Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Abstract

The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast-height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of normalised difference vegetation indices (NDVI) derived from all possible two-band combinations were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression was also assessed. New NDVIs based on the contrast between reflectance in the red-edge shoulder (756-820 nm) and the water absorption feature centred at 1200 nm (1172-1320 nm) were found to show higher correlations with the forest structural parameters than standard NDVIs derived from NIR and visible reflectance. PLS regression showed a slight improvement in estimating the beech forest structural attributes compared to NDVI using linear regression models. Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The DBH map could be useful to forest management in many ways e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.