Salt-dissolution-induced subsidence in the Dead Sea area detected by applying interferometric techniques to ALOS Palsar Synthetic Aperture Radar images

This paper discusses the interpretation of ground motions detected in the dried up Lynch Strait, Dead Sea area, by applying radar interferometric techniques to ALOS Palsar Synthetic Aperture Radar images. Four ALOS scenes spanning from December 15, 2007 to May 17, 2008 have been processed leading to the generation of five interferograms. Three ground deformation zones have been detected. One of them shows surface displacement which could be related to an earthquake (ML 3.1) that took place on April 13, 2008. High rates of subsidence have been measured in the northern Lynch Strait. They suggest that these subsidence phenomena follow the same trend of rapid increase as sinkholes. Additional measurements should be carried out in order to refine this observation.

The comparison between sinkholes' distributions in the Lynch Strait with that of Ghor Al Haditha, six kilometers eastward, supports the idea that the earthquake that hit the southern Dead Sea on April 23, 1979 (M 5.1) reactivated faults and fractures in the Lynch Strait triggering the development of sinkholes and subsidence in the frame of the Dead Sea recession.     

Application of multivariate model based on three simulated sensors for water quality variables estimation in Shitoukoumen Reservoir,Jilin Province,China

This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen Reservoir, Changchun City, Jilin Province, China, including concentration of total suspended matter, concentration of chlorophyll-a and non-pigment matter absorption. Two field campaigns for spectra measurements with a total of 40 samples were carried out on June 13 and September 23, 2008. The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments applied in this paper, i.e. Landsat TM, Alos and P6, by using the average method. And the recalculated spectra were used for estimating water quality variables by the single model and multivariate model. The results show that the multivariate model is superior to the single model as the multivariate model takes the combined effects of water components into consideration and can estimate water quality variables simultaneously. According to R ² and RMSE, Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption inversion performed the second.     

Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces

The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification.Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010.The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used.