An effective procedure for EUNIS and Natura 2000 habitat type mapping in estuarine ecosystems integrating ecological knowledge and remote sensing analysis

Valentini, Emiliana; Taramelli, Andrea; Filipponi, Federico; Giulio, Silvia

doi:10.1016/j.ocecoaman.2014.07.015

Cited by 33 publications

(27 citation statements)

References 66 publications

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“…For example, Carreño et al, 2008 mapped salt marshes, salt steppes and reeds in a 5 km 2 area of wetland; similarly, Li et al, 2010;Zhang et al, 2011 discriminated only three habitat classes (Suaeda salsa, Spartina anglica and Spartina alterniflora) on a 100 km 2 salt marsh site. Recent work by Valentini et al (2015) also aimed to map heterogeneous vegetation, approached by the dominant plant species, but the Table 4 Under-(A) and over-detection rates (B) expressed in percent per class for the vegetation classifications taken from mono-temporal and biseasonal Landsat-8 images, using the Maximum likelihood classifier (Géhu, 2011). As a result, mapping vegetation using geosigmetum may be used for localizing habitats with specific interest: for example, the Alopecuro-Juncetum gerardii, a plant community of European interest (CORINE Biotope code 15.331) is likely to occur where the geosigmetum BTrifolio maritimae-Oenantheto silaifoliae^was identied on the basis of landsat images (Table 1).…”

Section: Discussion Vegetation Classifications In Coastal Marshes Usimentioning

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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Section: Discussion Vegetation Classifications In Coastal Marshes Usimentioning

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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“…It is one of the last stretches of dunes with psammophytic vegetation, which is very important for the conservation of coastal biodiversity [33,34]. While limiting themselves to the vegetation, many authors have described the typical zonation for the north Adriatic coastal dunes based on strips parallel to the coastline [35,36]. In agreement with the definitions of Directive 92/43/EEC "Habitat", and in light of the recent publication of Merloni et al [37], from the sea going inland, the zonation consists of annual pioneer species, embryo dunes, While limiting themselves to the vegetation, many authors have described the typical zonation for the north Adriatic coastal dunes based on strips parallel to the coastline [35,36].…”

Section: Study Areamentioning

confidence: 99%

Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

et al. 2019

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Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

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“…We then built and implemented a decision tree classification algorithm that classified each pixel based on the abundance categories from fractions of cover typologies and named each pixel based on the strongest Pearson correlation between the selected spectral profiles and the field spectral library (Manzo et al, ; Valentini et al, ). Every pixel in the image was assigned to a certain class using a threshold value in fractional cover (i.e., ≥ 0.5552 for the wet class; ≥ 0.5950 for the soil class; ≥ 0.4992 for P. australis ; ≥ 0.5507 for E. athericus ; and ≥ 0.8945 for pioneer vegetation).…”

Section: Methodsmentioning

confidence: 99%

“…Once the SPOT images were exoatmospherically corrected, the Substrate Vegetation Dark (SVD) end‐member fractions and field work radiometry were used to spectrally unmix the scenes (Valentini et al, ). As in Small (), a unit sum constraint was applied.…”

Section: Methodsmentioning

confidence: 99%

“…However, the use of spatial variables derived from RS imagery offers an ideal tool to study the relationships among vegetation type, patch-size distribution, and channel sinuosity (Jefferies et al, 2006;Méléder et al, 2010;Moffett et al, 2015;Vande Castle, 1998;Wang et al, 2007). In fact, to capture smaller-scale variation, linear spectral mixture analysis (LSMA) allows the consideration of subpixel variation (Manzo et al, 2014;Valentini et al, 2014) and provides a validated tool that can be used to study temporal multiscale channel and vegetation variability in tidal ecosystems (Taramelli et al, 2017;Taramelli, Valentini, et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

et al. 2018

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Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development.

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An effective procedure for EUNIS and Natura 2000 habitat type mapping in estuarine ecosystems integrating ecological knowledge and remote sensing analysis

Cited by 33 publications

References 66 publications

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

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

Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

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