Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau

ISPRS International Journal of Geo-Information

卷:6

Sedigheh Maleki¹  Farhad Khormali¹  Tanja Kramm²  Dirk Hoffmeister²  Martin Kehl²  Constanze Curdt² 

[1] Department of Soil Sciences, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49138-15739, Iran;

[2] GIS and Remote Sensing, Institute of Geography, University of Cologne, Albertus Magnus Platz, 50923 Köln, Germany;

关键词: digital terrain models;    landform classification;    geomorphometry;    stereo satellite imagery;    ASTER GDEM;    SRTM;    loess;   

DOI  :  10.3390/ijgi6110366

来源: DOAJ

【 摘 要 】

An accurate geomorphometric description of the Iranian loess plateau landscape will further enhance our understanding of recent and past geomorphological processes in this strongly dissected landscape. Therefore, four different input datasets for four landform classification methods were used in order to derive the most accurate results in comparison to ground-truth data from a geomorphological field survey. The input datasets in 5 m and 10 m pixel resolution were derived from Pléiades stereo satellite imagery and the “Shuttle Radar Topography Mission” (SRTM), and “Advanced Spaceborne Thermal Emission and Reflection Radiometer” (ASTER GDEM) datasets with a spatial resolution of 30 m were additionally applied. The four classification approaches tested with this data include the stepwise approach after Dikau, the geomorphons, the topographical position index (TPI) and the object based approach. The results show that input datasets with higher spatial resolutions produced overall accuracies of greater than 70% for the TPI and geomorphons and greater than 60% for the other approaches. For the lower resolution datasets, only accuracies of about 40% were derived, 20–30% lower than for data derived from higher spatial resolutions. The results of the topographic position index and the geomorphons approach worked best for all selected input datasets.

【 授权许可】

Unknown