Pierre-Louis Frison

Spatio-temporal monitoring of evaporitic processes using multi-resolution C band radar remote sensing data: example over the Chott el Djerid

This paper illustrates the contribution of spatio-temporal multiresolution spaceborne radar data for the monitoring of land surfaces. More precisely, it illustrates the potential of C-band spaceborne radar data, by using in synergy scatterometer and Synthetic Aperture Radar (SAR) sensors, for the spatio-temporal monitoring of evaporitic processes over a vast playa, the Chott el Djerid, located in central Tunisia. Scatterometer data from the Advanced Scatterometer (ASCAT) instrument are characterized by a high temporal frequency of acquisition, about 3 days over the chott, with a spatial resolution of 25 km. It is well suited for an interpretation of radar temporal signatures in relationship with seasonal variations of surface states. SAR images obtained from both Advanced SAR (ASAR) and RADARSAT-2 sensors are less frequent (about 20 days) but provide a higher spatial resolution, allowing for the discrimination of spatial patterns related to evaporitic processes. ASAR Wide Swath mode, associated with 150 m of spatial resolution, allows for the monitoring of the whole chott area, whereas RADARSAT-2, realizing full polarimetric acquisitions with a spatial resolution of 8 m, allows for the discrimination of finer spatial patterns over a sub-area within the chott. Both scatterometer and SAR data show an overall good agreement in radiometry. Polarimetry, available for the RADARSAT-2 data, allows for the highlighting of striking spatial patterns in relation with the various sedimentation processes within the saline deposit over the chott.

Statistical characterization of the Sinclair matrix: Application to polarimetric image segmentation

This paper focuses on the flexibility of a multidimensional model of probability density function (pdf) to describe distribution of complex data in polarimetric SAR images. This model is based on Copulas Theory for characterizing the dependence between the polarimetric channels (HH, VV, HV, VH). This corresponds to finding a model based on multidimensional copulas to describe the behavior of the target vector. The advantage in using copulas theory is to extend correlation concept to a wider dependence one, which may be non-linear, especially when processing high-resolution data. So, from this point of view, the model is more flexible than the classical Wishart distribution since no speckle filtering is required as preprocessing step to model accurately the pdfs. The other advantage of copulas is to split dependence concept and marginal distributions. Then, this multidimensional characterization may be linked to pdf which are not necessary of circular Gaussian law. So, specific parametric distribution may be choosen to fit each component (modulus and phase) of the Sinclair matrix. It yields a flexible model, for characterizing statistical behavior of the polarimetric SAR data, that may be derived to produce a segmentation algorithm.

Observations and simulations of the ERS wind scatterometer response over a Sahelian region

ERS-1 wind-scatterometer data acquired over a saharo-sahelian region located in the Gourma (Mali) during the period 1992-1995, are analysed. Experimental observations show that /spl sigma//spl deg/(45/spl deg/) temporal data display a marked seasonality associated with the development and senescence of annual grasses during the rainy seasons. The interpretation of the temporal /spl sigma//spl deg/ plots is performed with the assistance of a semi-empirical backscattering model combined with an ecosystem grassland model. The use of this model allows the total biomass to be estimated with a 33% error.

Fusion of LiDAR and radar data for land-cover mapping in natural environments

Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the large number of forest and crop classes. A large amount of remote sensing possibilities is at the moment available and data fusion appears to be the most suitable solution for that purpose. The paper tackles the issue of land-cover mapping in such areas assuming the existence of a partly non-updated 5-class geodatabase: buildings, roads, water, crops, forests. Lidar point clouds and Radar images at two spatial resolutions and bands are merged at the feature level and fed into an efficient supervised classification framework. Results show that some classes benefit from the joint exploitation of multiple observations in terms of accuracy or recall.

UN-sensored very high resolution Land-Cover mapping

Land-Cover databases (LC-DB) are mandatory for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, an increasing number of sensors, such as optical and SAR satellite images or Lidar point cloud, allow to cover large areas regularly, and with a very high precision. Thus, automatic methods have to be developed to take into account the complementarity of available observations. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those methods are compared on several scenarii (based on optical, SAR and Lidar datasets), and evaluated thanks to a very high resolution LC-DB.

Contribution of textural information from TerraSAR-X image for forest mapping

This study evaluates the potential of High Resolution Spotlight TerraSAR-X image for forest type discrimination. Emphasis is put on textural analysis accessible with high resolution radar data. Textural attributes are extracted from GLCM matrices, wavelet, and Fourier Transform (i.e. FOTO method). Their contribution for classification is assessed by their performance through the SVM algorithm.