Earth observation for habitat mapping and biodiversity monitoring

Lang, Stefan; Mairota, Paola; Pernkopf, Lena; Padoa‐Schioppa, Emilio

doi:10.1016/j.jag.2014.10.007

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Numerous studies have pointed out the contribution of satellite imagery for measuring species richness (Rocchini et al., ) or predicting functional traits in grasslands (Lausch et al., ). However, it remains a challenging task to accurately map grassland plant communities using remote sensing data (Corbane et al., ; Lang, Mairota, Pernkopf, & Schioppa, ), due to floristic uncertainty and spectral uncertainty. The floristic uncertainty is the inevitable probability of misclassifying vegetation relevés (De Cáceres, Font, & Oliva, ), which is especially high when handling plant communities with similar species compositions.…”

Section: Introductionmentioning

confidence: 99%

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Rapinel

Rossignol

Hubert‐Moy

et al. 2018

Applied Vegetation Science

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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.

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Section: Introductionmentioning

confidence: 99%

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Rapinel

Rossignol

Hubert‐Moy

et al. 2018

Applied Vegetation Science

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“…The major limitation of Landsat images is their spatial resolution which is generally considered too coarse for mapping heterogeneous habitats: this objective is therefore still a challenge (Lang et al, 2015) and was addressed in this work. The procedure that we tested in this work consisted in aggregating fine-grained vegetation units that constitute repetitive combinations at a higher hierarchical level.…”

Section: Introductionmentioning

confidence: 99%

Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

et al. 2015

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International audienceCoastal marshlands may provide ecosystem services but their vegetation and related services may be impacted by environmental changes. Habitat mapping is a key step to monitor the spatio-temporal dynamics of vegetation and detect on-going changes. However, it is still a challenge to produce reliable vegetation maps at the regional scale. This study aims to evaluate the potential of new Landsat-8 imageries (acquired in September and December 2013) for mapping fine-grained plant communities in coastal marshlands. Fieldbased vegetation maps were collected for 270 km of marshlands along the French Atlantic coast. In order to be identifiable on the satellite image, fine-grained vegetationunits were aggregated into fewer plant community combinations. The classification accuracy was assessed by comparison with field-based vegetation data and compared between the supervised methods used, including Minimum Distance, Mahalanobis, Maximum Likelihood, Random Forest and Support Vector Machine. The best result was obtained with the Maximum Likelihood classifier and by combining the twoLandsat-8 images (85.9 % accuracy overall). Three main habitat types dominated the coastal Atlantic marshlands: croplands, Trifolio maritimae-Oenantheto silaifoliae geosigmetum and Puccinellio maritimae-Arthrocnemeto fruticosi geosigmetum. The reliability of the vegetation map produced will provide a good basis for monitoring the conservation status of the various habitats

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“…Lang et al (2015) addressed the challenges of data integration combining satellite and in situ sources. They also identified the need for a multiscale approach from biomes down to the individual species and their population studies to efficiently cope with the expectations of policy-making, like the CBD and the related EU directives.…”

Section: Data Standards and Integrationmentioning

confidence: 99%

The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study

et al. 2015

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The Aichi Biodiversity Targets of the United Nations' Strategic Plan for Biodiversity set ambitious goals for protecting biodiversity from further decline. Increased efforts are urgently needed to achieve these targets by 2020. The availability of comprehensive, sound and up-to-date biodiversity data is a key requirement to implement policies, strategies and actions to address biodiversity loss, monitor progress towards biodiversity targets, as well as to assess the current status and future trends of biodiversity. Key gaps, however, remain in our knowledge of biodiversity and associated ecosystem services. These are mostly a result of barriers preventing existing data from being discoverable, accessible and digestible. In this paper, we describe what regional Biodiversity Observation Networks (BONs) can do to address these barriers using the European Biodiversity Observation Network (EU BON) as an example. We conclude that there is an urgent need for a paradigm shift in how biodiversity data are collected, stored, shared and streamlined in order to tackle the many sustainable development challenges ahead. We need a shift towards an integrative biodiversity information framework, starting from collection to the final interpretation and packaging of data. This is a major objective of the EU BON project, towards which progress is being made.

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Earth observation for habitat mapping and biodiversity monitoring

Cited by 17 publications

References 38 publications

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Mapping grassland plant communities using a fuzzy approach to address floristic and spectral uncertainty

Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale

The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study

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