A deep learning framework to map riverbed sand mining budgets in large tropical deltas
Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited,...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2023.2285178 |
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| author | Sonu Kumar Edward Park Dung Duc Tran Jingyu Wang Huu Loc Ho Lian Feng Sameh A. Kantoush Doan Van Binh Dongfeng Li Adam D. Switzer |
| author_facet | Sonu Kumar Edward Park Dung Duc Tran Jingyu Wang Huu Loc Ho Lian Feng Sameh A. Kantoush Doan Van Binh Dongfeng Li Adam D. Switzer |
| author_sort | Sonu Kumar |
| collection | DOAJ |
| description | Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited, primarily due to insufficient data on sand extraction rates. Conventionally, bathymetry surveys and compilation of declared amounts have been used to quantify SM budgets, but they are often costly and laborious, or result in inaccurate quantification. Here, for the first time, we developed a Remote Sensing (RS)-based Deep Learning (DL) framework to map SM activities and budgets in the Vietnamese Mekong Delta (VMD), a global SM hotspot. We trained a near real-time object detection system to identify three boat classes in Sentinel-1 imagery: Barge with Crane (BC), Sand Transport Boat (STB), and other boats. Our DL model achieved a 96.1% Mean Average Precision (mAP) across all classes and 98.4% for the BC class, used in creating an SM boat density map at an Intersection over Union (IoU) threshold of 0.50. Applying this model to Sentinel-1, 256,647 boats were detected in the VMD between 2014–2022, of which 17.4% were BC. Subsequently, the annual SM budget was estimated by correlating it with a recent riverbed incision map. Our results showed that, between 2015–2022, about 366 Mm3 of sand has been extracted across the VMD. The annual budget has progressively increased from 34.92 Mm3 in 2015 to 53.25 Mm3 in 2022 (by 52%), with an annual increment of around 2.79 Mm3. At the provincial-scale, Dong Thap, An Giang, Vinh Long, Tien Giang, and Can Tho were the locations of intensive mining, accounting for 89.20% of the total extracted volume in the VMD. Finally, our estimated budgets were validated with previous research that yielded a correlation coefficient of 0.99% (with bias of 2.65%). The automatic DL framework developed in this study to quantify SM budgets has a high potential to be applied to other deltas worldwide also facing intensive SM. |
| format | Article |
| id | doaj-art-7d0c7e7c86564f38aa7dcc28e4453756 |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | GIScience & Remote Sensing |
| spelling | doaj-art-7d0c7e7c86564f38aa7dcc28e44537562024-12-06T13:51:50ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2023.2285178A deep learning framework to map riverbed sand mining budgets in large tropical deltasSonu Kumar0Edward Park1Dung Duc Tran2Jingyu Wang3Huu Loc Ho4Lian Feng5Sameh A. Kantoush6Doan Van Binh7Dongfeng Li8Adam D. Switzer9National Institute of Education, Nanyang Technological University, Singapore, SingaporeNational Institute of Education, Nanyang Technological University, Singapore, SingaporeNational Institute of Education, Nanyang Technological University, Singapore, SingaporeNational Institute of Education, Nanyang Technological University, Singapore, SingaporeWater Systems and Global Change Group, Wageningen University and Research, Wageningen, The NetherlandsSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, ChinaDisaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, JapanMaster Program in Water Technology, Reuse and Management, Vietnamese-German University, Bến Cát, Binh Duong Province, Viet NamKey Laboratory for Water and Sediment Sciences, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing, ChinaEarth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University, Singapore, SingaporeRapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our understanding of the geomorphic and social-ecological impacts arising from Sand Mining (SM) remains limited, primarily due to insufficient data on sand extraction rates. Conventionally, bathymetry surveys and compilation of declared amounts have been used to quantify SM budgets, but they are often costly and laborious, or result in inaccurate quantification. Here, for the first time, we developed a Remote Sensing (RS)-based Deep Learning (DL) framework to map SM activities and budgets in the Vietnamese Mekong Delta (VMD), a global SM hotspot. We trained a near real-time object detection system to identify three boat classes in Sentinel-1 imagery: Barge with Crane (BC), Sand Transport Boat (STB), and other boats. Our DL model achieved a 96.1% Mean Average Precision (mAP) across all classes and 98.4% for the BC class, used in creating an SM boat density map at an Intersection over Union (IoU) threshold of 0.50. Applying this model to Sentinel-1, 256,647 boats were detected in the VMD between 2014–2022, of which 17.4% were BC. Subsequently, the annual SM budget was estimated by correlating it with a recent riverbed incision map. Our results showed that, between 2015–2022, about 366 Mm3 of sand has been extracted across the VMD. The annual budget has progressively increased from 34.92 Mm3 in 2015 to 53.25 Mm3 in 2022 (by 52%), with an annual increment of around 2.79 Mm3. At the provincial-scale, Dong Thap, An Giang, Vinh Long, Tien Giang, and Can Tho were the locations of intensive mining, accounting for 89.20% of the total extracted volume in the VMD. Finally, our estimated budgets were validated with previous research that yielded a correlation coefficient of 0.99% (with bias of 2.65%). The automatic DL framework developed in this study to quantify SM budgets has a high potential to be applied to other deltas worldwide also facing intensive SM.https://www.tandfonline.com/doi/10.1080/15481603.2023.2285178Deep learningsand miningriverbed incisionMekong Deltaremote sensing |
| spellingShingle | Sonu Kumar Edward Park Dung Duc Tran Jingyu Wang Huu Loc Ho Lian Feng Sameh A. Kantoush Doan Van Binh Dongfeng Li Adam D. Switzer A deep learning framework to map riverbed sand mining budgets in large tropical deltas GIScience & Remote Sensing Deep learning sand mining riverbed incision Mekong Delta remote sensing |
| title | A deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| title_full | A deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| title_fullStr | A deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| title_full_unstemmed | A deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| title_short | A deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| title_sort | deep learning framework to map riverbed sand mining budgets in large tropical deltas |
| topic | Deep learning sand mining riverbed incision Mekong Delta remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2023.2285178 |
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