Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys
-
摘要: 潮滩地形与滩涂湿地生态系统的结构和功能密切相关,准确获取高精度的地形数据,对于分析潮滩的冲淤动态和盐沼植被扩散过程具有十分重要的意义。受自然潮滩观测时间有限、观测条件恶劣及植被覆盖等因素影响,传统的潮滩地形监测方法往往存在操作困难、效率较低、成本过高及覆盖范围有限等不足。文章通过无人机低空遥感方法获取航拍影像与其波段信息,基于运动结构技术提取影像三维坐标信息,构建研究区高精度数字表面模型(digital surface model,DSM),利用DSM模型直接获得无植被覆盖的光滩数字高程模型(digital elevation model,DEM); 对于有盐沼植被覆盖的区域,利用红、绿、蓝3个可见光波段信息计算可见光差异植被指数(visible-band difference vegetation index,VDVI),同时结合野外现场调查,获取潮滩盐沼植物株高与VDVI指数的定量关系,建立株高反演模型; 并利用株高反演模型从DSM中滤除植被,准确反演出潮滩植被区的DEM,从而整体获得潮滩地形的反演结果。结果表明,结合无人机低空遥感和现场调查的方法可以较好地实现对潮滩地形的精确反演: 光滩区地形均方根误差为0.07 m,其精度与高精度三维激光扫描仪测量结果接近; 经过植被滤除后,潮滩植被区地形均方根误差下降到0.14 m,数据精度可提升60%,优于传统的点云过滤方法。文章提供了一种基于无人机和现场调查的潮滩地形反演方法,实现了潮滩地形高效、大范围的监测,研究方法可应用到其他类似的潮滩或海岸区域,为海岸带滩涂湿地保护和管理提供重要的技术支撑。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.
-
Key words:
- tidal flat /
- topography /
- UAV /
- vegetation filtering /
- VDVI /
-
-
[1] Kirwan M L, Megonigal J P. Tidal wetland stability in the face of human impacts and sea-level rise[J]. Nature, 2013, 504(7478):53-60.
[2] Duarte C M, Losada I J, Hendriks I E, et al. The role of coastal plant communities for climate change mitigation and adaptation[J]. Nature Climate Change, 2013, 3(11):961-968.
[3] Murray N J, Phinn S R, DeWitt M, et al. The global distribution and trajectory of tidal flats[J]. Nature, 2019, 565(7738):222-225.
[4] Kulawardhana R W, Feagin R A, Popescu S C, et al. The role of elevation,relative sea-level history and vegetation transition in determining carbon distribution in Spartina alterniflora dominated salt marshes[J]. Estuarine,Coastal and Shelf Science, 2015, 154:48-57.
[5] Yuan L, Chen Y, Wang H, et al. Windows of opportunity for salt marsh establishment:The importance for salt marsh restoration in the Yangtze Estuary[J]. Ecosphere, 2020, 11(7):e3180.
[6] Cui L, Ge Z, Yuan L, et al. Vulnerability assessment of the coastal wetlands in the Yangtze Estuary,China to sea-level rise[J]. Estuarine,Coastal and Shelf Science, 2015, 156:42-51.
[7] Bang J H, Bae M, Lee E J. Plant distribution along an elevational gradient in a macrotidal salt marsh on the west coast of Korea[J]. Aquatic Botany, 2018, 147:52-60.
[8] 陈雅慧, 袁琳, 曹浩冰, 等. 基于胁迫梯度假说和互惠理论的海三棱藨草种群恢复技术[J]. 生态学报, 2019, 39(12):4233-4241.
[9] Chen Y H, Yuan L, Cao H B, et al. The stress-gradient hypojournal and facilitation theory-based restoration technique for Scirpus mariqueter population[J]. Acta Ecologica Sinica, 2019, 39(12):4233-4241.
[10] Widdows J, Blauw A, Heip C, et al. Role of physical and biological processes in sediment dynamics of a tidal flat in Westerschelde Estuary,SW Netherlands[J]. Marine Ecology Progress Series, 2004, 274:41-56.
[11] De Backer A, Van Colen C, Vincx M, et al. The role of biophysical interactions within the ijzermonding tidal flat sediment dynamics[J]. Continental Shelf Research, 2010, 30(9):1166-1179.
[12] Hu S, Niu Z, Chen Y, et al. Global wetlands:Potential distribution,wetland loss,and status[J]. Science of the Total Environment, 2017, 586:319-327.
[13] Nelson A, Reuter H, Gessler P. Chapter 3 DEM production methods and sources[J]. Developments in Soil Science, 2009, 33:65-85.
[14] 魏伟, 周云轩, 田波, 等. 基于地面激光扫描的典型海岸带盐沼潮滩地形反演[J]. 吉林大学学报(地球科学版), 2018, 48(6):1889-1897.
[15] Wei W, Zhou Y X, Tian B, et al. Topography retrieval on typical salt marsh of coastal zone based on terrestrial laser scanning[J]. Journal of Jilin University (Earth Science Edition), 2018, 48(6):1889-1897.
[16] Telling J, Lyda A, Hartzell P, et al. Review of earth science research using terrestrial laser scanning[J]. Earth-Science Reviews, 2017, 169:35-68.
[17] Valente D S M, Momin A, Grift T, et al. Accuracy and precision evaluation of two low-cost RTK global navigation satellite systems[J]. Computers and Electronics in Agriculture, 2020, 168:105142.
[18] Mancini F, Dubbini M, Gattelli M, et al. Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography:The structure from motion approach on coastal environments[J]. Remote Sensing, 2013, 5(12):6880-6898.
[19] Reshetyuk Y, Mårtensson S. Generation of highly accurate digital elevation models with unmanned aerial vehicles[J]. The Photogrammetric Record, 2016, 31(154):143-165.
[20] Wijesingha J, Moeckel T, Hensgen F, et al. Evaluation of 3D point cloud-based models for the prediction of grassland biomass[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 78:352-359.
[21] Westoby M J, Brasington J, Glasser N F, et al. ‘Structure-from-Motion’ photogrammetry:A low-cost,effective tool for geoscience applications[J]. Geomorphology, 2012, 179:300-314.
[22] Han X, Thomasson J A, Bagnall G C, et al. Measurement and calibration of plant-height from fixed-wing UAV images[J]. Sensors, 2018, 18(12):4092.
[23] Kalacska M, Chmura G L, Lucanus O, et al. Structure from motion will revolutionize analyses of tidal wetland landscapes[J]. Remote Sensing of Environment, 2017, 199:14-24.
[24] Dai W, Li H, Zhou Z, et al. UAV photogrammetry for elevation monitoring of intertidal mudflats[J]. Journal of Coastal Research, 2018, 85(85):236-240.
[25] 谢卫明, 何青, 章可奇, 等. 三维激光扫描系统在潮滩地貌研究中的应用[J]. 泥沙研究, 2015, 48(6):1889-1897.
[26] Xie W M, He Q, Zhang K Q, et al. Application of the terrestrial laser scanner to measuring geomorphology in tidal flats and salt marshes[J]. Journal of Sediment Research, 2015, 48(6):1889-1897.
[27] Zhao Z Y, Yuan L, Li W, et al. Re-invasion of Spartina alterniflora in restored saltmarshes:Seed arrival,retention,germination,and establishment[J]. Journal of Environmental Management, 2020, 266:110631.
[28] 汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报, 2015, 31(5):152-159.
[29] Wang X Q, Wang M M, Wang S Q, et al. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the Chinese Society Agricultural Engineering, 2015, 31(5):152-159.
[30] Meneses N C, Baier S, Reidelstürz P, et al. Modelling heights of sparse aquatic reed (Phragmites australis) using structure from motion point clouds derived from rotary- and fixed-wing unmanned aerial vehicle (UAV) data[J]. Limnologica, 2018, 72:10-21.
[31] Fonstad M A, Dietrich J T, Courville B C, et al. Topographic structure from motion:A new development in photogrammetric measurement[J]. Earth Surface Processes and Landforms, 2013, 38(4):421-430.
[32] 戴玮琦, 李欢, 龚政, 等. 无人机技术在潮滩地貌演变研究中的应用[J]. 水科学进展, 2019, 30(3):359-372.
[33] Dai W Q, Li H, Gong Z, et al. Application of unmanned aerial vehicle technology in geomorphological evolution of tidal flat[J]. Advances in Water Science, 2019, 30(3):359-372.
[34] Bendig J, Yu K, Aasen H, et al. Combining UAV-based plant height from crop surface models,visible,and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39:79-87.
[35] Rasmussen J, Ntakos G, Nielsen J, et al. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?[J]. European Journal of Agronomy, 2016, 74:75-92.
[36] Tian J, Wang L, Li X, et al. Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 61:22-31.
-
计量
- 文章访问数: 884
- PDF下载数: 130
- 施引文献: 0
下载: