Artificial Neural Networks for Landscape Analysis of the Biosphere Reserve “Eastern Carpathians” with Landsat ETM+ and SRTM data
2008 (English)In: The Problems of Landscape Ecology, ISSN 1899-3850, Vol. 20, 171-183 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2008. Vol. 20, 171-183 p.
SRTM, Self Organizing Map, landform, morphometric parameter, ETM+
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-8596OAI: oai:DiVA.org:kth-8596DiVA: diva2:13961
QC 20100728. Uppdaterad från in press till published (20100728).2008-06-022008-06-022010-07-28Bibliographically approved