Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty. Issue 4 (28th August 2018)

• Record Type: Journal Article
• Authors: Rapinel, Sébastien; Rossignol, Nicolas; Hubert‐Moy, Laurence; Bouzillé, Jan‐Bernard; Bonis, Anne
• Editors: Feilhauer, Hannes
• Is Part Of: Applied vegetation science. Volume 21:Issue 4(2018)
• Page Start: 678
• Page End: 693
• Publication Date: 2018-08-28
• Subjects: LiDAR -- noise clustering -- phytosociology -- Pléiades -- remote sensing -- vegetation typology -- wetlands; Plant ecology -- Periodicals; Plant communities -- Periodicals; Plant populations -- Periodicals; Nature -- Effect of human beings on -- Periodicals; 581.705
• DOI: 10.1111/avsc.12396
• Languages: English
• ISSNs: 1402-2001

Abstract

Abstract: Aims: The mapping and monitoring of natural vegetation is a challenging but important objective for environmental management. Although remote sensing has been used to map plant communities for several years, the maps produced are not sufficiently accurate to meet management requirements. This can be explained by the cumulative effects of floristic and spectral uncertainty. The objective of this study was to accurately map grassland plant communities using a comprehensive fuzzy approach in order to address floristic and spectral uncertainty. Location: Sub‐brackish wet grasslands, Marais Poitevin, France. Methods: We first created a compromise typology—floristically and spectrally consistent—to perform fuzzy noise clustering on a joint PCA matrix derived from vegetation relevés and remote sensing data. This typology had two levels, which corresponded to spectral signatures and plant communities, respectively. Second, we mapped grassland plant communities to predict the fuzzy model from the remote sensing data. We applied this approach using (1) a very high spatial resolution multispectral satellite image and a LiDAR‐derived Digital Terrain Model acquired on a 73 km 2 wet grassland site and (2) more than 200 relevés collected in the field. Results: The results show that (1) the compromise typology yields significantly higher mapping accuracy than classic phytosociological typology (62% and 26%, respectively); (2) compared to a crisp approach, the fuzzy approach improves mapping accuracy by 17 percentage points and (3) a single plant community can be defined by several (1–4) distinct spectral signatures. Conclusions: The comprehensive fuzzy procedure successfully mapped herbaceous plant communities at the ecosystem scale using inexpensive remote sensing data. Floristic and spectral uncertainty was considered in a fuzzy approach, resulting in the mapping of nine herbaceous plant communities with acceptable accuracy. As the natural habitats were characterized at the plant community level, correspondence with functional properties of the species or with ecosystem services can be easily inferred. These encouraging results open up new ways to meet the requirements for monitoring the conservation status of natural habitats in the EU Habitats Directive. Abstract : Mapping plant communities from satellite imagery remains challenging because of floristic and spectral uncertainties. Here, we address this issue using a fuzzy approach considering a 73 km 2 grassland area. We reveal that: (a) nine plant communities can be mapped from multispectral image with 62% accuracy, and (b) compared to a crisp approach, the fuzzy method improves mapping accuracy by ~20%.