Dear Colleagues,

Geographic Information Systems (GIS), remote sensing and environmental models have extensively been used lately in different applications in order to provide solutions that address societal and environmental management problems. They have, for instance, been widely employed for predictions, site suitability analysis and risk assessment. This growing usage of GIS and modelling has also been in line with the increasing availability of geospatial data, more powerful computers and improved technology.

However, the application of GIS and modelling requires different data, follows a chain of methods to process and reprocess data and involves several user choices. All of these factors affect model results, rendering them subject to uncertainties. Therefore, uncertainty and statistical analyses have to be included as part of the modelling process to assess how, for example, data and model parameters affect results. They are also important for determining model performance in terms of its accuracy as well as its limitations in providing information. Moreover, visualisation of uncertainty is essential for helping the visual identification of patterns and trends that allow better comprehension of their possible causes in modelling. In model results where geographic locations are identified, the inclusion of uncertainty in geovisualisation or in the map is a method of communicating information to its users.

This Special Issue on Risk, Uncertainty Analysis and Statistical Models in Environment welcomes original research or review contributions on uncertainty analyses and statistical models incorporated in the following topics, with applications in societal and environmental management and planning:

• Remote sensing;
• GIS/Spatial modelling;
• Environmental modelling;
• Natural hazard and/or risk assessment;
• Geovisualisation and/or mapping.

Dr. Nancy Joy Lim  
Prof. Dr. Natalia Nikolova
Prof. Dr. Mark Kelbert
Guest Editors

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• GIS
• geovisualisation
• mapping
• modelling
• remote sensing
• risk
• statistics
• uncertainty