Extraction of gully erosion using multi-level random forest model based on object-based image analysis
Gully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To impro...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000810 |
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| _version_ | 1849762786424389632 |
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| author | Mengxia Xu Mingchang Wang Fengyan Wang Xue Ji Ziwei Liu Xingnan Liu Shijun Zhao Minshui Wang |
| author_facet | Mengxia Xu Mingchang Wang Fengyan Wang Xue Ji Ziwei Liu Xingnan Liu Shijun Zhao Minshui Wang |
| author_sort | Mengxia Xu |
| collection | DOAJ |
| description | Gully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To improve the extraction accuracy of gully erosion, multi-level random forest (RF) extraction model based on object-based image analysis (OBIA) is proposed to extract gully erosion information. The Gaofen-2 (GF-2) image was selected as the main data source, supplemented by topographic data, to segment the features in Dehui City based on multi-scale segmentation method. Fusing spectral, textural and geometric feature information, the RF Gini index (GI) was used for feature optimization. Gully erosion extraction based on feature classes was performed using multi-level RF model based on OBIA in the southwestern part of Dehui City, with an overall accuracy (OA) of 96.71% and a Kappa coefficient (Kappa) of 0.865. Compared with the single-level extraction results, the OA and Kappa were improved by 8.4% and 0.102, which indicated that this model has better performance and has certain application value for the research of gully erosion information extraction and dynamic monitoring. |
| format | Article |
| id | doaj-art-787f1fc2d9be43e583cde4b80407882e |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-787f1fc2d9be43e583cde4b80407882e2025-08-20T03:05:39ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-03-0113710443410.1016/j.jag.2025.104434Extraction of gully erosion using multi-level random forest model based on object-based image analysisMengxia Xu0Mingchang Wang1Fengyan Wang2Xue Ji3Ziwei Liu4Xingnan Liu5Shijun Zhao6Minshui Wang7College of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science & Technology, Jilin University, Changchun, China; Corresponding author.College of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaCollege of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaChina Water Northeastern Investigation, Design & Research Co., Ltd., Changchun 130021, ChinaCollege of Geo-Exploration Science & Technology, Jilin University, Changchun, ChinaGully erosion cause soil organic matter loss, which poses a grave threat to food security and regional ecological sustainability. Remote sensing monitoring and information extraction of gully erosion are of great significance to protect cultivated land resources and agricultural production. To improve the extraction accuracy of gully erosion, multi-level random forest (RF) extraction model based on object-based image analysis (OBIA) is proposed to extract gully erosion information. The Gaofen-2 (GF-2) image was selected as the main data source, supplemented by topographic data, to segment the features in Dehui City based on multi-scale segmentation method. Fusing spectral, textural and geometric feature information, the RF Gini index (GI) was used for feature optimization. Gully erosion extraction based on feature classes was performed using multi-level RF model based on OBIA in the southwestern part of Dehui City, with an overall accuracy (OA) of 96.71% and a Kappa coefficient (Kappa) of 0.865. Compared with the single-level extraction results, the OA and Kappa were improved by 8.4% and 0.102, which indicated that this model has better performance and has certain application value for the research of gully erosion information extraction and dynamic monitoring.http://www.sciencedirect.com/science/article/pii/S1569843225000810Gully erosionRandom forestGF-2Object-basedMulti-levelRemote sensing extraction |
| spellingShingle | Mengxia Xu Mingchang Wang Fengyan Wang Xue Ji Ziwei Liu Xingnan Liu Shijun Zhao Minshui Wang Extraction of gully erosion using multi-level random forest model based on object-based image analysis International Journal of Applied Earth Observations and Geoinformation Gully erosion Random forest GF-2 Object-based Multi-level Remote sensing extraction |
| title | Extraction of gully erosion using multi-level random forest model based on object-based image analysis |
| title_full | Extraction of gully erosion using multi-level random forest model based on object-based image analysis |
| title_fullStr | Extraction of gully erosion using multi-level random forest model based on object-based image analysis |
| title_full_unstemmed | Extraction of gully erosion using multi-level random forest model based on object-based image analysis |
| title_short | Extraction of gully erosion using multi-level random forest model based on object-based image analysis |
| title_sort | extraction of gully erosion using multi level random forest model based on object based image analysis |
| topic | Gully erosion Random forest GF-2 Object-based Multi-level Remote sensing extraction |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000810 |
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