Bayesian Analysis of Uncertainty in the GlobCover 2009 Land Cover Product at Climate Model Grid Scale

Methodological considerations with data uncertainty in road safety analysis

The analysis of potential influencing factors that affect the likelihood of road accident occurrence has been of major interest for safety researchers throughout the recent decades. Even though steady methodological progresses were made over the years, several impediments pertaining to the statistical analysis of crash data remain. While issues related to methodological approaches have been subject to constructive discussion, uncertainties inherent to the most fundamental part of any analysis have been widely neglected: data. This paper scrutinizes data from various sources that are commonly used in road safety studies with respect to their actual suitability for applications in this area. Issues related to spatial and temporal aspects of data uncertainty are pointed out and their implications for road safety analysis are discussed in detail. These general methodological considerations are exemplary illustrated with data from Austria, providing suggestions and methods how to overcome these obstacles. Considering these aspects is of major importance for expediting further advances in road safety data analysis and thus for increasing road safety.

Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest

Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.