請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100936完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊振義 | zh_TW |
| dc.contributor.advisor | Jehn-Yih Juang | en |
| dc.contributor.author | 布萊德 | zh_TW |
| dc.contributor.author | Mbuya Bright Hubert | en |
| dc.date.accessioned | 2025-11-26T16:09:58Z | - |
| dc.date.available | 2025-11-27 | - |
| dc.date.copyright | 2025-11-26 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-11-05 | - |
| dc.identifier.citation | (NBS), N. B. o. S. (2022). Tanzania Population and Housing Census 2022 (Version Version 01).
Abril, G., & Borges, A. V. (2005). Carbon dioxide and methane emissions from estuaries. hydroelectric reservoirs and natural environments In Greenhouse gas emissions—fluxes and processes. Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., , Montgomery, J., & & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7), 842. Angle, J. C., Morin, T. H., Solden, L. M., Narrowe, A. B., Smith, G. J., Borton, M. A., Rey-Sanchez, C., Daly, R. A., Mirfenderesgi, G., Hoyt, D. W., Riley, W. J., Miller, C. S., Bohrer, G., & & Wrighton, K. C. (2017). Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions. Nature Communications, 8(1), 1567. Arai, H., Inubushi, K., & Chiu, C. Y. (2021). Dynamics of methane in mangrove forest: will it worsen with decreasing mangrove forests? Forests, 12(9). Araujo, J. K., Nogueira, J. D., & Moura, F. L. (2014). Methane emissions from mangrove estuaries under changing land use in tropical regions. Biogeochemistry, 119(1-3), 89–106. Balasus, N., Jacob, D. J., Lorente, A., Maasakkers, J. D., Parker, R. J., Boesch, H., Chen, Z. C., Kelp, M. M., Nesser, H., & Varon, D. J. (2023). A blended TROPOMI CGOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases. Atmospheric Measurement Techniques, 16(16), 3787–3807. https://doi.org/10.5194/amt-16-3787-2023 Bloom, A. A., Bowman, K. W., Lee, M., Turner, A. J., Schroeder, R., Worden, J. R., Weidner, R., McDonald, K. C., & & Jacob, D. J. (2010). A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0). Geoscientific Model Development, 10(6), 2141–2156. Boldini, A., Fernandes, J. A., & García, A. (2023). Machine learning approaches in modeling greenhouse gas emissions from estuarine ecosystems. Environmental Modelling and Software, 167(105717). Borges, A. V., & Abril, G. (2011). Carbon dioxide and methane dynamics in estuaries. Treatise on Estuarine and Coastal Science 5, 119–161. Breiman, L. (2001). Random forests Mchine Learning, 45(1), 5–32. Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., & Zhuang, Q. (2013). Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. . Global Change Biology, 19(5), 1325–1346. Brownlee, J. (2020). XGBoost with Python: Gradient boosting and hyperparameter tuning. Machine Learning Mastery. Bueno, I. T., Silva, C. A., Schlickmann, M. B., Donovan, V. M., Atkins, J. W., Brock, K. M., Xia, J. Y., Valle, D. R., Qiu, J. X., Vogel, J., Susaeta, A., Sharma, A., Klauberg, C., Mohan, M., & Dalla Corte, A. P. (2025). Upscaling Frameworks Drive Prediction Accuracy and Uncertainty When Mapping Aboveground Biomass Density from the Synergism of Spaceborne LiDAR, SAR, and Passive Optical Data. Remote Sensing, 17(14). Byarufu, D., Katima, J., & Said, M. (2021). Assessment of the Impacts of Climate Variability on Hydropower Generation: A case study of the New Pangani Falls, Tanzania. . Tanzania Journal of Engineering and Technology, 40(1), 50–61. Carmichael, M. J., Bernhardt, E. S., Bräuer, S. L., & Smith, W. K. (2014). The role of vegetation in methane flux to the atmosphere: should vegetation be included as a distinct category in the global methane budget? Biogeochemistry, 119(1-3), 1–24. https://doi.org/10.1007/s10533-014-9974-1 Chan, J. C. (2020). Predicting methane emissions using support vector regression and remote sensing data. Remote Sensing Applications: Society and Environment, 18(100305). Change, I. P. o. C. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. C. U. Press. Chen, C., Wang, J., Li, D., Sun, X. H., Zhang, J. Y., Yang, C. J., & Zhang, B. (2024). Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning. Scientific Reports, 14(1). Chen, S., Liu, L. C., Ma, Y. C., Zhuang, Q. L., & Shurpali, N. J. (2024). Quantifying Global Wetland Methane Emissions With In Situ Methane Flux Data and Machine Learning Approaches. Earths Future, 12(11). Chen, T., & Guestrin, C. (2016). A scalable tree boosting system 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining USA. Coalition, C. C. A. (2021). Global Methane Assesment: Benefits and Costs of mitigating Methane Emissions. Conrad, R. (2020). The Biogeochemical Methane cycle. Environmental Microbiology Reports, 1(5), 285–292. Cui, X., Chen, W., Wang, J., & Xie, B. (2024). Hydrological controls on methane fluxes in coastal wetlands under changing climate regimes. Environmental Science & Technology, 58(3), 1245–1256. Davis, K. (2008). Integrating Field Measurements with Flux Tower and Remote Sensing Data. Springer Netherlands, 207–225. Dengel, S., Zona, D., Sachs, T., Aurela, M., Jammet, M., Parmentier, F. J. W., Oechel, W. C., & Vesala, T. (2013). Testing the applicability of empirical models for estimating methane fluxes in northern wetlands. Biogeosciences, 10(12), 8185–8200. DotData. (2023). Automated feature engineering for complex environmental data ( Duncan, C., Owen, H. J. F., Thompson, J. R., Koldewey, H. J., Primavera, J. H., & Pettorelli, N. (2018). Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise. Methods in Ecology and Evolution, 9(8), 1837–1852. https://doi.org/10.1111/2041-210x.12923 Ecoregion-Programme, W.-E.-A.-M. (2012). Ruvuma Estuary: Transboundary Conservation Opportunities. El-Shahat, A., Singh, A., & Zhang, Y. . (2024). Evaluation of grid search optimization in remote sensing–based emission models. Sensors, 24(3). Ferreira, M. A., Andrade, F., Bandeira, S. O., Cardoso, P., Mendes, R. N., & Paula, J. (2009). Analysis of cover change (1995-2005) of Tanzania/Mozambique trans-boundary mangroves using Landsat imagery. Aquatic Conservation-Marine and Freshwater Ecosystems, 19, S38–S45. https://doi.org/10.1002/aqc.1042 Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11(9). Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. . (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(150066). Garcia, J. L., Patel, B. K. C., & Ollivier, B. (2000). Taxonomic phylogenetic and ecological diversity of methanogenic. Anaerobe, 6(4), 205–226. https://doi.org/10.1006/anae.2000.0345 Gardiner, T., Helmore, J., & Vaughan, A. (2017). Cross-validation techniques in environmental remote sensing: A case study on atmospheric CH4. Remote Sensing of Environment(199), 291–302. Ghorbanian, A., Zaghian, S., Asiyabi, R. M., Amani, M., Mohammadzadeh, A., & Jamali, S. (2021). Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sensing, 13(13). Golston, L. M., Tao, L., Brosy, C., Schäfer, K., Wolf, B., McSpiritt, J., Buchholz, B., Caulton, D. R., Pan, D., Zondlo, M. A., Yoel, D., Kunstmann, H., & McGregor, M. (2017). Lightweight mid-infrared methane sensor for unmanned aerial systems. Applied Physics B-Lasers and Optics, 123(6). Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 Hemes, K. S., Chamberlain, S. D., Eichelmann, E., Anthony, T., Valach, A., Kasak, K., Szutu, D., Verfaillie, J., Silver, W. L., & Baldocchi, D. D. . (2018). A biogeochemical compromise: The high methane cost of peatland restoration. Geophysical Research Letters, 45(11), 5489–5498. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... & Simmons, A. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. C. U. Press. Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., & Frankenberg, C. . (2016). Satellite observations of atmospheric methane and their value for quantifying methane emissions. Atmospheric Chemistry and Physics, 16(22), 14371–14396. Jahan, I., Mehana, M., Matheou, G., & Viswanathan, H. . (2024). Deep learning quantification of methane emissions from wetlands using chamber and satellite data. . International Journal of Applied Earth Observation and Geoinformation, 132(104018). Jain, A. K., Meiyappan, P., Song, Y., & & House, J. I. (2013). CO₂ emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Global Change Biology, 19(9), 2893–2906. Japhet, E., Mangora, M. M., Trettin, C. C., & Okello, J. A. (2019). Natural recovery of mangroves in abandoned rice farming areas of the Rufiji Delta, Tanzania. Western Indian Ocean Journal of Marine Science, 18(2), 25–36. Jongaramrungruang, S., Matheou, G., Thorpe, A. K., Zeng, Z.-C., & Frankenberg, C. (2021). Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale. Atmos. Meas. Tech, 14, 7999–8017. Kim, R., Lee, S. J., & & Son, Y. M. (2015). Estimation of National Greenhouse Gas Inventory in Wetland. Journal of Korean Environmental Research and Technology, 18, 61–72. Knox, S. H., Jackson, R. B., Poulter, B., McNicol, G., Fluet-Chouinard, E., Zhang, Z., Hugelius, G., Bousquet, P., Canadell, J. G., Saunois, M., Papale, D., Chu, H., Keenan, T. F., Baldocchi, D., Torn, M. S., Mammarella, I., Trotta, C., Aurela, M., Bohrer, G., & Zona, D. (2019). FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions. . Bulletin of the American Meteorological Society, 100(12), 2607–2632. Kristensen, E., Flindt, M. R., Ulomi, S., Borges, A. V., Abril, G., & Bouillon, S. . (2008). Emission of CO2 and CH4 to the atmosphere by sediments and open waters in two Tanzanian mangrove forests. . Marine Ecology Progress Series, 370, 53–67. Le Mer, J., & Roger, P. (2001). Production, oxidation, emission and consumption of methane by soils: A review. European Journal of Soil Biology, 37(1), 25–50. Lewinson, E. J. (2023). Interaction effects and lag-based predictors in ecological machine learning. Ecological Informatics, 72(101942). Liingilie, A. S., Kilawe, C., Kimaro, A., Rubanza, C., & & Jonas, E. (2015). Effects of salt making on growth and stocking of mangrove forests of south western Indian Ocean coast in Tanzania. Mediterranean Journal of Biosciences 1(1), 27–31. Liu, L., Wang, D., Chen, S., Yu, Z., Xu, Y., Li, Y., Ge, Z., & Chen, Z. (2019). Methane emissions from estuarine coastal wetlands: Implications for global change effect. Soil Science Society of America, 83(5), 1368–1377. Liu, W. C., & Liu, H. M. (2019). Integrating hydrodynamic model and Monte Carlo simulation for predicting extreme water levels in a river system. Terrestrial, Atmospheric & Oceanic Sciences, 30(4). Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Scarpelli, T. R., Nesser, H., Sheng, J.-X., Zhang, Y., Hersher, M., Bloom, A. A., Bowman, K. W., Worden, J. R., Janssens-Maenhout, G., & Parker, R. J. (2019). Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. . Atmospheric Chemistry and Physics, 19(12), 7859–7881. Majumder, M., Schüning, F., & Boehm, H. D. V. . (2023). Ensemble learning to improve methane emission estimates in tidal wetlands. Ecological Modelling, 478(110289). Majumder, R., Pollard, J., Salek, M. S., Werth, D., Comert, G., Gale, A., Khan, S. M., Darko, S., & Chowdhury, M. (2024). Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models. Environmental Health Insights, 18. McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/Doi 10.1080/01431169608948714 Mitsch, W. J., Nahlik, A., Wolski, P., Bernal, B., Zhang, L., & Ramberg, L. (2010). Tropical wetlands: seasonal hydrologic pulsing, carbon sequestration, and methane emissions. . Wetlands ecology and management, 18(5), 573–586. Monga, E., Mangora, M. M., & Mayunga, J. S. (2018). Mangrove cover change detection in the Rufiji Delta in Tanzania. Western Indian Ocean Journal of Marine Science, 17(2), 1–12. Morin, T. H., Bohrer, G., Stefanik, K. C., Rey-Sanchez, A. C., Matheny, A. M., & Mitsch, W. J. (2017). Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park. Agricultural and Forest Meteorology(237-238), 160–170. Mugabe, P., Kipkulei, H., Sieber, S., Mhache, E., & Löhr, K. (2024). Examining climate trends and patterns and their implications for agricultural productivity in Bagamoyo District, Tanzania. Frontiers in Climate, 6. Mwakalila, S., Clement, M., & Norbert, J. (2018). Implications of land use and climate change on water balance components in the Sigi Catchment, Tanzania. . Journal of the Geographical Association of Tanzania, 39(1), 1–18. Mwanja, R. I., Peter, K. H., & Said, M. K. (2024). The efficacy of management practices in combating mangrove forest degradation: A case study from Pangani District-Tanzania. East African Journal of Science, Technology and Innovation(6). Nazareth, D. R., & Gonsalves, M.-J. (2022). Influence of seasonal and environmental variables on the emission of methane from the mangrove sediments of Goa. . Environmental Monitoring and Assesment, 194(249). Oremland, R. S., & Polcin, S. . (1982). Methanogenesis and sulfate reduction: competitive and noncompetitive substrates in estuarine sediments. Applied and Environmental Microbiology, 44(6), 1270–1276. Parker, R. J., Boesch, H., McNorton, J., Comyn-Platt, E., Gloor, M., Wilson, C., Chipperfield, M. P., Hayman, G. D., & Bloom, A. A. . (2018). Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH₄ observations. Remote Sensing of Environment, 211, 261–275. Patel, L., Gowd, S. C., Singh, R., & Thottathil, S. D. (2025). Salinity and phosphorus as key regulators of methane emissions in tropical aquaculture ponds. Aquaculture International, 33(4), 302. https://doi.org/10.1007/s10499-025-01984-z Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., & Mammarella, I. (2019). Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations. Earth System Science Data, 11(3), 1263–1289. Philibert, A., Loyce, C., & & Makowski, D. (212). Assessment of the quality of meta-analysis in agronomy. Agriculture, Ecosystems & Environment, 148, 72–82. Pihlatie, M. K., Christiansen, J. R., Aaltonen, H., Korhonen, J. F. J., Nordbo, A., Rasilo, T., Benanti, G., Giebels, M., Helmy, M., Sheehy, J., Jones, S., J, uszczak, R., Klefoth, R., Lobo-do-Vale, R., Rosa, A. P., Schreiber, P., Serça, D., Vicca, S., Wolf, B., & & Pumpanen, J. (2013). Comparison of Static Chambers to measure CH4 emissions from soils. Agricultural and Forest Meteorology(171-172), 124–136. Poffenbarger, H. J., Needelman, B. A., & Megonigal, J. P. (2011). Salinity Influence on Methane Emissions from Tidal Marshes. Wetlands, 31(5), 831–842. Potter, C., Klooster, S., Hiatt, S., Fladeland, M., Genovese, V., & & Gross, P. (2006). Methane emissions from natural wetlands in the United States: Satellite-derived estimation based on ecosystem carbon cycling. Earth Interactions, 10(22), 1–12. Purdy, K. J., Eyice, Ö., Taylor, J., Dolfing, J., & Cetecioglu, Z. . (2018). COD/sulfate ratio does not affect the methane yield and microbial diversity in anaerobic digesters. Water Research, 155, 444–454. Purvaja, R., & Ramesh, R. (2001). Natural and Anthropogenic Methane Emission from Coastal Wetlands of South India. Environmental Management, 27(4), 547–557. QuarkML. (2022). Effective support vector regression for non-linear environmental systems. Machine Learning Reports, 8(1), 15–26. Radočaj, D., & Jurišić, D. . (2025). Ensemble modeling of greenhouse gas emissions: Performance assessment of hybrid approaches. Environmental Data Science, 4(e0025). Radocaj, D., & Jurisic, M. (2025). Comparative Evaluation of Ensemble Machine Learning Models for Methane Production from Anaerobic Digestion. Fermentation-Basel, 11(3). Räsänen, A., Manninen, T., Korkiakoski, M., Lohila, A., & Virtanen, T. (2025). Predicting catchment-scale methane fluxes with multi-source remote sensing. Landscape Ecology, 36, 1177–1195. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. Reji, L., Duan, J. S., Myneni, S. C. B., & Zhang, X. N. (2025). Distinct microbiomes underlie divergent responses of methane emissions from diverse wetland soils to oxygen shifts. Isme Communications, 5(1). Ruvuma-Basin-Water-Board. (2013). 2012-13 Ruvuma Basin Annual Hydrological Report (November 2012 – October 2013). Mtwara, Tanzania. https://www.scribd.com/document/369571657/2012-13-Ruvuma-Basin-Annual-Hydrological-Report Sandeep, K. S., Bhatia, A., & Pathak, H. (2022). Monte Carlo uncertainty analysis for methane flux modeling in subtropical wetlands. Agricultural and Forest Meteorology, 314(108784). Sandoval, D., Mendoza, J., & Estrada, F. (2022). Sensitivity analysis of methane flux models in tropical estuaries. Environmental Modelling & Software, 155(105481). Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., & Zhuang, Q. (2020). The Global Methane Budget 2000–2017. 12(3), 1561–1623. Scikit-learn. (2024). Recursive feature elimination with cross-validation ( Sebbar, B. E., Khabba, S., Merlin, O., Simonneaux, V., El Hachimi, C., Kharrou, M. H., & Chehbouni, A. (2023). Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions. Atmosphere, 14(4). Segarra, K. E. A., Schubotz, F., Samarkin, V., Yoshinaga, M. Y., Hinrichs, K. U., & Joye, S. B. (2015). High rates of anaerobic methane oxidation in freshwater wetlands reduce potential atmospheric methane emissions. Nature Communications, 6(7477). Sembhi, H., Parker, R., & Boesch, H. (2015). Tropospheric methane retrievals from GOSAT thermal infrared soundings. Sentinel-5P, C. (2024). Sentinel-5P OFFL CH4: Offline Methane. Google Earth Engine Data Catalog ( Serrano-Silva, N., Sarria-Guzmán, Y., Dendooven, L., & Luna-Guido, M. . (2014). Methanogenesis and methanotrophy in soil: A review. Pedosphere, 24(3), 291–307. Shanghude, Y. W. (2016). Estuarine environmental and socio-economic impacts associated with upland agricultural irrigation and hydropower developments: The case of Rufiji and Pangani estuaries, Tanzania. In S. Diop, P. Scheren, & J. F. Machiwa (Eds.), Estuaries: A Lifeline of Ecosystem Services in the Western Indian Ocean, 141–152. Springer, S. (2020). Monte Carlo simulation of greenhouse gas emissions in wetland modeling. Journal of Environmental Management, 260(110139). Tanzania, U. R. o. (2021a). National Environmental Policy 2021. https://www.vpo.go.tz/uploads/publications/en-1644923087-NATIONAL%20%20ENVIRONMENTAL%20POLICY%202021%20new.pdf Tanzania, U. R. o. (2021b). Nationally Determined Contribution (NDC). UNFCCC. https://unfccc.int/sites/default/files/NDC/2022-06/TANZANIA_NDC_SUBMISSION_30%20JULY%202021.pdf Tanzania, U. R. o. (2025). Tanzania Development Vision 2050. https://www.planning.go.tz/uploads/documents/en-1752762713-THE%20TANZANIA%20DEVELOPMENT%20VISION%202050.pdf Tong, C., Wang, W. Q., Zeng, C. S., & Marrs, R. (2010). Methane emission from a tidal marsh in the Min River estuary, southeast China. Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering, 45(4), 506–516. https://doi.org/10.1080/10934520903542261 Turetsky, M. R., Kotowska, A., Bubier, J., Dise, N. B., Crill, P., Hornibrook, E. R. C., Minkkinen, K., Moore, T. R., Myers-Smith, I. H., Nykänen, H., Olefeldt, D., Rinne, J., Saarnio, S., Shurpali, N., Tuittila, E. S., Waddington, J. M., White, J. R., Wickland, K. P., & Wilmking, M. (2014). A synthesis of methane emissions from 71 northern, temperate, and subtropical wetlands. Global Change Biology, 20(7), 2183–2197. https://doi.org/10.1111/gcb.12580 UC-Davis. (2023). Uncertainty quantification in environmental remote sensing. University of California, Davis—Center for Spatial Technologies. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks. 10(5), 988–999. Wagner, G. M., & Sallema-Mtui, R. (2016). The Rufiji Estuary: Climate change, anthropogenic pressures, vulnerability assessment and adaptive management strategies. In S. Diop, P. Scheren, & J. F. Machiwa (Eds.), Estuaries: A Lifeline of Ecosystem Services in the Western Indian Ocean, 153–168. Wang, N., Tian, J., Su, S. S., & Tian, Q. J. (2023). A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature. Remote Sensing, 15(18). Wang, Y., Song, Q., Wasif, D., Shahzad, M., Koller, C., Bamber, J., & Zhu, X. X. (2024). How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning. arXiv preprint arXiv:2412.06451. Welti, N., Hayes, M., & Lockington, D. (2017). Seasonal nitrous oxide and methane emissions across a subtropical estuarine salinity gradient. Biogeochemistry, 132(1), 55–69. https://doi.org/10.1007/s10533-016-0287-4 Whalen, S. C. (2005). Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environmental Engineering Science, 22(1), 73–94. Wójcik-Gront, E., Wnuk, A., & Gozdowski, D. . (2025). Spatio-Temporal Patterns of Methane Emissions from 2019 Onwards: A Satellite-Based Comparison of High-and Low-Emission Regions. Atmosphere, 16(6). World-Bank. (2024). Tanzania’s greener future: Reducing methane in waste and livestock sectors. https://www.worldbank.org/en/news/feature/2024/05/16/tanzania-afe-greener-future-reducing-methane-in-waste-and-livestock-sectors World-Rainforest-Movement. (2001). Tanzania: The death of the Rufiji Delta Prawn Project. Ye, R. Z., Jin, Q. S., Bohannan, B., Keller, J. K., McAllister, S. A., & Bridgham, S. D. (2012). pH controls over anaerobic carbon mineralization, the efficiency of methane production, and methanogenic pathways in peatlands across an ombrotrophic-minerotrophic gradient. Soil Biology & Biochemistry, 54, 36–47. https://doi.org/10.1016/j.soilbio.2012.05.015 Yvon-Durocher, G., Allen, A. P., Bastviken, D., Conrad, R., Gudasz, C., St-Pierre, A., Thanh-Duc, N., & del Giorgio, P. A. (2014). Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature, 507(7493), 488–491. Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N. E.,…Arino, O. (2022). ESA WorldCover 10 m 2021 v100. . International Institute for Applied Systems Analysis. Zhang, C., Comas, X., & Brodylo, D. . (2020). A remote sensing technique to upscale methane emission flux in a subtropical peatland. Journal of Geophysical Research: Biogeosciences, 125(10). Zheng, Y., Wu, S., Xiao, S., Yu, K., Fang, X., Xia, L., Wang, J., Liu, S., Freeman, C., & Zou, J. (2022). Global methane and nitrous oxide emissions from inland waters and estuaries. Global Change Biology, 28(15), 4713–4725. Zhu, X. D., Zhuang, Q. L., Qin, Z. C., Glagolev, M., & Song, L. L. (2013). Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks. Global Biogeochemical Cycles, 27(2), 592–604. https://doi.org/10.1002/gbc.20052 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100936 | - |
| dc.description.abstract | 本研究以整合性方法量化坦尚尼亞主要河口的甲烷排放，結合遙測技術、次級與衍生的實地測量資料，以及先進的機器學習技術。透過 Google Earth Engine（GEE）與多源衛星資料，本研究建立一套完整的方法論，以估算 Rufiji、Wami、Pangani 和 Ruvuma 四大河口的甲烷通量。研究納入多項環境變數，包括氣溫、降雨量、水體範圍、酸鹼值（pH）、標準化差異水指數（NDWI）及濕度，以探討其對甲烷生成與排放模式的影響。所採用的機器學習模型包括隨機森林（Random Forest）、支持向量迴歸（Support Vector Regression）與梯度提升（Gradient Boosting），成功解析多變量間的複雜關係，預測準確度高達 91%（R² = 0.91）。
研究結果顯示，各河口甲烷排放具有顯著的空間異質性，其中 Rufiji 河口的平均排放量最高（42.3 毫克 CH₄／平方米／日），依序為 Ruvuma（39.7 毫克）、Wami（35.6 毫克）與 Pangani（28.9 毫克）。季節變化亦十分明顯，雨季排放量普遍高於旱季 27–45%。氣溫為甲烷排放最強的預測因子（相關係數：0.69），而酸鹼值則呈顯著負相關（-0.45）。 本研究為坦尚尼亞河口甲烷動態提供首份全面性評估，並建立一套可應用於其他熱帶沿海系統的方法框架。研究成果有助於提升坦尚尼亞溫室氣體清冊的準確性，並為沿海濕地管理的氣候變遷緩解策略提供科學依據。 | zh_TW |
| dc.description.abstract | This study quantifies methane emissions from major estuaries in Tanzania using an integrated approach combining remote sensing, secondary/ derived field measurements, and advanced machine learning techniques. Using Google Earth Engine (GEE) and multi-source satellite data, this research develops a comprehensive methodology to estimate methane fluxes across the Rufiji, Wami, Pangani, and Ruvuma estuaries. The study incorporates multiple environmental variables including temperature, rainfall, water body extent, pH, Normalized Difference Water Index (NDWI), and humidity to understand their influence on methane production and emission patterns. Machine learning models including Random Forest, Support Vector Regression, and Gradient Boosting were employed to analyze complex multivariate relationships, achieving prediction accuracy of up to 91% (R² = 0.91). Results reveal significant spatial heterogeneity in methane emissions across estuaries, with the Rufiji estuary exhibiting the highest mean emissions (42.3 mg CH₄ m⁻² d⁻¹), followed by Ruvuma (39.7 mg CH₄ m⁻² d⁻¹), Wami (35.6 mg CH₄ m⁻² d⁻¹), and Pangani (28.9 mg CH₄ m⁻² d⁻¹). Pronounced seasonal variations were observed, with wet season emissions exceeding dry season values by 27-45% across all sites. Temperature emerged as the strongest predictor of methane emissions (correlation coefficient: 0.69), while pH showed a significant negative relationship (-0.45). The study provides the first comprehensive assessment of methane dynamics in Tanzanian estuaries and establishes a methodological framework applicable to other tropical coastal systems. These findings contribute to improved greenhouse gas inventories for Tanzania and inform climate change mitigation strategies for coastal wetland management. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-11-26T16:09:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-11-26T16:09:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Abstract ii
摘要 iii Contents iv List of figures v List of tables vi 1. Introduction 1 1.1 Background 1 1.2 Problem Statement 4 1.3 Research gaps 5 1.4 Research Objectives 6 1.5 Significance of the Study 7 1.6 Scope of the study 8 2. Literature Review 9 2.1 Methane Biogeochemistry in Estuarine Environments 9 2.2 Remote Sensing for Methane Quantification 10 2.3 Machine Learning Approaches for Environmental Modelling 12 2.4 In-situ Measurement Techniques 13 2.5 Environmental Drivers of Methane Emissions 14 2.6 Policy Frameworks for Methane Management 15 3. Methodology 16 3.1 Research framework 16 3.2 Study Area 19 3.3 Data Sources 23 3.4 Integrated Quantification Framework 26 4. Results and Analysis 32 4.1 Environmental Factor Analysis 32 4.2 Remote Sensing Analysis 42 4.3 Machine Learning model results 54 4.4 Quantification Results 59 4.5 Limitations and Uncertainties 62 5. Policy and Management Framework 63 5.1 Policy Context Analysis 63 5.2 Proposed Policy Framework 64 5.3 Implementation Strategies 67 6. Discussion 69 6.1 Methodological Advancements 69 6.2 Environmental Controls on Methane Emissions 71 6.3 Policy Implications 72 7. Limitations, Conclusion and Recommendations 74 7.1 Data and Methodological Limitations 74 7.2 Knowledge Gaps 75 7.3 Future Research Directions 75 7.4 Scientific Recommendations 76 7.6 Concluding Remarks 77 References 78 Appendix 86 | - |
| dc.language.iso | en | - |
| dc.subject | 甲烷排放 | - |
| dc.subject | 河口 | - |
| dc.subject | 坦尚尼亞 | - |
| dc.subject | 遙測 | - |
| dc.subject | TROPOMI | - |
| dc.subject | GOSAT | - |
| dc.subject | CH₄ 通量 | - |
| dc.subject | 氣候緩解 | - |
| dc.subject | Methane emissions | - |
| dc.subject | Estuaries | - |
| dc.subject | Tanzania | - |
| dc.subject | Remote Sensing | - |
| dc.subject | Machine Learning | - |
| dc.subject | TROPOMI | - |
| dc.subject | GOSAT | - |
| dc.subject | CH₄ flux | - |
| dc.subject | Climate mitigation | - |
| dc.title | 坦尚尼亞河口甲烷排放量之遙感技術量化研究 | zh_TW |
| dc.title | Quantification of Methane Emissions from Estuaries in Tanzania using Remote Sensing Techniques | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃倬英;孫烜駿 | zh_TW |
| dc.contributor.oralexamcommittee | Cho-Ying Huang;Syuan-Jyun Sun | en |
| dc.subject.keyword | 甲烷排放,河口坦尚尼亞遙測TROPOMIGOSATCH₄ 通量氣候緩解 | zh_TW |
| dc.subject.keyword | Methane emissions,EstuariesTanzaniaRemote SensingMachine LearningTROPOMIGOSATCH₄ fluxClimate mitigation | en |
| dc.relation.page | 86 | - |
| dc.identifier.doi | 10.6342/NTU202504630 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-11-06 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 氣候變遷與永續發展國際學位學程 | - |
| dc.date.embargo-lift | 2025-11-27 | - |
| 顯示於系所單位： | 氣候變遷與永續發展國際學位學程(含碩士班、博士班) | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf | 6.65 MB | Adobe PDF | 檢視/開啟 |
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。