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Stefan Lang   Dr.  Institute, Department or Faculty Head 
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Stefan Lang published an article in February 2019.
Top co-authors See all
Nicola Casagli

190 shared publications

Dipartimento di Scienze della Terra, Università di Firenze, Via G. La Pira 4, 50121 Firenze, ITALY

Dirk Tiede

70 shared publications

Department of Geoinformatics, University of Salzburg, Salzburg, Austria

Francesca Cigna

37 shared publications

British Geological Survey (BGS), Natural Environment Research Council (NERC), Environmental Science Centre, Nicker Hill, Keyworth, Nottingham, UK

S. Kienberger

32 shared publications

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

Daniel Hölbling

21 shared publications

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

61
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418
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Publication Record
Distribution of Articles published per year 
(2002 - 2018)
Total number of journals
published in
 
31
 
Publications See all
Article 0 Reads 0 Citations Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications ... Martin Sudmanns, Dirk Tiede, Hannah Augustin, Stefan Lang Published: 05 February 2019
International Journal of Digital Earth, doi: 10.1080/17538947.2019.1572799
DOI See at publisher website
Article 0 Reads 0 Citations Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation fo... Zahra Dabiri, Stefan Lang Published: 19 December 2018
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi7120488
DOI See at publisher website ABS Show/hide abstract
Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.
Article 0 Reads 0 Citations Earth observation based multi-scale assessment of logging activities in the Democratic Republic of the Congo Olaf Kranz, Elisabeth Schoepfer, Reiner Tegtmeyer, Stefan La... Published: 01 October 2018
ISPRS Journal of Photogrammetry and Remote Sensing, doi: 10.1016/j.isprsjprs.2018.07.012
DOI See at publisher website
PREPRINT 0 Reads 0 Citations Spectral-Spatial Dimensionality Reduction of APEX Hyperspectral Imagery for Tree Species Classification; a Case Study of... Zahra Dabiri, Stefan Lang Published: 12 June 2018
EARTH SCIENCES, doi: 10.20944/preprints201806.0188.v1
DOI See at publisher website ABS Show/hide abstract
Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.
ALGORITHMS-&-COMPLEXITY 0 Reads 0 Citations GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observati... Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan ... Published: 11 June 2018
PubMed View at PubMed ABS Show/hide abstract
ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.
ALGORITHMS-&-COMPLEXITY 0 Reads 0 Citations GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observati... Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan ... Published: 10 June 2018
PubMed View at PubMed ABS Show/hide abstract
ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2—Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.
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