Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions |
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| Authors: | Janne Heiskanen Miina Rautiainen Lauri Korhonen Matti Mõttus Pauline Stenberg |
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| Institution: | 1. Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland;2. School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;3. Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Finland |
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| Abstract: | Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (Le) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–Le regression performed best when applied to an independent test site in southern Finland RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory. |
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| Keywords: | Leaf area index Spectral invariants PARAS Vegetation index Neural networks kNN |
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