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Extraction of terraces on the loess plateau from high-resolution DEMs and imagery utilizing object-based image analysis
VerfasserZhao, Hanqing ; Fang, Xuan ; Ding, Hu ; Strobl, Josef ; Xiong, Liyang ; Na, Jiaming ; Tang, Guoan
Erschienen in
ISPRS International Journal of Geo-Information, Basel, 2017, Jg. 6, H. 6, S. 1-19
ErschienenMDPI, 2017
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)terraces / digital elevation model (DEM) / high-resolution imagery / object-based image analysis (OBIA) / terrain factor / terrain texture
URNurn:nbn:at:at-ubs:3-5659 Persistent Identifier (URN)
 Das Werk ist frei verfügbar
Extraction of terraces on the loess plateau from high-resolution DEMs and imagery utilizing object-based image analysis [5.23 mb]
Zusammenfassung (Englisch)

Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.

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