Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R² = 0.65). Estimators based on a single MODIS NDVI image performed worse (R² ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

Area of Habitat (AOH) is “the habitat available to a species, that is, habitat within its range”. It complements a geographic range map for a species by showing potential occupancy and reducing commission errors. AOH maps are produced by subtracting areas considered unsuitable for the species from their range map, using information on each species’ associations with habitat and elevation. We present AOH maps for 5,481 terrestrial mammal and 10,651 terrestrial bird species (including 1,816 migratory bird species for which we present separate maps for the resident, breeding and non-breeding areas). Our maps have a resolution of 100 m. On average, AOH covered 66 ± 28% of the range maps for mammals and 64 ± 27% for birds. The AOH maps were validated independently, following a novel two-step methodology: a modelling approach to identify outliers and a species-level approach based on point localities. We used AOH maps to produce global maps of the species richness of mammals, birds, globally threatened mammals and globally threatened birds.

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm ${}^{-1}$ (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land‐cover classes, which generates a source of uncertainty in AOH maps. We developed a data‐driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land‐cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land‐cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land‐cover class. We then compared the performance of our data‐driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land‐cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land‐cover classes with more confidence than others (e.g., wetlands and artificial). The data‐driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Observations are key to understanding the state of nature, the drivers of biodiversity loss and the impacts on ecosystem services and ultimately on people. Many EU policies and initiatives call for unbiased, integrated and regularly updated data on biodiversity and ecosystem services. However, biodiversity monitoring efforts are spatially and temporally fragmented, taxonomically biased and not integrated across Europe. EuropaBON has addressed this gap by developing an EU-wide framework for biodiversity monitoring. With this deliverable, EuropaBON proposes the terms of reference for an EU Biodiversity Observation Coordination Centre (EBOCC), a permanent infrastructure that could coordinate and foster the generation and use of high quality data to underpin the biodiversity knowledge-base used across EU policies, providing guidance and trainings when necessary. Such a centre represents one of the key solutions to overcome the critical challenges of biodiversity monitoring in Europe. Having this integrated and continuous monitoring capacity would allow more timely and efficient interventions that would optimise our capacity to revert biodiversity loss and prevent environmental degradation. It would also increase the value-added to the data flows, reaching high-value outputs with some existing low-value inputs. This deliverable offers a critical analysis of the existing monitoring landscape in Europe, extracting key messages about the main challenges, lessons learned and possible solutions. Based on a comprehensive analysis of needs and, most importantly, on an inclusive consultation process, the deliverable designs an EBOCC that tackles the key biodiversity monitoring challenges. The proposal specifies the mission, the tasks, the most urgent topics, the main policies and the key stakeholders that the EBOCC should serve and focus on during the first stage of its implementation. It also includes detailed analyses about governance models and potential costs. With this proposal, EuropaBON fosters the setting up and testing an operational EBOCC that could address the urgent need for coordination, integration, harmonisation and strengthening of biodiversity data collection and analysis, in order to inform policy-making at local, national, European and international level.