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
  • [31] Automation of Surface Karst Assessment Using Sentinel‑2 Satellite Imagery
    E. V. Drobinina
    Cosmic Research, 2023, 61 : S173 - S181
  • [32] Monitoring Riparian Vegetation in Urban Areas With Sentinel-2 Satellite Imagery
    Hislop, Samuel
    Soto-Berelov, Mariela
    Jellinek, Sacha
    Chee, Yung En
    Jones, Simon
    ECOLOGICAL MANAGEMENT & RESTORATION, 2025, 26 (01)
  • [33] Application of gradient boosting machine in satellite-derived bathymetry using Sentinel-2 data for accurate water depth estimation in coastal environments
    Liu, Yue
    Wu, Shulei
    Wu, Zhongqiang
    Zhou, Shuangshuang
    JOURNAL OF SEA RESEARCH, 2024, 201
  • [34] Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery
    Erinjery, Joseph J.
    Singh, Mewa
    Kent, Rafi
    REMOTE SENSING OF ENVIRONMENT, 2018, 216 : 345 - 354
  • [35] Extracting Citrus-Growing Regions by Multiscale UNet Using Sentinel-2 Satellite Imagery
    Li, Yong
    Liu, Wenjing
    Ge, Ying
    Yuan, Sai
    Zhang, Tingxuan
    Liu, Xiuhui
    REMOTE SENSING, 2024, 16 (01)
  • [36] ESTIMATION OF EVAPOTRANSPIRATION OF A VINEYARD OF TABLE GRAPES (Vitis vinifera) USING SENTINEL-2 SATELLITE IMAGERY
    Manuel Salvador-Castillo, Jose
    Alejandro Bolanos-Gonzalez, Martin
    Cesar Rodriguez, Julio
    Palacios-Velez, Enrique
    Alberto Palacios-Sanchez, Luis
    Watts, Christopher
    Lizarraga-Celaya, Carlos
    Ortega-Farias, Samuel
    Er-Raki, Salah
    AGROCIENCIA, 2021, 55 (05) : 369 - 387
  • [37] A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
    Mavraeidopoulos, Athanasios K.
    Oikonomou, Emmanouil
    Palikaris, Athanasios
    Poulos, Serafeim
    REMOTE SENSING, 2019, 11 (23)
  • [38] Index-Based Identification of Surface Water Resources Using Sentinel-2 Satellite Imagery
    Sekertekin, Aliihsan
    Cicekli, Sevim Yasemin
    Arslan, Niyazi
    2018 2ND INTERNATIONAL SYMPOSIUM ON MULTIDISCIPLINARY STUDIES AND INNOVATIVE TECHNOLOGIES (ISMSIT), 2018, : 610 - 614
  • [39] Object-based water body extraction model using Sentinel-2 satellite imagery
    Kaplan, Gordana
    Avdan, Ugur
    EUROPEAN JOURNAL OF REMOTE SENSING, 2017, 50 (01) : 137 - 143
  • [40] Port Bathymetry Mapping Using Support Vector Machine Technique and Sentinel-2 Satellite Imagery
    Mateo-Perez, Vanesa
    Corral-Bobadilla, Marina
    Ortega-Fernandez, Francisco
    Vergara-Gonzalez, Eliseo P.
    REMOTE SENSING, 2020, 12 (13)