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All issues Volume 157 (2025) BIO Web Conf., 157 (2025) 07007 References
Open Access
Issue
BIO Web Conf.
Volume 157, 2025
The 5th Sustainability and Resilience of Coastal Management (SRCM 2024)
Article Number 07007
Number of page(s) 12
Section Geo-Marine and Mapping Application for Coastal Area
DOI https://doi.org/10.1051/bioconf/202515707007
Published online 05 February 2025
  • Abidin, Z., Setiawan, B., Muhaimin, A. W., & Shinta, A. (2021). The role of coastal biodiversity conservation on sustainability and environmental awareness in mangrove ecosystem of southern Malang, Indonesia. Biodiversitas Journal of Biological Diversity, 22(2). [CrossRef] [Google Scholar]
  • Rizzetto, F. (2020). Effects of climate change on the morphological stability of the Mediterranean Coasts: Consequences for tourism. Climate Change, Hazards and Adaptation Options: Handling the Impacts of a Changing Climate, 761-775. [Google Scholar]
  • Melet, A., Teatini, P., Le Cozannet, G., Jamet, C., Conversi, A., Benveniste, J., & Almar, R. (2020). Earth observations for monitoring marine coastal hazards and their drivers. Surveys in Geophysics, 41, 1489-1534. [CrossRef] [Google Scholar]
  • Pujianiki, N. N. (2022, December). Coastline changes monitoring induced by man-made structures using synthetic aperture radar: A new simple approach. In IOP Conference Series: Earth and Environmental Science (Vol. 1117, No. 1, p. 012041). IOP Publishing. [CrossRef] [Google Scholar]
  • Dasari, K., Anjaneyulu, L., & Nadimikeri, J. (2022). Application of C-band Sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India. Marine Pollution Bulletin, 174, 113182. [CrossRef] [PubMed] [Google Scholar]
  • Li, C., Chen, W., Wang, Y., Wang, Y., Ma, C., Li, Y., ... & Zhai, W. (2022). Mapping winter wheat with optical and SAR images based on Google Earth Engine in Henan Province, China. Remote Sensing, 14(2), 284. [CrossRef] [Google Scholar]
  • Pramudya, F. S., Pan, J., Devlin, A. T., & Lin, H. (2021). Enhanced estimation of significant wave height with dual-polarization Sentinel-1 SAR imagery. Remote Sensing, 13(1), 124. [CrossRef] [Google Scholar]
  • Soudani, K., Delpierre, N., Berveiller, D., Hmimina, G., Vincent, G., Morfin, A., & Dufrene, E. (2021). Potential of C-band Synthetic Aperture Radar Sentinel-1 time-series for the monitoring of phenological cycles in a deciduous forest. Int. J. Appl. Earth Obs. Geoinformation, 104, 102505 [CrossRef] [Google Scholar]
  • Li, X. M., Sun, Y., & Zhang, Q. (2020). Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 3040-3053. [Google Scholar]
  • Amitrano, D., Martino, G.D., Guida, R., Iervolino, P., Iodice, A., Papa, M.N., Riccio, D., & Ruello, G. (2021). Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications. Remote. Sens., 13, 604. [CrossRef] [Google Scholar]
  • Armono, H. D., Citrosiswoyo, W., & Muzaki, F. K. (2024). Shoreline change model after artificial reefs deployment in Tlangoh, Bangkalan, Madura. In BIO Web of Conferences (Vol. 89, p. 09002). EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
  • Panjaitan, J. P., & Maulana, F. (2024). Mapping shoreline changes using Landsat Imagery at Pemalang, Central Java Province, Indonesia. In BIO Web of Conferences (Vol. 106, p. 04011). EDP Sciences. [CrossRef] [EDP Sciences] [Google Scholar]
  • Widiawaty, M. A., Nandi, H. M., & Murtianto, H. (2020). Physical and social factors of shoreline change in Gebang, Cirebon Regency 1915–2019. Geospatial Information, 4(1), 327-334. [CrossRef] [Google Scholar]
  • Dede, M., Susiati, H., Widiawaty, M. A., Kuok-Choy, L., Aiyub, K., & Asnawi, N. H. (2023). Multivariate analysis and modeling of shoreline changes using geospatial data. Geocarto International, 38(1), 2159070. [CrossRef] [Google Scholar]
  • Arum, Y. P. (2021, July). Analysis of shoreline changes using the bilko method on landsat imagery in Karawang regency (1999-2019). In Journal of Physics: Conference Series (Vol. 1943, No. 1, p. 012009). IOP Publishing. [CrossRef] [Google Scholar]
  • Dike, E. C., Oyetunji, A. K., & Amaechi, C. V. (2023). Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region. Journal of Marine Science and Engineering, 11(8), 1528. https://doi.org/10.3390/jmse11081528 [CrossRef] [Google Scholar]
  • Mascolo, L., Lopez-Sanchez, J. M., & Cloude, S. R. (2021). Thermal noise removal from polarimetric Sentinel-1 data. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. [Google Scholar]
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13(24), 13758. https://doi.org/10.3390/su132413758 [CrossRef] [Google Scholar]
  • Wale, P. B., Dhaigude, V., & Mishra, S. (2023). Comparative analysis of Image classification capabilities of Support Vector Machine (SVM) and Random Forest (RF) with Google Earth Engine (GEE) platform: A case study of Sangamner, Maharashtra. Intercontinental Geoinformation Days, 6, 113-116. [Google Scholar]
  • Masruroh, U. (2022). Konservasi dan Pemberdayaan “Peran CSR PHE WMO dalam Pemberdayaan Masyarakat Pesisir di Labuhan, Bangkalan”. Eastasouth Journal of Effective Community Services, 1(02), 01-16. [CrossRef] [Google Scholar]
  • Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021). Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics, 10(5), 593. [CrossRef] [Google Scholar]
  • Yang, Y., Yang, D., Wang, X., Zhang, Z., & Nawaz, Z. (2021). Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform. Remote Sensing, 13(24), 5064. https://doi.org/10.3390/rs13245064 [CrossRef] [Google Scholar]
  • Waleed, M., Mubeen, M., Ahmad, A., Habib-ur-Rahman, M., Amin, A., Farid, H. U., ... & El Sabagh, A. (2022). Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation. Scientific Reports, 12(1), 13210. [CrossRef] [PubMed] [Google Scholar]
  • Sabery, S. M., Bystrov, A., Navarro-Cía, M., Gardner, P., & Gashinova, M. (2021). Study of Low Terahertz Radar Signal Backscattering for Surface Identification. Sensors, 21(9), 2954. https://doi.org/10.3390/s21092954 [CrossRef] [PubMed] [Google Scholar]
  • Zhang, B., Wdowinski, S., Gann, D., Hong, S. H., & Sah, J. (2022). Spatiotemporal variations of wetland backscatter: The role of water depth and vegetation characteristics in Sentinel-1 dual-polarization SAR observations. Remote Sensing of Environment, 270, 112864. [CrossRef] [Google Scholar]
  • Hoffmeister, B. K., Gray, A. J., Sharp, P. C., Fairbanks, L. C., & Huang, J. (2020). Ultrasonic bone assessment using the backscatter amplitude decay constant. Ultrasound in Medicine & Biology, 46(9), 2412-2423. [CrossRef] [PubMed] [Google Scholar]
  • Gomez, C., Su, R., De Groot, P., & Leach, R. (2020). Noise reduction in coherence scanning interferometry for surface topography measurement. Nanomanufacturing and Metrology, 3(1), 68-76. [CrossRef] [Google Scholar]
  • Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., ... & Reiche, J. (2021). Sentinel-1 sar backscatter analysis ready data preparation in google earth engine. Remote Sensing, 13(10), 1954. [CrossRef] [Google Scholar]
  • Zehner, M., Dubois, C., Thiel, C., Schellenberg, K., Rüetschi, M., Brenning, A., Baade, J., & Schmullius, C. (2023). Accounting for Deciduous Forest Structure and Viewing Geometry Effects Improves Sentinel-1 Time Series Image Consistency. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13. [CrossRef] [Google Scholar]
  • Wang, Q., Lohse, J. P., Doulgeris, A. P., & Eltoft, T. (2023). Data augmentation for SAR sea ice and water classification based on per-class backscatter variation with incidence angle. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15. [CrossRef] [Google Scholar]
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13(24), 13758. https://doi.org/10.3390/su132413758 [CrossRef] [Google Scholar]
  • Irham, M., Suri, R., Setiawan, I., & Fuadi, A. (2021, February). Spatial analysis of accretion, abrasion and shoreline change in banda aceh costal area. In IOP Conference Series: Earth and Environmental Science (Vol. 674, No. 1, p. 012046). IOP Publishing. [CrossRef] [Google Scholar]
  • Owens, P. N. (2020). Soil erosion and sediment dynamics in the Anthropocene: a review of human impacts during a period of rapid global environmental change. Journal of Soils and Sediments, 20, 4115-4143. [CrossRef] [Google Scholar]
  • Sebastianelli, A., Del Rosso, M. P., Ullo, S. L., & Gamba, P. (2022). A speckle filter for Sentinel-1 SAR ground range detected data based on residual convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5086-5101. [CrossRef] [Google Scholar]
  • Sahithi, V. S., Iyyanki, M., & Giridhar, M. V. S. S. S. (2022). Analysing the sensitivity of SVM kernels on hyperspectral imagery for land use land cover classification. Journal of Image Processing and Artificial Intelligence, 8(2), 15-23. [CrossRef] [Google Scholar]
  • Pani, A. K. (2022). Non-linear process monitoring using kernel principal component analysis: A review of the basic and modified techniques with industrial applications. Brazilian Journal of Chemical Engineering, 39(2), 327-344. [CrossRef] [Google Scholar]
  • Razaque, A., Ben Haj Frej, M., Almi’ani, M., Alotaibi, M., & Alotaibi, B. (2021). Improved support vector machine enabled radial basis function and linear variants for remote sensing image classification. Sensors, 21(13), 4431. [CrossRef] [PubMed] [Google Scholar]
  • Hong, Y., Xie, T., Luo, L., Wang, M., Li, D., Zhang, Q., & Xu, T. (2024). Area extraction and growth monitoring of sugarcane from multi-source remote sensing images under a polarimetric SAR data compensation based on buildings. Geo-spatial Information Science, 1-18. [Google Scholar]
  • Gierszewska, M., & Berezowski, T. (2022). On the role of polarimetric decomposition and speckle filtering methods for C-Band SAR wetland classification purposes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2845-2860. [CrossRef] [Google Scholar]
  • Kumar, D. (2021). Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties. Scientific Reports, 11(1), 6241. [CrossRef] [PubMed] [Google Scholar]
  • Nesbit, P. R., Hubbard, S. M., & Hugenholtz, C. H. (2022). Direct georeferencing UAV-SfM in high-relief topography: Accuracy assessment and alternative ground control strategies along steep inaccessible rock slopes. Remote Sensing, 14(3), 490. [CrossRef] [Google Scholar]
  • Ferreira, Z. A., & Cabral, P. (2022). A Comparative study about vertical accuracy of four freely available digital elevation models: a case study in the Balsas river watershed, Brazil. ISPRS International Journal of Geo-Information, 11(2), 106. [CrossRef] [Google Scholar]
  • Tian, B., Zhang, F., Lang, F., Wang, C., Wang, C., Wang, S., & Li, J. (2022). A Novel Water Index Fusing SAR and Optical Imagery (SOWI). Remote Sensing, 14(21), 5316. [CrossRef] [Google Scholar]
  • Najem, S., Baghdadi, N., Bazzi, H., & Zribi, M. (2024). Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method. Remote Sensing, 16(20), 3838. [CrossRef] [Google Scholar]
  • Frolking, S., Milliman, T., Mahtta, R., Paget, A., Long, D. G., & Seto, K. C. (2022). A global urban microwave backscatter time series data set for 1993–2020 using ERS, QuikSCAT, and ASCAT data. Scientific Data, 9(1), 88. [CrossRef] [PubMed] [Google Scholar]
  • Manomba-Mbadinga, N., Niculescu, S., Zaabar, N., Mombo, J. B., & Xie, G. (2023, October). Grand Libreville (Gabon) coastline using machine learning and convolutional neural network detection and automatic extraction of the methods. In Earth Resources and Environmental Remote Sensing/GIS Applications XIV (Vol. 12734, p. 1273402). SPIE. [Google Scholar]
  • Huo, W., Li, W., Zhang, Z., Sun, C., Zhou, F., & Gong, G. (2021). Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection. Energy Conversion and Management, 243, 114367. [CrossRef] [Google Scholar]
  • Xie, G., & Niculescu, S. (2021). Mapping and monitoring of land cover/land use (LCLU) changes in the crozon peninsula (Brittany, France) from 2007 to 2018 by machine learning algorithms (support vector machine, random forest, and convolutional neural network) and by post-classification comparison (PCC). Remote Sensing, 13(19), 3899. [CrossRef] [Google Scholar]

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