Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

Long‐range terrestrial laser scanning (TLS) is an emerging method for the monitoring of alpine slopes in the vicinity of infrastructure. Nevertheless, deformation monitoring of alpine natural terrain is difficult and becomes even more challenging with larger scan distances. In this study we present approaches for the handling of spatially variable measurement uncertainties in the context of geomorphological change detection using multi‐temporal data sets. A robust distance measurement is developed, which deals with surface roughness and areas of lower point densities. The level of detection (LOD), i.e. the threshold distinguishing between real surface change and data noise, is based on a confidence interval considering the spatial variability of TLS errors caused by large laser footprints, low incidence angles and surface roughness. Spatially variable positional uncertainties are modelled for each point according to its range and the object geometry hit. The local point cloud roughness is estimated in the distance calculation process from the variance of least‐squares fitted planes. Distance calculation and LOD assessment are applied in two study areas in the Eastern Alps (Austria) using multi‐temporal laser scanning data sets of slopes surrounding reservoir lakes. At Finstertal, two TLS point clouds of high alpine terrain and scanned from ranges between 300 and 1800 m are compared. At Gepatsch, the comparison is done between an airborne laser scanning (ALS) and a TLS point cloud of a vegetated mountain slope scanned from ranges between 600 and 3600 m. Although these data sets feature different conditions regarding the scan setup and the surface conditions, the presented approach makes it possible to reliably analyse the geomorphological activity. This includes the automatic detection of rock glacier movement, rockfall and debris slides, even in areas where a difference in vegetation cover could be observed. Copyright © 2016 John Wiley & Sons, Ltd.

The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C + + in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing. Published by Copernicus Publications on behalf of the European Geosciences Union.Geosci. Model Dev.

ABSTRACT:Recently multispectral LiDAR became a promising research field for enhanced LiDAR classification workflows and e.g. the assessment of vegetation health. Current analyses on multispectral LiDAR are mainly based on experimental setups, which are often limited transferable to operational tasks. In late 2014 Optech Inc. announced the first commercially available multispectral LiDAR system for airborne topographic mapping. The combined system makes synchronic multispectral LiDAR measurements possible, solving time shift problems of experimental acquisitions. This paper presents an explorative analysis of the first airborne collected data with focus on class specific spectral signatures. Spectral patterns are used for a classification approach, which is evaluated in comparison to a manual reference classification. Typical spectral patterns comparable to optical imagery could be observed for homogeneous and planar surfaces. For rough and volumetric objects such as trees, the spectral signature becomes biased by signal modification due to multi return effects. However, we show that this first flight data set is suitable for conventional geometrical classification and mapping procedures. Additional classes such as sealed and unsealed ground can be separated with high classification accuracies. For vegetation classification the distinction of species and health classes is possible.

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