Abstract
Phytosociology is a reference method to classify vegetation that relies on field data. Its classification in hierarchical vegetation units, from plant associations to class level, hierarchically reflects the floristic similarity between different sites on different spatial scales. The development of remotely sensed multispectral platforms as satellites enormously contributes to the detection and mapping of vegetation on all scales. However, the integration between phytosociology and remotely sensed data is rather difficult and little practiced despite being a goal for the modern science of vegetation. In this study, we demonstrate how normalized difference vegetation index (NDVI) time series with functional principal component analysis (FPCA) could support the analyses of phytosociologists. The approach supports the recognition and characterization of forest plant communities identified on the ground by the phytosociological approach by using NDVI time series that encode phenological behaviors. The methodology was evaluated in two study areas of central Italy, and it could characterize and discriminate six different forest plant associations that have similar dominant tree species but distinct specific composition: three dominated by black hornbeam (Ostrya carpinifolia) and three dominated by holm oak (Quercus ilex). The methodology was also able to optimize the ground data collection of unexplored areas (from a phytosociological point of view) by using a phenoclustering approach. The obtained results confirmed that by using remote sensing, it is possible to separate and distinguish plant communities in an objective/instrumental way, thus overcoming the subjectivity intrinsic to the phytosociological method. In particular, FPCA functional components (NDVI seasonalities) were significantly correlated with vegetation abundance data variation (Mantel r = 0.76, p < 0.001).
Highlights
The recognition and characterization of plant communities can be achieved with various approaches
Valmontagnana area obtained with functional data clustering (FDC)
We presented a methodology for the recognition and characterization of forest plant communities through remote-sensing normalized difference vegetation index (NDVI) time series
Summary
The recognition and characterization of plant communities can be achieved with various approaches. According to present-day developments [4], an association is defined as “a system of plants having a floristic composition that is statistically repetitive; it has a range of different features, such as the structure, the ecological value (significant for different environmental parameters), and the quality of the dynamic and catenal relationships that it has with other communities”. It stands out for “a characteristic specific complex, consisting of the preferring plants which are linked to it in statistical terms and that are biogeographically and ecologically differential compared to similar synvicariant or geosynvicariant associations”. Phytosociology is a scientific discipline with high ecological and biogeographic connotation; it is based on three levels of analysis that can be considered stages of development reached over time: (1) the floristic–ecological approach, based on the recognition of plant associations; (2) the synphytosociological approach, based on the study of time–dynamical relationships among associations in order to identify dynamic vegetation series (integrated phytosociology); and (3) geosynphytosocology, known as landscape phytosociology, which, through the knowledge of the spatial distribution of different vegetation series, recognizes homogeneous landscape units in terms of bioclimatic and geographic features [5]
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