GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND.pptx
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
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3. One half of the total leaf surface area per unit ground surface areaSeveral 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
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5. Inversion of forest reflectance model (PARAS)Compare upscaled LAI maps with MODIS LAI (V005) 3 Objectives
6. > 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 fieldmeasurements
7. Requires min and max SWIR reflectancefactors Best modelfitifvaluesaredeterminedseparately for eachscene (scene-specific RSR)instead of general values (global RSR) 5 RSR-Le regression models Le RSR
8. PARAS forest reflectance model Rautiainen & Stenberg 2005, RSE groundcomponent canopycomponent 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 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
12. Red, NIR and SWIRPARAS simulations DIFN = ‘diffuse non-interceptance’ BRFNIR Empirical data 7 BRFred
13. 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
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15. 83 IRS P6 LISS and SPOT-4 HRVIR scenes, 2005 or 2006Input 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 Satelliteimagemosaics
16. 10 Satelliteimagemosaics(2000/2006) Landcovermaps (2000/2006) RSR Heiskanen et al. 2011, JAG LAI estimationmethods Validation Effective LAI (Le) Correction for shoot-levelclumping Fieldplots (6 sites) LAI MODIS LAI Intercomparison
21. 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)
27. 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 18
Hinweis der Redaktion
In our another estimation method, we use PARAS forest reflectance model for simulating training data for neural networksIn this model, canopy reflectance is calculated as a sum of ground and canopy componentsCanopy component is calculated using spectrally invariant parameter, photon recollision probability (p).p is a canopy structural parameter which links the canopy and leaf spectral albedos
![Leaf area index (LAI) ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)