Cartographie des grands types de végétation par télédétection : étude de faisabilité (Bretagne, Basse-Normandie et Pays-de-la-Loire)

annexes sur demanderapport de rechercheL'objectif de cette étude est d'évaluer les potentialités qu'offrent l'imagerie aérienne et satellitaire à haute et très haute résolution spatiale pour la cartographie des grands types de végétation des régions Bretagne, Basse-Normandie et Pays de la Loire. Pour ce faire, différents types d'images (BDORTHO® IRC, SPOT5, Worldview-2) ont été acquises sur quatre sites représentatifs de la diversité des végétations présentes sur ce territoire. Des procédures de classification ont été établies et leur reproductibilité à d'autres sites a été vérifiée. La typologie des végétations utilisée est celle proposée par le Conservatoire Botanique National de Brest (CBN de Brest) qui articule par une démarche " bottom-up ", la typologie phytosociologique utilisée sur le terrain, avec une typologie physionomique (structurale) pouvant être " comprise " par une approche télédétection. L'approche orientée-objet non supervisée a été privilégiée pour ce projet. Seules les procédures de classification des images Worldview-2 (démarche plus exploratoire) combinent l'approche orientée-objet non supervisée à la méthode pixel supervisée. Les performances globales des procédures pour chaque image sont calculées par l'intermédiaire d'un indice kappa pour les niveaux " occupation du sol ", " grands types de végétations " et " types de formations végétales ". Les résultats les plus concluants sont obtenus, pour la plupart des végétations étudiées, à partir de l'image Worldview-2, puis la BDORTHO® IRC et enfin les images SPOT5. L'identification de certaines végétations donne des résultats médiocres ; des recommandations sont proposées pour les améliorer

Contribution of remote sensing to functional assessment of wetlands : from observation to prospective modelling

Les zones humides, à l’interface entre terre et eau, sont des milieux riches et diversifiés, aux fonctions et valeurs multiples aujourd’hui largement reconnues. Face à la sensibilité grandissante des organisations gouvernementales, régionales et du public aux effets néfastes, directs ou indirects, de la régression, voire dans certains cas de la disparition des zones humides, l’inventaire, la délimitation, mais aussi la caractérisation et le suivi de ces milieux sont devenus une priorité. Si leur délimitation est aujourd’hui opérationnelle, l’évaluation de leurs fonctions n’a été opérée que sur des sites de quelques hectares, alors qu’il est nécessaire d’évaluer l’état fonctionnel des zones humides sur des territoires plus étendus pour les gérer. Les objectifs de cette thèse sont de développer une méthode permettant de spatialiser les fonctions des zones humides à l’échelle de territoires d’une centaine de Km² au minimum, d’évaluer des données de télédétection optiques à très haute résolution spatiale afin de produire des indicateurs de l’état fonctionnel des zones humides, et d’évaluer l’impact de changements d’occupation des sols sur ces fonctions. Pour cela, la démarche FAP a été adaptée et appliquée sur deux sites de 130 et 650 km² localisés en Bretagne et en Dordogne. Après avoir délimités et caractérisés les zones humides à partir de données de télédétection, des indicateurs spatialisés dérivés de ces données ont été utilisés pour évaluer des fonctions hydrologiques, biogéochimiques et écologiques. L’évolution de ces fonctions a ensuite été simulée selon différents scénarios de changements d’occupation des sols. Les résultats montrent l’intérêt des données de télédétection, en particulier LiDAR, pour caractériser avec précision la micro-topographie, le réseau hydrographique et la végétation des zones humides. Ces données permettent de cartographier le potentiel fonctionnel des zones humides à différentes échelles allant de la parcelle à l’ensemble du site, et ce pour différentes fonctions. La simulation des changements d’occupation des sols à l’horizon 2030 et l’évaluation de ceux-ci sur les fonctions des zones humides peuvent constituer un outil d’aide à la gestion de ces milieux.Interfacing between land and water systems, wetlands perform multiple functions and values that are now widely recognized. Inventory, delineation, but also characterization and monitoring of wetlands are now a priority to address the regression and in some cases the loss of these ecosystems. While wetland delineation is widely performed, the assessment of their functions has been only made on small sites of several hectares, whereas it is necessary to evaluate wetland functional status on larger areas to manage them. The objectives of this thesis are to develop a method to map wetland functions on areas greater than a hundred square kilometers, evaluate optical remote sensing data with very high spatial resolution to produce indicators of functional status of wetlands, and assess the impact of land use change on these functions. For this, the FAP approach has been adapted and applied to two sites located in Brittany and Dordogne. Once having defined and characterized wetlands from remotely sensed data, the spatial indicators derived from these data were used to evaluate hydrological, biogeochemical and ecological wetland functions. The evolution of these functions was then simulated under different scenarios of land use changes. The results show the usefulness of remotely sensed data, especially LiDAR data, to accurately characterize the micro-topography, drainage network and vegetation of wetlands. The functional potential of wetlands can therefore be mapped at different scales from the plot to the whole site for various functions. The simulation of land-use changes for the period 2000–2030 and the evaluation of their impact on wetland functions can be a tool for managing these environment

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

International audienceAdvances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities

Contribution of SPOT-7 multi-temporal imagery for mapping wetland vegetation

International audienceMapping the fine-grained pattern of vegetation is critical for assessing the functions and conservation status of wetlands. Although satellite time-series images can accurately model vegetation, the spatial resolution of these data is generally too coarse (> 6 m) to capture the fine-grained pattern of wetland vegetation. SPOT-7 satellite sensors address this issue since they acquire images at very high spatial resolution (1.5 m) with a potential high frequency revisit. While the ability of SPOT-7 images to discriminate wetland vegetation has yet to be assessed, this study investigates the contribution of SPOT-7 multi-temporal images for mapping the fine-grained pattern of 11 vegetation classes in a 470 ha fresh marsh (France). Random forest modeling, calibrated and validated using 170 vegetation plots, was conducted on four SPOT-7 pan-sharpened images collected from April-July 2017. The results highlight that (1) the wetland vegetation was accurately modeled (F1 score 0.88), (2) near-infrared spectral bands acquired in the spring are the most discriminating features, (3) the fine-grained pattern of vegetation plant communities is mapped well, and (4) model uncertainties reflect floristic transition, unconsidered classes or areas of shadow

Apport de la télédétection satellitaire pour la cartographie geosymphytosociologique des végétations des marais littoraux atlantiques. La phytosociologie paysagère : des concepts aux applications

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Natural grasslands across mainland France: A dataset including a 10 m raster and ground reference points

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Combined use of LidDAR data and multispectral earth observation imagery for wetland habitat mapping

International Journal of Applied Earth Observation and GeoinformationInternational audiencetAlthough wetlands play a key role in controlling flooding and nonpoint source pollution, sequesteringcarbon and providing an abundance of ecological services, the inventory and characterization of wetlandhabitats are most often limited to small areas. This explains why the understanding of their ecologicalfunctioning is still insufficient for a reliable functional assessment on areas larger than a few hectares.While LiDAR data and multispectral Earth Observation (EO) images are often used separately to mapwetland habitats, their combined use is currently being assessed for different habitat types. The aim ofthis study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data toprecisely map the distribution of wetland habitats. The image classification was performed combiningan object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5)and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately.Another classification was then applied integrating summer and winter multispectral image data andthree layers derived from LiDAR data: vegetation height, microtopography and intensity return. Thecomparison of classification results shows that some habitats are better identified on the winter image andothers on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classificationaccuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectralimages. Moreover, this study highlights the advantage of integrating vegetation height, microtopographyand intensity parameters in the classification process. This article demonstrates that information providedby the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment

Cartographie des zones humides par télédétection : approche multi-scalaire pour une planification environnementale

Wetlands contribute significantly to biodiversity and water resources supports. They have been deteriorated for fifty years, mainly due to human pressures, altering their functions. In order to carry out effective actions of protection and restoration, it is necessary to perform an inventory of these areas not only on specific sites of a few hectares but on entire watersheds. The objective of this article is to present a geomatics approach that was developed to delineate and characterize these environments characterized by high spatial and temporal dynamics on an entire watershed. We propose a new multiscalar approach based on remote sensing tools and spatial analysis. This approach was applied here on a sub-watershed of the Dordogne river basin of 650 km²(France) in order to, on the one hand, comprehensively and uniformly define wetlands and, on the other hand, map hydrologic, biogeochemical and ecological functions at the scale of management units. The results show that it can be a useful awareness and decision support tool for environmental planning and resource management

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

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

Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

International audienceGrassland habitats provide many ecosystem services but are threatened by agricultural intensification and urbanization. While the lack of accurate and comprehensive inventories at the national scale makes them difficult to manage, advances in spatial modeling using open remote sensing data and open-source software, as well as the increasing use of ecological archives, provide new perspectives for mapping natural habitats. In this context, this study evaluated the contribution of spectral and environmental variables to discriminate and then map grassland habitats throughout France. To this end, 92 spectral variables derived from moderate-resolution imaging spectroradiometer data, 19 bioclimatic variables derived from WorldClim data, 4 topographic variables derived from the European Union Digital Elevation Model (DEM), and 8 soil variables derived from SoilGrids data were combined at a spatial resolution of 250 m. Reference plots that characterized 6 and 21 grassland ecosystems at European Nature Information System (EUNIS) levels 2 (broad habitats) and 3 (habitats), respectively, were collected from vegetation archives. We first performed descriptive analysis that included habitat description, ordination, and pairwise separability. We then performed predictive analysis of grassland habitats using a cross-validated random forest model that included a spatial constraint. While environmental and spectral variables characterized most grassland habitats well and consistently, some confusion occurred between habitats with similar abiotic conditions. The main grassland habitat types were correctly mapped at EUNIS level 2 ( F1 score = 0.68), but not at EUNIS level 3 ( F1 score = 0.52). In addition, the two variables that contributed most to the model were the near-infrared spectral band in spring and the minimum temperature of the coldest month. The model’s prediction at EUNIS level 2 for mainland France provides the map of grassland habitats at a new spatial scale