Christos G. Karydas

Abstract: Risk assessment and management, are increasingly established as key procedures in dealing with the range of environmental issues at different scales and of different nature. Although at the EU and international policy level requirements for the use of risk assessment and management are being established through emerging policy and legislation, this demand has not been followed with common guidance on how to do so. This has proven to hinder the effective adoption of such processes, and posed a barrier more so in its implementation for large transboundary issues. In this paper is presented a Decision Support System (DSS) designed to provide a common framework and procedure for environmental risk assessment and management. The DSS is web-based and was developed to enable the formalized and more systematic utilization of risk assessment and management procedures in environmental decision making processes, in particular for users such as public authority officials charged with the responsibility of implementing risk management legal and policy obligations, yet which have limited know how in the field of risk. The DSS presented herein enables environmental administrators and decision makers to undertake generic risk assessment and management identifying areas where detailed risk assessment is required, proposing as well as appropriate risk management options. The web DSS was developed and piloted as part of the STRiM project funded by the European Union. Herein are shown results from the web application which has been trailed successfully in four pilot trials addressing risks of forest damage from storms, water pollution from olive mill waste discharges, wetland loss from water abstraction, and damage from flooding.

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: In this research, a rule-set of object-based classification of IKONOS imagery for fine-scale mapping of Mediterranean rural landscapes was developed. This study was conducted on the Mediterranean island of Crete (Greece). A three-level classification hierarchy was designed in a bottom-up approach containing a total number of 22 classes. The first level was associated with vegetation physiognomy (6 classes), the second level with linear features (6 classes) and the third level with land uses existing in the area (10 classes). Image objects were created with multiresolution segmentation, an algorithm supplied by eCognition software. The segmentation parameters were selected through a trial-and-error approach after visual evaluation of the resulting image objects. The rule-set comprised 100 classification rules described with the �Membership Function� classifier. The classification stability was found to lie between 0.59 and 0.77, inversely proportional to the complexity of each level's classification. For an accuracy assessment, the error matrix method was used in a set of 250 randomly selected points. The overall classification accuracy achieved at the first level was 74%, at the second level 50% and at the third level 64%. The geometric accuracy of the classification was beyond the scope of this research; and moreover, consistent reference data sets were not available. The conclusion is that the use of rules in an object-based image analysis (OBIA) process has the potential to produce accurate landscape maps even in the case of complex environments, in which ancillary data are not available. Future work should focus on testing the transferability of the rule-set in different Mediterranean study sites, in order to draw a conclusion in relation to its potential operational use.

Abstract: Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using objectoriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.

Abstract: WebGIS is becoming prominent in spatial decision support applications, as it allows researchers and stakeholders to benefit from sharing, analyzing and visualizing large, up-to-date geospatial data sets with minimal effort and cost. This paper addresses the integration of open-source, open-standards software packages and state-of-the-art web technology to develop an interactive web mapping portal for spatial analysis. It demonstrates that, open-source software offers a level of flexibility, availability and lowered cost that is typically unavailable with commercial software, while an architecture and design based on open standards ensures system interoperability and data reusability. The resulting system aims at enhancing collaboration and decision making among researchers and stakeholders in environmental decision-making, being highly accessible, and requiring minimal computing expertise. Where standard functionality is insufficient, the system can be extended via scripting to adapt to emerging needs. Keywords - WebGIS, interactive web applications, spatial decision support systems, raster algebra, environmental applications

Abstract: Fine scale mapping of agricultural landscape features, such as low vegetation, terraces, roads, hedges and fences contributes to support practice factor of soil erosion models and estimation of functions, such as waste material flow paths from olive mill waste tanks are prerequisites to water pollution risk assessment in an olive cultivation area. In this work, fine scale mapping of the aforementioned features and functions was tested in the island of Crete, Greece, more specifically in the area of Kolymvari, using a QuickBird image and a Digital Elevation Model. Visual photo-interpretation, object-oriented analysis and hydrological modelling were the basic methodologies. The results showed that eleven out of 15 waste tanks recognised in the visually interpreted image were validated. Low vegetation, bare land, roads, and terraces were mapped with object-oriented classification in a preliminary attempt. Some other features were identified only visually. QuickBird image background was used as a basis for drainage network correction and potential flow paths of the olive mill waste material were accurately traced.

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.

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