Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Abstract

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R = 0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.

Acknowledgements

The research was funded by the Academy of Finland through the TAITATOO-project and personal grant to PP (120190), and by the Natural Sciences and Engineering Research Council of Canada funding to DK. We are very grateful to Jon Pasher, PhD candidate at Carleton University, for the airborne imagery acquisition. A.G. and P.P. are also grateful for the working infrastructure and environment provided by the Geomatics and Landscape Ecology Laboratory (GLEL) and the Department of Geography and Environmental Studies of Carleton University during the fieldwork stay in August 2007. A.G. is financed partly by CIMO, Department of Geography of University of Helsinki, and the Finnish Graduate School of Geography.