Functional Analysis for Habitat Mapping in a Special Area of Conservation Using Sentinel-2 Time-Series Data

Simone Pesaresi,Giacomo Quattrini,Simona Casavecchia,Adriano Mancini

doi:10.3390/rs14051179

Abstract

The mapping and monitoring of natural and semi-natural habitats are crucial activities and are regulated by European policies and regulations, such as the 92/43/EEC. In the Mediterranean area, which is characterized by high vegetational and environmental diversity, the mapping and monitoring of habitats are particularly difficult and often exclusively based on in situ observations. In this scenario, it is necessary to automate the generation of updated maps to support the decisions of policy makers. At present, the availability of high spatiotemporal resolution data provides new possibilities for improving the mapping and monitoring of habitats. In this work, we present a methodology that, starting from remotely sensed time-series data, generates habitat maps using supervised classification supported by Functional Data Analysis. We constructed the methodology using Sentinel-2 data in the Mediterranean Special Area of Conservation “Gola di Frasassi” (Code: IT5320003). In particular, the training set uses 308 field plots with 11 target classes (five forests, two shrubs, one grassland, one mosaic, one extensive crop, and one urban land). Starting from vegetation index time-series data, Functional Principal Component Analysis was applied to derive FPCA scores and components. In particular, in the classification stage, the FPCA scores are considered as features. The obtained results out-performed a previous map derived from photo-interpretation by domain experts. We obtained an overall accuracy of 85.58% using vegetation index time-series, topography, and lithology data. The main advantages of the proposed approach are the capability to efficiently compress high dimensional data (dense remote-sensing time series) providing results in a compact way (e.g., FPCA scores and mean seasonal time profiles) that: (i) facilitate the link between remote sensing with habitat mapping and monitoring and their ecological interpretation and (ii) could be complementary to species-based approaches in plant community ecology and phytosociology.

Highlights

Introduction published maps and institutional affilPhytosociologyis the most widespread method of studying and classifying vegetation in Europe [2,3]
We evaluate the performance and applicability of the methodology proposed by Pesaresi et al [37] in order to map the plant associations and habitats of a Special
The main seasonal variations extracted from Sentinel-2 time-series data using Functional Principal Component Analysis (FPCA), according to the methodology proposed in Pesaresi et al [37], proved to be an effective tool for mapping several plant associations for an entire Special Area of Conservation (SAC)

Summary

Introduction

Introduction published maps and institutional affilPhytosociology (i.e., the Floristic–Sociologic Approach to Vegetation Classification [1])is the most widespread method of studying and classifying vegetation in Europe [2,3]. Phytosociology (i.e., the Floristic–Sociologic Approach to Vegetation Classification [1]). Plant communities, called plant associations, are the most detailed discrete units recognized in phytosociology. These plant associations, recurring in space and time, are characterized by a distinct floristic composition that reflects the current and past ecological–environmental conditions (e.g., bioclimatic features, biogeography, topographic conditions, lithology, and land-uses) [4]. Plant associations and the higher levels of phytosociological vegetation classification (classes, orders, and alliances) are useful for the diagnosis of most natural and semi-natural habitats listed in Annex I of the Habitats Directive [5,6]. Plant association mapping allows for understanding of the spatial distribution of the habitats and, if repeated over time, to evaluate and monitor their conservation status. Plant association mapping allows for understanding of the spatial distribution of the habitats and, if repeated over time, to evaluate and monitor their conservation status. iations.

Methods

Results

Discussion

Conclusion