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

The data provided here include the first 10 m raster of natural grasslands across mainland France and related ground reference points. The latter consist of 1770 field observations that describe natural and artificial grasslands from respectively a compilation of hundreds of field-based vegetation maps and the European Union Land Parcel Identification System (LPIS). Based on analysis of aerial images, ground reference points were manually extracted from grassland polygons of the field-based vegetation maps and the LPIS within herbaceous areas larger than 30 × 30 m. The raster data of natural grasslands were derived from five annual 10 m land cover maps of France from 2016-2020. Pixels classified as ``grassland'' every year from 2016-2020 were considered natural grasslands, while those classified as ``crop'' at least once were considered artificial grasslands. Validation using the ground reference points revealed that natural and artificial grasslands were accurately mapped (overall accuracy = 86%). The ground reference points, publicly available in GeoJSON vector format, can be used as training or test samples for spatial modeling. The natural grassland map, publicly available in GeoTIFF raster format, can be used as a predictor variable for spatial modeling or as a base map for landscape ecology analyses.

A 5 m dataset of digital terrain model derivatives across mainland France.

A dataset of three digital terrain model (DTM) derivatives was produced at 5 m spatial resolution across mainland France. This dataset includes (i) a topographic wetness index (TWI) that characterizes potential soil wetness as a function of the contributing area and local slope, (ii) a multi-scale topographic position color composite (MTPCC) that describes the position of a pixel relative to its neighborhood at three spatial scales, and (iii) a vertical distance to channel network index (VDCNI) that expresses the vertical height between the elevation of a pixel and the nearest channel. These three raster layers were derived from the French national airborne DTM at 5 m spatial resolution and the vector layer of the channel network of the national hydrological database. This unprecedented fine-scale dataset opens new insights for geomorphological analysis. It can be used for several purposes, such as environmental modeling, risk assessment, or water-resource management.

National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence.

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.

Field dataset of punctual observations of soil properties and vegetation types distributed along soil moisture gradients in France.

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.

Role of land use and land cover in residential exposures to agricultural pesticide models.

Exposure of the general population to pesticides, especially in agricultural areas, is a major public health concern. This review analyses the role of Land Use and Land Cover (LULC) in Residential Exposure to Agricultural Pesticides (REAP) and how it is measured and modelled. Some epidemiological studies have shown that basic LULC variables, such as distance to a crop and field size, are relevant for explaining REAP. However, the potential of LULC mitigation elements, such as vegetation barriers, grassy strips and buffer zones, to mitigate REAP has been poorly studied. The availability of recent low-cost and high-quality geospatial data enables REAP models to include alternative and more precise LULC variables. This review also highlights the need for (i) generic environmental sampling protocols, (ii) exposure and spraying datasets and (iii) assessment of the mitigation capacity of LULC to improve REAP modelling significantly.

Estimating crop parameters using Sentinel-1 and 2 datasets and geospatial field data.

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

Bioclimatic dataset of Metropolitan France under current conditions derived from the WorldClim model.

Several studies have shown that adequate bioclimatic information is of major importance for mapping ecological niches or for modelling the distribution ranges of species and communities, particularly from a climate change perspective [1,2]. However, in France, there are few data sources that provide consistent information, available data being produced at low spatial resolution and based on classification systems that are not suitable for mapping French ecological systems. This paper presents bioclimatic maps produced on Metropolitan France and based on the Worldwide Bioclimatic Classification System, which are called Global Bioclimatics [3]. This data paper documents a set of variables that includes 23 bioclimatic maps generated according to the Worldwide Bioclimatic Classification System. These maps describe current bioclimatic conditions in Metropolitan France at a resolution of 30 arc-seconds. Climatic parameters and bioclimatic indices usually used for the analysis or modelling of species and communities' distribution, and bioclimatic typological units, were calculated using the temperature and precipitation data derived from the WorldClim 2 model. These maps can be used in GIS or models by researchers for mapping ecological conditions, but can also provide natural resource managers with analytical tools to assess Nature conservation policies.

Time-series spectral dataset for croplands in France (2006-2017).

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.

Polarimetric SAR Time-Series for Identification of Winter Land Use.

In the past decade, high spatial resolution Synthetic Aperture Radar (SAR) sensors have provided information that contributed significantly to cropland monitoring. However, the specific configurations of SAR sensors (e.g., band frequency, polarization mode) used to identify land-use types remains underexplored. This study investigates the contribution of C/L-Band frequency, dual/quad polarization and the density of image time-series to winter land-use identification in an agricultural area of approximately 130 km² located in northwestern France. First, SAR parameters were derived from RADARSAT-2, Sentinel-1 and Advanced Land Observing Satellite 2 (ALOS-2) time-series, and one quad-pol and six dual-pol datasets with different spatial resolutions and densities were calculated. Then, land use was classified using the Random Forest algorithm with each of these seven SAR datasets to determine the most suitable SAR configuration for identifying winter land-use. Results highlighted that (i) the C-Band (F1-score 0.70) outperformed the L-Band (F1-score 0.57), (ii) quad polarization (F1-score 0.69) outperformed dual polarization (F1-score 0.59) and (iii) a dense Sentinel-1 time-series (F1-score 0.70) outperformed RADARSAT-2 and ALOS-2 time-series (F1-score 0.69 and 0.29, respectively). In addition, Shannon Entropy and SPAN were the SAR parameters most important for discriminating winter land-use. Thus, the results of this study emphasize the interest of using Sentinel-1 time-series data for identifying winter land-use.

Mapping wetland functions using Earth observation data and multi-criteria analysis.

Wetland functional assessment is commonly conducted based on field observations, and thus, is generally limited to small areas. However, there is often a need for wetland managers to obtain information on wetland functional performance over larger areas. For this purpose, we are proposing a new field-based functional assessment procedure in which wetland functions are evaluated and classified into hydrogeomorphic units according to a multi-criteria analysis approach. Wetland-related geographic information system layers derived from Earth observation data (LiDAR, multispectral and radar data) are used in this study for a large-scale functional evaluation. These include maps of a hydrogeomorphic units, ditches, vegetation, annual flood duration, biomass, meadows management, and wetland boundaries. To demonstrate the feasibility of this approach, a 132 km2 international long-term ecological research site located in the west of France was assessed. Four wetland functions were evaluated: flood peak attenuation, low water attenuation, denitrification, and habitat. A spatial distribution map of the individual wetland functions was generated, and the intensity levels of the functions were highlighted. Antagonisms between functions within individual hydrogeomorphic units were also identified. Mapping of hydrological, biogeochemical, and ecological wetland functions over large areas can provide an efficient tool for policy makers and other stakeholders including water authorities, nature conservation agencies, and farmers. Specifically, this tool has the potential to provide a mapping of ecosystem services, conservation management priorities, and possible improvements in water resources management.

Agricultural practices in grasslands detected by spatial remote sensing.

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index = 0.82) at a field scale over large agricultural areas using a series of satellite images.

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers.

Environmental determinants of the urinary concentrations of herbicides during pregnancy: the PELAGIE mother-child cohort (France).

Herbicides are generally the most extensively used of the pesticides applied to agricultural crops. However, the literature contains little evidence useful in assessing the potential sources of the general population's exposure to herbicides, including by residential proximity to crops. The objective of this study was to take advantage of data from the PELAGIE mother-child cohort to identify the main determinants of the body burden of exposure to the chloroacetanilide and triazine herbicides commonly used on corn crops in Brittany, France, before 2006. Urine samples from a randomly selected subcohort of women in the first trimester of pregnancy (n=579) were assayed for herbicide metabolites. The residential exposure resulting from proximity to corn crops was assessed with satellite-image-based scores combined with meteorological data. Data on diet, drinking tap water (from the public water supply), occupations, and household herbicide use were collected by questionnaires. Herbicides were quantified in 5.3% to 39.7% of urine samples. Alachlor and acetochlor were found most frequently in the urine of women living in rural areas. The presence of dealkylated triazine metabolites in urine samples was positively associated with residential proximity to corn crops (OR=1.38, 95% CI: 1.05-1.80). Urinary metabolites of both atrazine and dealkylated triazine were correlated with tap water consumption (OR=2.94, 1.09-7.90, and OR=1.82, 1.10-3.03, respectively); hydroxylated triazine metabolites were correlated with fish intake (OR=1.48, 1.09-1.99). This study reinforces previous results that suggest that environmental contamination resulting from agricultural activities may contribute to the general population's exposure to herbicides.

A method for improving the management of controversial wetland.

Valley bottom wetlands in agricultural landscapes often are neglected in national and regional wetland inventories. Although these areas are small, located in the bottomlands of the headwater catchments, and scattered in the rural landscape, they strongly influence hydrology, water quality, and biodiversity over the whole catchment area. Valley bottom wetlands often are considered as controversial wetlands. Awareness of the functional role of wetlands is increasing, in parallel with their progressive disappearance in intensive farming landscapes. The need to improve tools for controlling wetland management is a primary consideration for decision makers and land users. This article proposes a method for the inventory of valley bottom wetlands. The method is based on the functional analysis of potential, existing, and efficient valley bottom wetlands (the PEEW approach). Several indicators are proposed for checking the validity of such an approach. Potential wetlands are delineated by means of a topographic index using topographic and pedoclimatic criteria computed from a Digital Elevation Model and easily accessible databases. Existing wetlands are identified from observed surface moisture, the presence of specific wetland vegetation, or soil feature criteria. Efficient wetlands are defined through a given function, such as flow or pollutant regulation or biodiversity control. An analysis of areas at the limits between potential, existing, and efficient wetlands highlights land cultivated or drained in the past, which currently represents negotiating areas in which rehabilitation and other intended management actions can be implemented.