Samuel Corgne

Land cover change prediction with a new theory of plausible and paradoxical reasoning

The spatial prediction of land cover ot the Jeld scale in winter appears useful for the issue of bare soils reduction in agricultural intensive regions. High variability of the factors that motivate the land cover changes between each winter involves integration of uncertainty in the modelling process. Fusion process with Dempster-Shafer Theoiy (DST) presents some limits in generating errors in decision making when the degree of conflict, between the sources of evidence that suppor? land cover hypotheses, becomes important. This paper focuses on the application of Dezeri-Smarandache Theory (DSmT) method to the fusion of multiple land-use attributes for land cover prediction purpose. Results are discussed and compared with prediction levels achieved with DST. Through this

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

rst application of the Dezert- Smarandache Theory, we show an example of this new approach abiliry to solve some of practical problems where the Dempster-Shafer Theory usuolly foils.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

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.

Performance of change detection using remotely sensed data and evidential fusion : comparison of three cases of application

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

Multitemporal classification of TerraSAR-X data for wetland vegetation mapping

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological and climatic modelling. Using remote sensing data, numerous change indices have already been proposed. Previous work showed the interest of combining several of these to improve change detection performance, using the Dempster–Shafer evidence theory framework. This study analyses the performance of different change indices and their combination in different cases of application: forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas, in comparison to the forest fire case presented in previous work. The interest of indices derived from Information Theory, some of which are original, is shown.

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Abstract This paper is concerned with wetland vegetation mapping using multitemporal synthetic aperture radar imagery. Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to precisely map the distribution of vegetation formations considering flood duration. A series of six dual-polarization TerraSAR-X images (HH-VV) was acquired in 2012 during dry and wet seasons. One polarimetric parameter, the Shannon entropy (SE), and two intensity parameters ( σ ° HH and σ ° VV), which vary with wetland flooding status and vegetation roughness, were first extracted. These parameters were then classified using support vector machine techniques based on a specific kernel adapted to the comparison of time-series data, K-nearest neighbors, and decision tree (DT) algorithms. The results show that the vegetation formations can be identified very accurately ( kappa index = 0.85 ) from the classification of SE temporal profiles derived from the TerraSAR-X images. They also reveal the importance of the use of polarimetric parameters instead of backscattering coefficients alone (HH or VV) or combined (HH and VV).

Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of France

In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

Follow-up and modeling of the land use in an intensive agricultural watershed in France

In intensive agricultural regions, accurate assessment of the spatial and temporal variation of winter vegetation covering is a key indicator of water transfer processes, essential for controlling land management and helping local decision making. Spatial prediction modeling of winter bare soils is complex and it is necessary to introduce uncertainty in modeling land use and cover changes, especially as high spatial and temporal variability are encountered. Dempsters fusion rule is used in the present study to spatially predict the location of winter bare fields for the next season on a watershed located in an intensive agricultural region. It expresses the model as a function of past-observed bare soils, field size, distance from farm buildings, agro-environmental action, and production quotas per ha. The model well predicted the presence of bare soils on 4/5 of the total area. The spatial distribution of misrepresented fields is a good indicator for identifying change factors.

Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

In intensive agricultural regions, monitoring land use and cover change represents an important stake. Some land cover changes in agro-systems cause modifications in the management of land use that contribute to increase environmental problems, including an important degradation of water quality. In this context, the identification of land-cover dynamics at high spatial scales constitutes a prior approach for the restoration of water resources. The modeling approach used to study land use and cover changes at a field-scale is adapted from a vector change analysis method generally applied to assess land cover changes from regional to global scales. The main objective of this study is to identify vegetation changes at the field scale during winter, in relation with crop successions. Magnitude and direction of the vector of changes with remote sensing data and GIS, calculated on a small watershed located in Western France for a six-year period (1996-2001) indicate both intensity and nature of observed changes in this area. The results allow to qualify accurately (i.e. at the scale of the field) the type of changes, to quantify them and weigh up their intensity. Then, all the results are integrated in a probabilistic model to build-up a short time land use prediction.

Agricultural practices in grasslands detected by spatial remote sensing

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.