Open Access
| Issue |
BIO Web Conf.
Volume 228, 2026
Biospectrum 2025: International Conference on Biotechnology and Biological Science
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 8 | |
| Section | Use of AI and ML in Biotechnology | |
| DOI | https://doi.org/10.1051/bioconf/202622801002 | |
| Published online | 11 March 2026 | |
- D. M. Alongi, “Mangrove forests: resilience, protection from tsunamis, and responses to global climate change,” Estuarine, Coastal and Shelf Science, vol. 76, no. 1, pp. 1–13, 2008. [Google Scholar]
- I. Valiela, J. L. Bowen, and J. K. York, “Mangrove forests: One of the world’s threatened major tropical environments,” BioScience, vol. 51, no. 10, pp. 807–815, 2001. [Google Scholar]
- F. Dahdouh-Guebas et al., “How effective were man-groves as a defence against the recent tsunami?” Current Biology, vol. 15, no. 12, pp. R443–R447,2005. [Google Scholar]
- K. Ewel, R. Twilley, and J. I. N. Ong, “Different kinds of mangrove forests provide different goods and services,” Global Ecology & Biogeography Letters, vol. 7, no. 1, pp. 83–94, 1998. [Google Scholar]
- D. Alongi, The energetics of mangrove forests. Springer, 2009. [Google Scholar]
- S. E. Hamilton and D. Casey, “Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21),” Global Ecology and Biogeography, vol. 25, no. 6, pp. 729–738, 2016. [CrossRef] [Google Scholar]
- G. Lassalle et al., “Advances in multi-and hyper-spectral remote sensing of mangrove species,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 195, pp. 298–312, 2023. [Google Scholar]
- G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in hyperspectral image classification: Earth monitoring with statistical learning methods,” IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 45–54, 2013. [Google Scholar]
- L. He, J. Li, C. Liu, and S. Li, “Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1579–1597, 2017. [Google Scholar]
- C.-I. Chang and Q. Du, “Interference and noise-adjusted principal components analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 5, pp. 2387–2396, 1999. [Google Scholar]
- G. Luo, G. Chen, L. Tian, K. Qin, and S. Qian, “Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising,” Canadian Journal of Remote Sensing, vol. 42, no. 2, pp. 106–116, 2016. [Google Scholar]
- C.-I. Chang et al., “Comparative study and analysis among ATGP, VCA, and SGA for finding endmembers in hyperspectral imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4280–4306, 2016. [Google Scholar]
- M. E. Winter, “N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data,” in Imaging Spectrometry V, vol. 3753, pp. 266–275, 1999. [Google Scholar]
- D. R. Hidalgo, B. B. Corte´s, and E. Caicedo, “Dimensionality reduction of hyperspectral images of vegetation and crops based on self-organized maps,” Information Processing in Agriculture, vol. 8, no. 2, pp. 310–327, 2021. [Google Scholar]
- M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A systematic review on supervised and unsupervised machine learning algorithms for data science,” in Supervised and unsupervised learning for data science, Springer, 2020. [Google Scholar]
- S. Chakravortty and S. Chakrabarti, “Preprocessing of hyperspectral data: a case study of Henry and Lothian Islands in Sunderban Region,” International Journal of Geomatics And Geosciences, vol. 2, no. 2, p. 490, 2011. [Google Scholar]
- Zhang, Zhen, Md Rasel Ahmed, Qian Zhang, Yi Li, and Yangfan Li. “Monitoring of 35-year mangrove wetland change dynamics and agents in the sundarbans using temporal consistency checking.” Remote Sensing 15, no. 3 (2023): 625. [Google Scholar]
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