Abstract: Currently, many soil erosion studies at local, regional, national or continental scale use models based on the USLE-family approaches. Applications of these models pay little attention to seasonal changes, despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology. This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing. The latter, together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000. This potential service has led to the establishment of a new modelling approach (called the G2 model) based on the inheritance of USLE-family models. The G2 model proposes innovative techniques for the estimation of vegetation and protection factors. The model has been applied in a 14,500 km2 study area in SE Europe covering a major part of the basin of the cross-border river, Strymonas. Model results were verified with erosion and sedimentation figures from previous research. The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.
Abstract: Medium resolution imagery, such as SPOT4 Xi imagery, has often proved to be inadequate when mapping heterogeneous Mediterranean landscapes. Although very high resolution satellite imagery, such as IKONOS, is expected to bring new insight into land cover mapping of such landscapes, there are also limitations in its use. The aim of this study was to investigate an alternative means of mapping Mediterranean landscapes, namely, the potential offered by combining SPOT4 Xi with IKONOS imagery. More specific objectives were: (i) to examine the advantages and disadvantages of IKONOS imagery when used for the aforementioned purpose, and (ii) to investigate if the combined use of the two sensors is technically and financially feasible for this purpose. A region with a typical complex Mediterranean landscape in the island of Crete, Greece, was chosen as the study area. The CORINE classification scheme and the Maximum Likelihood (ML) technique were used. The accuracy derived was low for both classification attempts (SPOT: 36%, IKONOS: 52%) mainly due to the fact that, in many classes, DNs within the same class did not have normal distribution, as it is assumed by the ML algorithm. Nevertheless, the accuracy was improved when classification was based on the use of IKONOS instead of SPOT imagery (+16%). The main advantages of IKONOS imagery were the facilitation with which the sampling-sites were collected for the classification procedure and the accuracy assessment, as well as its appropriateness for use as a reference for the geometric correction of the SPOT image. The main disadvantage was the observed deterioration in accuracy with regard to the highly heterogeneous classes. In conclusion, the combined use of the two types of imagery proved to be far more favourable than the use of the SPOT imagery alone.
