EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 599 (2024) E3S Web Conf., 599 (2024) 03002 References
Open Access
Issue
E3S Web Conf.
Volume 599, 2024
6th International Conference on Science and Technology Applications in Climate Change (STACLIM 2024)
Article Number 03002
Number of page(s) 9
Section Land and Forest
DOI https://doi.org/10.1051/e3sconf/202459903002
Published online 10 January 2025
  1. D.M. Alongi, Carbon sequestration in mangrove forests. Carbon Manag. 3, 313–322 (2012). https://doi.org/10.4155/cmt.12.20 [CrossRef] [Google Scholar]
  2. M. De Dominicis, J. Wolf, R. van Hespen, P. Zheng, Z. Hu, Mangrove forests can be an effective coastal defence in the Pearl River Delta, China. Commun. Earth Environ. 4, 1–13 (2023). https://doi.org/10.1038/s43247-022-00672-7 [CrossRef] [Google Scholar]
  3. M.M. Igulu, I. Nagelkerken, M. Dorenbosch, M.G.G. Grol, A.R. Harborne, I.A. Kimirei, et al., Mangrove Habitat Use by Juvenile Reef Fish: Meta-Analysis Reveals that Tidal Regime Matters More than Biogeographic Region. PLoS One 9, e114715 (2014). https://doi.org/10.1371/journal.pone.0114715 [CrossRef] [PubMed] [Google Scholar]
  4. S. Thorn, S. Seibold, A.B. Leverkus, T. Michler, J. Müller, R.F. Noss, et al., The living dead: acknowledging life after tree death to stop forest degradation. Front. Ecol. Environ. 18, 505–512 (2020) [CrossRef] [Google Scholar]
  5. M.S. Islam, C. Saha, M. Hossain, Biomass and carbon stocks in mangrove-afforested areas, central coastal areas of Bangladesh. Environ. Challenges 13, 100784 (2023) [CrossRef] [Google Scholar]
  6. M. Stankovic, A.K. Mishra, Y.P. Rahayu, J. Lefcheck, D. Murdiyarso, D.A. Friess, et al., Blue carbon assessments of seagrass and mangrove ecosystems in South and Southeast Asia: Current progress and knowledge gaps. Sci. Total Environ. 904, 166618 (2023) [CrossRef] [Google Scholar]
  7. X. Liu, Y. Xiong, B. Liao, Relative contributions of leaf litter and fine roots to soil organic matter accumulation in mangrove forests. Plant Soil 421, 493–503 (2017). https://doi.org/10.1007/s11104-017-3477-5 [CrossRef] [Google Scholar]
  8. Y. Zhang, L. Xiao, D. Guan, Y. Chen, M. Motelica-Heino, Y. Peng, et al., The role of mangrove fine root production and decomposition on soil organic carbon component ratios. Ecol. Indic. 125, 107510 (2021). https://doi.org/10.1016/j.ecolind.2021.107510 [CrossRef] [Google Scholar]
  9. K.L. Mckee, D.R. Cahoon, I.C. Feller, Caribbean mangroves adjust to rising sea level through biotic controls on change in soil elevation. Glob. Ecol. Biogeogr. 16, 545–556 (2007). https://doi.org/10.1111/j.1466-8238.2007.00317.x [CrossRef] [Google Scholar]
  10. I.G.M.C.P. Liyanaralalage, A.S.K.K. Arachchilage, P.K.M. Indeewari, U. Jayasinghe, S. K. Madarasinghe, F. Dahdouh-Guebas, et al., Climate and intertidal zonation drive variability in the carbon stocks of Sri Lankan mangrove forests. Geoderma 389, 114929 (2021) [CrossRef] [Google Scholar]
  11. H. Akram, S. Hussain, P. Mazumdar, K.O. Chua, T.E. Butt, J.A. Harikrishna, Mangrove Health: A Review of Functions, Threats, and Challenges Associated with Mangrove Management Practices. Forests 14, 1698 (2023). https://doi.org/10.3390/f14091698 [CrossRef] [Google Scholar]
  12. M. Aguilos, N. Liu, D.M. Alongi, Impacts of Climate Change on Blue Carbon Stocks and Fluxes in Mangrove Forests. Forests 13, 149 (2022). https://doi.org/10.3390/f13020149 [Google Scholar]
  13. Global Mangrove Alliance, A breakthrough moment for mangroves: Delivering Global Action on Mangrove Restoration and Protection Climate Champions (2023). https://climatechampions.unfccc.int/a-breakthrough-moment-for-mangroves-delivering-global-action-on-mangrove-restoration-and-protection/ [Google Scholar]
  14. D.H. Zainal Abidin, S. Lavoué, N. Mohd Abu Hassan Alshari, M. Siti-Azizah, M. A. Rahim, N.A. Mohammed Akib, Ichthyofauna of Sungai Merbok Mangrove Forest Reserve, northwest Peninsular Malaysia, and its adjacent marine waters. Check List 17, 601–631 (2021). https://doi.org/10.15560/17.2.601 [CrossRef] [Google Scholar]
  15. J.E. Ong, W.J. Wan Ahmad, J.W.H. Yong, M. Mohamed, Y.Y. Wong, H. Mohd. Nasir, The Merbok mangroves: Present status and the way forward. In: Hutan Paya Laut Merbok, Kedah: Pengurusan hutan, Persekitaran fizikal dan kepelbagaian flora (Jabatan Perhutanan Semenanjung Malaysia, Kuala Lumpur, 2015), pp. 21–33. https://doi.org/10.13140/RG.2.2.35389.15846 [Google Scholar]
  16. I. Mohd Hasmadi, H.Z. Pakhriazad, K. Norlida, Remote Sensing for Mapping RAMSAR Heritage Site at Sungai Pulai Mangrove Forest Reserve, Johor, Malaysia. Sains Malays. 40, 83–88 (2011). https://www.researchgate.net/publication/289349412_Remote_Sensing_for_Mapping_RAMSAR_Heritage_Site_at_Sungai_Pulai_Mangrove_Forest_Reserve_Johor_Malaysia [Google Scholar]
  17. S.M. Hossain, M. Hashim, J.S. Bujang, M.H. Zakaria, A.M. Muslim, Assessment of the impact of coastal reclamation activities on seagrass meadows in Sungai Pulai estuary, Malaysia, using Landsat data (1994– 2017). Int. J. Remote Sens. 2018, (cited 2024 May 21). https://sci-hub.se/10.1080/01431161.2018.1547931 [Google Scholar]
  18. V.C. Chong, A. Sasekumar, Fish communities and fisheries of Sungai Johor and Sungai Pulai Estuaries (Johor, Malaysia). Malayan Nat. J. 56, 279–302 (2002) [Google Scholar]
  19. M. Abdullah, U.K. MalaysiaPP, Biodiversity of Sungai Pulai, Ramsar Site, Johor. Earth Observation Centre, Faculty of Social Science and Humanities, Universiti Kebangsaan Malaysia. 2008. https://books.google.com.my/books?id=7IJWtwAACAAJ [Google Scholar]
  20. L. Schimleck, J. Dahlen, L.A. Apiolaza, G. Downes, G. Emms, R. Evans, et al., Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation. Forests 10, 728 (2019). https://www.mdpi.com/1999-4907/10/9/728/htm [CrossRef] [Google Scholar]
  21. N. Devi, A. Thakur, H. Singh, Allometric equations for evaluating above-ground biomass and carbon storage capability of Indian bamboos: Review approach. Environ. Ecol. 29, 921–923 (2023). https://doi.org/10.53550/EEC.2023.v29i02.063 [Google Scholar]
  22. A. Komiyama, S. Poungparn, S. Kato, Common allometric equations for estimating the tree weight of mangroves. J. Trop. Ecol. 21, 471–477 (2005) [CrossRef] [Google Scholar]
  23. H.H. Nguyen, H.D. Vu, A. Röder, Estimation of above-ground mangrove biomass using landsat-8 dataderived vegetation indices: A case study in quang ninh province, Vietnam. For. Soc. 5, 506–525 (2021) [Google Scholar]
  24. C.E. Shannon, A Mathematical Theory of Communication. Bell Syst. Tech. J. 27, 379–423 (1948) [CrossRef] [Google Scholar]
  25. U.A.R. Th, G. Canaria, Joint Modeling of Spaceborne Radar and Lidar Data with Ensemble Learning for Forest Aboveground Biomass Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 19–24 (2024) [Google Scholar]
  26. G. Fang, H. Yu, L. Fang, X. Zheng, Synergistic Use of Sentinel-1 and Sentinel-2 Based on Different Preprocessing for Predicting Forest Aboveground Biomass. Forests 14, 1615 (2023). https://www.mdpi.com/1999-4907/14/8/1615/htm [CrossRef] [Google Scholar]
  27. T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 2016, 785–794. https://dl.acm.org/doi/10.1145/2939672.2939785 [Google Scholar]
  28. T. Wang, W. Zhou, J. Xiao, L. Xie, Estimating the grassland aboveground biomass based on remote sensing data and machine learning algorithm. J. Glaciol. Geocryol. 45, 1–10 (2023) [Google Scholar]
  29. H.Y. Gao, M.J. Hou, J. Ge, X.Y. Bao, Y.C. Li, J. Liu, et al., Hyperspectral estimation of aboveground biomass of alpine grassland based on random forest algorithm. Acta Agrestia Sin. 29, 1757–1767 (2021) [Google Scholar]
  30. G. De’Ath, Boosted trees for ecological modeling and prediction. Ecology 88, 243–251 (2007) [CrossRef] [PubMed] [Google Scholar]
  31. J. Elith, J.R. Leathwick, T. Hastie, A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008). https://doi.org/10.1111/j.1365-2656.2008.01390.x [CrossRef] [PubMed] [Google Scholar]
  32. M. Vojtek, J. Vojteková, R. Costache, Q.B. Pham, S. Lee, A. Arshad, et al., Comparison of multi-criteriaanalytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Geomatics Nat. Hazards Risk 12, 1153–1180 (2021). https://doi.org/10.1080/19475705.2021.1912835 [CrossRef] [Google Scholar]
  33. B. Meng, J. Gao, T. Liang, X. Cui, J. Ge, J. Yin, et al., Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China. Remote Sens. 10, 320 (2018). https://doi.org/10.3390/rs10020320 [CrossRef] [Google Scholar]
  34. J. Ge, B. Meng, T. Liang, Q. Feng, J. Gao, S. Yang, et al., Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China. Remote Sens. Environ. 218, 162–173 (2018). [CrossRef] [Google Scholar]
  35. IPCC, 2006 IPCC guidelines for national greenhouse gas inventories, S. Eggleston, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, eds. (2006). [Google Scholar]
  36. J.B. Kauffman, D.C. Donato, Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests (2012). [Google Scholar]
  37. J.F. De Queljoe, F.Y. Rumlawang, S. Si, M. Si, L.J. Sinay, Analisis Kruskal-Wallis Untuk Mengetahui Konsentrasi Belajar Mahasiswa Berdasarkan Bidang Minat Program Studi Statistika Fmipa Unpatti. Parameter: J. Matematika Stat. Terapannya 1, 29–34 (2022). https://doi.org/10.24843/JMST.2022.1 [CrossRef] [Google Scholar]
  38. R.A.K. Sherwani, H. Shakeel, W.B. Awan, M. Faheem, M. Aslam, Analysis of COVID-19 data using neutrosophic Kruskal-Wallis H test. BMC Med. Res. Methodol. 21, 1–7 (2021). https://doi.org/10.1186/s12874-021-01410-x [CrossRef] [Google Scholar]
  39. T.M.Z.T. Hashim, M.N. Suratman, H.R. Singh, J. Jaafar, A.N. Bakar, Predictive model of mangroves carbon stocks in Kedah, Malaysia using remote sensing. IOP Conf. Ser. Earth Environ. Sci. 540, 012002 (2020). https://doi.org/10.1088/1755-1315/540/1/012002 [CrossRef] [Google Scholar]
  40. L.C. Hong, Z. Hemati, R. Zakaria, Carbon stock evaluation of selected mangrove forests in peninsular Malaysia and its potential market value. J. Environ. Sci. Manag. 20, 2 (2017). [Google Scholar]
  41. O. Hamdan, M.R. Khairunnisa, A.A. Ammar, M. Hasmadi, K.A. Khali, Mangrove carbon stock assessment by optical satellite imagery. J. Trop. For. Sci. 25, (2013). [Google Scholar]
  42. S. Solberg, E. Næsset, T. Gobakken, O.M. Bollandsås, Forest biomass change estimated from height change in interferometric SAR height models. Carbon Balance Manag. 9, 1–12 (2014). https://doi.org/10.1186/s13021-014-0005-2 [CrossRef] [PubMed] [Google Scholar]
  43. J. Fan, D. Chen, J. Wen, Y. Sun, C.P. Gomes, Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net. arXiv 2207.08022v1 (2022). https://doi.org/10.48550/arXiv.2207.08022 [Google Scholar]
  44. Y. Han, R. Tang, Z. Liao, B. Zhai, J. Fan, A novel hybrid GOA-XGB model for estimating wheat aboveground biomass using UAV-based multispectral vegetation indices. Remote Sens. 14, 3506 (2022). https://doi.org/10.3390/rs14143506 [CrossRef] [Google Scholar]
  45. S.D. Madundo, E.W. Mauya, C.J. Kilawe, Comparison of multi-source remote sensing data for estimating and mapping above-ground biomass in the West Usambara tropical montane forests. Sci. Afr. 21, e01763 (2023). https://doi.org/10.1016/j.sciaf.2023.e01763 [Google Scholar]
  46. Y. Li, M. Li, C. Li, Z. Liu, Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci. Rep. 10, 1–12 (2020). https://doi.org/10.1038/s41598-020-67024-3 [CrossRef] [Google Scholar]
  47. C. Li, L. Zhou, W. Xu, Estimating aboveground biomass using Sentinel-2 MSI data and ensemble algorithms for grassland in the Shengjin Lake Wetland, China. Remote Sens. 13, 1595 (2021). https://doi.org/10.3390/rs13081595 [CrossRef] [Google Scholar]
  48. C. Huang, S. Li, H.S. He, Y. Liang, W. Xu, M.M. Wu, et al., Effects of forest management practices on carbon dynamics of China’s boreal forests under changing climates. J. Environ. Manage. 335, 117497 (2023). [CrossRef] [Google Scholar]
  49. V. Shanin, A. Komarov, R. Mäkipää, Tree species composition affects productivity and carbon dynamics of different site types in boreal forests. Eur. J. For. Res. 133, 273–286 (2014). [CrossRef] [Google Scholar]
  50. S.K. Sahu, K. Kathiresan, The age and species composition of mangrove forest directly influence the net primary productivity and carbon sequestration potential. Biocatal. Agric. Biotechnol. 20, 101235 (2019). [CrossRef] [Google Scholar]
  51. X. Wang, Z. Yuan, C. Chu, Y. Wang, Z. Liu, T. Tang, et al., Analysis of the relative importance of stand structure and site conditions for the productivity, species diversity, and carbon sequestration of Cunninghamia lanceolata and Phoebe bournei mixed forest. Plants 12, 1633 (2023). https://doi.org/10.3390/plants12081633 [CrossRef] [PubMed] [Google Scholar]
  52. GEC, Community-based mangrove conservation and sustainable livelihoods in Sungai Johor, Malaysia (2024). https://www.gec.org.my/index.cfm?&menuid=421&parentid=409 [Google Scholar]
  53. GEC, Community-based forest and wetland landscape management and restoration programme (2023). www.gec.org.my [Google Scholar]
  54. USM, Mangrove conservation: global effort in greening Merbok (2024). https://news.usm.my/index.php/english-news/9978-mangrove-conservation-global-effort-in-greening-merbok [Google Scholar]
  55. M.S. Hossain, M. Hashim, J.S. Bujang, M.H. Zakaria, A.M. Muslim, Assessment of the impact of coastal reclamation activities on seagrass meadows in Sungai Pulai estuary, Malaysia, using Landsat data (1994– 2017). Int. J. Remote Sens. 40, 3571–3605 (2019). https://doi.org/10.1080/01431161.2018.1547931 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.