Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data

Abstract

Forest canopy cover (C) is needed in forest area monitoring and for many ecological models. Airborne scanning lidar sensors can produce fairly accurate C estimates even without field training data. However, optical satellite images are more cost-efficient for large area inventories. Our objective was to use airborne lidar data to obtain accurate estimates of C for a set of sample plots in a boreal forest and to generalize C for a large area using a satellite image. The normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) were calculated from the satellite image and used as predictors in the regressions. RSR, which combines information from the red, near-infrared, and shortwave infrared bands, provided the best performance in terms of absolute root mean square error (RMSE) (7.3%) in the training data. NDVI produced a markedly larger RMSE (10.0%). However, in an independent validation data set, RMSE increased (13.0–17.1%) because the systematic sample of validation plots contained more variation than the training plots. Our results are better than those reported earlier, which is probably explained by more consistent C estimates derived from the lidar. Our approach provides an efficient method for creating C maps for large areas.

Acknowledgements

This research was supported by the strategic funding of the University of Eastern Finland. We are thankful to Dr Timothy Warner and the three anonymous reviewers for their feedback regarding this study.