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

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)

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

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  1. 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. Introduction • 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

  3. Objectives • 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)

  4. LAI fieldmeasurements > 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)

  5. RSR-Le regression models • Requires min and max SWIR reflectancefactors • Best modelfitifvaluesaredeterminedseparately for eachscene (scene-specific RSR)instead of general values (global RSR) Le RSR

  6. PARAS forest reflectance model Rautiainen & Stenberg 2005, RSE • θ1 and θ2: view and Sun zenith angles cgf =canopy gap fraction ρground = BRF of the forest background f= canopy upward scattering phase function i0(θ2 ) = canopy interceptance ωL = leaf albedo groundcomponent canopycomponent p p • 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 p p

  7. PARAS simulations • 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 DIFN = ‘diffuse non-interceptance’ BRFNIR Empirical data BRFred

  8. Accuracy at an independentvalidationsite Heiskanen et al. 2011, JAG RSR (scene-specific) PARAS RMSE = 0.57 (24.2%) Bias = -0.30 (-12.7%) r = 0.90 RMSE = 0.59 (25.1%) Bias = -0.27 (-11.4%) r = 0.88 EstimatedLe EstimatedLe MeasuredLe MeasuredLe

  9. Satelliteimagemosaics • 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

  10. Satelliteimagemosaics(2000/2006) Landcovermaps (2000/2006) RSR Heiskanen et al. 2011, JAG LAI estimationmethods Validation Effective LAI (Le) Fieldplots (6 sites) Correction for shoot-levelclumping LAI MODIS LAI Intercomparison

  11. Scene-specific RSR SWIR BRF Scene-boundaries (2006) Forestmask + +

  12. Scene-specific RSR:ρSWIR_min,ρSWIR_max Global values based on sample plots ρSWIR_min = 0.063 ρSWIR_max = 0.244

  13. Accuracy at modellingsites RSR (scene-specific) RSR (global) PARAS EstimatedLe MeasuredLe

  14. LAI maps(global RSR) 2000 2006

  15. 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 and MODIS LAI (V005) MODIS LAI (IMAGE2006 dates) MODIS LAI (Julyaverage 2002–2010) LAI 2006 White = non-forest (< 50% forest), Black = clouds Good quality (main algorithm with or without saturation)

  16. Comparisonwith MODIS LAI Scene-wiseaverages • MODIS LAI includesalsounderstory LAI

  17. Conclusions • Empirical and forest reflectance model based methods for estimating LAI • Empiricalmodelbased on RSR (global) wasselected for generating LAI maps for Finnishforests • Realistic LAI patternsbut the highestvaluesareunderestimated • Reflectance data and landcovermaps • Systematicdifference in red and SWIR bands • Phenological differencesbetween the images • Clumpingcorrection • Furthervalidation of MODIS LAI (V005)

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

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