southern BOREAS study area

SOJP (old jack pine) SOBS

age, years 60-75 100-155

height, m 16-19 10-11

LAI 2.61 4.0

Eastern Maryland, Tulip poplar association

Mature Intermediate

age, years 99 41

height, m 32-37 31-34

LAI 5.16 5.26

Averaged SLICER waveforms

with multiple scattering

without multiple scattering

Model simulations:

Retrieval of canopy structural parameters

)(

16

Single interaction: , where is the round trip time between

the sensor and the interaction point

pstotal tt pst

Several interactions: , where is the extra time due to

multiple scattering

pmpstotal ttt pmt

c

d)1N(t pm

)(

1~d

Lu~)(

d - photon mean free path

Lu

Multiply scattered photons carry information on canopy structural

parameters, namely, foliage density and gap fraction.

- extinction coefficient

- leaf area volume density

Task 1: Conclusions

17

S. Y. Kotchenova, N. V. Shabanov, Y. Knyazikhin, A. B. Davis, R. Dubayah, and R. B. Myneni,

Modeling lidar waveforms with time-dependent stochastic radiative transfer theory for remote

estimations of forests structure. Journal of Geophysical Research, 108 (D15), 2003.

1. The inclusion of multiple scattering leads to a better approximation of

the return signal.

2. Multiply scattered photons magnify the signal and significantly enhance the

lower part of it.

3. In case of sparse canopies, effects of multiple scattering are insignificant and

single-scattering approximation models are expected to provide good simulations

of lidar-recorded signals.

4. Multiply scattered photons carry information on canopy structural parameters

Task 2: New application for lidar measurements

18

Canopy height profiles retrieved from lidar waveforms can be used

for modeling gross primary productivity of deciduous forests

Gross Primary Production

19

For several decades, monitoring and modeling the terrestrial carbon cycle have been among the main goals for many ecological and climate change studies.

[NPP] = [GPP] – [Ra]

(the total amount of CO2 taken up by land plants from the atmosphere to participate in photosynthesis)

(the carbon lost by photo-respiration and by internal plant metabolism; about half of the total carbon uptake)

- Annual terrestrial GPP is estimated as about 120 Pg/yr.

- Forests cover about 26% of the total land surface area.

Land surface: 148,300,000 km2

Forests: 38,700,000 km2

Net Primary Production

Autotrophic respiration

Gross Primary Production

Overview of photosynthesis models

20

1) Production Efficiency Models (global and regional scales)

GPP=g(T, soil moisture, VPD).FAPAR.PAR

NPP=n(T, soil moisture, VPD).FAPAR.PAR

(g and n are the light use efficiencies, T is the ambient temperature, VPD is the vapor

pressure deficit, FAPAR is the fraction of absorbed Photosynthetically Active Radiation (PAR))

2) Models based on Farquhar’s equations (global, regional and local scales)a) the big-leaf model

b) the sunlit/shaded leaf

separation model

c) the multiple layer model

d) the combined leaf-separation

multiple-layer model

scaling from leaf

to canopy

Farquhar’s model

Modeling photosynthesis for

a unit leaf area

Representation of the canopy as two big leaves with different

illumination conditions

Unit leaf area

Farquhar’s model+ gs(RAD, T, VPD)

Ph (RAD, T, CO2 , Ni, VPD, O2, wind)

Use of actual foliage profiles

Division of the canopy into N layers with equal LAI + separation of sunlit

and shaded leaves in each layer

Advanced photosynthesis models

N

ishadeshadesunsun )i(Ph)i(L)i(Ph)i(LPh

21

Lidar measurements shadeshadesunsun LPhLPhPh

N

i

N

ishadeshadesunsun )i(PhL)i(PhLPh

Combined two-leaf multiple-layer model

Two-leaf model

Drawbacks of the existing models

22

1. The two-leaf model (the leaf-separation model)

Use of the average values of diffuse and direct PAR absorbed by the canopy. Fails

to capture the attenuation of incident PAR with height.

2. The coupled model (the combined leaf-separation multiple-layer model)

Assumption of a uniform distribution of foliage with height. Wrong sunlit/shaded

leaf and PAR distributions except for the canopies with approximately uniform vertical

structures of foliage.

Will the accounting for the vertical distribution of

foliage lead to a better estimation of photosynthesis?

Distributions of PAR, sunlit/shaded leaves, and GPP

N

ishadeshade

sunsun

N

ilayer

)i(Ph)i(L

)i(Ph)i(L

)i(PhPh

23

Comparison between the uniform and actual distributions of foliage

Radiation model

24

Reference - Shabanov et al., Stochastic modeling of radiation regime in discontinuous vegetation

canopies. Remote Sensing of Environment. 74, 125-144, 2000.

Model inputs

Canopy structural parameters

tree height vertical foliage profiles leaf inclination

Optical properties of leaves and soils

Characteristics of the incoming radiation

direct PAR flux diffuse PAR flux solar zenith angle

the distribution of direct PAR with height the distribution of diffuse PAR with height

Model outputs

Comparison of photosynthesis models

25

100 profiles & 3 days with different weather conditions

3 different profiles & 1 monthAugust, 1997

Comparison

(SLICER data collected over the mixed deciduous forest stands + environmental data)

1 2

Daily GPP rates calculated by

the two-leaf model

(the sunlit/shaded leaf separation model)

and

the coupled model with uniform foliage profiles

(the combined leaf-separation multiple-layer model)

will be compared with the GPP rates calculated by

the coupled model with actual (lidar measured) foliage profiles

Daily GPP rates as functions of weather conditions (1)

26

Measurements of incident PAR, temperature and humidity were taken at a 3-min time-step

Wye Research and Education Center, Maryland, August 1997 http://uvb.nrel.colostate.edu/UVB/uvb_climate_network.html

Daily GPP rates as functions of weather conditions (2)

27

GPP rates were calculated at a half-hour step and then integrated over a whole day-length period.

more than 80% of foliage in the first 2/3 of the tree

more than 80% of foliage in the last 2/3 of the tree

approximately uniform distribution of foliage

remaining CHP

CHP classification

Mature-aged stand

- dailyGPP rates calculated by the two-leaf model

Intermediate-aged stand

Daily GPP rates as functions of foliage profiles (1)

28

SLICER waveforms (September 07, 1995)

PAR, temperature, humidity (August 1997)Wye Research and Education Center, Marylandhttp://uvb.nrel.colostate.edu/UVB/uvb_climate_network.html

Mean daily wind speed values (August 1997)Andrews AFB station, Marylandhttp://www.ncdc.noaa.gov/oa/climate/onlineprod/drought/

xmgr.html

Daily GPP rates as functions of foliage profiles (2)

29

3 different canopy profiles:

(1) the majority of the foliage in the first half of the canopy

(2) a nearly uniform distribution

(3) the majority of the foliage in the lower part of the canopy

the two-leaf model

the coupled model with uniform profiles

the coupled model with actual profiles

(For each day, the GPP rate was calculated at a half-hour step and then integrated over a whole day-length period.)

Task 2: Conclusions

30

S. Y. Kotchenova, X. Song, N. V. Shabanov, C. S. Potter, Y. Knyazikhin, and R. B. Myneni,

Lidar remote sensing for modeling gross primary production of deciduous forests. Remote

Sensing of Environment, in Press.

1. The use of actual foliage profiles might lead to more accurate estimates of daily

GPP rates in the photosynthesis models relying on the extrapolation of Farquhar’s

equations from a unit leaf area to the whole canopy.

2. The disagreement range was almost the same for the two-leaf model and the

coupled model with uniform CHPs during the comparison with the coupled model

with actual CHPs. This means it might be useless to apply a multiple layer division

in addition to the two-leaf separation if the actual foliage profile is not known.

3. An increase of the incident diffuse PAR due to the partial cloudiness could

significantly enhance the daily carbon assimilation rate. The performed study also

demonstrates the importance of separate measurements of diffuse and direct PAR.

Task 3: Improvement of the algorithm for retrieval of CHPs

31

Drawbacks of the current algorithm:

1) the assumption of a uniform horizontal distribution of needles

2) ignoring the effects of multiple scattering

3) the use of canopy material distribution instead of foliage distribution

*

*

The shoot silhouette

area to total area ratio:

- the shoot silhouette area in direction ;

- the total needle area of the shoot.

The mean projection of unit shoot area:

TNA

1. Use of shoots as basic structural elements instead of needles Description

of the canopy structure in terms of spatial and angular distribution of shoots

Shoots as basic structural elements

TNA

)(SSA)(G

TNA

)(SSA)(STAR

32

)(SSA

)(SSA

Reference – Stenberg, P. (1996). Simulations of the effects of shoot structure and orientation on vertical gradients in intercepted light by coniferous canopies. Tree Physiology, 16, 99-108.

STAR depends on the shoot structure, shoot orientation and position in the crown.

The shoot ( ) and needle ( ) scattering

coefficients are related as

where is the probability for a scattered photon to hit the same shoot again.

Assumption: = constant

Ray-tracing simulations:

Accounting for multiple scattering

33

2b. Scattering of radiation between shoots

(The RT model developed in Task 1 will be used to account for this type of scattering)

2a. Scattering of radiation between the needles of a shoot

(The scattering properties of needles are replaced with those of shoots)

shp

STAR41psh shp

Reference – Smolander, S., & Stenberg, P. (2003). A method to account for shoot scale clumping in coniferous canopy reflectance models. Remote Sensing of Environment, 88(4), 363-373.

nsh

)(p1

)(p)()(

nsh

nshnsh

Modification of the algorithm

34

the current (M-H) algorithm

The modified algorithm

Step 1: accounting for scattering of radiation between the needles of a shoot ( )

Step 2: step 1 + accountingfor the shoot inclination

Step 3: steps 1-2 + accounting for multiple scattering of radiation between the shoots

shn

gr

gr

shv

vc

R)0(R

)z(R1ln)z(CHP

)z(R v

)0(R v

grR

- the vegetation return at height z

- the total vegetation return

- the ground return

1. The horizontal distribution of shoots is uniform; all layers are characterized, on average,

by the same value – the standard simulation. mod 1

2. Shoots are uniformly distributed, but the mean increases from 0.1 to 0.25 with the

degree of shading. The increase is modeled in two ways:

a) as a linear function of a degree of shading: mod 2a

b) as a quadratic function of the degree of shading exceeding 50%

mod 2b

3. ...

Conifer canopy models

STAR

STAR

)DS(15.01.0STAR

35

5.0DSfor)5.0/)5.0DS((15.01.0STAR

5.0DSfor1.0STAR2

DS is calculated from the field measurements of PAR transmittance. Reference - W. Ni, X. Li, C. Woodcock, J.-L. Roujean, & R. E. Davis (1997). Transmission of solar radiation in boreal conifer forests: measurements and models. Journal of Geophysical Research, 102 (D24), 29555-29566.

Retrieval of Canopy Height Profiles (1)

36

the M-H algorithm

The modified

algorithm

mod 1

mod 2a

mod 2b

SLICER data:

the southern old black spruce site, BOREAS, Canada. July 1996.

Retrieval of Canopy Height Profiles (2)

37

Average canopy area indices (CAIs) of 50 SOBS CHPs retrieved by

the M-H algorithmthe modified algorithm

Step 1 Step 2 Step 3 final CAI

mod 1

0.45

0.56 (23.0% ) 2.09 (14.1% ) 1.80

mod 2a 0.50 (11.0% ) 1.56 (12.9% ) 1.36

mod 2b 0.56 (24.9% ) 2.61 (14.8% ) 2.22

Field measurements of CAI: 1.87

Reference - J. M. Chen, P. M. Rich, S. T. Gower, J. M. Norman, & S. Plummer (1997). Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research, 102 (D24), 29429-29443.

Task 3: Conclusions

38

1. Clumping of needles into a shoot, shoots inclination and multiple scattering of

radiation between the shoots are the only effects that can be taken into account

without contradicting the basis of the M-H algorithm.

2. The main advantage of the modified algorithm is the use of the mathematically

corrected approach for transferring from the distribution of needles to the distribution

of shoots.

S. Y. Kotchenova, N. V. Shabanov, Y. Knyazikhin, and R. B. Myneni (2004). Retrieval of canopy

height profiles from lidar measurements over coniferous forests. IEEE Transactions on

Geoscience and Remote Sensing, in Review.

Case study 4. New algorithm for biomass estimation *

33

Drawbacks of the current algorithm:

Requirement of extensive ground measurements to relate canopy

height profiles and biomass volumes (in particular, each tree diameter

within a footprint should be measured).

Research plan for this study:

A new algorithm is planned to be developed with the help of allometric

scaling theory of plant energetics and geometry.

Allometric scaling theory

4/3maxmax MNQNR

34

A general model for the structure and allometry of plant vascular systems

Relationships between density and mass in resource-limited plants

Reference – Enquist et al. (1998). Allometric scaling of plant energetics and population density. Nature, 305 (10), 163-165.

4/3max MN

3/1maxmaxtot )N(MNM

- the max number of individuals that can be supported by unit

area

- the rate of resource supply per unit area

- the average rate of resource use per individual

- the average plant mass

- the total plant masstotM

maxN

QR

M

Assumptions: (1) the branch structure of a tree is considered a space-filling fractal system; (2) energy required to distribute resources is minimized.

Results: the rate of fluid transport through this system scales as ¾ of the tree mass

Reference – West et al. (1998). A general model for the origin of allometric scaling laws in biology. Science, 276, 122-126.

Application of the scaling model

35

Problems:

1. Lidar instruments do not distinguish between foliage and woody material.

How to extract foliage distribution from the retrieved CHP?

2. The general model for the structure and allometry of plant vascular systems is developed for an individual tree, while the lidar footprint seizes more than one tree.

How to determine the number of trees within a footprint?

Solutions:

1. Evaluation of the percentage of branch surface in the CHP using the structure and allometry general model.

2. Combination of the lidar technique with other remote sensing tools capable to capture the horizontal structure of vegetation.

Mapping of the horizontal structure of vegetation

36

2. Polarimetric SAR Interferometry

1. Aerial photography

Reference - S. R. Cloude, D. Corr. Tree height retrieval using single baseline polarimetric interferometry. Proceedings of ESA Workshop, POLInSAR 2003, Frascati, Italy, 14-16 January.

Scots Pine forest model Simulated SAR image

It is one of the most widely used remote sensing tools in forestry. A large number of air photos are available.

Reference – http://www.eoc.csiro.au

Conclusions

37

- The proposed research will potentially make an important contribution

into further development of lidar remote sensing technique.

- The lack of lidar data is a significant obstacle of the developed program.

Thank you!Thank you!

Questions?..Questions?..

38