# Presentation: 051942

## Current and Future Approaches to Mapping Canopy Foliage Distribution over National Extents at NRCan, CCRS

- Richard Fernandes, Sylvain Leblanc, Peter White, Goran Pavlic,Josef Cihlar

## Current Method

- Based on nadir vegetation indices.
- Simple ratio and reduced simple ratio.
- Applied to
-  9 TM/ETM scenes (40+ planned)
- 10-day composited AVHRR from 1993-1999, SPOT-VGT from 1998-1999, 16 day MODIS composites for 2000 (in testing)
-  3 Methods Tested: Empirical, Physical Model Inversion via LUT, Semi-Empirical

## Empirical Method	

- (LAI,Vegetation Index) measured for 3x3 TM pixel patches.
- Measurement error modelled.
- Type II regressions applied (errors cannot be ignored in both LAI and VI) after tranformation of axes for homeoscedacity.
- p(LAI|VI) reported but m.l.e. recorded.

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## Slide 11

## Physical Models

- Use of **validated reflectance models** to define p(LAI|SR, parameters x) - for nadir viewing only currently.
- Broadleaf - Sellers 1985; Conifer - 5 Scale.
- Simulate over p(x) = uniform distn of valid range for Canada.
- Populate p(LAI|SR) using each model run with a distribution of LUT entries reflecting model errors.
- Reporting m.l.e. of p(LAI|SR) can be ill-posed.

## Semi-Empirical Model

- p(x) is a joint p.d.f. of model parameters.
- Previous studies did not use “equal steps”: biased p(x).
- Physical model use of p(x)=u(x) is wrong.
- Calibrate p(x) by weighting model trajectories  based on the best match to empirical (LAI|SR) data.
- Assumes we have adequately sampled marginal distribution of p(x) in our empirical data.
- calibrates out errors in physics - model is “physically valid” in terms of LAI vs SR.
- Comparison of p.d.f. of empirical and semi-empirical model allows check of  representativeness of data.

## LAI

- SR

- Conditional Needleaf LAI Distributions Given SR

## LAI

- SR

- Conditional Broadleaf LAI Distributions Given SR

## Conditional Needleaf LAI Distributions Given SR

- Physical Model

- Semi-Empirical Model

- Empirical Model

- SR = 5 for solid lines.
- SR = 10 for dashed lines.

## LAI

- Conditional Broadleaf LAI Distributions Given SR

- Physical Model

- Semi-Empirical Model

- Empirical Model

- SR = 5 for solid lines.
- SR = 10 for dashed lines.

## Scaling To AVHRR/VGT

- Approach I
- Make TM LAI maps and relate to AVHRR/VGT VI.
- p(LAI|SRcoarse)=p(SRcoarse|LAI)*P(LAI)/p(SRcoarse)
- prior P(LAI) is spatially variable => bias can be serious.
- Approach II
- Relate TM VI to TM SR and apply TM based
- p(SRTM|SRcoarse)=p(SRcoarse|SRTM)P(SRTM)/p(SRcoarse)
- p(LAI|Srcoarse) = p(LAI|SRTM)p(SRTM|SRcoarse)
- P(SRTM) is known.
- Larger uncertainty in p(LAI|SRcoarse) due to sensor cross-calibration errors and due to scaling errors but hopefully less bias

## Spectral response function effect in NDVI observed from AVHRRNOAA-14 and NOAA-15. July 15, 2000. Northern Ontario. Canada. 

- model

- observations

- Channel 1

- Channel 2

- Green
- vegetation

## Leaf Area Index - Scaling

**LAI**
**AVHRR July 20-30, 1994**

## Slide 21

## 1km Water Fraction Mask of Canada

-  from 1:50000 Topographic Maps

## Flowchart for LAI from NADIR VI

- Scaling
- relationship
- between TM and Coarse |
- ecozone/lifeform.

- Is Type II
- within range
- of Physical

- Stratify by
- ecozone
- and lifeform

- Surface
- LAI & TM
- Nadir VI

- Type II
- Model

- Reflectance
- Model

- Survey
- parameter
-  ranges

- p(x)=u(x)

- Model
- choice

- p(LAI|
- SR,u(x))

- Unconstr.
- LUT

- (LAI,
- SRTM)

- More data

- Validate model

- Constrain
- Physical
- Model

- Apply empirical
- m.l.e. if
- sufficent
- data else model.

**p(LAI|**
**SR)**

**m.l.e.**
**LAI|SR**

- Subjective

- Measurement

- Objective

- Outputs

## Phenology - LAI(t)	 from VGT

## Foliage Clumping - Why Monitor It?

## BRDF Clumping Index 

## Physically Based Estimation of Clumping

- NDHD = (HotSpot- DarkSpot)/(HotSpot + DarkSpot)

## Slide 28

## Clumping Index

## Future Work

- Simultaneous estimation of sub-pixel land cover and LAI.
- Produce LAI from multi-angle data (waiting for MISR pre-processing!).
- Compare error budget of multi-angle LAI vs LAI from nadir VI and anisotropy index.
- Evaluate sensitivity of LAI estimates to variations in leaf biochemistry and leaf/site moisture status (Using 5Scale)
- Compare 3D RT with 5-scale in forward and inverse mode.