請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103639完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭友晉 | zh_TW |
| dc.contributor.advisor | Yo-Jin Shiau | en |
| dc.contributor.author | 洪藝豪 | zh_TW |
| dc.contributor.author | I-Hao Hung | en |
| dc.date.accessioned | 2026-08-18T17:23:32Z | - |
| dc.date.available | 2026-08-19 | - |
| dc.date.copyright | 2026-08-18 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-08-10 15:02:03 | - |
| dc.identifier.citation | Alexander, P., Paustian, K., Smith, P., & Moran, D. (2015). The economics of soil C sequestration and agricultural emissions abatement. Soil, 1(1), 331–339.
Alluhaidan, A. S., Bashir, R. N., Jahangir, R., Marzouk, R., Saidani, O., & Alroobaea, R. (2024). Machine learning and fog computing-enabled sensor drift management in precision agriculture. IEEE Sensors Journal, 24(22), 36953–36970. Alongi, D. M. (2012). Carbon sequestration in mangrove forests. Carbon management, 3(3), 313–322. Atwood, T. B., Connolly, R. M., Almahasheer, H., Carnell, P. E., Duarte, C. M., Ewers Lewis, C. J., Irigoien, X., Kelleway, J. J., Lavery, P. S., & Macreadie, P. I. (2017). Global patterns in mangrove soil carbon stocks and losses. Nature Climate Change, 7(7), 523–528. Aula, L., Tomaz de Oliveira, M. M., Easterly, A. C., & Creech, C. F. (2026). Predicting Soil Organic Carbon in Lower Depths from Surface Soil Features Using Machine Learning Methods. Agronomy, 16(7), 758. Baisad, K., Chutsagulprom, N., & Moonchai, S. (2023). A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression. Mathematics, 11(23), 4799. Baldocchi, D., Chu, H., & Reichstein, M. (2018). Inter-annual variability of net and gross ecosystem carbon fluxes: A review. Agricultural and Forest Meteorology, 249, 520–533. Baldocchi, D. D. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global change biology, 9(4), 479–492. https://doi.org/https://doi.org/10.1046/j.1365-2486.2003.00629.x Barnes, R. J. (1991). The variogram sill and the sample variance. Mathematical Geology, 23(4), 673–678. Ben Ghorbal, A., Grine, A., Eid, M. M., & El-kenawy, E.-S. M. (2025). Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm. Frontiers in Environmental Science, 13, 1630762. Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Allen, A., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J. A., & Dong, H. (2025). A foundation model for the Earth system. Nature, 641(8065), 1180–1187. Borgen, S. K., Grønlund, A., Andrén, O., Kätterer, T., Tveito, O. E., Bakken, L. R., & Paustian, K. (2012). CO2 emissions from cropland in Norway estimated by IPCC default and Tier 2 methods. Greenhouse Gas Measurement and Management, 2(1), 5–21. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 Buyukdemircioglu, M., Kocaman, S., & Kada, M. (2022). Deep learning for 3D building reconstruction: A review. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 359–366. Camuffo, D. (2018). Standardization activity in the evaluation of moisture content. Journal of Cultural Heritage, 31, S10–S14. Chapman, H. (1965). Cation‐exchange capacity. Methods of soil analysis: Part 2 Chemical and microbiological properties, 9, 891–901. Chen, G., Ge, X., Zhang, Z., & Han, L. (2026). Detecting Drivers and Predicting Spatial Distribution of Soil Organic Carbon in an Arid Region Using Machine Learning. Remote Sensing, 18(4), 535. Chen, P., Luo, Z., Jiang, X., Yin, Z., & Li, J. (2025). Maps for Autonomous Driving: Full-process Survey and Frontiers. arXiv preprint arXiv:2509.12632. Chen, Q., Wang, Y., & Zhu, X. (2024). Soil organic carbon estimation using remote sensing data-driven machine learning. PeerJ, 12, e17836. https://doi.org/10.7717/peerj.17836 Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, Chiles, J.-P., & Delfiner, P. (2012). Geostatistics: modeling spatial uncertainty. John Wiley & Sons. Chou, M.-Q., Lin, W.-J., Lin, C.-W., Wu, H.-H., & Lin, H.-J. (2022). Allometric equations may underestimate the contribution of fine roots to mangrove carbon sequestration. Science of The Total Environment, 833, 155032. Convention, R. (1971). Ramsar Convention. In: IUCN Gland, Switzerland. Council, N. R., Earth, D. o., Studies, L., Geosciences, C. o., & Wetlands, C. o. C. o. (1995). Wetlands: Characteristics and boundaries. National Academies Press. Craft, C. B. (2001). Biology of wetland soils. Wetland soils: genesis, hydrology, landscapes, and classification, 107–137. Dolatabadian, A., & Kariman, K. (2026). Bridging Pedology and Data Science: Machine Learning Applications for Soil Organic Matter and Carbon Analysis. Applied Sciences, 16(11), 5412. Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature geoscience, 4(5), 293–297. Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of animal ecology, 77(4), 802–813. Elwin, A., Bukoski, J. J., Jintana, V., Robinson, E. J., & Clark, J. M. (2019). Preservation and recovery of mangrove ecosystem carbon stocks in abandoned shrimp ponds. Scientific Reports, 9(1), 18275. Ettema, C. H., & Wardle, D. A. (2002). Spatial soil ecology. Trends in ecology & evolution, 17(4), 177–183. FAO. (2021). Standard operating procedure for soil electrical conductivity (soil/water, Issue. Fourqurean, J., Johnson, B., Kauffman, J. B., Kennedy, H., Lovelock, C., Alongi, D. M., Cifuentes, M., Copertino, M., Crooks, S., & Duarte, C. (2015). Field sampling of soil carbon pools in coastal ecosystems. Franz, D., Acosta, M., Altimir, N., Arriga, N., Arrouays, D., Aubinet, M., Aurela, M., Ayres, E., Lopez-Ballesteros, A., & Barbaste, M. (2018). Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review. International Agrophysics, 32(4), 439–455. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189–1232. G.V.V, S. R., Joshi, P. D., Dandekar, R. A., Dandekar, R., & Panat, S. (2025). A study of Universal ODE approaches to predicting soil organic carbon. arXiv preprint arXiv:2509.24306. Gao, S. (2026). Artificial intelligence and human geography. In The encyclopedia of human geography (pp. 63–69). Springer. García-Zambrano, M. A., Cusme-Zambrano, V. E., Cedeño-Valarezo, L., & Reyna-Bowen, J. L. (2026). Accurate prediction of soil organic carbon using spectroscopic techniques and machine learning. Revista Brasileira de Engenharia Agrícola e Ambiental, 30(01), e294870. Gomez, C., Lagacherie, P., & Bacha, S. (2012). Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in the Cap Bon region, Tunisia. Digital sail assessments and beyond, 387–392. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford university press. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in neural information processing systems, 35, 507–520. Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., & Smith, P. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650. Guo, P.-T., Li, M.-F., Luo, W., Tang, Q.-F., Liu, Z.-W., & Lin, Z.-M. (2015). Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma, 237, 49–59. Harris, P., Brunsdon, C., & Fotheringham, A. S. (2011). Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic environmental Research and Risk assessment, 25(2), 123–138. Hastie, T. (2009). The elements of statistical learning: data mining, inference, and prediction. In: springer. Hengl, T., Heuvelink, G. B., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers & geosciences, 33(10), 1301–1315. Hengl, T., Heuvelink, G. B., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1-2), 75–93. Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., & Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748. Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. IPCC. (2006). Guidelines for national greenhouse gas inventories. Prepared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. Published: IGES, Japan. IPCC. (2023). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/9781009157896 Jiang, C., Zhao, J., Ding, Y., & Li, G. (2023). Vis–NIR spectroscopy combined with GAN data augmentation for predicting soil nutrients in degraded Alpine Meadows on the Qinghai–Tibet Plateau. Sensors, 23(7), 3686. Jiménez, J. J., Lal, R., Leblanc, H. A., & Russo, R. O. (2007). Soil organic carbon pool under native tree plantations in the Caribbean lowlands of Costa Rica. Forest Ecology and Management, 241(1-3), 134–144. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141–151. Kaiser, K., & Guggenberger, G. (2003). Mineral surfaces and soil organic matter. European Journal of Soil Science, 54(2), 219–236. Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81, 401–418. Kowsher, M., Zhan, W., & Chen, C. (2026). A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery. arXiv preprint arXiv:2604.16663. Lal, R. (2004). Soil carbon sequestration impacts on global climate change and food security. science, 304(5677), 1623–1627. Lennan, M., & Morgera, E. (2022). The Glasgow climate conference (COP26). The International Journal of Marine and Coastal Law, 37(1), 137–151. Li, H., Wu, Y., Liu, S., Xiao, J., Zhao, W., Chen, J., Alexandrov, G., & Cao, Y. (2022). Decipher soil organic carbon dynamics and driving forces across China using machine learning. Global change biology, 28(10), 3394–3410. Li, J., Nie, M., Powell, J. R., Bissett, A., & Pendall, E. (2020). Soil physico-chemical properties are critical for predicting carbon storage and nutrient availability across Australia. Environmental Research Letters, 15(9), 094088. Luo, Z., Feng, W., Luo, Y., Baldock, J., & Wang, E. (2017). Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions. Global change biology, 23(10), 4430–4439. https://doi.org/https://doi.org/10.1111/gcb.13767 Macreadie, P. I., Anton, A., Raven, J. A., Beaumont, N., Connolly, R. M., Friess, D. A., Kelleway, J. J., Kennedy, H., Kuwae, T., & Lavery, P. S. (2019). The future of Blue Carbon science. Nature communications, 10(1), 3998. Macreadie, P. I., Costa, M. D., Atwood, T. B., Friess, D. A., Kelleway, J. J., Kennedy, H., Lovelock, C. E., Serrano, O., & Duarte, C. M. (2021). Blue carbon as a natural climate solution. Nature Reviews Earth & Environment, 2(12), 826–839. Manxuan, M., Zhang, N., Li, Y., Wang, X., Shaowen, X., Li, T., Liu, S., Zhou, H., & Haofan, X. (2026). Towards Spatial Mapping and Local Interpretation of Soil Organic Carbon Contents in a Subtropical Mountainous Region Using Integrated Machine Learning Approaches. Sustainability, 18(10), 4943. Manzhos, S., Lüder, J., & Ihara, M. (2023). Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data. The Journal of Chemical Physics, 159(23). Masters, D., & Luschi, C. (2018). Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612. Matthews, G. V. T. (1993). The Ramsar Convention on Wetlands: its history and development. McBratney, A., & Webster, R. (1986). Choosing functions for semi‐variograms of soil properties and fitting them to sampling estimates. Journal of soil Science, 37(4), 617–639. McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3–52. Millard, P., Lattanzi, F. A., Simmons, A., Emoke Madari, B., Lukoye Fungo, B., Henry, B., Lalljee, B., McConkey, B. G., Hedley, C., & Piccini, C. (2019). Measuring and modelling soil carbon stocks and stock changes in livestock production systems: guidelines for assessment; Version 1-Advanced copy. Mirabito, A. J., Pereira, G., Hurst, N. R., Berkowitz, J. F., & Chambers, L. G. (2026). Field evidence of carbon and nitrogen stabilization through mineral‐associated organic matter formation in coastal wetland soils. Journal of Geophysical Research: Biogeosciences, 131(5), e2025JG009429. Moyeed, R. A., & Papritz, A. (2002). An empirical comparison of kriging methods for nonlinear spatial point prediction. Mathematical Geology, 34(4), 365–386. Muñoz, A. S. M., Alvis, Á. I. G., & Martínez, I. F. B. (2024). A random forest model to predict soil organic carbon storage in mangroves from Southern Colombian Pacific coast. Estuarine, Coastal and Shelf Science, 299, 108674. Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. Methods of soil analysis: Part 3 Chemical methods, 5, 961–1010. Oliveira, A., Fachada, N., & Matos-Carvalho, J. P. (2024). Data science for geographic information systems. 2024 8th International Young Engineers Forum on Electrical and Computer Engineering (YEF-ECE), Oliver, M., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena, 113, 56–69. Olson, K. R. (2010). Impacts of tillage, slope, and erosion on soil organic carbon retention. Soil science, 175(11), 562–567. Olson, K. R. (2013). Soil organic carbon sequestration, storage, retention and loss in US croplands: Issues paper for protocol development. Geoderma, 195, 201–206. Padarian, J., Minasny, B., & McBratney, A. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, e00198. Parman, R. P., Kamarudin, N., Ibrahim, F. H., Nuruddin, A. A., Omar, H., & Abdul Wahab, Z. (2022). Geostatistical Analysis of Mangrove Ecosystem Health: Mapping and Modelling of Sampling Uncertainty Using Kriging. Forests, 13(8). https://doi.org/10.3390/f13081185 Pfaff, J. D. (1993). Method 300.0 Determination of inorganic anions by ion chromatography. US Environmental protection agency, Office of research and development, Environmental monitoring systems Laboratory, 28. Pham, T. D., Yokoya, N., Nguyen, T. T. T., Le, N. N., Ha, N. T., Xia, J., Takeuchi, W., & Pham, T. D. (2021). Improvement of mangrove soil carbon stocks estimation in North Vietnam using Sentinel-2 data and machine learning approach. GIScience & Remote Sensing, 58(1), 68–87. Probst, P., Wright, M. N., & Boulesteix, A. L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: data mining and knowledge discovery, 9(3), e1301. Saidi, S., Idbraim, S., Karmoude, Y., Masse, A., & Arbelo, M. (2024). Deep-learning for change detection using multi-modal fusion of remote sensing images: A review. Remote Sensing, 16(20), 3852. Samarinas, N., Tsakiridis, N. L., Kalopesa, E., & Tziolas, N. (2025). An Agricultural Hybrid Carbon Model for National-Scale SOC Stock Spatial Estimation. Environments, 12(12), 477. Sarun, H. (2026). The impacts of mangrove destruction on carbon emissions—A comprehensive. Journal of Agriculture and Environment, 3(2), 297–301. Seddon, N., Chausson, A., Berry, P., Girardin, C. A., Smith, A., & Turner, B. (2020). Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1794). Seddon, N., Sengupta, S., García-Espinosa, M., Hauler, I., Herr, D., & Rizvi, A. R. (2019a). Nature-based solutions in nationally determined contributions. IUCN and University of Oxford. Gland, Switzerland and Oxford. Seddon, N., Sengupta, S., García-Espinosa, M., Hauler, I., Herr, D., & Rizvi, A. R. (2019b). Nature-based solutions in nationally determined contributions: Synthesis and recommendations for enhancing climate ambition and action by 2020. Seddon, N., Smith, A., Smith, P., Key, I., Chausson, A., Girardin, C., House, J., Srivastava, S., & Turner, B. (2021). Getting the message right on nature‐based solutions to climate change. Global change biology, 27(8), 1518–1546. Sekulić, A., Kilibarda, M., Heuvelink, G. B., Nikolić, M., & Bajat, B. (2020). Random forest spatial interpolation. Remote Sensing, 12(10), 1687. Shamrikova, E. V., Kondratenok, B. M., Tumanova, E. A., Vanchikova, E. V., Lapteva, E. M., Zonova, T. V., Lu-Lyan-Min, E. I., Davydova, A. P., Libohova, Z., & Suvannang, N. (2022). Transferability between soil organic matter measurement methods for database harmonization. Geoderma, 412, 115547. https://doi.org/https://doi.org/10.1016/j.geoderma.2021.115547 Smith, P., Soussana, J. F., Angers, D., Schipper, L., Chenu, C., Rasse, D. P., Batjes, N. H., Van Egmond, F., McNeill, S., & Kuhnert, M. (2020). How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global change biology, 26(1), 219–241. Solly, E. F., Weber, V., Zimmermann, S., Walthert, L., Hagedorn, F., & Schmidt, M. W. I. (2020). A Critical Evaluation of the Relationship Between the Effective Cation Exchange Capacity and Soil Organic Carbon Content in Swiss Forest Soils [Original Research]. Frontiers in Forests and Global Change, Volume 3 - 2020. https://doi.org/10.3389/ffgc.2020.00098 Somarathna, P., Malone, B., & Minasny, B. (2016). Mapping soil organic carbon content over New South Wales, Australia using local regression kriging. Geoderma Regional, 7(1), 38–48. Song, H., Liu, Z., Lin, B., Zhao, Y., Siringan, F. P., & You, C.-F. (2024). Clay mineral nanostructures regulate sequestration of organic carbon in typical fluvial sediments. Heliyon, 10(3). Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC bioinformatics, 9(1), 307. Suello, R. H., Hernandez, S. L., Bouillon, S., Belliard, J.-P., Dominguez-Granda, L., Van de Broek, M., Rosado Moncayo, A. M., Ramos Veliz, J., Ramirez, K. P., & Govers, G. (2021). Mangrove sediment organic carbon storage and sources in relation to forest age and position along a deltaic salinity gradient. Biogeosciences Discussions, 2021, 1–32. Tadić, J. M., Ilić, V., Ilić, S., Pavlović, M., & Tadić, V. (2024). Hybrid machine learning and geostatistical methods for gap filling and predicting solar-induced fluorescence values. Remote Sensing, 16(10), 1707. Takoutsing, B., & Heuvelink, G. B. M. (2022). Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors. Geoderma, 428. https://doi.org/10.1016/j.geoderma.2022.116192 Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., & Jiang, L. (2020). Deep learning optimizes data-driven representation of soil organic carbon in earth system model over the conterminous United States. Frontiers in Big Data, 3, 17. Toloşi, L., & Lengauer, T. (2011). Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics, 27(14), 1986–1994. Tziachris, P., Aschonitis, V., Chatzistathis, T., Papadopoulou, M., & Doukas, I. D. (2020). Comparing machine learning models and hybrid geostatistical methods using environmental and soil covariates for soil pH prediction. ISPRS International Journal of Geo-Information, 9(4), 276. USEPA. (2004). SW-846 Test Method 9045D: Soil and Waste pH. Vaysse, K., & Lagacherie, P. (2017). Using quantile regression forest to estimate uncertainty of digital soil mapping products. Geoderma, 291, 55–64. Velde, B. (2013). Origin and mineralogy of clays: clays and the environment. Springer Science & Business Media. Vepraskas, M. J., & Richardson, J. (2001). Morphological features of seasonally reduced soils. Wetland soils: genesis, hydrology, landscapes, and classification, 163–182. Wang, F., Guo, R., Zhang, N., Yang, S., & Cao, W. (2023). Soil organic carbon storages and bacterial communities along a restored mangrove soil chronosequence in the Jiulong River Estuary: From tidal flats to mangrove afforestation. Fundamental research, 3(6), 880–889. Wang, J.-F., Li, L.-F., & Christakos, G. (2009). Sampling and kriging spatial means: efficiency and conditions. Sensors, 9(7), 5224–5240. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. John Wiley & Sons. Wen, W., Zhu, Y., Guo, J., Pan, X., Li, J., Guo, Y., Guo, J., Wu, G., Wang, Y., & Gong, M. (2022). Spatial variation of soil organic carbon from Bamen bay mangrove in Southern China. Water, 14(20), 3278. Wendt, J., & Hauser, S. (2013). An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers. European Journal of Soil Science, 64(1), 58–65. Yaldiz, S. G., Agaccioglu, H., & Verstraeten, G. (2026). A Step Toward Rainfall Erosivity Mapping Over Türkiye Using Kriging With External Drift. Hydrological Processes, 40(3), e70477. Yong, Z.-J., Lin, W.-J., Lin, C.-W., & Lin, H.-J. (2024). Tidal influence on carbon dioxide and methane fluxes from tree stems and soils in mangrove forests. Biogeosciences, 21(22), 5247–5260. Young, M. T., Bechle, M. J., Sampson, P. D., Szpiro, A. A., Marshall, J. D., Sheppard, L., & Kaufman, J. D. (2016). Satellite-based NO2 and model validation in a national prediction model based on universal kriging and land-use regression. Environmental science & technology, 50(7), 3686–3694. Zhang, J., Gan, S., Yang, P., Zhou, J., Huang, X., Chen, H., He, H., Saintilan, N., Sanders, C. J., & Wang, F. (2024). A global assessment of mangrove soil organic carbon sources and implications for blue carbon credit. Nature communications, 15(1), 8994. Zhang, J., Li, Y., Yang, X., Jiang, R., & Zhang, L. (2025). Rsam-seg: A sam-based model with prior knowledge integration for remote sensing image semantic segmentation. Remote Sensing, 17(4), 590. Zimmermann, C. F., Keefe, C. W., & Bashe, J. (1997). Method 440.0: determination of carbon and nitrogen in sediments and particulates of estuarine/coastal waters using elemental analysis. USEPA, Office of Research and Development, National Exposure Research Laboratory. Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological bulletin, 99(3), 432. 林俊成, 邱祈榮, 許正一, 周文臣, 洪慶章, 林幸助, 薛美莉, 柳婉郁, & 郭鴻裕. (2022). 碳匯之發展趨勢及國內策略研析 (陳綠蔚、盧虎生, Ed.). 財團法人中技社. 林茂文. (2022). 臺灣 2050 淨零排放路徑及策略之綜析. 石油季刊, 58(2), 1–39. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103639 | - |
| dc.description.abstract | 海岸紅樹林濕地具備極高的藍碳封存潛力，為達成 2050 淨零排放目標之關鍵自然碳匯。然而，於微觀尺度下，土壤有機碳（SOC）密度呈現顯著之空間異質性，且傳統現地調查受限於高昂成本，常面臨小樣本限制，使得高解析度之空間製圖面臨嚴峻挑戰。本研究旨在透過比較純地理統計、純機器學習與混合空間模型，建構適用於微觀高噪聲環境之最佳化 SOC 空間預測框架，並鑑定關鍵環境驅動因子，以優化國家藍碳之測量、報告與驗證效率。
研究首先於紅樹林濕地現場進行大規模現地採樣，透過實驗室化學分析測定土壤理化性質與多項離子濃度。隨後執行特徵工程與主成分分析以挑選最適合參數，並採用 Bootstrap 袋外交叉驗證與漸進式樣本縮放實驗進行模型評估。結果顯示，空間座標（特別是經度）對預測模型具壓倒性之主導權，證實微觀尺度下的 SOC 分佈深受巨觀環境梯度影響。排除地理座標後，氯與鉀離子為最關鍵之化學預測因子。在演算法性能比較中，深度類神經網路受限於小樣本數據，展現嚴重的「數據依賴性」；反之，隨機森林憑藉自助聚合機制，展現極佳之預測強健性與穩定性。 最終，本研究實證「結合機器學習之外部漂移克里金（KED-ML）」為最優化之混合製圖框架。透過將 RF 萃取之非線性化學趨勢作為外部漂移項注入克里金系統，KED-ML平衡了特徵空間之化學精準度與地理空間之連續性。本研究建立之「現地採樣與實驗室分析 → RF → KED 空間差值」分析，不僅提升了預測準確度，也為未來發展低成本、高精度之國家藍碳空間調查提供科學研究基礎。 | zh_TW |
| dc.description.abstract | Coastal mangrove wetlands possess exceptional blue carbon sequestration potential, acting as a critical natural carbon sink for achieving 2050 Net-Zero emissions targets. However, at micro-scales (e.g., 1-hectare), Soil Organic Carbon (SOC) density exhibits extreme spatial heterogeneity. Coupled with the small-sample constraints inherent to labor-intensive field coring campaigns, high-resolution spatial mapping faces significant challenges. This study aims to construct an optimized SOC predictive framework by systematically comparing pure geostatistical models, standalone machine learning algorithms, and hybrid spatial architectures. Furthermore, it seeks to identify key environmental drivers to enhance the efficiency of national Measurement, Reporting, and Verification (MRV) protocols for blue carbon.
The methodology began with systematic field soil sampling in coastal mangrove wetlands and comprehensive laboratory chemical analysis to determine physicochemical properties and ion concentrations. Subsequently, rigorous feature engineering and Principal Component Analysis were applied to select the most suitable parameters, followed by Bootstrap Out-of-Bag cross-validation and progressive sample scaling experiments. Variable importance analyses revealed that macroscopic spatial geometric gradients fundamentally dictate SOC distribution. Upon excluding geometric coordinates, Chloride and Potassium emerged as the most critical chemical predictors. Algorithmically, Deep Neural Networks exhibited severe "data hunger" under small-sample constraints, whereas Random Forest (RF) demonstrated robust predictive stability through its bootstrap aggregating mechanism. Additionally, Universal Kriging outperformed Ordinary Kriging by successfully capturing the macroscopic trend surface. Ultimately, this study confirms that Kriging with External Drift by Machine Learning (KED-ML) is the optimal hybrid mapping architecture for micro-scale blue carbon. By injecting the non-linear chemical thresholds extracted by RF as an external drift into the kriging matrix, KED-ML successfully eliminated the artificial, perfectly balancing feature-space chemical precision with geographic-space continuity. The standardized analytical pipeline developed herein—comprising systematic field/lab analysis, RF driver extraction, and KED spatial smoothing—not only substantially enhances predictive accuracy but also provides a cost-effective and scientifically defensible foundation for future national blue carbon MRV spatial verification protocols. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-08-18T17:23:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-08-18T17:23:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii Table of Contents v List of Figures ix List of Tables xi Chapter 1. Introduction 1 1.1Background 1 1.2 Research Goals 3 Chapter 2. Literature Review 4 2.1 Domestic and International Policies and Trends 4 2.1.1 International Policies 4 2.1.2 Domestic carbon reduction development and future planning 5 2.2 Soil Carbon 6 2.2.1 Wetland Carbon 6 2.2.2 Mangrove Soil Carbon 7 2.3 Methodology for Measurement of Soil Carbon 9 2.3.1 Direct Methods of measuring SOC Change 9 2.3.2 Novel Methods of measuring SOC Change 11 2.3.3 Blue Carbon SOC Calculate 12 2.4 Factors Affecting Soil Organic Carbon 12 2.5 Prediction Technique for Spatial Points 14 2.5.1 Kriging 14 2.5.2 Machine Learning 16 2.5.2.1 The Evolution of Machine Learning 16 2.5.2.2 Comparison of machine learning models 17 2.5.2.3 Machine learning models predict soil carbon 18 2.5.3 Hybrid Models 21 Chapter 3. Methodology 23 Research Framework 23 3.1 Sample and Data Collecting 24 3.1.1 Site Description 24 3.1.2 Sample Collecting 24 3.2 Analysis of Soil Physical and Chemical Properties 25 3.2.1 Gravimetric Soil Moisture Content 25 3.2.2 Soil pH 26 3.2.3 Soil Electrical Conductivity 26 3.2.4 Total Organic Carbon and Total Nitrogen 27 3.2.5 Bulk Density Calculation 29 3.2.6 Determination of Cations and Anions by Hot Water Extraction 29 3.3 Model Building 31 3.3.1 Spatial Data Preprocessing and Biogeochemical Feature Engine 31 3.3.1.1 Outlier Mitigation and Logarithmic Transformation 31 3.3.1.2 Spatial Data Preprocessing and Projection Transformation 31 3.3.2 Spatial Variability Diagnostics and Geostatistical Modeling 32 3.3.2.1 Semivariogram Analysis 32 3.3.2.2 Pure Geostatistical Interpolation: Ordinary and Universal Kriging 33 3.3.3 Machine Learning Modeling for Non-linear Biogeochemical Features 35 3.3.3.1 Random Forest 35 3.3.3.2 Extreme Gradient Boosting 35 3.3.3.3 Deep Neural Network 36 3.3.4 The Architecture and Operational Workflow of KED-ML 37 3.3.5 Machine Learning Hyperparameter Configuration 38 3.3.6 Model Validation and Evaluation Protocols 40 3.3.7 Permutation Importance 41 3.4 Statistical Analysis and Computational Environment 42 3.4.1 Fundamental Statistics and Multivariate Dimensionality Reduction 42 3.4.2 Model Performance Evaluation Metrics 42 3.4.3 Software Tools and Spatial Computational Environment 44 Chapter 4. Results and Discussion 45 4.1 Soil Physical and Chemical Properties 45 4.1.1 Correlation Analysis among Parameters 47 4.1.1.1 Pearson Correlation 47 4.1.1.2 Principal Components Analysis 49 4.1.2 Spatial distribution analysis of various parameters 51 4.2 Kriging Prediction 53 4.2.1 Overall Performance and Superiority of Kriging 53 4.2.2 Semivariogram of Kriging 57 4.2.3 Sample Size Sensitivity and Algorithmic Convergence Analysis 60 4.3 Machine Learning Prediction 63 4.3.1 Overall Performance and Superiority of ML Models 65 4.3.2 Feature Importance Analysis 69 4.3.3 Sample Size Sensitivity and Algorithmic Convergence Analysis for ML 73 4.4 Hybrid Model 77 4.4.1 Model Performance 78 4.4.2 Semivariogram of KED-ML 80 4.4.3 Sample Size Sensitivity and Algorithmic Convergence Analysis 82 4.5 General Discussion 84 Chapter 5. Conclusion and Recommendation 90 5.1 Conclusion 90 5.2 Recommendation 93 References 95 Appendices 110 | - |
| dc.language.iso | en | - |
| dc.subject | 藍碳 | - |
| dc.subject | 土壤有機碳 | - |
| dc.subject | 機器學習 | - |
| dc.subject | 克里金 | - |
| dc.subject | 數位土壤製圖 | - |
| dc.subject | Blue Carbon | - |
| dc.subject | Soil Organic Carbon | - |
| dc.subject | Machine Learning | - |
| dc.subject | Kriging | - |
| dc.subject | Digital Soil Mapping | - |
| dc.title | 以人工智慧建立海岸濕地土壤碳匯空間分布推估之研究 | zh_TW |
| dc.title | Spatial Distribution Mapping of Soil Organic Carbon in Coastal Wetlands Using Artificial Intelligence | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡瑞彬;許健輝 | zh_TW |
| dc.contributor.oralexamcommittee | Jui-Pin Tsai;Chein-Hui Syu | en |
| dc.subject.keyword | 藍碳; 土壤有機碳; 機器學習; 克里金; 數位土壤製圖 | zh_TW |
| dc.subject.keyword | Blue Carbon; Soil Organic Carbon; Machine Learning; Kriging; Digital Soil Mapping | en |
| dc.relation.page | 111 | - |
| dc.identifier.doi | 10.6342/NTU202600967 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-08-12 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物環境系統工程學系 | - |
| dc.date.embargo-lift | 2031-08-05 | - |
| 顯示於系所單位： | 生物環境系統工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-2.pdf 此日期後於網路公開 2031-08-05 | 25.92 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。