Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe

Abstract

Tree species maps derived from satellite imagery increasingly support forest administrations and nature conservation authorities with large-scale and up-to-date information. However, many species are often excluded or aggregated in classification tasks due to a limited knowledge of the most suitable predictors. Our study aims to gain a better understanding of optical and polarimetric traits for tree species mapping by examining Sentinel-1 and Sentinel-2 time series from 61 tree species in temperate Europe. For a selection of 32 optical, polarimetric and structural variables, the principal component analysis revealed that Sentinel-2 variables mainly explain the variance in the data by contributing to the “seasonality” and “foliage color” components. Sentinel-1 contribute most to the “texture” component. The Normalized Difference Vegetation Index (NDVI), Tasseled Cap Greenness (TCG) and Radar Vegetation Index (RVI) were chosen as key variables for further analysis. Seasonality was found to be the most dominant aspect in all vegetation indices. Furthermore, the TCG was found to be useful to distinguish between early and late budding species. The RVI showed a large potential to discriminate conifers, which is attributed to the crown volume effect of C-band SAR. Using exploratory data analysis, we further examined the influence of management, biogeographical and meteorological factors on the time series from Fagus sylvatica, Pinus sylvestris, and Picea abies. The NDVI and TCG are relatively robust to different conditions. For the two conifer species however, we found strong spatial variations of the RVI which are presumably caused by different crown conditions across the study area. Using Sentinel-1 data could therefore lead to uncertainties in tree species mapping across large biogeographical gradients. This study contributes to the improvement of tree species mapping based on optical and dual-polarimetric data and thus benefits forest authorities and other stakeholders in their monitoring tasks and decision-making.