Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

  1. Luo, Weidong 12
  2. Gan, Shu 12
  3. Yuan, Xiping 23
  4. Gao, Sha 12
  5. Bi, Rui 12
  6. Chen, Cheng 12
  7. He, Wenbin 12
  8. Hu, Lin 12
  9. González Aguilera, Diego 4
  10. Rodríguez-Gonzálvez, Pablo
  1. 1 School of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
  2. 2 Plication Engineering Research Center of Spatial Information Surveying, Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunming 650093, China
  3. 3 Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities, West Yunnan University of Applied Sciences, Dali 671006, China
  4. 4 Universidad de Salamanca
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    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2023

Volumen: 7

Número: 5

Páginas: 298

Tipo: Artículo

DOI: 10.3390/DRONES7050298 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Resumen

As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

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Financiadores

  • National Natural Science Foundation of China
    • 62266026

Referencias bibliográficas

  • Uzkeda, (2022), J. Struct. Geol., 157, pp. 104568, 10.1016/j.jsg.2022.104568
  • He, H., Ye, H., Xu, C., and Liao, X. (2022). Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China. ISPRS Int. J. Geo-Inf., 11.
  • Hussain, Y., Schlogel, R., Innocenti, A., Hamza, O., Iannucci, R., Martino, S., and Havenith, H.B. (2022). Review on the Geophysical and UAV-Based Methods Applied to Landslides. Remote Sens., 14.
  • Ilinca, (2022), Landslides, 19, pp. 1717, 10.1007/s10346-022-01877-9
  • Marques, (2022), IEEE Access, 10, pp. 20514, 10.1109/ACCESS.2022.3151897
  • Cho, (2022), KSCE J. Civ. Eng., 26, pp. 1904, 10.1007/s12205-021-1374-1
  • Vollgger, (2016), J. Struct. Geol., 85, pp. 168, 10.1016/j.jsg.2016.02.012
  • Gomez, C., Setiawan, M.A., Listyaningrum, N., Wibowo, S.B., Suryanto, W., Darmawan, H., Bradak, B., Daikai, R., Sunardi, S., and Prasetyo, Y. (2022). LiDAR and UAV SfM-MVS of Merapi Volcanic Dome and Crater Rim Change from 2012 to 2014. Remote Sens., 14.
  • Vecchi, E., Tavasci, L., De Nigris, N., and Gandolfi, S. (2021). GNSS and Photogrammetric UAV Derived Data for Coastal Monitoring: A Case of Study in Emilia-Romagna, Italy. J. Mar. Sci. Eng., 9.
  • Qian, (2019), J. Desert Res., 39, pp. 18
  • Zhang, (2018), J. Remote Sens., 22, pp. 185
  • Gao, (2017), Seismol. Geol., 39, pp. 793
  • Clapuyt, (2016), Geomorphology, 260, pp. 4, 10.1016/j.geomorph.2015.05.011
  • Yu, J.J., Kim, D.W., Lee, E.J., and Son, S.W. (2020). Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models. Drones, 4.
  • Gao, S., Gan, S., Yuan, X.P., Bi, R., Li, R.B., Hu, L., and Luo, W.D. (2021). Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface. Processes, 10.
  • Barba, S., Barbarella, M., Di Benedetto, A., Fiani, M., Gujski, L., and Limongiello, M. (2019). Accuracy Assessment of 3D Photogrammetric Models from an Unmanned Aerial Vehicle. Drones, 3.
  • Farella, E.M., Torresani, A., and Remondino, F. (2020). Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures. Remote Sens., 12.
  • Mousavi, V., Varshosaz, M., Rashidi, M., and Li, W.L. (2022). A New Multi-Criteria Tie Point Filtering Approach to Increase the Accuracy of UAV Photogrammetry Models. Drones, 6.
  • Xi, (2011), Bull. Surv. Mapp., 4, pp. 23
  • Wang, (2022), Bull. Surv. Mapp., 2, pp. 20
  • Huang, H., Ye, Z.H., Zhang, C., Yue, Y., Cui, C.Y., and Hammad, A. (2022). Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space. Remote Sens., 14.
  • James, (2017), Earth Surf. Process. Landf., 42, pp. 1769, 10.1002/esp.4125
  • Yan, L., Fei, L., Chen, C.H., Ye, Z.Y., and Zhu, R.X. (2016). A Multi-View Dense Image Matching Method for High-Resolution Aerial Imagery Based on a Graph Network. Remote Sens., 8.
  • Dong, (2019), J. China Univ. Min. Technol., 48, pp. 459
  • Gao, (2021), Bull. Geol. Sci. Technol., 40, pp. 283
  • Keshtkar, (2017), Arab. J. Geosci., 10, pp. 1813, 10.1007/s12517-017-2899-y
  • Ai, (2020), Int. J. Remote Sens., 41, pp. 1813, 10.1080/01431161.2019.1675324
  • Fang, (2013), Geomat. Inf. Sci. Wuhan Univ., 38, pp. 1353
  • Barnhart, (2013), Remote Sens., 5, pp. 2813, 10.3390/rs5062813
  • Lague, (2013), ISPRS J. Photogramm. Remote Sens., 82, pp. 10, 10.1016/j.isprsjprs.2013.04.009
  • Gong, (2015), Spectrosc. Spectr. Anal., 35, pp. 1325
  • Mao, (2017), Trans. Chin. Soc. Agric. Mach., 48, pp. 152
  • DiFrancesco, P.M., Bonneau, D., and Hutchinson, D.J. (2020). The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds. Remote Sens., 12.
  • Mahe, (2020), Water, 12, pp. 2478
  • Zhou, (2008), J. Wuhan Univ. Technol., 2, pp. 172
  • Zhou, (2021), Geomat. Inf. Sci. Wuhan Univ., 46, pp. 1186