A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data
The dune density is an important parameter for representing the characteristics of desert geomorphology, providing a precise depiction of the undulating topography of the desert. Owing to the limitations of estimation methods and data availability, accurately quantifying dune density has posed a sig...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/297 |
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| author | Mingyu Wang Yongqiang Liu Huoqing Li Minzhong Wang Wen Huo Zonghui Liu |
| author_facet | Mingyu Wang Yongqiang Liu Huoqing Li Minzhong Wang Wen Huo Zonghui Liu |
| author_sort | Mingyu Wang |
| collection | DOAJ |
| description | The dune density is an important parameter for representing the characteristics of desert geomorphology, providing a precise depiction of the undulating topography of the desert. Owing to the limitations of estimation methods and data availability, accurately quantifying dune density has posed a significant challenge; in response to this issue, we propose an innovative model to estimate dune density using a dune vertex search combined with four-directional orographic spectral decomposition. This study reveals several key insights: (1) Taklimakan Desert distributes approximately 5.31 × 10<sup>7</sup> dunes, with a linear regression fit <i>R</i><sup>2</sup> of 0.79 between the estimated and observed values. The average absolute error and root mean square error are calculated as 25.61 n/km<sup>2</sup> and 30.48 n/km<sup>2</sup>, respectively. (2) The distribution of dune density across the eastern, northeastern, southern, and western parts of the Taklimakan Desert is relatively lower, while there is higher dune density in the central and northern areas. (3) The observation data constructed using the improved YOLOv8s algorithm and remote sensing imagery effectively validate the estimation results of dune density. The new algorithm demonstrates a high level of accuracy in estimating sand dune density, thereby providing crucial parameters for sub-grid orographic parameterization in desert regions. Additionally, its application potential in dust modeling appears promising. |
| format | Article |
| id | doaj-art-52719b57895141f68097bd5edf584b63 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-52719b57895141f68097bd5edf584b632025-01-24T13:48:02ZengMDPI AGRemote Sensing2072-42922025-01-0117229710.3390/rs17020297A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing DataMingyu Wang0Yongqiang Liu1Huoqing Li2Minzhong Wang3Wen Huo4Zonghui Liu5Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaThe dune density is an important parameter for representing the characteristics of desert geomorphology, providing a precise depiction of the undulating topography of the desert. Owing to the limitations of estimation methods and data availability, accurately quantifying dune density has posed a significant challenge; in response to this issue, we propose an innovative model to estimate dune density using a dune vertex search combined with four-directional orographic spectral decomposition. This study reveals several key insights: (1) Taklimakan Desert distributes approximately 5.31 × 10<sup>7</sup> dunes, with a linear regression fit <i>R</i><sup>2</sup> of 0.79 between the estimated and observed values. The average absolute error and root mean square error are calculated as 25.61 n/km<sup>2</sup> and 30.48 n/km<sup>2</sup>, respectively. (2) The distribution of dune density across the eastern, northeastern, southern, and western parts of the Taklimakan Desert is relatively lower, while there is higher dune density in the central and northern areas. (3) The observation data constructed using the improved YOLOv8s algorithm and remote sensing imagery effectively validate the estimation results of dune density. The new algorithm demonstrates a high level of accuracy in estimating sand dune density, thereby providing crucial parameters for sub-grid orographic parameterization in desert regions. Additionally, its application potential in dust modeling appears promising.https://www.mdpi.com/2072-4292/17/2/297dune densityremote sensingYOLOv8sTaklimakan Desert |
| spellingShingle | Mingyu Wang Yongqiang Liu Huoqing Li Minzhong Wang Wen Huo Zonghui Liu A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data Remote Sensing dune density remote sensing YOLOv8s Taklimakan Desert |
| title | A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data |
| title_full | A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data |
| title_fullStr | A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data |
| title_full_unstemmed | A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data |
| title_short | A Novel Algorithm for Estimating the Sand Dune Density of the Taklimakan Desert Based on Remote Sensing Data |
| title_sort | novel algorithm for estimating the sand dune density of the taklimakan desert based on remote sensing data |
| topic | dune density remote sensing YOLOv8s Taklimakan Desert |
| url | https://www.mdpi.com/2072-4292/17/2/297 |
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