Digital elevation model (DEM) generation and accuracy assessment from ASTER stereo data

Süzen, Mehmet Lütfi
Digital elevation models (DEM) are the indispensable quantitative environmental variable in most of the research studies in remote sensing. The improvement of sensor and satellite imaging technologies enabled the researchers to generate DEM using remotely sensed data. These data can be started to use as not only the two-dimensional (2-D) but also three-dimensional (3-D) information sources with usage of the DEM. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is one of the sensor systems capable of DEM generation and during the study, ASTER level 1A (L1A) data were used. Due to presence of many geological features and different landcover types, the test site is selected as the watershed of Asarsuyu River, located in north-western Anatolia in between Duzce and Bolu plains. The aim of this study is to check the best effort of 15 in spatial resolution DEM generation from ASTER L1A data by collecting different numbers of ground control points (GCP) (30, 45, and 60) and tie points (TP). During the study, three different techniques-spatial correlation, image differencing and profiling were used for both planimetric and vertical accuracy assessment. The obtained results from both of the techniques show that the accuracy of the DEM increases by increasing the number of GCP. However, there is an only slight difference between the result of 45 GCPs and 60 GCPs.


Integration of environmental variables with satellite images in regional scale vegetation classification
Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01)
The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
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DOĞAN, Hakan Mete; Celep, Ferhat; Karaer, Fergan (Informa UK Limited, 2009-01-01)
Mapping the composition of plant community types requires reliable spatial data obtained from field surveys and satellite-derived indices. The normalized difference vegetation index (NDVI) is the simplest and most frequently used index in plant applications. If relationships between the NDVI and plant cover abundance are determined, this information can be used in the mapping process. In this study, we investigated these possible connections for mapping the plant community composition of Tersakan Valley in ...
Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping
San, B. Taner; Süzen, Mehmet Lütfi (Informa UK Limited, 2011-01-01)
Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (similar to 7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82-2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noi...
Neural Network Based Pavement Condition Assessment with Hyperspectral Images
Özdemir, Okan Bilge; Soydan, Hilal; Yardimci Cetin, Yasemin; Düzgün, Hafize Şebnem (MDPI AG, 2020-12-01)
Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM s...
Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey
Bulut, Burak; Yılmaz, Mustafa Tuğrul; Afshar, Mahdı Hesamı ; Sorman, A. Unal; Yücel, İsmail; Cosh, Michael H.; Simsek, Osman (MDPI AG, 2019-08-01)
This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations coll...
Citation Formats
B. T. SAN and M. L. Süzen, “Digital elevation model (DEM) generation and accuracy assessment from ASTER stereo data,” INTERNATIONAL JOURNAL OF REMOTE SENSING, pp. 5013–5027, 2005, Accessed: 00, 2020. [Online]. Available: