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ISPRS Int. J. Geo-Inf. 2016, 5(12), 238; doi:10.3390/ijgi5120238

Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau

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Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
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State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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The First Institute of Photogrammetry and Remote Sensing, National Administration of Surveying, Mapping and Geoinformation, Xi’an 710054, China
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Author to whom correspondence should be addressed.
Academic Editors: Jason K. Levy and Wolfgang Kainz
Received: 26 October 2016 / Revised: 23 November 2016 / Accepted: 5 December 2016 / Published: 16 December 2016
View Full-Text   |   Download PDF [9356 KB, uploaded 16 December 2016]   |  

Abstract

The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to extract gully features in this region. Two typical catchments in Changwu and Ansai were selected to represent loess tableland and loess hilly regions, respectively. A high-powered quadrocopter (md4-1000) equipped with a non-metric camera was used for image acquisition. InPho and MapMatrix were applied for semi-automatic workflow including aerial triangulation and model generation. Based on the stereo-imaging and the ground control points, the highly detailed digital elevation models (DEMs) and ortho-mosaics were generated. Subsequently, an object-based approach combined with the random forest classifier was designed to detect gully-affected areas. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The overall extraction accuracy in Changwu and Ansai achieved was 84.62% and 86.46%, respectively, which indicated the potential of the proposed workflow for extracting gully features. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research. View Full-Text
Keywords: unmanned aerial vehicle (UAV); gully erosion; gully-affected areas; object-based image analysis; random forest; Loess Plateau unmanned aerial vehicle (UAV); gully erosion; gully-affected areas; object-based image analysis; random forest; Loess Plateau
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Liu, K.; Ding, H.; Tang, G.; Na, J.; Huang, X.; Xue, Z.; Yang, X.; Li, F. Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau. ISPRS Int. J. Geo-Inf. 2016, 5, 238.

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