Leaf Area Index (LAI) is an important variable in environmental processes modelling. Already several approaches were proposed to model wetlands LAI with remote sensing data. However, none of these methods was based on upscaling the field spectral reflectance measurements, which is a matter of this paper. In this study, we used combined measurements of spectral reflectance (350-2500 nm) and LAI to establish a regression model of LAI. The spectral reflectance was resampled to the spectral resolution of a satellite hyperspectral sensor (CHRIS-PROBA) beforehand and then used to calculate NDVI-based spectral indices. From the set of spectral indices the one with the strongest correlation with LAI was chosen for the regression. Finally, the regression was applied to the CHRIS satellite images and the results were analysed within the scope of different wetland communities of the study area. The results show that the optimal regression model gives statistically different LAI values for the majority of different ground truth plant communities, rivers and urban areas.

Leaf Area Index (LAI) is one of the crucial characteristics describing forest canopy structure and is significant for biomass assessments which are important for characterizing forest ecosystems and rational management of wood resources. The main goal of this research was to estimate Leaf Area Index of forests located within borders of the Magura National Park (MNP) situated in the area of the Flysch Carpathians, Poland. Examined forest communities belong to two different vegetation layers in altitudinal zonation: foothills zone, up to 530 m a.s.l., and forest zone, located higher. In situ ground indirect measurements of LAI were performed using a LAI-2000 Plant Canopy Analyzer. They were achieved within the scanned swath of the airborne laser scanning (ALS) with a density of 4 points/m²> and Landsat images. Both Landsat images and ALS data were used to calculate the LAI. Field measurements were carried out between 23 and 29 August 2013 using two LAI-2000 Plant Canopy Analyzers. The campaign was organized just after the date of ALS data acquisition (22.08.2013). Several spectral vegetation indices (NDVI, IPVI, MSR, GNDVI among others) were tested in order to obtain the spatial distribution of LAI estimated on the basis of Landsat images as a comparison to LAI derived from ALS data. The GNDVI index was chosen as the best predictor of Leaf Area Index (R² = 0.705; r = 0.840). The results indicate that ALS offers an accurate tool for mapping leaf area index for forests at local or regional scale and that it is suitable for verification of LAI derived through passive optical remote sensing techniques over large areas. The results indicate that ALS-derived point density and Landsat vegetation indices are correlated and that ALS results present an acceptable accuracy of LAI estimations for all forest classes (R² = 0.5526). The comparison of ALS LAI and field measurements gave satisfactory results. The coefficient of determination for all forest classes in this case was equal to 0.456.