Could land surface phenology be used to discriminate Mediterranean pine species?

Land surface phenology (LSP) can improve the monitoring of forest areas and their change processes. The aim of this study was to characterize the temporal dynamics in Mediterranean pines and evaluate the potential of LSP for species discrimination. We used 661 mono-specific plots for five different Pinus species (Pinus halepensis, P. pima, P. pinaster, P. sylvestris, P. nigra) and the MOD13Q1-NDVI time series (2000-2016) to perform the analyses. The time series were smoothed to extract the phenological parameters and calculate multi-temporal metrics, to synthesize the inter-annual variability. The potential of ISP for discriminating between Pinus species was evaluated by the application of the Random Forest (RF) classifier from different subsets of explanatory variables: i) the smooth time series; ii) the multi-temporal metrics; and iii) the multi-temporal metrics plus the auxiliary physical variables. This latter subset was also used as input to a Classification and Regression Tree (CART) algorithm to better explain the differences between Pinus species regarding LSP parameters and other environmental drivers. The analysis showed two different patterns: an important NDVI decrease during the summer for P. halepensis, P. pinea, and P. pinaster, and lower NDVI variation along the year for P. sylvestris. P. nigra showed a heterogeneous infra-specific behavior, having locations with different patterns. We distinguished Pinus species plots with a global accuracy of 0.82, when we used multi-temporal metrics of LSP and auxiliary physical variables. More generally, the Mediterranean Pinus species could be differentiated considering the 23rd of July as the start of season and 179 km and 1100 m as distance to the coastline and elevation, respectively.