Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau
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...
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| Format: | Article |
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MDPI AG
2016-12-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/5/12/238 |
| _version_ | 1818176415019827200 |
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| author | Kai Liu Hu Ding Guoan Tang Jiaming Na Xiaoli Huang Zhengguang Xue Xin Yang Fayuan Li |
| author_facet | Kai Liu Hu Ding Guoan Tang Jiaming Na Xiaoli Huang Zhengguang Xue Xin Yang Fayuan Li |
| author_sort | Kai Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T20:15:49Z |
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| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-11T20:15:49Z |
| publishDate | 2016-12-01 |
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| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0abd071afa574871a60135a85f8110652022-12-22T00:52:11ZengMDPI AGISPRS International Journal of Geo-Information2220-99642016-12-0151223810.3390/ijgi5120238ijgi5120238Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess PlateauKai Liu0Hu Ding1Guoan Tang2Jiaming Na3Xiaoli Huang4Zhengguang Xue5Xin Yang6Fayuan Li7Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaThe First Institute of Photogrammetry and Remote Sensing, National Administration of Surveying, Mapping and Geoinformation, Xi’an 710054, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaThe 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.http://www.mdpi.com/2220-9964/5/12/238unmanned aerial vehicle (UAV)gully erosiongully-affected areasobject-based image analysisrandom forestLoess Plateau |
| spellingShingle | Kai Liu Hu Ding Guoan Tang Jiaming Na Xiaoli Huang Zhengguang Xue Xin Yang Fayuan Li Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau ISPRS International Journal of Geo-Information unmanned aerial vehicle (UAV) gully erosion gully-affected areas object-based image analysis random forest Loess Plateau |
| title | Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau |
| title_full | Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau |
| title_fullStr | Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau |
| title_full_unstemmed | Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau |
| title_short | Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau |
| title_sort | detection of catchment scale gully affected areas using unmanned aerial vehicle uav on the chinese loess plateau |
| topic | unmanned aerial vehicle (UAV) gully erosion gully-affected areas object-based image analysis random forest Loess Plateau |
| url | http://www.mdpi.com/2220-9964/5/12/238 |
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