Due to a high population growth (approx. 2.5 % p.a) the food-sector in Syria is facing in-creasing problems. An enormous increase in population results in increased demand for food. This has adversely affected the socio-economic and ecological development in the country. Intensive use of various natural resources has led to significant changes in land use pattern, especially due to use of inappropriate methods in the agricultural sector. The increasing anthropogenic pressure on the sensitive ecological structure of the respective area causes environmental damages, in particular degradation of soil characteristics. In the semi-arid and arid eco-climatic zones vast areas are facing desertification. Soil erosion through water represents the main form of land degradation in the north-west of Syria. Particularly vulnerable are the soils with a shallow or no vegetation cover, such as the soils found in the Mediterranean hills, where olives are cultivated. For this research the Afrin region, located in the northwest of Syria, was selected as study area, in order to analyse and assess the extent of degradation. For estimation of erosion the relevant parameters of the “Universal Soil Loss Equation USLE” were used. These para-meters were adapted and integrated through remote sensing and GIS. LANDSAT TM and ASTER satellite imagery of the investigated area were used for this purpose. Data were acquired at the end of the dry season. In order to achieve an accurate evaluation and high-quality comparison of multi-temporal satellite data, imagery was firstly geometrically and atmospherically corrected and then analysed. The vegetation coverage and its current de-gradation level were investigated by spectral mixture analysis (SMA). The digital elevation model (DEM) derived from ASTER data was utilized to generate the slope gradient (S) and the slope length (L). In addition to the laboratory analysis, grain size index (GSI) and SMA were used for the characterization and mapping of soil erodibility. Land-use/land-cover classification and change detection were determined by using pixel-based classification procedures (maximum likelihood classification) and post classification methods respectively. Required samples for land cover classification of the remotely sensed data were collected during the field work, in addition to the soil samples for soil analysis. The results of this study show that advanced methods of remote sensing and GIS provide powerful tools not only for a better understanding of the land use changes, but also for an accurate assessment of land degradation and desertification. This knowledge, in turn, con-tributes highly towards developing effective and appropriate management strategies for sustainable use and conservation of natural resources in the north-west of Syria