In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly ...understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.
While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to ...their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping. The objective of this study was to map wetlands over mainland France (ca. 550,000 km2) by applying AI to environmental variables derived from remote sensing and archive field data. A random forest model was calibrated using spatial cross-validation according to the precision-recall area under the curve (PR-AUC) index using ca. 135,000 soil or flora plots from archive databases, as well as 5 m topographical variables derived from an airborne DTM and a geological map. The model was validated using an experimentally designed sampling strategy with ca. 3000 plots collected during a ground survey in 2021 along non-wetland/wetland transects. Map accuracy was then compared to those of nine existing wetland maps with global, European, or national coverage. The model-derived suitability map (PR-AUC 0.76) highlights the gradual boundaries and fine-grained pattern of wetlands. The binary map is significantly more accurate (F1-score 0.75, overall accuracy 0.67) than existing wetland maps. The approach and end-results are of important value for spatial planning and environmental management since the high-resolution suitability and binary maps enable more targeted conservation measures to support biodiversity conservation, water resources maintenance, and carbon storage.
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•A national wetland mapping was conducted using an airborne DTM and a geological map.•An RF model was calibrated and validated using ca. 135,000 soil or flora field plots.•Uncertainty in delineating wetlands was addressed by providing a suitability map.•A new fine-grained (5 m) wetland suitability map was developed at the national scale.•The binary map is more accurate (F1-score 0.75) than existing wetland maps.
The interface between wetlands and uplands is characterized by gradients in hydrological, soil and biological components. Consequently, the exact spatial distribution of this transitional area is not ...well known because it often occurs as a fuzzy moisture gradient. However, ecological assessment and conservation require mapping and characterizing this interface to better understand and model biotic and abiotic interactions between wetlands and uplands. To this end, in 2021 and 2022, we observed soil properties and vegetation types along soil moisture gradients throughout the Atlantic, Continental, Mediterranean and Alpine biogeographic regions of France. The dataset contains 2 236 georeferenced plots (accuracy ± 5 m) distributed along 1 088 transects placed along the slope at 377 sites. Each plot in the database is characterized by 21 fields that describe the vegetation habitat type based on the European Nature Information System (EUNIS) and soil properties (i.e. depth of appearance and thickness of redoximorphic features in the soil profile, moisture). These data are useful for researchers and engineers in a variety of disciplines (e.g. Earth and life sciences) to calibrate and validate models to predict the spatial distribution of habitats or to analyze flows.
Monitoring grassland plant communities is crucial for understanding and managing biodiversity. Previous studies indicate that mapping these natural habitats from single-date remotely sensed imagery ...remains challenging because some communities have similar physiognomy. The recently launched Sentinel-2 satellites are a promising opportunity for monitoring vegetation. This article assesses the advantages of Sentinel-2 time-series for discriminating plant communities in wet grasslands. An annual Sentinel-2 time-series was compared respectively to single-date and single-band datasets derived from this time-series for mapping grassland plant communities in a temperate floodplain located near Mont-Saint-Michel Bay, which is included in the long-term ecological research network “ZA Armorique” (France). At this 475 ha site, 123 vegetation relevés were collected and assigned to seven plant communities to calibrate and validate the Sentinel-2 data. Satellite images were classified using support vector machine (SVM) and random forest (RF) classifiers. Results show that the SVM classifier performs slightly better than the RF classifier (overall accuracy 0.78 and 0.71, respectively). They highlight that accuracy is lower when using single-date (0.67) or single-band images (0.70). The results also reveal that discrimination of plant communities is more sensitive to temporal resolution (Δ = 0.34 in overall accuracy) than spectral resolution (Δ = 0.12 in overall accuracy).
•Grassland plant communities were accurately classified using Sentinel-2 time-series.•SVM slightly outperforms RF (overall accuracy 0.78 and 0.71, respectively).•Accuracy is lower when using single-date (0.67) or single-band images (0.70).•Spring and early summer are the most discriminating seasons.•Temporal resolution is more important than spectral resolution.
Mapping natural habitats remains challenging, especially at a national scale. Although new open‐access variables for vegetation and its environment and increased spatial resolution derived from ...satellite remote sensing data are available at the global scale, the relevance of these new variables for fine‐grained mapping of natural habitats at a national scale remains underexplored. This study aimed to map the fine‐grained pattern of four heathland habitats throughout France (550 000 km2). Environmental (bioclimatic, soil and topographic) and spectral (vegetation) variables derived from MODerate resolution Imaging Spectroradiometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Sentinel‐2 satellite data were analyzed using the MaxEnt classifier. Open‐access field databases were used to calibrate and validate the classification, based on the threshold‐independent area under the curve (AUC) index and the conventional F1‐score. For each heathland habitat, potential and actual areas were mapped using environmental and spectral variables, respectively. The results showed high classification accuracy for potential (AUC 0.92–0.99) and actual (AUC 0.88–0.99) suitability maps of the four heathland habitats. Visual interpretation of maps of the probability of occurrence indicated that the fine‐grained distribution of heathland habitat was detected satisfactorily. However, although the accuracy of the crisp map of combined classifications of actual heathland habitats was high (overall accuracy 0.72), estimated producer's accuracies in terms of proportion of area were low (<0.25). This study provides the first fine‐grained pattern maps of heathland habitats at a national scale, thus highlighting the value of combining environmental and spectral variables derived from open‐remote sensing data and open‐source field databases. These suitability maps could support the identification of heathland habitats in the framework of national conservation policies.
Mapping natural habitats remains challenging, especially at the national scale. Here, we mapped the fine‐grained pattern of four heathland habitats throughout France (550 000 km2) using open‐source remote sensing data, including Sentinel‐2 time series. This study provides the first fine‐grained map of heathland habitats at a national scale, highlighting the value of combining environmental and vegetation variables. This map will support the monitoring and evaluation of the conservation status of natural habitats.
Mapping plant communities, which is essential to assess the conservation status of natural habitats, is currently based mainly on time-consuming field surveys without the use of satellite data. ...However, free image time-series with high spatial and temporal resolution have been available since 2015. This study assessed the contribution of Sentinel-2 time-series images to mapping the spatial distribution of 18 plant communities within a Natura 2000 site (1978 ha) located on the Mediterranean biogeographical region (Corsica, France). The method was based on random forest modeling of six Sentinel-2 images acquired from 26 February to 24 October 2017, which were calibrated and validated using a field vegetation map. The results showed that the 18 plant communities were modeled correctly, with 72% overall accuracy. The uncertainty map associated with the model indicated areas that required additional field observations.
La délimitation des zones humides est un enjeu majeur pour la protection de ces écosystèmes. La démarche réglementaire décrite dans la circulaire du 18 janvier 2010 relative à la délimitation des ...zones humides présente des imprécisions, et son application requiert une expertise phytosociologique. La démarche basée sur l’indice d’Ellenberg, qui permet de caractériser directement le degré d’humidité d’un relevé à partir de sa composition floristique, n’a jusqu’à présent été utilisée qu’à une échelle locale. Cette étude vise à évaluer l’intérêt d’utiliser l’indice d’humidité d’Ellenberg pour délimiter les zones humides sur l’ensemble des régions biogéographiques et des grands types d’habitats présents en France métropolitaine. Pour cela, 76 284 relevés phytosociologiques archivés dans plusieurs bases de données ont été analysés. Le caractère humide de chaque relevé a d’abord été déterminé selon la démarche réglementaire puis en utilisant l’indice d’Ellenberg. Les résultats montrent une forte corrélation entre les deux approches (variant de 90,6 à 96,3 % selon les régions biogéographiques, et de 82,4 à 99,2 % selon les habitats) pour une valeur d’Ellenberg de 5,7 ± 0,2. Ils confirment que la démarche basée sur l’indice d’humidité d’Ellenberg est une alternative simple et robuste à la démarche réglementaire pour délimiter les zones humides.
Decadal time-series derived from satellite observations are useful for discriminating crops and identifying crop succession at national and regional scales. However, use of these data for crop ...modeling is challenged by the presence of mixed pixels due to the coarse spatial resolution of these data, which influences model accuracy, and the scarcity of field data over the decadal period necessary to calibrate and validate the model. For this data article, cloud-free satellite “Vegetation Indices 16-Day Global 250 m” Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as the Land Parcel Information System (LPIS) vector field data, were collected throughout France for the 12-year period from 2006 to the end of 2017. A GIS workflow was developed using R software to combine the MOD13Q1 and MYD13Q1 products, and then to select “pure” MODIS pixels located within single-crop parcels over the entire period. As a result, a dataset for 21,129 reference plots (corresponding to “pure” pixels) was generated that contained a spectral time-series (red band, near-infrared band, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI)) and the associated annual crop type with an 8-day time step over the period. This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.
Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists ...need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.
Monitoring the structural and functional dimensions of natural vegetation is a critical issue to ensure effective management of biodiversity. While coarse-resolution satellite image time-series have ...been used extensively to monitor vegetation physiognomies, their potential to describe plant species composition remains understudied. The objective of this study is to assess the potential of annual time-series of MODIS images to discriminate combinations of plant communities, called "vegetation series," and characterize their structural and functional dimensions at the landscape scale. Twelve vegetation series were mapped in a 16 574 ha study area in a Mediterranean context located in Corsica (France). First, the structural dimension of vegetation series was examined using a random forest (RF) model calibrated with a reference field map to (i) measure the importance of each MODIS image in discriminating vegetation series; (ii) quantify the influence of the number of dates on model accuracy; and (iii) map the vegetation series with the optimal subset of MODIS images. Second, the functional dimension of vegetation series was analyzed by ordinating three functional indices through principal component analysis. These indices were the annual sum of normalized difference vegetation index (NDVI), the annual amplitude of NDVI, and the date of maximum NDVI, considered as a proxy for annual primary production, seasonality of carbon fluxes, and vegetation phenology, respectively. Results showed that (i) vegetation series were mapped accurately (median Kappa index 0.70, median overall accuracy 0.76), preferably using images acquired from February to August; (ii) at least 10 MODIS images were required to achieve sufficient accuracy; and (iii) a functional gradient was detected, ranging from high annual net primary production with low seasonality of carbon fluxes and early phenology in Mediterranean vegetation series to low annual net primary production with high seasonality of carbon fluxes and late phenology in alpine vegetation series.
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