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ABSTRACT:Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden's J-statistic on cooccurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.

The data from several weather stations in Western Algeria show a semi-arid climate during last decades. The entire study region showed a great variability in the occurrences of the first and second rainy days in the year. This variability is associated with a positive trend, showing a continuous increasing aridity in the south Mediterranean and the late arrival of the rainy season is well marked. The rainy season in the north of Algeria, spreads from September to June. The origin of the rains differ according to the seasons. The rainfall from June to October is of localized stormy origin, whereas in winter, the rainfall comes from the classical atmospherically perturbations arriving from North or North West. This work objective was to give a definition of the rainy season onset and to show its inter - annual variability according to the Niño and Niña years. The El - Niño phenomenon by its positive and negative phases seems to affect the start of the rainy season. The variability of the rainfall onset indices is very significant. There is a relative stability of the rainy season length over the long term period. A significant delay in the precipitation onset was observed during certain years. A method to define rainy season onset based on daily rainfall data from a weather station in the Algerian highlands was proposed. This approach is based on a climatic point of view, using a frequency analysis of precipitation and dates of their first occurrence. It delays the first heavy rain day (20 mm) when La - Niña settles. If EL-Niño settles, the first heavy rain (20 mm) day will be earlier. These results will improve the probabilistic forecasts of the beginning of the rainy seasons, the cessation as well as the lengths. This work is a preliminary confirmation that the El-Niño phenomenon really affects the Mediterranean climate.

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Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remote sensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400 km corridor of sand dunes. We used image processing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. The process we developed combined two main parts: The first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern. For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001;Liao et al., 2007). Then, we merged positive bounding boxes exceeding a defined overlapping ratio. The positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an image processing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different descriptors for the profiles matching: HOG features were used to construct a multivariate Gaussian model, and then SURF descriptors were fed an SVM. The result was a recursive model that successfully mapped the contours of barchans dunes. We experimented with IKONOS high resolution satellite images. The use of IKONOS high resolution satellite images proved useful not only to have a good accuracy, but also allowed to map the contours of barchans sand dunes with a high precision. Overall, the execution time of the combined methods was very satisfying.