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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 80-88     DOI: 10.6046/zrzyyg.2020336
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Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys
LI Yang1(), YUAN Lin1,2(), ZHAO Zhiyuan1, ZHANG Jinlei1, WANG Xianye1, ZHANG Liquan1
1. State Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China Normal University,Shanghai 200241, China
2. Yangtze Delta Estuarine Wetland Ecosystem Observation and Research Station (Ministry of Education & Shanghai Science and Technology Committee), Shanghai 202162, China
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Abstract  

Tidal flat topography is closely related to the structure and function of the ecosystem in intertidal wetlands. Therefore, it is significant for the analyses of tidal flat dynamics and the monitoring of the diffusion process of saltmarsh vegetation to obtain high-precision topography data. However, owing to limited ebb time, muddy tidal flats, and saltmarsh vegetation, traditional geographic observation techniques suffer the shortcomings such as low accuracy and efficiency, high cost, and limited coverage. In this study, unmanned aerial vehicle (UAV) low-altitude remote sensing was employed to obtain aerial images and their band information. Then the 3D and spectral information with precise coordinates were extracted based on the structure obtained using motion technology. They were used to construct a high-precision digital surface model (DSM) of the study area. The DSM of bare flats can be directly used as the digital elevation model (DEM) of the tidal flat. In the areas with saltmarsh vegetation, the information of red, green, and blue bands was used to calculate the visible-band vegetation index (VDVI), which was combined with field surveys to build an inversion model for vegetation height. Finally, vegetation was filtered out from the DSM using the height inversion model to obtain accurate DEM. In this way, the elevation of the vegetation zone in the tidal flat can be reflected. As indicated by the results of this study, the method that combines UAV low-altitude remote sensing with field surveys can realize precise inversion of tidal flat topography. The root mean square error (RMSE) of the topography in bare flat obtained using the method was 0.07 m and the accuracy was close to the terrestrial laser scanner (TLS). For areas with saltmarsh vegetation, the RMSE was reduced to 0.14 m and the geographical accuracy can be improved by 60% after the vegetation was filtered out. Therefore, the method is superior to traditional point cloud filtering. Overall, this study provided an inversion method of tidal flat topography based on UAV remote sensing and field surveys, which can effectively monitor large-scale natural tidal flat systems. The method can be applied to other similar natural tidal flat systems or coastal areas, providing important technological support for the protection and management of coastal tidal flat wetlands.

Keywords tidal flat      topography      UAV      vegetation filtering      VDVI     
ZTFLH:  TP79  
Corresponding Authors: YUAN Lin     E-mail: bigliyang123@126.com;lyuan@sklec.ecnu.edu.cn
Issue Date: 24 September 2021
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Yang LI
Lin YUAN
Zhiyuan ZHAO
Jinlei ZHANG
Xianye WANG
Liquan ZHANG
Cite this article:   
Yang LI,Lin YUAN,Zhiyuan ZHAO, et al. Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys[J]. Remote Sensing for Natural Resources, 2021, 33(3): 80-88.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020336     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/80
Fig.1  Location of Chongming Dongtan
Fig.2  Location of study area
Fig.3  UAV oblique photography picture in study area
Fig.4  UAV orthophoto picture of saltmarsh plants
Fig.5  UAV orthophoto picture of bare flat
Fig.6  Location of sampling points distribution
Fig.7  Flow chart of tidal flat topographic reconstruction
Fig.8  VDVI inversion model of plant height
Fig.9  VDVI distribution of saltmarsh in study area
Fig.10  Saltmarsh height distribution in study area
Fig.11  Tidal flat topography after filtering vegetation
Fig.12  Comparison between elevation of UAV and RTK before and after vegetation filtering
生境类型 UAV扫描 植被滤除
光滩区 0.07
植被区 0.33 0.14
整体平均 0.28 0.12
Tab.1  Terrain error of inversion before and after vegetation filtering in different habitats(m)
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