1. 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

2. www.helsinki.fi/yliopisto 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 2 Introduction

3. www.helsinki.fi/yliopisto 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) 3 Objectives

4. www.helsinki.fi/yliopisto > 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

5. www.helsinki.fi/yliopisto 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) 5 RSR-L e regression models LeLe RSR

6. www.helsinki.fi/yliopisto PARAS forest reflectance model θ 1 and θ 2 : view and Sun zenith angles cgf = canopy gap fraction ρ ground = BRF of the forest background f= canopy upward scattering phase function i 0 (θ 2 ) = canopy interceptance ω L = 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 p p p

7. www.helsinki.fi/yliopisto 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 7 Empirical data BRF red BRF NIR DIFN = ‘diffuse non-interceptance’

8. www.helsinki.fi/yliopisto Accuracy at an independent validation site PARAS RMSE = 0.59 (25.1%) Bias = -0.27 (-11.4%) r = 0.88 Measured L e Estimated L e RSR (scene-specific) Measured L e Estimated L e RMSE = 0.57 (24.2%) Bias = -0.30 (-12.7%) r = 0.90 Heiskanen et al. 2011, JAG

9. www.helsinki.fi/yliopisto 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 9 Satellite image mosaics

10. www.helsinki.fi/yliopisto10 RSR Effective LAI (L e ) LAI estimation methods Correction for shoot- level clumping LAI Validation Field plots (6 sites) MODIS LAIIntercomparison Satellite image mosaics (2000/2006) Land cover maps (2000/2006) Heiskanen et al. 2011, JAG

11. www.helsinki.fi/yliopisto Scene-specific RSR SWIR BRFForest maskScene-boundaries (2006) ++

12. www.helsinki.fi/yliopisto Data setρ SWIR RangeMeanSD IMAGE2000 (n = 37) Min0.057–0.1180.0820.016 Max0.208–0.2760.2350.012 IMAGE2006 (n = 83) Min0.063–0.1330.0890.019 Max0.193–0.2850.2210.015 Global values based on sample plots ρ SWIR_min = 0.063 ρ SWIR_max = 0.244 Scene-specific RSR: ρ SWIR_min, ρ SWIR_max

13. www.helsinki.fi/yliopisto Accuracy at modelling sites RSR (scene-specific)RSR (global)PARAS Measured L e Estimated L e Measurement siteDate RSR (scene-specific)RSR (global)PARAS LAIImageR2R2 RMSER2R2 R2R2 Puumala (n = 395)6/20002.8.19990.640.520.640.540.690.56 Saarinen (n = 370)7/200127.6.20010.610.650.610.720.660.84 Hirsikangas (n = 24)5–6/20056.8.20060.420.550.420.550.480.57 Rovaniemi (n = 20)6/20052.7.20060.700.540.750.290.790.46 Tähtelä (n = 261)6/20067.6.20060.450.550.450.350.38 Hyytiälä (n = 73)6–7/200817.7.20060.580.730.570.790.630.88

14. www.helsinki.fi/yliopisto LAI maps (global RSR) 20002006

15. www.helsinki.fi/yliopisto 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 = cloudsGood quality (main algorithm with or without saturation) LAI 2006 and MODIS LAI (V005)

16. www.helsinki.fi/yliopisto Comparison with MODIS LAI MODIS LAI includes also understory LAI Scene-wise averages

17. www.helsinki.fi/yliopisto 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) 17 Conclusions

18. www.helsinki.fi/yliopisto Thank you! http://www.mm.helsinki.fi/~mxrautia/lai/index.htm 18 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