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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 闕蓓德 | zh_TW |
| dc.contributor.advisor | Pei-Te Chiueh | en |
| dc.contributor.author | 古育寧 | zh_TW |
| dc.contributor.author | Yu-Nien Ku | en |
| dc.date.accessioned | 2024-08-16T16:13:34Z | - |
| dc.date.available | 2024-08-31 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-31 | - |
| dc.identifier.citation | Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., & Bochtis, D. (2019). Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sensing, 11(6), 676. https://doi.org/10.3390/rs11060676
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Soil, 9(1), 21-38. https://doi.org/10.5194/soil-9-21-2023 Wang, L., Zhang, Y., Yao, Y., Xiao, Z., Shang, K., Guo, X., Yang, J., Xue, S., & Wang, J. (2021). Gbrt-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets. Remote Sensing, 13(6), 1054. https://doi.org/10.3390/rs13061054 Wang, M. M., Guo, X. W., Zhang, S., Xiao, L. J., Mishra, U., Yang, Y. H., Zhu, B. A., Wang, G. C., Mao, X. L., Qian, T., Jiang, T., Shi, Z., & Luo, Z. K. (2022). Global Soil Profiles Indicate Depth-Dependent Soil Carbon Losses under a Warmer Climate. Nature Communications, 13(1), Article 5514. https://doi.org/10.1038/s41467-022-33278-w Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H.-J., & Kögel-Knabner, I. (2019). Soil Organic Carbon Storage as a Key Function of Soils - a Review of Drivers and Indicators at Various Scales. Geoderma, 333, 149-162. https://doi.org/10.1016/j.geoderma.2018.07.026 World Bank. (2021). Soil Organic Carbon Mrv Sourcebook for Agricultural Landscapes. © World Bank, Washington, DC. Yang, L., Cai, Y., Zhang, L., Guo, M., Li, A., & Zhou, C. (2021). A Deep Learning Method to Predict Soil Organic Carbon Content at a Regional Scale Using Satellite-Based Phenology Variables. International Journal of Applied Earth Observation and Geoinformation, 102, 102428. https://doi.org/10.1016/j.jag.2021.102428 Zhang, L., Cai, Y., Huang, H., Li, A., Yang, L., & Zhou, C. (2022). A Cnn-Lstm Model for Soil Organic Carbon Content Prediction with Long Time Series of Modis-Based Phenological Variables. Remote Sensing, 14(18), 4441. https://doi.org/10.3390/rs14184441 Zhang, M., Shi, W., & Xu, Z. (2020). Systematic Comparison of Five Machine-Learning Models in Classification and Interpolation of Soil Particle Size Fractions Using Different Transformed Data. Hydrology and Earth System Sciences, 24(5), 2505-2526. https://doi.org/10.5194/hess-24-2505-2020 Zhang, N., Chen, X., Wang, J., Dong, H., Han, X., Lu, X., Yan, J., & Zou, W. (2023). Anthropogenic Soil Management Performs an Important Role in Increasing Soil Organic Carbon Content in Northeastern China: A Meta-Analysis. Agriculture, Ecosystems & Environment, 350, 108481. https://doi.org/10.1016/j.agee.2023.108481 Zhang, X., Xie, E., Chen, J., Peng, Y., Yan, G., & Zhao, Y. (2023). Modelling the Spatiotemporal Dynamics of Cropland Soil Organic Carbon by Integrating Process-Based Models Differing in Structures with Machine Learning. Journal of Soils and Sediments. https://doi.org/10.1007/s11368-023-03516-9 Zhang, Y., Fan, M., Xu, Z., Jiang, Y., Ding, H., Li, Z., Shu, K., Zhao, M., Feng, G., Yong, K.-T., Dong, B., Zhu, W., & Xu, G. (2023). Machine-Learning Screening of Luminogens with Aggregation-Induced Emission Characteristics for Fluorescence Imaging. Journal of Nanobiotechnology, 21(1), 107. https://doi.org/10.1186/s12951-023-01864-9 洪蕾音、劉子揚、丁雅珠 (2018)。 各項農作物主要種植期間，斗南鎮公所。 許健輝、顏淳阡、劉滄棽 (2024)。 利用RothC模式進行土壤有機碳固存潛力時空變化分析-以濁水溪流域為例，農業試驗所技術服務季刊。 郭鴻裕、劉滄棽、朱戩良、江志峰、吳懷國 (1995)。 臺灣地區土壤有機質含量及有機資材之施用狀況，有機質肥料合理施用技術研討會。 陳柱中、戴宏宇、蘇子珊、廖崇億、許龍欣 (2023)。 應用DNDC模擬作物生育與溫室氣體排放，作物永續栽培體系國際研討會，國立臺灣大學。 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94468 | - |
| dc.description.abstract | 隨著工業快速發展伴隨而來的氣候變遷現象，減碳議題近年來受到國內外組織規劃與政策實施之重視，為因應減少大氣中的二氧化碳總量，以實現減緩氣候變遷的現象。我國農業部門為達到減量的目標，亦提出永續農耕方式，並嘗試提升農業土壤碳匯量。然而，目前尚未有明確訂定用於分析土壤碳匯量之動態變化的模型框架。
本研究為解決該問題，參考國外相關之方法學基礎，考量極端氣候及非氣候因子對農業土壤有機碳儲存潛量帶來不確定性，以臺灣主要糧食水稻產區－－雲嘉南為研究對象，探討未來約十五年的土壤有機碳動態分布。本文利用機器學習整合氣候、土壤條件等資料建立模型，以評估淨初級生產量及土壤有機碳之動態變化。其中包含兩階段模型，其一為淨初級生產量之時空動態模型，推估其隨時間之分布變化；其二則為地上部與地下部之數值迴歸模型，建立遙測與土壤調查資料之關係。藉由統計方法，於研究結果說明參數相關性特徵，檢視參數對於模型之顯著性，並分析環境變量對於碳潛量的分布特性。 本研究進而探討氣候變遷情境與土地管理實踐情境之碳潛量變化。於劇烈的氣候變遷條件 (SSP5-8.5) 下，其變化率相對較和緩的氣候情境 (SSP1-2.6) 對於歷史情境的23%，提升為29%，顯示土壤碳儲存潛量穩定性有下降的可能性。接著，以SSP1-2.6為基線推估未來在土地管理實踐措施下，土壤有機碳儲存潛量的變化情形，相對2000年，至2035年可提升27%以上。這些結果顯示面對不同氣候變遷情境下，土地管理實踐對於土壤碳潛量的重要性。 本研究透過衛星遙測資料、地理資訊系統與機器學習方法，呈現並說明時空間農業土壤碳潛量之連續性變化；並致力於提升對水稻田土壤碳儲存潛量變化之理解，分析環境變量對土壤碳指標的影響，以供未來相關研究與政策之參考。 | zh_TW |
| dc.description.abstract | Climate change has accompanies the rapid development of industrialization, and carbon reduction issues have received attention from domestic and foreign organizations in planning and policy implementation, aiming at reducing the sum of carbon dioxide, and mitigating the phenomenon of climate change. To address carbon reduction, Taiwan’s Ministry of Agriculture (MOA) has proposed sustainable agricultural practices to increase soil carbon storage. However, Taiwan lacks a clearly defined model framework for analyzing the dynamic changes in soil carbon sinks.
In order to solve this problem, this study refers to foreign methodological foundations, considers the uncertainty caused by environmental variables on soil organic carbon (SOC) potential, and focuses on the primary rice-growing regions of Yulin, Chiayi, and Tainan in Taiwan. The goal is to explore the future spatiotemporal patterns of SOC distribution. This study employs machine learning to develop a model to evaluate the dynamic changes in net primary production (NPP) and SOC. The model comprises two stages: a dynamic potential change model of NPP distribution with the spatiotemporal resolution, and a regression model for aboveground and underground parts to establish a remote sensing relationship with soil survey data. Furthermore, statistical methods are used to analyze the significance of climate variables on soil carbon indicators. This study explores the changes in carbon potential under further climate change scenarios and land management practices (LMPs). Under the severe climate scenario (SSP5-8.5), the climate scenario with a relatively mild changing rate (SSP1-2.6) increased from 23% of the historical scenario to 29%, indicating that the stability of SOC storage potential has declined. Using SSP1-2.6 as a baseline, it is showed that future land management practices could increase soil carbon storage potential by up to 27% by 2035 compared to the year 2000. These results indicated the importance of land management practices for SOC in the face of climate change. This study focuses on using satellite remote sensing data, Geographic Information Systems (GIS), and machine learning methodologies to present and explain changes in spatiotemporal continuity. It is also committed to understanding changes in soil carbon storage potential in rice fields and analyzing the dynamic changes of various environmental variables on soil carbon indicators to provide a reference for future studies and policy development. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T16:13:34Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T16:13:34Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 v 圖目錄 viii 表目錄 x 第一章、緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 第二章、文獻回顧 5 2.1 國內外政策與趨勢說明 5 2.1.1 國際規範與國內政策 5 2.1.1.1 國際規範 5 2.1.1.2 國內碳減量發展與未來規劃 6 2.1.2 國內外碳移除與自然碳匯 7 2.1.2.1 自然碳匯 7 2.1.2.2 陸域生態系統 7 2.2 農業土壤碳系統 8 2.2.1 土壤碳動態平衡與重要性 9 2.2.2 影響土壤碳平衡之因子 10 2.3 衛星遙測於土壤碳系統之應用 13 2.3.1 遙測指標之應用 13 2.3.2 地下部土壤碳系統與地上部遙測資料之關聯性 15 2.4 土壤碳量之動態模擬方法 16 2.4.1 已建立之土壤模擬系統 16 2.4.2 查表法 17 2.4.3 機器學習 18 2.5 土壤碳儲存潛量分析與研究案例 21 2.5.1 時間解析度之趨勢探討 21 2.5.2 空間解析度之動態分布分析 22 第三章、研究方法 24 3.1 研究範疇 26 3.2 研究材料 28 3.2.1 參數設定與選擇 28 3.2.2 參數來源與軟體工具 28 3.2.2.1參數來源 28 3.2.2.2軟體工具與運用 30 3.2.3 情境分析與設計 31 3.2.3.1 氣候情境之設定 31 3.2.3.2 土地管理實踐之設定 33 3.3 研究之建模 34 3.3.1 模型架構與建模簡述 34 3.3.2 模型設置與資料處理 35 3.3.3 淨初級生產量之時空動態模型 36 3.3.3.1 演算法說明：CNN、LSTM、RF 37 3.3.3.2 使用不同環境變數組合作為預測變量 41 3.3.4 地上部(NPP)與地下部(SOC)之數值迴歸模型 41 3.3.4.1 演算法說明：RF、GBRT、KNN、MLP、XGB 43 3.3.4.2 使用不同環境變數組合預測變量 46 3.3.5 驗證與分析階段 47 第四章、結果與討論 49 4.1 模型預測能力 49 4.1.1 模型一－淨初級生產量之時空動態模型 49 4.1.1.1 模型驗證與特性說明 52 4.1.1.2 模型一選用與分析 57 4.1.2 模型二－地上部(NPP)與地下部(SOC)之數值迴歸模型 58 4.1.2.1 模型驗證與特性說明 60 4.1.2.2 模型二選用與分析 65 4.1.2.3 土壤碳潛量指標之趨勢分析－SOC、NPP 66 4.2 氣候情境下之土壤碳潛量變化 68 4.3 不同土地管理實踐下之土壤碳儲存動態潛量 75 4.4 綜合討論 78 第五章、結論與建議 81 5.1 結論 81 5.2 未來研究建議 83 參考文獻 87 附錄 97 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 農業土壤碳匯 | zh_TW |
| dc.subject | 碳儲存潛量 | zh_TW |
| dc.subject | 時間與空間動態評估 | zh_TW |
| dc.subject | 氣候變遷 | zh_TW |
| dc.subject | 土地管理實踐 | zh_TW |
| dc.subject | Carbon sink potential | en |
| dc.subject | Machine learning (ML) | en |
| dc.subject | Land management practices (LMPs) | en |
| dc.subject | Climate change | en |
| dc.subject | Spatio-temporal | en |
| dc.subject | Agriculture soil | en |
| dc.title | 應用機器學習推估水稻田土壤有機碳之時空動態與儲存潛力 | zh_TW |
| dc.title | Machine Learning Assessment of Soil Organic Carbon in Paddy Fields: Spatiotemporal Dynamics and Sequestration Potential | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 駱尚廉;蕭友晉 | zh_TW |
| dc.contributor.oralexamcommittee | Shang-Lien Lo;Yo-Jin Shiau | en |
| dc.subject.keyword | 機器學習,農業土壤碳匯,碳儲存潛量,時間與空間動態評估,氣候變遷,土地管理實踐, | zh_TW |
| dc.subject.keyword | Machine learning (ML),Agriculture soil,Carbon sink potential,Spatio-temporal,Climate change,Land management practices (LMPs), | en |
| dc.relation.page | 104 | - |
| dc.identifier.doi | 10.6342/NTU202402830 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-08-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 環境工程學研究所 | - |
| 顯示於系所單位： | 環境工程學研究所 | |
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