[1] Surface roughness is a key parameter for surface-atmosphere exchanges of mass and energy. Only a few field measurements have been performed in arid or semiarid areas where it is an important control of the aeolian erosion threshold. An intensive field campaign was performed in southern Tunisia to measure the lateral cover, L c , and the aerodynamic roughness length, Z 0 , over 10 sites with different surface roughnesses. L c was determined by combining field measurements of the geometry of the roughness elements and simple assumptions on their shapes. Z 0 was experimentally determined from high-precision wind velocity and air temperature profiles. The resulting data were found to be in good agreement with the existing relationships linking the geometric and the aerodynamic roughness. This suggests that for natural surfaces, Z 0 can be estimated on the basis of the geometric characteristics of the roughness elements. This data set was then used to investigate the capabilities of radar backscatter coefficients, s 0 , to retrieve L c and/or Z 0 . Significant relationships were found between s 0 and both L c and Z 0 . The SAR/ERS data set is in agreement with the SIR-C SLR data set from Greeley et al. (1997). On the basis of these two data sets including data from different arid and semiarid areas (North Africa, South Africa, North America), we propose an empirical relationship to retrieve Z 0 using radar observations in the C band from operational sensors.
Abstract-The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows to consider unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, we describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g., corresponding to conflicting information) thank to an iterative process.Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L and C bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified.
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