# Presentation: 698460

## DEFINITION

**LEAF AREA INDEX is defined as one half the total foliage**
**surface area per (unit of ground area projected on the local**
**datum).**
- Why “one half”?  - Reduces uncertainties in reporting due to leaf shape.
- Why “foliage” ? - Stomates are on foliage.
- Why “ground area projected ..” ? - Independent to local slope.
- Problems with definition- Bryophytes, litter/dead foliage (foliage health).

**Notes:**
This definition given in Fernandes et al. 2000.  Evaluating Image based estimates of leaf area index over boreal conifer stands using high spatial resolution CASI imagery.  Proceedings of 22 Canadian Remote Sensing Confernce.

## Surface Measurement Methods

- Cut all leaves and measure (volume displacement + scaling by mass).
- Inclined point quadrats.
- Direct gap fraction.
- Diffuse hemispherical.
- Invert a stand ray tracing model (to complex)
- Allometric methods (may not be locally representative)

## Inclined Point Quadrats

- 

- 

- Gives LAI directly if we
- know G function!!
- Costly and difficult for
- high canopies but ...

**Notes:**
Schematic explaining point-quadrat method of measuring LAI (Levy and Madden 1932) extended to include G function (Warren Wilson, 1960).  The G function is the ratio of foliage area projected normal to the transect relative to foliage area projected onto the local surface datum.

## Canopy Cranes

**Notes:**
Canopy crane in Panama.  Line transects using cables suspended from the crane and binoculars to measure contacts have been applied to estimate LAI based on point-quadrat analysis.

## Direct Gap Fraction

**TRAC ALSO ESTIMATES ******
**STILL NEED G!**

**Notes:**
Equation cited is modification of Nilson 1971 given in Fernandes, R.A., H.P. White, S.G. Leblanc, G. Pavlic, H. McNairn, J.M. Chen, R.Hall. 2001.  Examination of Error Propagation in Relationships between leaf area index and spectral vegetation indices from Landsat TM and ETM, 23 Canadian Remote Sensing Symposium Proceedings.

Figure shows TRAC (left) and recorded photon flux density along a transect.  Gap size distribution and gap fractions is derived from thresholding photon flux density.   Clumping is derived from gap size distribution by removing largest gaps until gap size distribution matches an negative exponential distribution. 

## Diffuse Hemispherical

**Unlike point quadrat**
**we must integrate over **
**non-negligible azimuth!**
**STILL NEED ******

**Notes:**
Equation is modification of Miller’s Theorem to account for clumping (Fernandes, R.A., H.P. White, S.G. Leblanc, G. Pavlic, H. McNairn, J.M. Chen, R.Hall. 2001.  Examination of Error Propagation in Relationships between leaf area index and spectral vegetation indices from Landsat TM and ETM, 23 Canadian Remote Sensing Symposium Proceedings.
Simulated mixed forest using a ray tracing model.

## TRAC Spatial Sampling

**Notes:**
Use of ray tracing software to identify TRAC spatial sampling footprint.  

## Hemispherical Lens Issues

- Sensitivity to Zenith
- Sampling

- Sensitivity to
- Thresholding (exposure)

**Notes:**
Both screen captures are from NRCan, CCRS, hemispherical photograph processing software (S.G. Leblanc author) showing simulated canopies using a ray tracing software.  Sensitivity to Zenith indicates large variability in gap fraction with both high and low zenith angles suggesting care must be taken in approximating the integral in Miller’s Theorem.  Sensitivity to thresholding suggests flux density distribution curves should be examined to identify potential for errors due to thresholding.

## What Affects Le Estimates More: G() with Hemispherical or () with Gap Fraction?

**Notes:**
Clumping measurements from Chen, J. 1996.  Optically based methods for measuring seasonal variation of leaf area index in boreal conifer stands.  Agriculture and Forest Meteorology, 80:135-163.

G function estimated using:  Goudriaan, J. 1977.  Crop Micrometeorology:  A Simulation Study.  Wageningen Centre for Agricultural Publishing and Documentation: Wageningen, Netherlands.

## Answer:  We need both!

**Notes:**
Based on hemispherical photograph of Boreal Jack Pine stand.  Actual uses Miller’s theorem modified as in Fernandes et al. 2001 to account for ().  Measured  at 46 degrees was used together with linear fit to data of () shown in previous slides for Jack Pine stands.

Slide shows that, for this stand both hemispherical and lai-2000 estimates are similar (they differ in that LAI-2000 uses analogue methods for measuring gap fraction in 5 zenith rings while the hemispherical uses a large number (>20) aziumthal steps.  The actual value currently is not exact as well since it is based on hemispherical photos that do not extend past 80 degrees and uses an assumed variation of omega with theta (but using measured omega at 46 degrees).  We simulate the trac gap fraction by using a single narrow zenith range from the hemispherical photo.  The clumping is still based on the TRAC transect and assumed omega vs theta relationship.  The variation in TRAC estimates is due to the change in angle at which gap fraction is assumed to be measured.  

## CCRS Re-Calibration of LAI-2000 Effective LAI

- (average of 7-10 samples/site)

**Notes:**
This is one approach to standardize LAI-2000 measurements so they are more similar to TRAC measurements.  The approach also removes some errors due to multiple scattering.  From Leblanc, S.G. And J.M. Chen. 2001.  A practical scheme for correcting multiple scattering effects on optical LAI measurements.  Submitted to Ag. Forest Met.

## BOREAS NSA-OBS 2m CASI Reflectance Image

- Red (as red), NIR (as green), Blue (as blue)

## BOREAS SSA-OJP 2m CASI Reflectance Image

- Red (as red), NIR (as green), Blue (as blue)

## CASI 2m vs. “BIGFOOT” Krigged Le: NOBS

- CASI 2m vs. “BIGFOOT” Krigged Le: NOBS

- LAI-2000 Footprint (r=35m)

**Notes:**
Left image shows CASI effective lai image together with approximate footprints of LAI-2000 measurements from Bigfoot.  Right image shows krigged result (optimized to match site mean Le).  Variograms are included at end of presentation.

## CASI 2m vs. “BIGFOOT” Krigged Le: SOJP

- CASI 2m vs. “BIGFOOT” Krigged Le: SOJP

- LAI-2000 Footprint (r=45m)

**Notes:**
Left image shows CASI effective lai image together with approximate footprints of LAI-2000 measurements from Bigfoot.  Right image shows krigged result (optimized to match site mean Le).  Variograms are included at end of presentation.

## Summary: CASI 2m vs. “BIGFOOT” Le and Arithmetic Mean

- Error bars = mean absolute difference
- between actual and kriging at 2m resolution.

**Notes:**
Graph indicates that there is no substantial difference in Le between all methods compared to actual (assuming CASI image is representative for Le).  Relative error in Le estimates from LAI-2000 are on the order of 15%.  Error bar is the mean absolute deviation between CASI and krigged patterns at 2m resolution over the region within the BIGFOOT sampling area.  The large relative mean absolute error suggests the spatial pattern of Le is not captured from krigging and use of the BIGFOOT sampling within the fine grid.  It is
possible that adding the large area coarse grid may improve the pattern.

## BOREAS

- a.

- c.

- d.

- b.

- Figure 1

- CASI
- 2m, Feb 06,1996

- TM
- 30m, June 6, 1996

- Reflectance Color Composites

- Estimated LAI

- NSA-OBS

**Notes:**
CASI LAI from FLIM-CLUS algorithm and TM LAI from Reduced Simple Ratio regression:  Fernandes, R.A., Hu, B., Miller, J.R., and Rubinstein, I. (2001), A multi-scale approach to mapping effective leaf area index in Boreal Picea mariana stands using high spatial resolution CASI imagery. In pressInt. J. Remote Sens.

## Evaluation of CASI 2m FLIM-CLUS Effective LAI

**Notes:**
CASI Le from FLIM-CLUS algorithm:  Fernandes, R.A., Hu, B., Miller, J.R., and Rubinstein, I. (2000), A multi-scale approach to mapping effective leaf area index in Boreal Picea mariana stands using high spatial resolution CASI imagery. In press Int. J. Remote Sens.

Surface Le from LAI-2000 transects.

## Density Plots of CASI FLIM-CLUS vs.

-  Aggregated TM Reduced Simple Ratio (RSR) LAI over BOREAS NOBS

**Notes:**
Results suggest both 30m and 1km TM differ from CASI and hence surface LAI (due to validation of CASI as being unbiased).  TM bias error at 30m is in part due to atmospheric correction errors in TM.   But, improvement in correlation between TM and CASI at all scales suggest open areas control scaling errors for this stand.  Grey scale proportion to frequency.  White lines are 1:1 lines.  Aggregation to 1km produced by a moving window average so scatter shown is likely smaller than expected from independent window aggregation.

Fernandes, R.A., J.R. Miller, J. Chen and I. Rubinstein, 2001. Evaluating image based estimates of leaf area index in Boreal conifer Stands over a range of scales using high-resolution CASI imagery, in revision  Remote Sensing of Environment.

## “1km” AVHRR/VGT vs. Aggregated TM LAI: Worst Case

- “1km” AVHRR/VGT vs. Aggregated TM LAI: Worst Case

- Water correction - removal of sub-pixel water body
- effect assuming SR=1 for water.

**Notes:**
Comparison of 4km averaged TM and AVHRR/VGT LAI estimates over LAI-validation sites across Canada.  Water correction used modifies AVHRR or VGT reflectances by assuming constant water vegetation index value and using TM water fraction for co-registered AVHRR pixel.  
From Chen et al. 2001.  Validation of Canada wide LAI maps using ground measurements and high and moderate resolution satellite imagery, submitted to RSE.

## Correction of Scaling Errors with Sub-Grid Land Cover

- Open area correction - removal of sub-pixel water body
- effect assuming constant open area SR.

**Notes:**
Open areas mapped from TM land cover as non-forested.  Correction based on using constant open area SR and subtracting from AVHRR pixels based on co-registered TM land cover.  Blue points are ALL areas but without open area correction (only water correction), pink points are after open area correction.  Negligible broadleaf overstory is present in the region near James Bay, Canada.

From Chen et al. 2001.  Validation of canada wide LAI maps using ground measurements and high and moderate resolution satellite imagery, submitted to RSE.

## Conclusions

- Surface LAI needs multiple angle and transect measurements
- 1km LAI/pixel is a fn of subpixel LAI distribution
- Subpixel LAI governed by Land Cover as the critical factor
- Land cover spatial scale varies with biome/landscape characteristics
-  Spatially fixed sampling design may not generally optimal
-  Better estimate of ‘true’ LAI for validation:
- measure LAI of Land Cover types (a range for each),
- multiply these by fractions of LC present/pixel
- need hi-res LC data)
-  Similar approach can be used for calibration of models.

## What’s Next for CCRS

- Multitemporal Validation
- Site revisits
- Burn chronosequence
- Forest health.
- More spatial sampling
- Grasslands, Tundra, Mountains
- New field and airborne instruments.
- LIDAR, IKONOS, TRAC II, Digital Cameras
- Closer ties to reflectance calibration.