Laurence Hubert‐Moy

Littoral, Environnement, Télédétection, Géomatique

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Samuel Corgne

Centre National de la Recherche Scientifique

Thomas Corpetti

Littoral, Environnement, Télédétection, Géomatique

Julie Betbeder

Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Jean Nabucet

Littoral, Environnement, Télédétection, Géomatique

Rahim Aguejdad

Territoires, Environnement, Télédétection et Information Spatiale

Sébastien Rapinel

Université Rennes 2

Marc Lennon

Hytech (Czechia)

Simon Dufour

Littoral, Environnement, Télédétection, Géomatique

Marianne Laslier

Centre National de la Recherche Scientifique

Pauline Dusseux

Centre National de la Recherche Scientifique

Cooperative Institutions

Centre National de la Recherche Scientifique

Université de Rennes

Littoral, Environnement, Télédétection, Géomatique

Université Rennes 2

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Ecosystèmes, Biodiversité, Evolution

Institut Agro Rennes-Angers

Institut Universitaire Européen de la Mer

Nantes Université

Université de Montpellier

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Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

Remote Sensing (2014)

Pauline Dusseux Thomas Corpetti Laurence Hubert‐Moy Samuel Corgne

The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

10.3390/rs6076163

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Citations (98)

Vineyard identification and characterization based on texture analysis in the Helderberg Basin (South Africa)

Alain Lefebvre Thomas Corpetti Valérie Bonnardot Hervé Quénol Laurence Hubert‐Moy

In this paper, a methodology for the spatial identification and characterization of vineyards using texture analysis is proposed to meet the need of ongoing and further viticultural "terroir" studies. The proposed method is based on the maximization of a criteria that deals with the coefficients enclosed in the different bands of a wavelet decomposition of the original image. More precisely, we search for the orientation that best concentrates the energy of the coefficients in a single direction. For each texture pattern, a degree of anisotropy and the angle of the main orientation is extracted. The methodology is validated on aerial-photographs in the Helderberg Basin (South Africa). The degree of anisotropy is a reliable information able to discriminate vineyards to other land-uses. Moreover, the row orientation turns out to be a relevant information for all applications related to mesoscale atmospheric modeling in vineyard areas.

Vineyard

Texture (cosmology)

Identification

Maximization

Characterization

Submodular set function

Aerial image

10.1109/igarss.2010.5653645

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Citations (3)

Large dam removal and early spontaneous riparian vegetation recruitment on alluvium in a former reservoir: Lessons learned from the pre‐removal phase of the Sélune River project (France)

River Research and Applications (2019)

Charlotte Ravot Marianne Laslier Laurence Hubert‐Moy Simon Dufour Didier Le Cœur

Abstract The restoration of ecological continuity along the Sélune River (Normandy, France) involves the removal of two tall hydroelectric dams (36 m removed in 2019 and 16 m in 2021), a project without precedent in Europe. During the pre‐removal phase (2014–2018), we performed scientific monitoring of the vegetation that was colonizing alluvium in the former dam reservoir (length: 19 km; surface area: 151 ha). Our study aimed to analyse if spontaneous vegetation could ecologically restore the riparian zone and help maintain fine sediment after dam removal. We used colonization indicators related to vegetation structure, taxonomic richness and diversity, and composition. These indicators were calculated at two spatial scales (local, at a single site, and broad, along the reservoir). The aim was to (a) characterize the spontaneously established species pool; (b) analyse longitudinal patterns in vegetation colonization; and (c) assess temporal changes in the species community. Our results show that diverse plant communities have developed. Slight differences in longitudinal and lateral patterns existed; they were linked with habitat heterogeneity and the reservoir's slow pace of draining. We observed fast spontaneous terrestrialization, which has resulted in cover stabilization, decreased diversity, and the development of herbaceous riverbank communities, with very few invasive species. This finding suggests stabilization potential is high and passive ecological restoration could occur, at least locally. Further analyses focusing on functional traits could help inform future management decisions regarding revegetation on reservoir alluvium.

Stream Restoration

Dam removal

Revegetation

Restoration Ecology

10.1002/rra.3535

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Citations (15)

Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Remote Sensing (2014)

Julie Betbeder Jean Nabucet Éric Pottier Jacques Baudry Samuel Corgne

Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

Fragmentation

10.3390/rs6053752

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Citations (21)

Estimation of the orientation of textured patterns via wavelet analysis.

Antoine Lefèbvre Thomas Corpetti Laurence Hubert‐Moy

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Citations (1)

How to manage human-wildlife interactions? An interdisciplinary spatially-explicit adaptive method

HAL (Le Centre pour la Communication Scientifique Directe) (2004)

Gwenaëlle Le Lay Laurence Hubert‐Moy Philippe Clergeau United States Regional of The International Association For Landscape Ecology

Adaptive Management

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Object-oriented Image Analysis for Mapping Urban Expansion in Western France

Rahim Aguejdad Laurence Hubert‐Moy Philippe Clergeau

Monitoring urban expansion is critical to understanding the effects of urbanization on ecological processes. However, it presents important challenges due to the heterogeneity of the urban areas and the rapid changes in land cover and land use that occur over short time periods. Conventional surveying and mapping methods are not adequate to produce accurate and timely land-use and cover change information on urbanization. To remedy to these problems, object-oriented methods using remote sensing data with very high resolution have been recently developed. The main objective of this paper is to assess the ability of an object-oriented approach to classify urban areas and their spatial expansion with a moderate/high resolution satellite image time series. This paper focuses on the monitoring of urban expansion of a medium-size city (Rennes metropolitan area, North-western France, 384 000 inhabitants) during the 21 year period from 1984 until 2005 with a multiscale object-oriented method. The image object-oriented classification approach we used consists of a two step method : A segmentation technique has been performed prior to classification. Preliminary results show an important urban expansion (+ 62%) during the 21-year investigated period, with a large increase since the early nineties. The object-oriented method used in this research has proven to be reliable to discriminate pixels that are truly urban from pixels that have similar spectral properties, such as clear cuts or bare soils, from moderate/high resolution images, on the contrary of per-pixel classifications.

Land Cover

Object based

10.1109/igarss.2006.599

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Citations (3)

Quels outils et quel mode opératoire

Philippe Clergeau Laurence Hubert‐Moy Jean-Noël Consalès Laure Cormier

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Preprocessing of Low-Resolution Time Series Contaminated by Clouds and Shadows

IEEE Transactions on Geoscience and Remote Sensing (2008)

Bassam Abdel Latif Rémi Lecerf Grégoire Mercier Laurence Hubert‐Moy

Monitoring changes in the vegetation cover during the intercrop season is of special interest in intensive agricultural region, such as the Brittany region in France, to locate bare soils and control their influence to the environment. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. Therefore, identification and monitoring of bare soils at a regional scale in the winter season are required for any agricultural management program. Data from the Moderate Resolution Imaging Spectroradiometer have been selected for this paper due to their low spatial resolution, which decreases the cost of processing and storage, and the high revisit frequency, which increases the probability to acquire scenes free of clouds and shadows during the winter season. Unfortunately, few images per season only are free of cloud contamination and associated shadows. Therefore, the specific objective of this paper is to develop and implement a preprocessing method to recover spectral values of contaminated data by weather conditions for subsequent bare-soil mapping. In the context of this paper, Kohonen's self-organizing map (SOM) is used for recovering data contaminated by weather conditions that have been considered as erroneous data. This nonparametric regression procedure has proved its success to deal with missing-values problem. Hence, the erroneous-values problem, reflectance values contaminated by clouds or shadows, has been converted to the missing-values problem by using a cloud and shadow (outlier) detector. The SOM algorithm was tested also on the erroneous data directly, but better results were found with the "missing values" formulation. The idea is to, first, train SOM onto clear temporal profiles free of clouds and shadows during the winter season. Second, erroneous values are converted to missing values by an outlier detector which operates on each temporal profile (set of colocated pixels acquired at different dates). Finally, the SOM algorithm for missing values is used to estimate contaminated reflectance values.

Moderate-resolution imaging spectroradiometer

Spectroradiometer

10.1109/tgrs.2008.916473

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Citations (30)

Mapping dynamic wetland processes with a one year RADARSAT-2 quad pol time-series

Cécile Marechal Éric Pottier Sophie Allain-Bailhache Stéphane Méric Laurence Hubert‐Moy

Remotely sensed data are widely used to identify, delineate and characterize wetlands. Optical data provide interesting information on land-use and land cover but are limited to cloud-free periods and to a description of the top layer of the vegetation strata because penetration depth is very small. For these reasons, it is not possible to precisely inventory wetland vegetation and agricultural practices, as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time series datasets to determine the water cycle dynamics, in order to delineate precisely potential, effective and efficient wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multi-temporal analysis of wetland evolution, and the investigation of polarimetric decomposition methods for quantitative physical parameter inversion algorithms are presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results have been validated using soil moisture measurements in the field and a LiDAR image. They show that it is possible to produce detailed water feature maps useful for delineating water tables as well as water-saturated areas, and for monitoring water area dynamics. These products provide useful information to identify and delineate wetlands in order to support conservation and management in these ecosystems across large areas.

Water cycle

Land Cover

Ancillary data

10.1109/igarss.2011.6048914

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Citations (9)