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Stefan Lang   Dr.  Institute, Department or Faculty Head 
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Stefan Lang published an article in February 2018.
Top co-authors See all
Dirk Tiede

52 shared publications

Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstr. 30, 5020 Salzburg, Austria

Nicola Casagli

22 shared publications

Department of Earth Sciences, University of Florence, 50121 Florence, Italy

Daniel Hölbling

16 shared publications

Department of Geoinfomatics-Z_GIS, University of Salzburg, Schillerstrasse 30, Salzburg 5020, Austria

Stefan Kienberger

13 shared publications

Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, Austria

Francesco Antolini

12 shared publications

Politecnico di Torino

Publication Record
Distribution of Articles published per year 
(1970 - 2018)
Total number of journals
published in
Publications See all
BOOK-CHAPTER 2 Reads 0 Citations Earth Observation for Humanitarian Operations Stefan Lang, Petra Füreder, Edith Rogenhofer Published: 23 February 2018
The Yearbook on Space Policy, doi: 10.1007/978-3-319-72465-2_10
DOI See at publisher website
Article 0 Reads 0 Citations Urban green valuation integrating biophysical and qualitative aspects Stefan Lang Published: 12 December 2017
European Journal of Remote Sensing, doi: 10.1080/22797254.2017.1409083
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Urban green mapping has become an operational task in city planning, urban land management, and quality of life assessments. As a multi-dimensional, integrative concept, urban green comprising several ecological, socio-economic, and policy-related aspects. In this paper, the author advances the representation of urban green by deriving scale-adapted, policy-relevant units. These so-called geons represent areas of uniform green valuation under certain size and homogeneity constraints in a spatially explicit representation. The study accompanies a regular monitoring scheme carried out by the urban municipality of the city of Salzburg, Austria, using optical satellite data. It was conducted in two stages, namely SBG_QB (10.2 km², QuickBird data from 2005) and SBG_WV (140 km², WorldView-2 data from 2010), within the functional urban area of Salzburg. The geon delineation was validated by several quantitative measures and spatial analysis techniques, as well as ground documentation, including panorama photographs and visual interpretation. The spatial association pattern was assessed by calculating Global Moran’s I with incremental search distances. The final geonscape, consisting of 1083 units with an average size of 13.5 ha, was analyzed by spatial metrics. Finally, categories were derived for different types of functional geons. Future research paths and improvements to the described strategy are outlined.
BOOK-CHAPTER 0 Reads 0 Citations Land Use/Land Cover Classification of the Natural Environment Rajesh Thapa, Stefan Lang, Elisabeth Schoepfer, Stefan Kienb... Published: 30 January 2015
Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River basin, doi: 10.1007/978-81-322-1967-5_5
DOI See at publisher website
Article 0 Reads 25 Citations A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures... Daniel Hölbling, Petra Füreder, Francesco Antolini, Francesc... Published: 07 May 2012
Remote Sensing, doi: 10.3390/rs4051310
DOI See at publisher website ABS Show/hide abstract
Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.
Article 1 Read 25 Citations Earth observation (EO)-based ex post assessment of internally displaced person (IDP) camp evolution and population dynam... Stefan Lang, Dirk Tiede, Daniel Hölbling, Petra Füreder, Pet... Published: 08 November 2010
International Journal of Remote Sensing, doi: 10.1080/01431161.2010.496803
DOI See at publisher website
BOOK-CHAPTER 3 Reads 2 Citations Segmentation and Object-Based Image Analysis Elisabeth Schöpfer, Stefan Lang, Josef Strobl Published: 08 May 2010
Multitemporal Remote Sensing, doi: 10.1007/978-1-4020-4385-7_10
DOI See at publisher website