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

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.