Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping
The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, appr...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/5/793 |
| _version_ | 1819294259935707136 |
|---|---|
| author | Hu Ding Kai Liu Xiaozheng Chen Liyang Xiong Guoan Tang Fang Qiu Josef Strobl |
| author_facet | Hu Ding Kai Liu Xiaozheng Chen Liyang Xiong Guoan Tang Fang Qiu Josef Strobl |
| author_sort | Hu Ding |
| collection | DOAJ |
| description | The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran’s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (<i>ANI</i>), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process. |
| first_indexed | 2024-12-24T04:23:29Z |
| format | Article |
| id | doaj.art-e72cc776afb4488db9c56bf050f84208 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:23:29Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e72cc776afb4488db9c56bf050f842082022-12-21T17:15:43ZengMDPI AGRemote Sensing2072-42922020-03-0112579310.3390/rs12050793rs12050793Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully MappingHu Ding0Kai Liu1Xiaozheng Chen2Liyang Xiong3Guoan Tang4Fang Qiu5Josef Strobl6Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, 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, ChinaDepartment of Geospatial Science, University of Texas at Dallas, Richardson, TX 75080-3021, USADepartment of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, AustriaThe Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran’s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (<i>ANI</i>), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.https://www.mdpi.com/2072-4292/12/5/793object-based image analysisgully mappingsegmentation optimizationunmanned aerial vehicle (uav)xgboost |
| spellingShingle | Hu Ding Kai Liu Xiaozheng Chen Liyang Xiong Guoan Tang Fang Qiu Josef Strobl Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping Remote Sensing object-based image analysis gully mapping segmentation optimization unmanned aerial vehicle (uav) xgboost |
| title | Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping |
| title_full | Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping |
| title_fullStr | Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping |
| title_full_unstemmed | Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping |
| title_short | Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping |
| title_sort | optimized segmentation based on the weighted aggregation method for loess bank gully mapping |
| topic | object-based image analysis gully mapping segmentation optimization unmanned aerial vehicle (uav) xgboost |
| url | https://www.mdpi.com/2072-4292/12/5/793 |
| work_keys_str_mv | AT huding optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT kailiu optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT xiaozhengchen optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT liyangxiong optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT guoantang optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT fangqiu optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping AT josefstrobl optimizedsegmentationbasedontheweightedaggregationmethodforloessbankgullymapping |