An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

• Published in: Chinese geographical science 2017-06, Vol.27 (3), p.415-430
• Main Authors: Liu, Kai, Ding, Hu, Tang, Guoan, Zhu, A-Xing, Yang, Xin, Jiang, Sheng, Cao, Jianjun
• Format: Article
• Language: English
• Subjects: Case studies; DEM; Digital imaging; Earth and Environmental Science; Erosion control; Geography; Gullies; Gully erosion; Mapping; Mountains; Remote sensing; Topography; 基于对象; 影像; 沟壑; 特征映射; 高分辨率; 黄土丘陵区; 黄土高原地区
• ISSN: 1002-0063; 1993-064X
• DOI: 10.1007/s11769-017-0874-x

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model（DEM） and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.