Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET

被引:7
|
作者
Pardo-Pascual, J. E. [1 ]
Almonacid-Caballer, J. [1 ]
Cabezas-Rabadan, C. [1 ,2 ]
Fernandez-Sarria, A. [1 ]
Armaroli, C. [3 ]
Ciavola, P. [4 ,5 ]
Montes, J. [4 ,6 ]
Souto-Ceccon, P. E. [4 ]
Palomar-Vazquez, J. [1 ]
机构
[1] Univ Politecn Valencia, Geoenvironm Cartog & Remote Sensing Grp CGAT UPV, Dept Cartog Engn Geodesy & Photogrammetry, Cami Vera S-N, Valencia 46022, Spain
[2] Univ Bordeaux, CNRS, Bordeaux INP, EPOC,UMR 5805, F-33600 Pessac, France
[3] Univ Bologna, Alma Mater Studiorum, Dept Biol Geol & Environm Sci, Bologna, Italy
[4] Univ Ferrara, Dept Phys & Earth Sci, Ferrara, Italy
[5] Consorzio Futuro Ric, Ferrara, Italy
[6] Univ Cadiz, Dept Earth Sci, Int Campus Excellence Sea CEI MAR, Cadiz, Spain
基金
欧盟地平线“2020”;
关键词
DIFFERENCE WATER INDEX; SURFACE-WATER; BEACH; DELTA; TOOLS; NDWI;
D O I
10.1016/j.coastaleng.2023.104426
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The definition of the shoreline position from satellite imagery is of great interest among coastal monitoring techniques. Understanding the reality mapped by the resulting shorelines and defining their accuracy is of paramount importance. The assessment described in this paper constitutes a validation of the shorelines ob-tained by using the novel tool SAET (Shoreline Analysis and Extraction Tool) for automatic shoreline extraction. The resulting shorelines applying the different parameters available in SAET are assessed in 9 test sites with diverse morphology and oceanographic conditions along the Atlantic European and Western Medi-terranean coasts. The reference data is obtained along large coastal segments (covering up to about 240 km) from nearly coincident very high-resolution satellite images. Different image processing levels and extraction methods have been tested, showing their key role in the accuracy of shoreline position. When defining the approximate shoreline position the Automated Water Extraction Index for images without shadows (AWEInsh) with a 0 threshold generally constitutes the best segmentation method. In turn, the employment of the mathematical morphological operations of dilation or erosion considerably improves the results in certain coastal typologies. On the contrary, the employment of atmospherically-corrected images has a smaller influence on the accuracy of the SDSs. Results support the idea that the magnitude of the errors is strongly related to the specific coastal conditions-In general, the lowest errors appear in low-energetic microtidal sites, contrary to the energetic and mesotidal coasts with gentle slopes. The shoreline errors range between 3.7 m and 13.5 m RMSE (root-mean-square error) among the different coastal types when selecting the most appropriate extraction parameters. The shoreline position identified with SAET shows a similar or better accuracy to that obtained by other tools.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Sentinel-2 Satellite Imagery for Agronomic and Quality Variability Assessment of Pistachio (Pistacia vera L.)
    Barajas, Enrique
    Alvarez, Sara
    Fernandez, Elena
    Velez, Sergio
    Rubio, Jose Antonio
    Martin, Hugo
    SUSTAINABILITY, 2020, 12 (20) : 1 - 12
  • [42] Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal
    Gerardo, Romeu
    de Lima, Isabel P.
    AGRICULTURE-BASEL, 2022, 12 (09):
  • [43] Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery
    Christina Corbane
    Vasileios Syrris
    Filip Sabo
    Panagiotis Politis
    Michele Melchiorri
    Martino Pesaresi
    Pierre Soille
    Thomas Kemper
    Neural Computing and Applications, 2021, 33 : 6697 - 6720
  • [44] Medium-resolution Dynamic Habitat Indices from Landsat and Sentinel-2 satellite imagery
    Razenkova, Elena
    Lewinska, Katarzyna E.
    Anand, Akash
    Yin, He
    Farwell, Laura S.
    Pidgeon, Anna M.
    Hostert, Patrick
    Coops, Nicholas C.
    Radeloff, Volker C.
    ECOLOGICAL INDICATORS, 2025, 173
  • [45] Seasonal and Spatial Variability in Patchiness of Microphytobenthos on Intertidal Flats From Sentinel-2 Satellite Imagery
    Daggers, Tisja D.
    Herman, Peter M. J.
    van der Wal, Daphne
    FRONTIERS IN MARINE SCIENCE, 2020, 7
  • [46] Characterizing the Relationship between the Sediment Grain Size and the Shoreline Variability Defined from Sentinel-2 Derived Shorelines
    Cabezas-Rabadan, Carlos
    Pardo-Pascual, Josep E.
    Palomar-Vazquez, Jesus
    REMOTE SENSING, 2021, 13 (14)
  • [47] Stand density estimation based on fractional vegetation coverage from Sentinel-2 satellite imagery
    Zhang, Zhichao
    Dong, Xinyu
    Tian, Jia
    Tian, Qingjiu
    Xi, Yanbiao
    He, Dong
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 108
  • [48] ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
    Giacco, Giovanni
    Marrone, Stefano
    Langella, Giuliano
    Sansone, Carlo
    FUTURE INTERNET, 2022, 14 (10)
  • [49] Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery
    Huryna, Hanna
    Cohen, Yafit
    Karnieli, Arnon
    Panov, Natalya
    Kustas, William P.
    Agam, Nurit
    REMOTE SENSING, 2019, 11 (19)
  • [50] Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery
    Corbane, Christina
    Syrris, Vasileios
    Sabo, Filip
    Politis, Panagiotis
    Melchiorri, Michele
    Pesaresi, Martino
    Soille, Pierre
    Kemper, Thomas
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (12): : 6697 - 6720