Mapping natural habitats using remote sensing and Sparse partial least square discriminant analysis

This work presents a novel approach for mapping the spatial distribution of natural habitats in the "Foothills of Larzac" Natura 2000 listed site located in a French Mediterranean Biogeographical Region. Sparse Partial Least Square Discriminant Analysis was used to analyze two RapidEye datasets (June 2009 and July 2010) with the purpose of choosing the most informative spectral, textural and thematic variables that allow discriminating the classes of habitats. The Sparse Partial Least Square Discriminant Analysis selected relevant and stable variables for the discrimination of habitat classes that could be linked to ecological or biophysical characteristics. It also gave insight into the similarities and the differences between habitats classes with comparable physiognomic characteristics. The highest user accuracy was obtained for dry improved grasslands (u=91.97%) followed by riparian ash woods (u= 88.38%). These results are very encouraging given that these two classes were identified in Annex 1 of the EC Habitats Directive as of community interest. Due to limited data input requirements and to its computational efficiency, the approach developed in this paper is a good alternative to other types of variable selection approaches in a supervised classification framework and can be easily transferred to other Natura 2000 sites

Comparative study on the performance of multiparameter SAR Data for operational urban areas extraction using textural features

The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF)model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m

Exploring high repetitivity remote sensing time series for mapping and monitoring natural habitats ; A new approach combining OBIA and k-partite graphs

International audienc

A pilot study of methodology for the development of farmland habitat reports for sustainability assessments

peer reviewedThe inclusion of farm maps of habitat features is becoming an urgent requirement for assessments of farm-scale sustainability and for compliance or benchmarking with national and international sustainability certification and accreditation schemes. Traditional methods of habitat assessment rely strongly on field-based surveys, which are logistically demanding and relatively costly. We describe and investigate a process that relies on information technology to develop a scalable method that can be applied across multiple farms to reduce the significant logistical challenges and financial costs of traditional habitat surveys. A key impediment to the routine development of farm habitat maps is the lack of information on the type of habitats that occur on a land parcel. Within a pilot project comprising 187 farms, we developed and implemented a process for creating farm habitat reports and investigate the accuracy of visual interpretation of satellite imagery by an ecologist aiming to identify habitat types. We generated customised farm reports that included a colour-coded farm habitat map and habitat information (type, area, relative wildlife importance). Visual assessment of satellite imagery achieved an overall accuracy of 96% in its ability to discriminate between land parcels with habitats categorised by this study as being of either high or low nature conservation value. Assessment of satellite imagery achieved an overall accuracy of 90% in its ability to discriminate among Fossitt level II habitat classes, and an overall accuracy of 81% when using individual habitat classes (Fossitt level III). There was, however, considerable variation in the accuracy associated with individual habitat classes. We conclude that this methodology based on satellite imagery is sufficiently accurate to be used for the incorporation of farmland habitats into farm-scale sustainability assurance, but should, at most, use Fossitt level II habitat classes. We discuss future challenges and opportunities for the development of farm habitat maps and plans for their use in sustainability certification schemes

Sandy coastlines under threat of erosion

Sandy beaches occupy more than one-third of the global coastline(1) and have high socioeconomic value related to recreation, tourism and ecosystem services(2). Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones(3). However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological(4,5), geological(6) and anthropogenic factors(1,7). A substantial proportion of the world's sandy coastline is already eroding(1,7), a situation that could be exacerbated by climate change(8,9). Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world's sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures. Erosion is a major problem facing sandy beaches that will probably worsen with climate change and sea-level rise. Half the world's beaches, many of which are in densely populated areas, could disappear by the end of the century under current trends; mitigation could lessen retreat by 40%.info:eu-repo/semantics/publishedVersio

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas

Cartographie des habitats naturels dans le site Natura 2000 des étangs Palavasiens : potentiel et limites des séries temporelles Landsat

Dans le cadre des travaux de recherche menés actuellement au sein de l’UMR TETIS sur la cartographie des habitats naturels par télédétection, une trentaine d’images Landsat produites au CESBIO et couvrant le Sud de la France en particulier le Languedoc-Roussillon ont été analysées. Le but étant d’explorer le potentiel des séries temporelles Landsat pour la cartographie et le suivi des habitats naturels sur les étangs palavasiens qui font partie du réseau Natura 2000. Ce rapport préliminaire réalisé dans le contexte du programme TOSCA, a pour objectifs 1) de rappeler la problématique de cartographie et de suivi des habitats naturels dans les zones humides et 2) de présenter les résultats préliminaires sur l’utilisation des séries temporelles pour le suivi phénologique de quelques habitats d’intérêts communautaires caractéristiques des étangs palavasiens

Meilleure interfaçage des projets de recherche UE et de la prise de décision politique : leçons retenues et pistes d'avancement

International audienceIn September 2012, a joint workshop was organized by the EU-FP7 funded project SPIRAL (www.spiral-project.eu), the European Environment Agency (EEA) and EU Directorate General Research and Innovation (DG RI). The aim was to explore how the science-policy interfaces of EU-funded biodiversity research projects can be improved to more efficiently support European policy-making processes. Representatives from the MS.MONINA project participated to share experiences on the challenges and successes in interfacing the project's research component with the decision-making process at the local, national and EU levels. The direct contact to EU stakeholders contributed to a better understanding of their needs in terms of an EU service for mapping biodiversity. The exchange with other FP7 projects resulted in several ideas on how to better communicate with decision-makers (e.g. innovative tools for communicating the project's results, etc.)