RESUMEN
A network of 137 cultivated fields covering the wide diversity of soils, crop rotations and cropping practices throughout the region of Brittany (France) was monitored to collect data on soil organic nitrogen (SON) mineralization and to identify the factors that explain the observed variability. The dataset presented in this article contains all of the information about the soils, which were subjected to pedological description and in-depth analysis of their topsoil properties. The topsoil (0-30 cm) was sampled by mixing 30 samples to obtain one composite per field, which was divided into one sub-sample sieved at 5 mm to analyze soil microbial biomass (SMB) and SON mineralization via anaerobic incubation, and one subsample dried at 40 °C and sieved at 2 mm. The physico-chemical analyses included the particle-size distribution of five fractions; organic matter (OM); organic C; organic N; pH (water); pH KCl; CEC (Metson); CEC (hexamminecobalt); exchangeable Al, Ca, Fe, K, Mg, Mn and Na (hexamminecobalt); Olsen P; Dyer P; and total Al, Ca, Fe, K, Mg, Mn, Na and P. Physical OM fractionation was used to characterize the 200-2000 µm and 50-200 µm fractions of particulate organic matter (POM). Finally, three chemical methods were used to determine extractable organic nitrogen (EON): hot KCl, hot water and phosphate buffer tests. This dataset covers a wide range of pedological situations and cropping systems, and is of great interest to scientists searching for soil properties that can explain SON mineralization. It provides original data on EON indices, SMB and multiple forms of P. This paper supports and supplements information presented in a previous article [1].
RESUMEN
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.
RESUMEN
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.
RESUMEN
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.
