Open Access
Issue |
BIO Web Conf.
Volume 136, 2024
The 13th International and National Seminar of Fisheries and Marine Science (ISFM XIII 2024)
|
|
---|---|---|
Article Number | 03002 | |
Number of page(s) | 17 | |
Section | Aquatic, Biodiversity, Ecology and Conservation | |
DOI | https://doi.org/10.1051/bioconf/202413603002 | |
Published online | 11 November 2024 |
- A.C. Ferreira, R. Borges, L.D. de Lacerda, Can Sustainable Development Save Mangroves? Sustainability. 14, 1263 (2022). https://doi.org/10.3390/su14031263 [CrossRef] [Google Scholar]
- S. Temmerman, E.M. Horstman, K.W. Krauss, J.C. Mullarney, I. Pelckmans, K. Schoutens, Marshes and Mangroves as Nature-Based Coastal Storm Buffers. Annu. Rev. Mar. Sci. 15, 95–118 (2023). https://doi.org/10.1146/annurev-marine-040422-092951 [CrossRef] [PubMed] [Google Scholar]
- S. Song, Y. Ding, W. Li, Y. Meng, J. Zhou, R. Gou, C. Zhang, S. Ye, N. Saintilan, K.W. Krauss, S. Crooks, S. Lv, G. Lin, Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change. Nat. Commun. 14, 756 (2023). https://doi.org/10.1038/s41467-023-36477-1 [CrossRef] [Google Scholar]
- A.C. Ximenes, K.C. Cavanaugh, D. Arvor, D. Murdiyarso, N. Thomas, G. Arcoverde, P. C. da Bispo, T. Van der Stocken, A comparison of global mangrove maps: Assessing spatial and bioclimatic discrepancies at poleward range limits. Sci. Total Environ. 860, 160380 (2023). https://doi.org/10.1016/j.scitotenv.2022.160380 [CrossRef] [Google Scholar]
- M. Domínguez-Domínguez, J. Zavala-Cruz, J.A. Rincón-Ramírez, P. Martínez-Zurimendi, Management Strategies for the Conservation, Restoration and Utilization of Mangroves in Southeastern Mexico. Wetlands. 39, 907–919 (2019). https://doi.org/10.1007/s13157-019-01136-z [CrossRef] [Google Scholar]
- R. Borges, A.C. Ferreira, L.D. Lacerda, Systematic Planning and Ecosystem-Based Management as Strategies to Reconcile Mangrove Conservation with Resource Use. Front. Mar. Sci. 4, 327 (2017). https://doi.org/10.3389/fmars.2017.00353 [CrossRef] [Google Scholar]
- K. Maurya, S. Mahajan, N. Chaube, Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review. Complex Intell. Syst. 7, 2797–2818 (2021). https://doi.org/10.1007/s40747-021-00457-z [CrossRef] [Google Scholar]
- P.M. Hai, P.H. Tinh, N.P. Son, T.V. Thuy, N.T. Hong Hanh, S. Sharma, D.T. Hoai, VC Duy, Mangrove health assessment using spatial metrics and multi-temporal remote sensing data. PLOS ONE. 17, e0275928 (2022). https://doi.org/10.1371/journal.pone.0275928 [CrossRef] [Google Scholar]
- T. Taulli, Introduction to Generative AI, in Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business (Apress, Berkeley, 2023), pp. 1–20. https://doi.org/10.1007/978-1-4842-9367-6_1 [Google Scholar]
- P. Pérez-Cutillas, A. Pérez-Navarro, C. Conesa-García, D.A. Zema, J.P. Amado-Álvarez, What is going on within google earth engine? A systematic review and metaanalysis. Remote Sens. Appl. Soc. Environ. 29, 100907 (2023). https://doi.org/10.1016/j.rsase.2022.100907 [Google Scholar]
- R. Tao, J. Xu, Mapping with ChatGPT. ISPRS Int. J. Geo-Inf. 12, 284 (2023). https://doi.org/10.3390/ijgi12070284 [CrossRef] [Google Scholar]
- N. Abate, F. Visone, M. Sileo, M. Danese, A. Minervino Amodio, R. Lasaponara, N. Masini, Potential Impact of Using ChatGPT-3.5 in the Theoretical and Practical MultiLevel Approach to Open-Source Remote Sensing Archaeology, Preliminary Considerations. Heritage. 6, 7640–7659 (2023). https://doi.org/10.3390/heritage6120402 [CrossRef] [Google Scholar]
- J.J. Szczesniewski, A. Ramoso Alba, P.M. Rodríguez Castro, M.F. Lorenzo Gómez, J. Sainz González, L. Llanes González, Calidad de información de ChatGPT, BARD y Copilot acerca de patología urológica en inglés y en español. Actas Urol. Esp. 48, 398403 (2024). https://doi.org/10.1016/j.acuro.2023.12.002 [Google Scholar]
- Y. Liu, T. Han, S. Ma, J. Zhang, Y. Yang, J. Tian, H. He, A. Li, M. He, Z. Liu, Z. Wu, L. Zhao, D. Zhu, X. Li, N. Qiang, D. Shen, T. Liu, B. Ge, Summary of ChatGPT-Related research and perspective towards the future of large language models. Meta-Radiology. 1, 100017 (2023). https://doi.org/10.1016/j.metrad.2023.100017 [CrossRef] [Google Scholar]
- B. Yetistiren, I. Ozsoy, E. Tuzun, Assessing the quality of GitHub copilot's code generation, in Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering, Singapore, Singapore (2022), pp. 62–71. https://doi.org/10.1145/3558489.3559072 [Google Scholar]
- A.J. Adetayo, Microsoft Copilot and Anthropic Claude AI in education and library service. Libr. Hi Tech News (to be published). https://doi.org/10.1108/LHTN-01-2024-0002 [Google Scholar]
- V. Chan, A Relationship Model between the Perceived Economic Value of Computer Operating Systems and their Usability: All Variables Evaluated by Copilot AI (2024). https://doi.org/10.54941/ahfe1004595 [Google Scholar]
- A. Jungherr, Using ChatGPT and Other Large Language Model (LLM) Applications for Academic Paper Assignments. (2023). https://doi.org/10.31235/osf.io/d84q6 [Google Scholar]
- M.M. Al Rahhal, Y. Bazi, S.O. Alsaleh, M. Al-Razgan, M.L. Mekhalfi, M. Al Zuair, N. Alajlan, Open-ended remote sensing visual question answering with transformers. Int. J. Remote Sens. 43, 6809–6823 (2022). https://doi.org/10.1080/01431161.2022.2145583 [CrossRef] [Google Scholar]
- L.P. Osco, E.L. de Lemos, W.N. Gonçalves, A.P.M. Ramos, J. Marcato Junior, The Potential of Visual ChatGPT for Remote Sensing. Remote Sens. 15, 3232 (2023). https://doi.org/10.3390/rs15133232 [CrossRef] [Google Scholar]
- R. Jhonnerie, V.P. Siregar, B. Nababan, L.B. Prasetyo, S. Wouthuyzen, Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries. Procedia Environ. Sci. 24, 215–221 (2015). https://doi.org/10.1016/j.proenv.2015.03.028 [CrossRef] [Google Scholar]
- L.N. Rosa, M. Duarte de Paula Costa, DM de Freitas, Modelling spatial-temporal changes in carbon sequestration by mangroves in an urban coastal landscape. Estuar. Coast. Shelf Sci. 276, 108031 (2022). https://doi.org/10.1016/j.ecss.2022.108031 [CrossRef] [Google Scholar]
- D. Stuart, Coastal Ecosystems and Agricultural Land Use: New Challenges on California's Central Coast. Coast. Manage. 38, 42–64 (2010). https://doi.org/10.1080/08920750903363190 [CrossRef] [Google Scholar]
- C. Kuenzer, A. Bluemel, S. Gebhardt, T.V. Quoc, S. Dech, Remote Sensing of Mangrove Ecosystems: A Review. Remote Sens. 3, 878–928 (2011). https://doi.org/10.3390/rs3050878 [CrossRef] [Google Scholar]
- R. Jhonnerie, V.P. Siregar, B. Nababan, Comparison of random forest algorithm which implemented on object and pixel based classification for mangrove land cover mapping, in International Conference on Science and Technology (ICST) 2016, Pekanbaru (2016) [Google Scholar]
- A. Asmala, M.K. Abd Ghani, S. Razali, H. Sakidin, N. Hashim, Haze reduction from remotely sensed data. Appl. Math. Sci. 8, 1755–1762 (2014). https://doi.org/10.12988/ams.2014.4289 [CrossRef] [Google Scholar]
- X. Wang, L. Tan, J. Fan, Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province. Remote Sens. 15, 1386 (2023). https://doi.org/10.3390/rs15051386 [CrossRef] [Google Scholar]
- D. Wang, B. Wan, P. Qiu, Y. Su, Q. Guo, R. Wang, F. Sun, X. Wu, Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sens. 10, 1468 (2018). https://doi.org/10.3390/rs10091468 [CrossRef] [Google Scholar]
- A.D. Purwanto, K. Wikantika, A. Deliar, S. Darmawan, Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia. Remote Sens. 15, 16 (2023). https://doi.org/10.3390/rs15010016 [Google Scholar]
- C. Xue, S. Chen, Z. Lee, L. Hu, X. Shi, M. Lin, J. Liu, C. Ma, Q. Song, T. Zhang, Iterative near-infrared atmospheric correction scheme for global coastal waters. ISPRS J. Photogramm. Remote Sens. 179, 92–107 (2021). https://doi.org/10.1016/j.isprsjprs.2021.07.005 [CrossRef] [Google Scholar]
- H. Zhang, J. Li, Q. Liu, S. Lin, A. Huete, L. Liu, H. Croft, JGPW Clevers, Y. Zeng, X. Wang, C. Gu, Z. Zhang, J. Zhao, Y. Dong, F. Mumtaz, W. Yu, A novel red-edge spectral index for retrieving the leaf chlorophyll content. Methods Ecol. Evol. 13, 2771–2787 (2022). https://doi.org/10.1111/2041-210X.13994 [CrossRef] [Google Scholar]
- A. Bannari, A. El-Battay, R. Bannari, H. Rhinane, Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape. Remote Sens. 10, 855 (2018). https://doi.org/10.3390/rs10060855 [CrossRef] [Google Scholar]
- A.B. Baloloy, A.C. Blanco, R.R.C. Sta. Ana, K. Nadaoka, Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J. Photogramm. Remote Sens. 166, 95–117 (2020). https://doi.org/10.1016/j.isprsjprs.2020.06.001 [CrossRef] [Google Scholar]
- M. Jia, Z. Wang, C. Wang, D. Mao, Y. Zhang, A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery. Remote Sens. 11, 2043 (2019). https://doi.org/10.3390/rs11172043 [CrossRef] [Google Scholar]
- C. Strobl, A.-L. Boulesteix, A. Zeileis, T. Hothorn, Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics. 8, 25 (2007). https://doi.org/10.1186/1471-2105-8-25 [CrossRef] [Google Scholar]
- A. Behnamian, K. Millard, S.N. Banks, L. White, M. Richardson, J. Pasher, A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values. IEEE Geosci. Remote Sens. Lett. 14, 1988–1992 (2017). https://doi.org/10.1109/LGRS.2017.2745049 [CrossRef] [Google Scholar]
- D. Smit, H. Smuts, P. Louw, J. Pielmeier, C. Eidelloth, The impact of GitHub Copilot on developer productivity from a software engineering body of knowledge perspective (2024) [Google Scholar]
- C. Bird, D. Ford, T. Zimmermann, N. Forsgren, E. Kalliamvakou, T. Lowdermilk, I. Gazit, Taking Flight with Copilot: Early insights and opportunities of AI-powered pairprogramming tools. Queue. 20, 10 (2023). https://doi.org/10.1145/3582083 [Google Scholar]
- A.R. Soffianian, N.B. Toosi, A. Asgarian, H. Regnauld, S. Fakheran, L.T. Waser, Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran. Nat. Conserv. 52, 6985 (2023). https://doi.org/10.3897/natureconservation.52.89639 [Google Scholar]
- A.M. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M.C. Desmarais, Z.M. Jiang, Github copilot AI pair programmer: Asset or liability? J. Syst. Softw. 203, 111734 (2023). https://doi.org/10.1016/j.jss.2023.111734 [CrossRef] [Google Scholar]
- F. Pillodar, P. Suson, M. Aguilos, R. Amparado, Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis. Forests. 14, 1080 (2023). https://doi.org/10.3390/f14061080 [CrossRef] [Google Scholar]
- P. Denny, V. Kumar, N. Giacaman, Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language, in Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, Toronto ON, Canada (2023), pp. 1136–1142. https://doi.org/10.1145/3545945.3569823 [Google Scholar]
- B.W. Heumann, Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progress in Physical Geography: Earth and Environment. 35, 87108 (2011). https://doi.org/10.1177/0309133310385371 [CrossRef] [Google Scholar]
- L. Wang, W.P. Sousa, Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. Int. J. Remote Sens. 30, 1267–1281 (2009). https://doi.org/10.1080/01431160802474014 [CrossRef] [Google Scholar]
- Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature. 521, 436–444 (2015). https://doi.org/10.1038/nature14539 [CrossRef] [PubMed] [Google Scholar]
- X.W. Chen, X. Lin, Big Data Deep Learning: Challenges and Perspectives. IEEE Access. 2, 514–525 (2014). https://doi.org/10.1109/ACCESS.2014.2325029 [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.