Laurence Hubert-Moy’s research while affiliated with French National Centre for Scientific Research and other places

Urban trees contribute to urban well-being but face challenging environments that can reduce their lifespan and increase young tree mortality. Although many studies have used remote sensing data to monitor the functional status of trees in rural areas, few have done so in urban areas to assess the health or estimate the biomass of large green areas. This study assessed the suitability of using Sentinel-2 images to characterize two urban tree functional traits—leaf chlorophyll content (Cab) and leaf area density (LAD)—in isolated trees and tree rows. Simulated Sentinel-2 images were generated using the DART radiative transfer model, considering 16 tree-endogenous and 14 tree-exogenous parameters, with 15 vegetation indices (VIs) analyzed. Sensitivity analysis was performed in four contrasting urban environments using local climate zone taxonomy. The accuracy of the simulated images was validated with real Sentinel-2 images, field measurements, and ancillary data collected for four tree species in Rennes, France. The results showed that the tree parameters significantly influenced Sentinel-2 spectral bands, with NGBDI and OSAVI VIs being most sensitive to Cab and LAD. The model showed high accuracy, with a mean RMSE of 0.016 for key spectral bands. The results also highlighted the importance of considering ancillary data to capture specific urban characteristics.

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R² values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.

A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured in situ and in the laboratory; (ii) plant area density measured in situ under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter and plant area density for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.

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.

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.

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.

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.

While fine-scale maps of vegetation alliances are required for conservation, their distribution is usually known at broad scales. Open-access field and spatial data are increasingly available worldwide, but their contribution to modelling vegetation alliances remains to be assessed. This study aimed to map the suitability area of six heath alliances distributed along ecological gradients throughout France. We used 20 broad and local variables derived from WorldClim, the European Union DEM, SoilGrids, and the Global Wind and Solar Atlases that describe bioclimatic and environmental conditions. For each alliance, two nested MaxEnt models were calibrated and validated using archive field data: one at a broad scale (1000 × 1000 m) to define its bioclimatic area, and the second at a local scale (25 × 25 m) to predict its environmental area. The results showed complementarity of the variables used to discriminate the alliances, and the accuracy of spatial modelling at broad (AUC = 0.83-0.99) and local scales (AUC = 0.91-0.99). They also highlighted the gradients of continentality and temperature that differentiate the alliances. These new maps provide additional knowledge about alliance distribution areas and could support natural vegetation conservation at a national scale.

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.