氣候變遷與土地利用調整皆會對農業環境與生態系服務造成顯著影響，特別是在高度依賴灌溉與施肥的農業平原地區，其水資源供給、碳儲存能力及營養鹽循環機制正面臨多重壓力。嘉南平原作為臺灣最重要的農業生產區域，其土地利用型態與水文條件的變化，對區域永續發展具有關鍵影響。因此本研究以嘉南平原為研究區域，探討環境變遷下農業環境生態系服務之時空變化，並進一步評估未來情境下可能面臨的挑戰與發展趨勢。
本研究整合地理資訊系統與InVEST生態系服務評估模型，針對年產水量、碳儲存與封存，以及營養鹽傳輸三項關鍵生態系服務進行量化分析。首先利用1995、2005與2016年土地利用資料，搭配氣候、土壤與集水區等空間資料，分析歷史時段嘉南平原土地利用變遷及其對生態系服務的影響。其次透過MOLUSCE模組建構土地利用變化模型，並以2016年為基準，結合氣候推估資料，模擬2030與2050年未來情境下生態系服務變化，藉以探討未來氣候變遷與土地利用之交互作用。
研究結果顯示，1995至2016年間嘉南平原土地利用結構發生顯著變化，森林面積整體呈現增加趨勢，而旱田與部分水田面積則有所縮減。此一變化使得山區碳儲存量明顯提升，平原地區則因農業型態與土地利用調整而呈現較大的空間差異。年產水量分析結果指出，山區因降雨量較高且蒸散較低，長期維持較高的產水量，而平原地區則對氣候變動與土地利用調整高度敏感。營養鹽傳輸結果顯示，旱作農地與坡地區域為氮輸出主要區域，森林與水田則具備相對較高的氮滯留能力，顯示不同土地利用類型在營養鹽調節服務上具有顯著差異。未來情境模擬結果顯示，2030年嘉南平原部分地區因土地利用調整而出現短期碳儲存增加現象，至2050年，隨著建地擴張與植生覆蓋減少，平原地區碳儲存量呈現下降趨勢。年產水量方面，未來山區仍為主要水源供應區，而平原地區水量下降趨勢加劇。氮營養鹽輸出在未來情境下亦呈現整體上升趨勢，顯示若缺乏有效的土地管理與農業調適措施，水質風險可能進一步升高。
綜合研究結果，本研究指出嘉南平原未來生態系服務供給將受到氣候變遷與土地利用變化的共同影響，並呈現明顯的空間差異。

Climate change and land-use adjustments exert significant impacts on agricultural environments and ecosystem services, particularly in agricultural plains that are highly dependent on irrigation and fertilization, where water resource availability, carbon storage capacity, and nutrient cycling processes are under increasing pressure. As the most important agricultural production region in Taiwan, the Chianan Plain plays a critical role in regional sustainability, and changes in its land-use patterns and hydrological conditions have profound implications for long-term development. Accordingly, this study focuses on the Chianan Plain to investigate the spatiotemporal dynamics of agricultural ecosystem services under environmental change and to further assess potential challenges and development trends under future scenarios.
This study integrates geographic information systems (GIS) with the InVEST ecosystem service assessment model to quantitatively evaluate three key ecosystem services: annual water yield, carbon storage and sequestration, and nutrient (nitrogen) transport. Land-use data from 1995, 2005, and 2016 were combined with climate, soil, and watershed datasets to analyze historical land-use changes and their impacts on ecosystem service provision in the Chianan Plain. Subsequently, a land-use change model was developed using the MOLUSCE module, with 2016 as the baseline year, and future ecosystem service changes for 2030 and 2050 were simulated by incorporating climate projection data. This approach enables an integrated assessment of the interactions between future climate change and land-use dynamics.
The results indicate that significant changes in land-use structure occurred in the Chianan Plain between 1995 and 2016, characterized by an overall increase in forest area and a reduction in dry farmland and portions of paddy fields. These changes led to a substantial increase in carbon storage in mountainous regions, while the plain areas exhibited pronounced spatial heterogeneity due to variations in agricultural practices and land-use adjustments. Analysis of annual water yield shows that mountainous areas maintain consistently higher water yields due to greater precipitation and lower evapotranspiration, whereas plain areas are highly sensitive to both climatic variability and land-use change. Nutrient transport results reveal that dry farmland and sloping agricultural areas are the primary sources of nitrogen export, while forests and paddy fields demonstrate relatively high nitrogen retention capacity, highlighting marked differences in nutrient regulation services among land-use types.
Future scenario simulations suggest that certain areas of the Chianan Plain may experience short-term increases in carbon storage by 2030 due to land-use adjustments; however, by 2050, carbon storage in plain areas is projected to decline as a result of urban expansion and reduced vegetation cover. In terms of water yield, mountainous regions are expected to remain the primary sources of water supply, while water availability in plain areas shows an increasing downward trend. Nitrogen export is projected to increase overall under future scenarios, indicating that without effective land management and agricultural adaptation measures, the risk of water quality degradation may intensify. Overall, the findings indicate that future ecosystem service provisioning in the Chianan Plain will be jointly influenced by climate change and land-use transformation, resulting in pronounced spatial heterogeneity across the region.

謝誌	i
摘要	ii
ABSTRACT	iv
目錄	vii
圖目錄	ix
表目錄	xi
第一章	前言	1
1.1 研究背景	1
1.2 研究架構	2
第二章	文獻回顧	4
2.1 生態系服務之定義與應用	4
2.2 農業環境之生態系服務範疇	8
2.3 生態系服務評估方式	9
第三章	材料與方法	13
3.1 研究區域概況	13
3.2 數據蒐集及資料處理	14
3.3 生態系服務評估工具	21
3.3.1 年產水	22
3.3.2 碳儲存與封存	25
3.3.3 營養鹽傳輸	26
3.4 土地利用變化預測方法	28
3.4.1資料前處理	29
3.4.2 空間驅動因子資料來源與處理	29
3.4.3 驅動因子標準化	30
3.4.4 未來土地模擬及驗證	31
第四章	結果與討論	34
4.1 土地利用變化	34
4.2 年產水變化	38
4.2.1 年產水量的空間分布	38
4.2.2 各集水區年產水量變化	40
4.3 碳儲存與封存變化	44
4.3.1 碳儲存空間分布特徵	44
4.3.2 碳儲存年間分布變化	51
4.4 營養鹽傳輸變化	54
4.4.1 氮輸出之空間特徵	55
4.4.2 氮輸出之時間變化	58
4.5 土地利用、水文、氮輸出的交互作用	60
4.6 未來情境	61
4.6.1 未來情境模擬之驗證	62
4.6.2 未來土地利用變化	64
4.6.3 年產水未來變化	67
4.6.4 碳儲存與封存未來變化	71
4.6.5 營養鹽傳輸未來變化	78
4.6.6 未來情境整體討論	83
第五章	結論與建議	87
5.1 結論	87
5.2 建議	88
參考文獻	91

圖目錄
圖 1、研究流程.......................................................................................................3 
圖 2、嘉南平原位置及集水區分布.....................................................................14
圖 3、蒸發散量推估選用測站.............................................................................17
圖 4、年產水量水平衡概念示意.........................................................................23
圖 5、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原土地利用之面積變化 (平方 公里, 百分比).........................................................................................................35
圖 6、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原土地利用之空間變化.....36
圖 7、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原年產水量之空間變化.....40
圖 8、嘉南平原各集水區之單位面積產水量.....................................................44
圖 9、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原地上生物碳量之空間變化 .................................................................................................................................47
圖 10、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原地下生物碳量之空間變化 .................................................................................................................................48
圖 11、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原土壤碳量之空間變化...49
圖 12、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原死亡有機物碳量之空間變 化.............................................................................................................................50
圖 13、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原總含碳量之空間變化...51
圖 14、(a)1995~2005 年、(b)2005~2016 年嘉南平原總含碳量之年間變化....54
圖 15、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原透過地表輸出氮量之空間 變化.........................................................................................................................56
圖 16、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原透過地下輸出氮量之空間 變化.........................................................................................................................57
圖 17、(a)1995 年、(b)2005 年、(c)2016 年嘉南平原總輸出氮量之空間變化 .................................................................................................................................58
圖 18、嘉南平原各集水區之氮輸出量...............................................................60
圖 19、嘉南平原土地利用模擬:(a)2016 年實際土地利用情形、(b)2016 年模擬 土地利用情形.........................................................................................................63
圖 20、嘉南平原土地利用模擬之 ROC 曲線.....................................................64
圖 21、(a)2016 年、(b)2030 年、(c)2050 年嘉南平原之土地利用 (平方公里,百 分比)........................................................................................................................67
圖 22、嘉南平原土地利用之空間變化:(a)2030 年、(b)2050 年.......................67
圖 23、(a)2030 年、(b)2050 年嘉南平原之年產水量........................................70
圖 24、嘉南平原未來情境各集水區單位面積產水量.......................................71
圖 25、(a)2030 年、(b)2050 年嘉南平原地上生物碳量之空間變化................74
圖 26、(a)2030 年、(b)2050 年嘉南平原地下生物碳量之空間變化................74
圖 27、(a)2030 年、(b)2050 年嘉南平原土壤碳量之空間變化........................75 
圖 28、(a)2030 年、(b)2050 年嘉南平原死亡有機物碳量之空間變化............75 
圖 29、(a)2030 年、(b)2050 年嘉南平原總含碳量之空間變化........................76 
圖 30、(a)2016~2030 年、(b)2030~2050 年嘉南平原碳儲存之年間變化........77 
圖 31、(a)2030 年、(b)2050 年嘉南平原透過地表輸出氮量之空間變化........79 
圖 32、(a)2030 年、(b)2050 年嘉南平原透過地下輸出氮量之空間變化........80 
圖 33、(a)2030 年、(b)2050 年嘉南平原總輸出氮量之空間變化....................80
圖 34、嘉南平原未來情境各集水區之氮輸出量...............................................82

表目錄
表 1、TEEB 生態系服務細項分類........................................................................5
表 2、農業環境的生態系服務範疇.......................................................................8 
表 3、常見生態系服務評估模型之優缺點比較.................................................11 
表 4、土地利用分類表.........................................................................................15 
表 5、植物可利用之水份含量.............................................................................20 
表 6、嘉南平原不同土地利用類型之碳庫.........................................................26 
表 7、營養鹽傳輸生物物理表.............................................................................28 
表 8、驅動因子.....................................................................................................30 
表 9、嘉南平原集水區總產水量.........................................................................43 
表 10、嘉南平原 2030、2050 年產水量.............................................................70

Allan, J. D. (2004). Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems. Annu. Rev. Ecol. Evol. Syst, 35, 257–284. https://doi.org/10.1146/annurev.ecolsys.35.120202.110122
Aloui, S., Mazzoni, A., Elomri, A., Aouissi, J., Boufekane, A., & Zghibi, A. (2023). A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions. Journal of Environmental Management, 326, 116799. https://doi.org/10.1016/j.jenvman.2022.116799
Ayesu, S., Agbyenyaga, O., Barnes, V. R., Gyamfi, A., & Asante, R. K. (2024). Advancing multiple ecosystem service assessment in the tropics: Evidence from Barekese and Owabi watersheds in Ghana. Heliyon, 10(18), e37499. https://doi.org/10.1016/j.heliyon.2024.e37499
Bagstad, K. J., Semmens, D. J., Waage, S., & Winthrop, R. (2013). A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosystem Services, 5, 27–39. https://doi.org/10.1016/j.ecoser.2013.07.004
Barrios, E. (2007). Soil biota, ecosystem services and land productivity. Special Section - Ecosystem Services and Agriculture, 64(2), 269–285. https://doi.org/10.1016/j.ecolecon.2007.03.004
Beusen, A. H. W., Doelman, J. C., Van Beek, L. P. H., Van Puijenbroek, P. J. T. M., Mogollón, J. M., Van Grinsven, H. J. M., Stehfest, E., Van Vuuren, D. P., & Bouwman, A. F. (2022). Exploring river nitrogen and phosphorus loading and export to global coastal waters in the Shared Socio-economic pathways. Global Environmental Change, 72, 102426. https://doi.org/10.1016/j.gloenvcha.2021.102426
Caro, C., Marques, J. C., Cunha, P. P., & Teixeira, Z. (2020). Ecosystem services as a resilience descriptor in habitat risk assessment using the InVEST model. Ecological Indicators, 115, 106426. https://doi.org/10.1016/j.ecolind.2020.106426
Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O’Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P., & van den Belt, M. (1997). The value of the world’s ecosystem services and natural capital. Nature, 387(6630), 253–260. https://doi.org/10.1038/387253a0
Daily, G., Postel, S., Bawa, K., & Kaufman, L. (1997). Nature’s Services: Societal Dependence On Natural Ecosystems. Bibliovault OAI Repository, the University of Chicago Press.
De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity, 7(3), 260–272. https://doi.org/10.1016/j.ecocom.2009.10.006
de Groot, R. S., Wilson, M. A., & Boumans, R. M. J. (2002). A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics, 41(3), 393–408. https://doi.org/10.1016/S0921-8009(02)00089-7
Fu B. P. (1981). On the calculation of the evaporation from land surface. Scientia Atmospherica Sinica, 5(1), 23–31. https://doi.org/10.3878/j.issn.1006-9895.1981.01.03
Gómez-Baggethun, E., de Groot, R., Lomas, P. L., & Montes, C. (2010). The history of ecosystem services in economic theory and practice: From early notions to markets and payment schemes. Special Section - Payments for Environmental Services: Reconciling Theory and Practice, 69(6), 1209–1218. https://doi.org/10.1016/j.ecolecon.2009.11.007
Hamel, P., & Guswa, A. J. (2015). Uncertainty analysis of a spatially explicit annual water-balance model: Case study of the Cape Fear basin, North Carolina. Hydrology and Earth System Sciences, 19(2), 839–853. https://doi.org/10.5194/hess-19-839-2015
Hasan, S. S., Zhen, L., Miah, Md. G., Ahamed, T., & Samie, A. (2020a). Impact of land use change on ecosystem services: A review. Environmental Development, 34, 100527. https://doi.org/10.1016/j.envdev.2020.100527
Hasan, S. S., Zhen, L., Miah, Md. G., Ahamed, T., & Samie, A. (2020b). Impact of land use change on ecosystem services: A review. Resources Use, Ecosystem Restoration and Green Development, 34, 100527. https://doi.org/10.1016/j.envdev.2020.100527
Islam, Md. Y., Nasher, N. M. R., Karim, K. H. R., & Rashid, K. J. (2023). Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh. Heliyon, 9(5), e15617. https://doi.org/10.1016/j.heliyon.2023.e15617
Lal, R. (2004). Soil carbon sequestration to mitigate climate change. Geoderma, 123(1), 1–22. https://doi.org/10.1016/j.geoderma.2004.01.032
Metabolic. (2017). The Global Food System: An Analysis. Metabolic Institute. https://www.metabolic.nl/publication/global-food-system-an-analysis/
Nelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, Dr., Chan, K. M., Daily, G. C., Goldstein, J., Kareiva, P. M., Lonsdorf, E., Naidoo, R., Ricketts, T. H., & Shaw, Mr. (2009). Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment, 7(1), 4–11. https://doi.org/10.1890/080023
Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G. P., & Smith, P. (2016). Climate-smart soils. Nature, 532(7597), 49–57. https://doi.org/10.1038/nature17174
Plaas, E., Meyer-Wolfarth, F., Banse, M., Bengtsson, J., Bergmann, H., Faber, J., Potthoff, M., Runge, T., Schrader, S., & Taylor, A. (2019). Towards valuation of biodiversity in agricultural soils: A case for earthworms. Ecological Economics, 159, 291–300. https://doi.org/10.1016/j.ecolecon.2019.02.003
Redhead, J. W., Stratford, C., Sharps, K., Jones, L., Ziv, G., Clarke, D., Oliver, T. H., & Bullock, J. M. (2016). Empirical validation of the InVEST water yield ecosystem service model at a national scale. Science of The Total Environment, 569–570, 1418–1426. https://doi.org/10.1016/j.scitotenv.2016.06.227
Reid, W., Mooney, H., Cropper, A., Capistrano, D., Carpenter, S., & Chopra, K. (2005). Millennium Ecosystem Assessment. Ecosystems and human well-being: Synthesis.
Rimal, B., Sharma, R., Kunwar, R., Keshtkar, H., Stork, N. E., Rijal, S., Rahman, S. A., & Baral, H. (2019). Effects of land use and land cover change on ecosystem services in the Koshi River Basin, Eastern Nepal. Ecosystem Services, 38, 100963. https://doi.org/10.1016/j.ecoser.2019.100963
Schirpke, U., & Tasser, E. (2024). Potential impacts of climate change on ecosystem services in Austria. Ecosystem Services, 68, 101641. https://doi.org/10.1016/j.ecoser.2024.101641
Sharp, R., Douglass, J., Wolny, S., & Arkema, K. (2020). InVEST User Guide. The Natural Capital Project, Stanford University. https://naturalcapitalproject.stanford.edu/software/invest
Swinton, S. M., Lupi, F., Robertson, G. P., & Hamilton, S. K. (2007). Ecosystem services and agriculture: Cultivating agricultural ecosystems for diverse benefits. Special Section - Ecosystem Services and Agriculture, 64(2), 245–252. https://doi.org/10.1016/j.ecolecon.2007.09.020
TEEB. (2010). The Economics of Ecosystems and Biodiversity: National and International Policy Making. Earthscan.
Theis, S., Affinito, F., Rodriguez, P., Fortin, M.-J., & Gonzalez, A. (2025). Advancing ecosystem service monitoring by mapping the current use of essential ecosystem service variables. Ecological Indicators, 178, 113940. https://doi.org/10.1016/j.ecolind.2025.113940
Valencia, J. B., Guryanov, V. V., Mesa-Diez, J., Tapasco, J., & Gusarov, A. V. (2023). Assessing the Effectiveness of the Use of the InVEST Annual Water Yield Model for the Rivers of Colombia: A Case Study of the Meta River Basin. Water, 15(8), 1617. https://doi.org/10.3390/w15081617
Verma, P., Siddiqui, A. R., Mourya, N. K., & Devi, A. R. (2024). “Forest carbon sequestration mapping and economic quantification infusing MLPnn-Markov chain and InVEST carbon model in Askot Wildlife Sanctuary, Western Himalaya”. Ecological Informatics, 79, 102428. https://doi.org/10.1016/j.ecoinf.2023.102428
Viana, C. M., Freire, D., Abrantes, P., Rocha, J., & Pereira, P. (2022). Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Science of The Total Environment, 806, 150718. https://doi.org/10.1016/j.scitotenv.2021.150718
Vilhar, U. (2025). Runoff and Evapotranspiration–Precipitation Ratios as Indicators of Water Regulation Ecosystem Services in Urban Forests. Land, 14(4), 809. https://doi.org/10.3390/land14040809
Villa, F., Bagstad, K. J., Voigt, B., Johnson, G. W., Portela, R., Honzák, M., & Batker, D. (2014). A Methodology for Adaptable and Robust Ecosystem Services Assessment. PLOS ONE, 9(3), e91001. https://doi.org/10.1371/journal.pone.0091001
Weiskopf, S. R., Rubenstein, M. A., Crozier, L. G., Gaichas, S., Griffis, R., Halofsky, J. E., Hyde, K. J. W., Morelli, T. L., Morisette, J. T., Muñoz, R. C., Pershing, A. J., Peterson, D. L., Poudel, R., Staudinger, M. D., Sutton-Grier, A. E., Thompson, L., Vose, J., Weltzin, J. F., & Whyte, K. P. (2020). Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of The Total Environment, 733, 137782. https://doi.org/10.1016/j.scitotenv.2020.137782
Yu, C., Huang, X., Guo, Q., Yang, Y., & Xu, Z. (2024). Predicting water ecosystem services under prospective climate and land-use change scenarios in typical watersheds distributed across China. Ecological Indicators, 159, 111744. https://doi.org/10.1016/j.ecolind.2024.111744
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
Zhang, L., Dawes, W. R., & Walker, G. R. (2001). Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research, 37(3), 701–708. https://doi.org/10.1029/2000WR900325
林蕙萱(Hui-Hsuan Lin), 錢玉蘭(Yu-Lan Chien), & 林幸助(Hsing-Juh Lin). (2019). 臺灣西部海岸濕地碳儲存效益. 農業經濟叢刊, 25(1), 1–26. https://doi.org/10.6196/TAER.201906_25(1).0001
詹為巽, 林俊成, 吳孟珊, & 邱祈榮. (2018). 世界的森林生態系服務功能評價方法. 林業研究專訊, 25(6), 29–34.
詹碧連, 吳炎融, & 游添榮. (2011). 有效利用水資源－建立雲嘉南地區適當的耕作制度. 臺南區農業專訊, 78, 25–27. https://doi.org/10.29557/YYWYLL.201112.0006
鄭立沛. (2023). 整合永續發展目標之生態系服務與土地治理：以濁水溪流域為例－生態系服務之參與式情境建構應用於集水區的未來環境治理（子計畫四） (成果報告) [MOST 109-2621-M-002-007-MY3]. 國家科學及技術委員會.
闕雅文(Ya-Wen Chiueh). (2009). 區域水資源調配管理行爲分析模型. 農業與資源經濟, 6(1), 31–46. https://doi.org/10.29796/ARE.200906.0002