Artificial neural networks for landscape analysis of the Biosphere Reserve “Eastern Carpathians” with Landsat ETM+ and SRTM data

Amir Houshang Ehsani, Friedrich Quiel


In this paper we propose a semi-automatic method for landscape analysis with both spectral and morphometric constituents. SRTM data are used to calculate first derivatives (slope) and second derivatives of elevation such as minimum curvature, maximum curvatures and cross-sectional curvature by fitting a bivariate quadratic surface with a window size 9 by 9. Together with multi-spectral remote sensing data like Landsat 7 ETM+ with 28.5 meter raster elements, these data provide comprehensive information for the analysis of the landscape in the study area. Unsupervised neural network algorithm -Self Organizing map- divided all input vectors into inclusive and exhaustive classes on the basis of similarity between attribute vectors. morphometric analysis, spectral signature analysis, feature space analysis are used to assign semantic meaning to the classes as landscape elements according to form, cover and slope e.g. deciduous forest on ridge (convex landform) with steep slopes.

Słowa kluczowe

SRTM, Self Organizing Map, landform, morphometric parameter, EMT+

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