Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

The ecological causes of functional distinctiveness in communities

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Visual mining of large DEM and image data bases

The availability of Earth Observation (EO) imagery is relatively recent, i.e. less than 20 years. With the development of REMOTE Sensing (RS) technology, (e.g. high-resolution images, higher acquisition frequencies and new sensor types), the scope of RS Images allows users to perform a huge variety of specific applications, such as land cover mapping, thanks to increased levels of information and the possibility of synergy between data sets. Information in forms of geo-referenced DEMs / Images should be used for 3D real-time visualisation over large area at the country scale while preserving high resolution data/content and realistic rendering. Up to now, the exploitation / integration of large and complex databases integration in the overall interactive 3D visualisation process is currently extremely difficult. However, the demand for such technologies exploded in the last years and lead to a reinforcement of world acquisition programs (SRTM2). Therefore, RS and Virtual Reality communities are faced to several challenges to deal in real-time with such amount of data: e.g. Landsat images mosaic / ERS Tandem DEM over the whole Germany (gridding: 25m) Ô (40000² pixel images) × (R-G-B channels + elevation information). In addition to the rendering time constraints, realistic visualisations require to enhance / regularise the database. In this purpose, an important level of understanding and content extraction have to be reached in order to perform significant improvements in the data and particularly in the DEMs, which bring the geometry information. Indeed, DEM information is now recognised as one of the most important data structures used for geo-spatial analysis and 3D rendering. Nevertheless, despite high accuracies, EO DEM are still pervaded with errors and artefacts mainly due to the acquisition / generation techniques. As a consequence, the elevation data have to be analysed, filtered and enhanced. These pre-processing steps are determinant in order to cope the artefacts, generate a higher level of realism and simplify the data for the virtual reality enhancing processes (meshes simplification, hierarchical decomposition...). The article presents / evaluates several signal processing techniques and information extraction algorithms: Non-stationary approach (Bayesian approach, Gauss Markov Random Fields(GMRF)) Multi-resolution approach (fBm, Wavelets) Segmentation algorithms Object Extraction /Topology analysis Since world DEM coverage are available (SRTM, generated by SAR interferometry), a Bayesian filter has been developed to deal with non-stationary data such as DEM. It intends to reduce the thermal noise, smooth the Phase Unwrapping (PU) artefacts while preserving contours and objects included in the data. Despite the good results obtained in term of statistical analyses and rendering aspects, the filtering is still not enough to cope with important artefacts (specular reflexion, PU). Indeed, complementary information have to be added to reach very realistic flight simulation. The principle of the DEM regularization is the following: Based on the image data, relevant information are extracted. An interactive learning procedure is done through a Graphical User Interface is used to link the signal classes to the semantic ones, e.g. to include human knowledge in the system. The selected information, in form of objets are merged with the DEM by assigning regularisation constraints. In this purpose, a segmentation algorithm based on region growing method is presented and an object extraction algorithm. The extracted objects are classified and stored in a tree structure in the sense to preserve topology relations between the objects and reflect their dependencies. A database preparation line for Virtual Reality scenarios has been presented. It constitutes a key step for realistic 3D visualisations of EO data and for the rendering optimisation to manage flight simulation in real time of large areas. 1. DEM: Digital Elevation Models: digital representation of the Earths relief 2. SRTM : Shuttle Radar Topography Mission: Generation of World DEM coverage, largest homogenous DE

Rebound in functional distinctiveness following warming and reduced fishing in the North Sea

Functionally distinct species (i.e. species with unique trait combinations in the community) can support important ecological roles and contribute disproportionately to ecosystem functioning. Yet, how functionally distinct species have responded to recent climate change and human exploitation has been widely overlooked. Here, using ecological traits and long-term fish data in the North Sea, we identified functionally distinct and functionally common species, and evaluated their spatial andtemporaldynamics in relation to environmental variables and fishing pressure. Functionally distinct specieswere characterized by late sexualmaturity, few, large offspring, and high parental care,many being sharks and skates that play critical roles in structuring food webs. Both functionally distinct and functionally common species increased in abundance as ocean temperatures warmed and fishing pressure decreased over the last three decades; however, functionally distinct species increased throughout the North Sea, but primarily in southern North Sea where fishing was historically most intense, indicating a rebound following fleet decommissioning and reduced harvesting. Yet, some of the most functionally distinct species are currently listed as threatened by the IUCN and considered highly vulnerable to fishing pressure. Alarmingly these species have not rebounded. This work highlights the relevance and potential of integrating functional distinctiveness into ecosystem management and conservation prioritization

The dimensionality and structure of species trait spaces

International audienc

The dimensionality and structure of species trait spaces

Trait- based ecology aims to understand the processes that generate the overarching diversity of organismal traits and their influence on ecosystem functioning. Achieving this goal requires simplifying this complexity in synthetic axes defining a trait space and to cluster species based on their traits while identifying those with unique combinations of traits. However, so far, we know little about the dimensionality, the robustness to trait omission and the structure of these trait spaces. Here, we propose a unified framework and a synthesis across 30 trait datasets representing a broad variety of taxa, ecosystems and spatial scales to show that a common trade- off between trait space quality and operationality appears between three and six dimensions. The robustness to trait omission is generally low but highly variable among datasets. We also highlight invariant scaling relationships, whatever organismal complexity, between the number of clusters, the number of species in the dominant cluster and the number of unique species with total species richness. When species richness increases, the number of unique species saturates, whereas species tend to disproportionately pack in the richest cluster. Based on these results, we propose some rules of thumb to build species trait spaces and estimate subsequent functional diversity indices

A functional vulnerability framework for biodiversity conservation

Number: 1 Publisher: Nature Publishing GroupSetting appropriate conservation strategies in a multi-threat world is a challenging goal, especially because of natural complexity and budget limitations that prevent effective management of all ecosystems. Safeguarding the most threatened ecosystems requires accurate and integrative quantification of their vulnerability and their functioning, particularly the potential loss of species trait diversity which imperils their functioning. However, the magnitude of threats and associated biological responses both have high uncertainties. Additionally, a major difficulty is the recurrent lack of reference conditions for a fair and operational measurement of vulnerability. Here, we present a functional vulnerability framework that incorporates uncertainty and reference conditions into a generalizable tool. Through in silico simulations of disturbances, our framework allows us to quantify the vulnerability of communities to a wide range of threats. We demonstrate the relevance and operationality of our framework, and its global, scalable and quantitative comparability, through three case studies on marine fishes and mammals. We show that functional vulnerability has marked geographic and temporal patterns. We underline contrasting contributions of species richness and functional redundancy to the level of vulnerability among case studies, indicating that our integrative assessment can also identify the drivers of vulnerability in a world where uncertainty is omnipresent

The ecological causes of functional distinctiveness in communities

Although how rare species persist in communities is a major ecological question, the critical phenotypic dimension of rarity is broadly overlooked. Recent work has shown that evaluating functional distinctiveness, the average trait distance of a species to other species in a community, offers essential insights into biodiversity dynamics, ecosystem functioning, and biological conservation. However, the ecological mechanisms underlying the persistence of functionally distinct species are poorly understood. Here we propose a heterogeneous fitness landscape framework, whereby functional dimensions encompass peaks representing trait combinations that yield positive intrinsic growth rates in a community. We identify four fundamental causes leading to the persistence of functionally distinct species in a community. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (either positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and some guidelines to distinguish among them. In addition to these deterministic processes, we also explore how stochastic dispersal limitation can yield functional distinctiveness