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Innsbruck, Austria

Conrad O.,University of Hamburg | Bechtel B.,University of Hamburg | Bock M.,University of Hamburg | Bock M.,scilands GmbH | And 7 more authors.
Geoscientific Model Development | Year: 2015

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. © Author(s) 2015. Source


Rieg L.,University of Innsbruck | Wichmann V.,Alpnter for Climate Change Adaptation Technologies | Wichmann V.,LASERDATA GmbH | Rutzinger M.,University of Innsbruck | And 5 more authors.
Computers, Environment and Urban Systems | Year: 2014

The application of multi-temporal topographic LiDAR data has become a standard for many mapping and monitoring applications in man-made and natural environments. With increasing availability of area-wide, high-resolution, multi-temporal datasets and the increasing interest in working with the original measured 3D topographic LiDAR point clouds, challenges regarding optimal data storage and management are gaining in importance. During the last decade, an unique LiDAR dataset, consisting of over 30 flight campaigns covering different areas in western Austria and northern Italy, has been acquired with the purpose to analyse surface changes in high mountain environments. The datasets from each flight campaign are stored and managed in a LiDAR specific information system (LIS, Laserdata Information System), which has a client-server architecture and a spatial relational database as a core. The system is integrated into an open source Geographical Information System, which allows a seamless integration into operational spatial data processing and analysis workflows. It enables multi-user access to 3D point cloud data and additional attributes, such as intensity, return number, global positioning system time, flight path trajectory information and fullwaveform attributes if available. In this paper we describe the available dataset, the approach to 3D point cloud storage and management and the structure of the database. The value of direct access to the stored 3D point cloud data is assessed as well as the importance of storing point cloud attributes. A further focus lies on the importance of the data management and infrastructure for the analysis of large areas and long time series of topographic LiDAR data. © 2013 Elsevier Ltd. Source


Jochem A.,University of Innsbruck | Jochem A.,Alpnter for Climate Change Adaptation Technologies | Hofle B.,University of Heidelberg | Wichmann V.,Alpnter for Climate Change Adaptation Technologies | And 3 more authors.
Computers, Environment and Urban Systems | Year: 2012

Most algorithms performing segmentation of 3D point cloud data acquired by, e.g. Airborne Laser Scanning (ALS) systems are not suitable for large study areas because the huge amount of point cloud data cannot be processed in the computer's main memory. In this study a new workflow for seamless automated roof plane detection from ALS data is presented and applied to a large study area. The design of the workflow allows area-wide segmentation of roof planes on common computer hardware but leaves the option open to be combined with distributed computing (e.g. cluster and grid environments). The workflow that is fully implemented in a Geographical Information System (GIS) uses the geometrical information of the 3D point cloud and involves four major steps: (i) The whole dataset is divided into several overlapping subareas, i.e. tiles. (ii) A raster based candidate region detection algorithm is performed for each tile that identifies potential areas containing buildings. (iii) The resulting building candidate regions of all tiles are merged and those areas overlapping one another from adjacent tiles are united to a single building area. (iv) Finally, three dimensional roof planes are extracted from the building candidate regions and each region is treated separately. The presented workflow reduces the data volume of the point cloud that has to be analyzed significantly and leads to the main advantage that seamless area-wide point cloud based segmentation can be performed without requiring a computationally intensive algorithm detecting and combining segments being part of several subareas (i.e. processing tiles). A reduction of 85% of the input data volume for point cloud segmentation in the presented study area could be achieved, which directly decreases computation time. © 2011 Elsevier Ltd. Source


Fey C.,Alpnter for Climate Change Adaptation | Fey C.,TIWAG Tiroler Wasserkraft AG | Rutzinger M.,Austrian Academy of Sciences | Rutzinger M.,University of Innsbruck | And 5 more authors.
GIScience and Remote Sensing | Year: 2015

Information on geometries and kinematics of landslides are necessary to establish geological slope deformation models. We present two complementary geospatial methods to analyze landslide surface changes even in areas affected by strong surface pattern changes, making use of airborne laser scanning (ALS) data. An image correlation method based on shaded relief images with a uniformly diffuse lighting and a feature tracking based on terrain breaklines are applied on a data set of eight ALS flight campaigns analyzing an active deep-seated rockslide in the Eastern Alps (Austria). Both tracking methods are described in detail, including parameter assessment. Additionally, an accuracy assessment of the input data sets has been conducted. 3D vector displacement maps derived from image correlation are well suited for the study of landslides if only slight surface pattern changes occur. The smallest detectable displacements strongly depend on the accuracy of the ALS data and for image correlation results lie within the range of 0.24 and 0.75 m for this study. Displacement vectors derived by breakline tracking only allow to detect displacements greater than 2 m. However, in comparison to image correlation, breakline tracking is not limited to areas with slight surface pattern changes and allows us to detect displacements even in areas with strong surface pattern changes. For a comprehensive interpretation of landslide activity a combination of both methods, with consideration of additional supportive data such as elevation change images and orthoimages, is recommended. © 2015 Taylor & Francis. Source

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