Integrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images. Issue 3 (3rd March 2020)

• Record Type: Journal Article
• Authors: Dai, Wen; Na, Jiaming; Huang, Nan; Hu, Guanghui; Yang, Xin; Tang, Guoan; Xiong, Liyang; Li, Fayuan
• Is Part Of: International journal of geographical information science. Volume 34:Issue 3(2020)
• Page Start: 484
• Page End: 503
• Publication Date: 2020-03-03
• Subjects: Agricultural terraces -- edge detection -- directional detection -- DEM -- contour direction; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1650363
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Agricultural terraces are important for agricultural production and soil-and-water conservation. They comprise treads and risers that require manual construction and maintenance. If managed improperly, risers will collapse, causing soil loss, gully erosion, and cultivation threats. However, mapping terrace risers remains a challenge. This study presents a novel approach to automatically map terrace risers by combining remote sensing images and digital elevation models (DEMs). First, a terraced hillslope was extracted via a hill-shading method and edges in the image were detected using a Canny edge detector. Next, the DEM was used to generate the contour direction, and edges along this direction were searched and coded as candidate terrace risers via directional detection. Finally, the results of directional detection and the edge image obtained from the Canny detector were overlaid to backtrack complete terrace risers. The approach was validated using four study areas with different topographic characteristics in the Loess Plateau, China. The results verify that the approach achieves outstanding performance and robustness in mapping terrace risers. The precision, recall, and F-measure were 90.81%–97.57%, 88.53%–94.10%, and 90.13%–95.80%, respectively. This approach is flexible and applicable with freely available images and DEM sources.