generating fine resolution leaf area index maps for boreal forests of finland

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www.helsinki.fi/ yliopisto Generating fine resolution leaf area index maps for boreal forests of Finland Janne Heiskanen, Miina Rautiainen, Lauri Korhonen, Matti Mõttus, Pauline Stenberg IGARSS 2011, 24–29 July 2011, Vancouver, Canada

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Generating fine resolution leaf area index maps for boreal forests of Finland. Janne Heiskanen, Miina Rautiainen, Lauri Korhonen, Matti Mõttus , Pauline Stenberg. Introduction. Leaf area index (LAI) - PowerPoint PPT Presentation

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Page 1: Generating fine resolution leaf area index maps for boreal forests of Finland

www.helsinki.fi/yliopisto

Generating fine resolution leaf area index maps for boreal forests of

FinlandJanne Heiskanen, Miina Rautiainen, Lauri Korhonen,

Matti Mõttus, Pauline Stenberg

IGARSS 2011, 24–29 July 2011, Vancouver, Canada

Page 2: Generating fine resolution leaf area index maps for boreal forests of Finland

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Leaf area index (LAI)• Key variable in modeling vegetation-atmosphere interactions,

particularly carbon and water cycle• One half of the total leaf surface area per unit ground surface

area

Several global-scale LAI products, but finer spatial resolution (e.g. Landsat and SPOT) is needed to describe the spatial heterogeneity of LAI

Empirical, vegetation index (VI) based methods are typically used in fine resolution mapping, but more physically-based approach could generalize better in space and time, and between sensors

Introduction

Page 3: Generating fine resolution leaf area index maps for boreal forests of Finland

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Generate fine-resolution forest LAI maps for Finland using satellite image mosaics at 25 m resolution

LAI estimation methods• Empirical model based on reduced simple ratio (RSR)• Inversion of forest reflectance model (PARAS)

Compare upscaled LAI maps with MODIS LAI (V005)

Objectives

Page 4: Generating fine resolution leaf area index maps for boreal forests of Finland

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> 1000 field plots measured with LAI-2000 PCA or hemispherical photography (2000–2008)

SPOT HRVIR and Landsat ETM+ images from the same summer (atmospherically corrected)

LAI field measurements

Page 5: Generating fine resolution leaf area index maps for boreal forests of Finland

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Requires min and max SWIR reflectance factors

Best model fit if values are determined separately for each scene (scene-specific RSR) instead of general values (global RSR)

RSR-Le regression models

min_max_

max_

SWIRSWIR

SWIRSWIR

red

NIRRSR

L e

RSR

Page 6: Generating fine resolution leaf area index maps for boreal forests of Finland

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PARAS forest reflectance model

L

LLgroundcanopy

ppifcgfcgfBRF

1202121 )(),()()(

θ1 and θ2: view and Sun zenith anglescgf = canopy gap fractionρground = BRF of the forest background

f= canopy upward scattering phase function i0(θ2 ) = canopy interceptance ωL = leaf albedo

Photon recollision probability (p): the probability by which a photon scattered from a leaf (or needle) in the canopy will interact within the canopy again

Rautiainen & Stenberg 2005, RSE

ground component canopy component

p

pp

p

Page 7: Generating fine resolution leaf area index maps for boreal forests of Finland

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Can use field measurements of canopy structure and optical properties of foliage and understory

Calculation of p from LAI-2000 PCA data (Stenberg 2007, RSE)

30,000 simulations for training neural networks

• LAI-2000 PCA (cgf, p)• Leaf (needle) albedo from images• Mixtures of forest understory spectra

(Lang et al. 2001)• Red, NIR and SWIR

PARAS simulations

Empirical data

BRFred

BR

F NIR

LAIDIFNp

11

DIFN = ‘diffuse non-interceptance’

Page 8: Generating fine resolution leaf area index maps for boreal forests of Finland

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Accuracy at an independent validation site

PARAS

RMSE = 0.59 (25.1%)Bias = -0.27 (-11.4%)

r = 0.88

Measured Le

Estim

ated

Le

RSR (scene-specific)

Measured Le

Estim

ated

Le

RMSE = 0.57 (24.2%)Bias = -0.30 (-12.7%)

r = 0.90

Heiskanen et al. 2011, JAG

Page 9: Generating fine resolution leaf area index maps for boreal forests of Finland

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Country-wide mosaics (IMAGE2000/2006) produced by Finnish Environmental Institute (SYKE)

• 37 Landsat ETM+ scenes, 1999–2002 • 83 IRS P6 LISS and SPOT-4 HRVIR scenes, 2005 or 2006

Input data for Finnish Corine Land Cover databases (CLC2000/2006)

Images have been atmospherically corrected, but red and SWIR reflectance factors were calibrated using satellite data from the field sites

Satellite image mosaics

Page 10: Generating fine resolution leaf area index maps for boreal forests of Finland

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RSR

Effective LAI (Le)LAI

estimation methods

Correction for shoot-level clumping

LAI

Validation

Field plots(6 sites)

MODIS LAI Intercomparison

Satellite image mosaics

(2000/2006)

Land cover maps

(2000/2006)

Heiskanen et al. 2011, JAG

Page 11: Generating fine resolution leaf area index maps for boreal forests of Finland

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Scene-specific RSR

SWIR BRF Forest maskScene-boundaries (2006)

+ +

Page 12: Generating fine resolution leaf area index maps for boreal forests of Finland

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Data set ρSWIR Range Mean SD

IMAGE2000(n = 37)

Min 0.057–0.118 0.082 0.016

Max 0.208–0.276 0.235 0.012

IMAGE2006(n = 83)  

Min 0.063–0.133 0.089 0.019

Max 0.193–0.285 0.221 0.015

Global values based on sample plotsρSWIR_min = 0.063ρSWIR_max = 0.244

Scene-specific RSR:ρSWIR_min, ρSWIR_max

Page 13: Generating fine resolution leaf area index maps for boreal forests of Finland

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Accuracy at modelling sites

RSR (scene-specific) RSR (global) PARAS

Measured Le

Est

imat

ed L

e

Measurement site Date  RSR (scene-specific) RSR (global) PARAS  LAI Image R2 RMSE R2 RMSE R2 RMSEPuumala (n = 395) 6/2000 2.8.1999 0.64 0.52 0.64 0.54 0.69 0.56Saarinen (n = 370) 7/2001 27.6.2001 0.61 0.65 0.61 0.72 0.66 0.84Hirsikangas (n = 24) 5–6/2005 6.8.2006 0.42 0.55 0.42 0.55 0.48 0.57Rovaniemi (n = 20) 6/2005 2.7.2006 0.70 0.54 0.75 0.29 0.79 0.46Tähtelä (n = 261) 6/2006 7.6.2006 0.45 0.55 0.45 0.35 0.38 0.38Hyytiälä (n = 73) 6–7/2008 17.7.2006 0.58 0.73 0.57 0.79 0.63 0.88

Page 14: Generating fine resolution leaf area index maps for boreal forests of Finland

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LAI maps (global RSR)

2000 2006

Page 15: Generating fine resolution leaf area index maps for boreal forests of Finland

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LAI≤ 1.0

1.1–2.0

2.1–3.0

3.1–4.0

4.1–5.0

5.1–6.0

> 6.0

LAI 2006 MODIS LAI(IMAGE2006 dates)

MODIS LAI(July average 2002–2010)

White = non-forest (< 50% forest), Black = clouds Good quality (main algorithm with or without saturation)

LAI 2006 and MODIS LAI (V005)

Page 16: Generating fine resolution leaf area index maps for boreal forests of Finland

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Comparison with MODIS LAI

MODIS LAI includes also understory LAI

Scene-wise averages

Page 17: Generating fine resolution leaf area index maps for boreal forests of Finland

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Empirical and forest reflectance model based methods for estimating LAI

• Empirical model based on RSR (global) was selected for generating LAI maps for Finnish forests

• Realistic LAI patterns but the highest values are underestimated

Reflectance data and land cover maps• Systematic difference in red and SWIR bands• Phenological differences between the images• Clumping correction

Further validation of MODIS LAI (V005)

Conclusions

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Thank you!

http://www.mm.helsinki.fi/~mxrautia/lai/index.htm

Heiskanen, J, M Rautiainen, L Korhonen, M Mõttus & P Stenberg (2011). Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions. International Journal of Applied Earth Observation and Geoinformation 13: 595–606. doi:10.1016/j.jag.2011.03.005