Relationship between tree species composition and phenology extracted from satellite data in Swedish forests

Abstract: This study investigated the “relationship between tree species composition and phenology extracted from satellite data in Swedish forests”. The proposed method investigated in this study aims at mapping the fractional composition of deciduous/coniferous tree species and also the fractional composition of Norway spruce (Picea abies) and several pine (Pinus sp.). The fractions can then be used to classify a forest into forest types such as uniform deciduous/coniferous or mixed forests. The method uses field measurements for training a regression model against satellite derived seasonality parameters. The satellite derived phenological parameters consists of a time series of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) values where parameters such as maximum, length, start and end of growing season were extracted with the software TIMESAT. The satellite system used was the Moderate-Resolution Imaging Spectroradiometer (MODIS). The result indicates that the EVI derived seasonality parameters correlated stronger against the fractional composition of deciduous/coniferous tree species as compared with NDVI derived seasonality. The correlation coefficient for the EVI derived seasonality was estimated to 0.88 for the best performing dataset and parameter. However, when validated against an independent dataset the accuracy proved to be low when the tested regression models were used to predict the fraction of deciduous/coniferous tree species composition. A source of error derives from differences in mapping scale between the satellite system (250x250 m) and that of the field data (either a plot size of a 7 m circle or a plot size 30x30 m). The differences in mapping scale are assumed be a major source of error. However, the correlation between satellite derived seasonality and the fractional tree species composition is strong enough to consider it worthwhile to investigate for future studies when better data will become available.