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  • MDPI AG  (3)
  • Lu, Weiqiang  (3)
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  • MDPI AG  (3)
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  • 1
    Online Resource
    Online Resource
    Research on Intelligent Crack Detection in a Deep-Cut Canal Slope in the Chinese South–North Water Transfer Project
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 21 ( 2022-10-27), p. 5384-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 21 ( 2022-10-27), p. 5384-
    Abstract: The Chinese South–North Water Transfer Project is an important project to improve the freshwater supply environment in the Chinese interior and greatly alleviates the water shortage in the Chinese North China Plain; its sustainable, healthy, and safe operation guarantees ecological protection and economic development. However, due to the special expansive soil and deep excavation structure, the first section of the South–North Water Transfer Project canal faces serious disease risk directly manifested by cracks in the slope of the canal. Currently, relying on manual inspection not only consumes a lot of human resources but also unnecessarily repeats and misses many inspection areas. In this paper, a monitoring method combining depth learning and Uncrewed Aerial Vehicle (UAV) high-definition remote sensing is proposed, which can detect the cracks of the channel slope in time and accurately and can be used for long-term health inspection of the South–North Water Transfer Project. The main contributions are as follows: (1) aiming at the need to identify small cracks in reinforced channels, a ground-imitating UAV that can obtain super-clear resolution remote-sensing images is introduced to identify small cracks on a complex slope background; (2) to identify fine cracks in massive images, a channel crack image dataset is constructed, and deep-learning methods are introduced for the intelligent batch identification of massive image data; (3) to provide the geolocation of crack-extraction results, a fast field positioning method for non-modeled data combined with navigation information is investigated. The experimental results show that the method can achieve a 92.68% recall rate and a 97.58% accuracy rate for detecting cracks in the Chinese South–North Water Transfer Project channel slopes. The maximum positioning accuracy of the method is 0.6 m, and the root mean square error is 0.21 m. It provides a new technical means for geological risk identification and health assessment of the South–North Water Transfer Central Project.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14215384
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Research on Soil Moisture Inversion Method for Canal Slope of the Middle Route Project of the South to North Water Transfer Based on GNSS-R and Deep Learning
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 17 ( 2023-09-03), p. 4340-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 17 ( 2023-09-03), p. 4340-
    Abstract: The soil moisture from the South-to-North Water Diversion Middle Route Project is assessed in this study. Complex and variable geological conditions complicate the prediction of soil moisture in the study area. To achieve this aim, we carried out research on soil moisture inversion methods for channel slopes in the study area using massive monitoring data from multiple GNSS observatories on channel slopes, incorporating GNSS-R techniques and deep learning algorithms. To address the issue of low accuracy in linear inversion when using a single satellite, this study proposes a multi-satellite and multi-frequency data fusion technique. Furthermore, three soil moisture inversion models, namely, the linear model, BP neural network model, and GA-BP neural network model, are established by incorporating deep learning techniques. In comparison with single-satellite data inversion, with the data fusion technique proposed in this study, the correlation is improved by 12.7%, the root mean square error is reduced by 0.217, the mean square error is decreased by 0.884, and the mean absolute error is decreased by 0.243 with the linear model. With the BP neural network model, the correlation is increased by 15.4%, the root mean square error is decreased by 0.395, the mean square error is decreased by 0.465, and the mean absolute error is reduced by 0.353. Moreover, with the GA-BP neural network model, the correlation is improved by 6.3%, the root mean square error is decreased by 1.207, the mean square error is decreased by 0.196, and the mean absolute error is reduced by 0.155. The results indicate that performing data fusion by using multiple satellites and multi-frequency bands is a feasible approach for improving the accuracy of soil moisture inversion. These research findings provide new technical means for the risk analysis of deformation disasters in the expansive soil channel slopes of the South-to-North Water Diversion Middle Route Project.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15174340
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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    Online Resource
  • 3
    Online Resource
    Online Resource
    TerraSAR-X and GNSS Data for Deformation Detection and Mechanism Analysis of a Deep Excavation Channel Section of the China South–North Water-Diversion Project
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 15 ( 2023-07-29), p. 3777-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 15 ( 2023-07-29), p. 3777-
    Abstract: Due to expansive soils and high slopes, the deep excavated channel section of the China South–North Water-Diversion Middle-Route Project has a certain risk of landslide disaster. Therefore, examining the deformation law and mechanism of the channel slope in the middle-route section of the project is an extreme necessity for safe operation. However, the outdated monitoring method limits research on the surface deformation law and mechanism of the entire deep excavation channel section. For these reasons, we introduced a novel approach that combines SBAS-InSAR and GNSS, enabling the surface domain monitoring of the study area at a regional scale as well as real-time monitoring of specific target regions. By using SBAS-InSAR technology and leveraging 11-view high-resolution TerraSAR-X data, we revealed the spatiotemporal evolution law of surface deformations in the channel slopes within the study area. The results demonstrate that the predominant deformation in the study area was uplifted, with limited evidence of subsidence deformation. Moreover, there is a distinct region of significant uplift deformation, with the highest annual uplift rate reaching 19 mm/y. Incorporating GNSS and soil-moisture-monitoring timeseries data, we conducted a study on the correlation between soil moisture and the three-dimensional deformation of the ground surface, revealing a positive correlation between the soil moisture content and vertical displacement of the channel slope. Furthermore, combining field investigations on surface uplift deformation characteristics, we identified that the main cause of surface deformation in the study area was attributed to the expansion of the soil due to water absorption in expansive soils. The research results not only revealed the spatiotemporal evolution law and mechanism of the channel slope deformation in the studied section of the deep excavation channel but also provide successful guidance for the prevention and control of channel slope-deformation disasters in the study area. Furthermore, they offer effective technical means for the safe monitoring of the entire South–North Water-Diversion Middle-Route Project and similar long-distance water-conveyance canal projects.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15153777
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
    Location Call Number Limitation Availability
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    Online Resource
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