Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data
Accurate vegetation type information is essential for the scientific assessment of ecological restoration in open pit mining areas. However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the prec...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2515269 |
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| author | Suling He Yuncheng Deng Jinliang Wang Mengjia Luo |
| author_facet | Suling He Yuncheng Deng Jinliang Wang Mengjia Luo |
| author_sort | Suling He |
| collection | DOAJ |
| description | Accurate vegetation type information is essential for the scientific assessment of ecological restoration in open pit mining areas. However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the precision of ecological evaluations. To address this issue, this study proposed a multi-scale hierarchical classification (MSHC) method for fine-scale mapping of vegetation types in open phosphate mining areas by combining unmanned aerial vehicle (UAV) RGB remote sensing imagery and light detection and ranging (LiDAR) data with the support of object-oriented methods. The results showed that (1) The all features (AF) scheme achieved an overall accuracy (OA) of 97.04% and a Kappa coefficient of 0.98. (2) The proposed MSHC method reached an OA of 97.69% and a Kappa of 0.97. (3) The vegetation in the study area was predominantly grassland (approximately 29.57% of the total area), with Alnus nepalensis being the dominant tree species (16.07% of the area). Overall, this study provides a detailed classification of vegetation types, offering a valuable dataset for ecological monitoring and assessment. The proposed method also presents a transferable and effective approach for the fine-scale vegetation mapping of other open-pit mining environments. |
| format | Article |
| id | doaj-art-de43edc0c04544e6b16cc144fd665e8d |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-de43edc0c04544e6b16cc144fd665e8d2025-08-25T11:31:56ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2515269Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud dataSuling He0Yuncheng Deng1Jinliang Wang2Mengjia Luo3Faculty of Geography, Yunnan Normal University, Kunming, People’s Republic of ChinaFaculty of Geography, Yunnan Normal University, Kunming, People’s Republic of ChinaFaculty of Geography, Yunnan Normal University, Kunming, People’s Republic of ChinaFaculty of Geography, Yunnan Normal University, Kunming, People’s Republic of ChinaAccurate vegetation type information is essential for the scientific assessment of ecological restoration in open pit mining areas. However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the precision of ecological evaluations. To address this issue, this study proposed a multi-scale hierarchical classification (MSHC) method for fine-scale mapping of vegetation types in open phosphate mining areas by combining unmanned aerial vehicle (UAV) RGB remote sensing imagery and light detection and ranging (LiDAR) data with the support of object-oriented methods. The results showed that (1) The all features (AF) scheme achieved an overall accuracy (OA) of 97.04% and a Kappa coefficient of 0.98. (2) The proposed MSHC method reached an OA of 97.69% and a Kappa of 0.97. (3) The vegetation in the study area was predominantly grassland (approximately 29.57% of the total area), with Alnus nepalensis being the dominant tree species (16.07% of the area). Overall, this study provides a detailed classification of vegetation types, offering a valuable dataset for ecological monitoring and assessment. The proposed method also presents a transferable and effective approach for the fine-scale vegetation mapping of other open-pit mining environments.https://www.tandfonline.com/doi/10.1080/17538947.2025.2515269Multi-source datamachine learningclassification of tree speciesmulti-scale hierarchical classificationvegetation patterns |
| spellingShingle | Suling He Yuncheng Deng Jinliang Wang Mengjia Luo Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data International Journal of Digital Earth Multi-source data machine learning classification of tree species multi-scale hierarchical classification vegetation patterns |
| title | Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data |
| title_full | Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data |
| title_fullStr | Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data |
| title_full_unstemmed | Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data |
| title_short | Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data |
| title_sort | fine identification of vegetation types in open pit mining regions using combined uav rgb imagery and lidar point cloud data |
| topic | Multi-source data machine learning classification of tree species multi-scale hierarchical classification vegetation patterns |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2515269 |
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