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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 邱祈榮(Chyi-Rong Chiou) | |
| dc.contributor.author | Chuan-Chou Lu | en |
| dc.contributor.author | 呂權周 | zh_TW |
| dc.date.accessioned | 2022-11-24T09:24:45Z | - |
| dc.date.available | 2022-11-24T09:24:45Z | - |
| dc.date.copyright | 2022-02-16 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-02-12 | |
| dc.identifier.citation | 中華民國內政部 (2010) 國家重要濕地保育計畫(100-105年)合訂本。 中華民國內政部 (2017) 夢幻湖重要濕地（國家級）保育利用計畫。 中華民國內政部 (2018) 雙連埤重要濕地（國家級）保育利用計畫（草案）。 中華民國內政部 (2020) 濕地保育電子公報109年。 李界木 (2015) 巡田水遊記-冷埤和粗坑。民報。專題文章。 李璟芳、張守陽、林志平 (2004) 紅外線熱影像應用於土石流監測之可行性研究。中華水土保持學報。35(3)。261-274。 吳振發、詹士樑 (2003) 常態化差異植生指數應用於都市綠地品質管制之探討。台灣土地研究。6(2)。17-42。 林金樹、焦國模 (1997) 遙測資訊在生態環境變遷監測上應用之研究－以台南地區為例。航測及遙測學刊。2(3)。37-76。 林裕彬、鄧東波、吳振發 (2001) 利用衛星影像探討都市綠地空間之碎形現象。社區規劃與環境改造研討會論文集，台北市。 林榮潤、許世孟、李鳳梅 (2015) 遙測技術應用於山區地下水潛能場址之研究。中興工程。127。11-19。 宜蘭縣政府農業局 (2007) 雙連埤整體發展先期計畫：湖沼生態系之監測與基礎資料建立。 宜蘭縣政府環境保護局 (2017) 水污染源稽查與水污費徵收查核暨活力海洋與綠色港灣等相關計畫網-地面水體水質水量監測。 邱祈榮、鍾智昕、黃俊元、林庭安 (2017) 應用衛星影像 Data Cube 進行植生復育監測之探討。第十二屆環境保護林經營管理研討會論文集。p.99-108。 邱錦和、黃朝慶 (2003) 雙連埤整地後水生植物調查報告。 洪明仕 (2007) 台灣第一類群的生態保育。漁業推廣。251。10-19。 陳貝貞 (2009) 以景觀生態學觀點探討濕地廊道評估架構之建立。東海大學景觀學系所碩士論文。 陳宜清 (2009) 濕地分類及其功能涵容評價之簡介。自然保育季刊。60。3-20。 陳添水 (2002) 遙測應用於大肚溪口地區環境變遷分析。特有生物研究。4（1）。61-74。 詹海柏 (2017) 以衛星熱紅外影像資料探勘及監測北台灣的地熱與火山活動。國立中央大學地球科學系博士論文。 羅英瑞 (2013) 應用多尺度方法探討雙連埤野生動物保護區水位變動與植物回復之關係。宜蘭大學森林暨自然資源學系學位論文。 Abdi A.M. (2019) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience and Remote Sensing. 57(1). 1-20. Adam E.M., Mutanga O., Rugege D., Ismail R. (2009) Field spectrometry of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands of St Lucia, South Africa Field spectrometry of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands of St Lucia, South Africa. Geoscience and Remote Sensing Symposium, 2009 IEEE International. 260-263. Adams, J. M., H. Faure, L. Faure‐Denard, J. M. McGlade, and F. I. Woodward, Increases in terrestrial carbon storage from the last glacial maximum to the present, Nature, 348, 711–714, 1990. Bakker S.A., van den Berg N.J., Speleers B.P. (1994) Vegetation transitions of floating wetlands in a complex of turbaries between 1937 and 1989 as determined from aerial photographs with GIS. Vegetatio, 114, 161-167. Behera M.D., Chitale V.S., Shaw A., Roy P. S., Murthy M.S.R. (2012) Wetland Monitoring, Serving as an Index of Land Use Change-A Study in Samaspur Wetlands, Uttar Pradesh, India. Journal of the Indian Society of Remote Sensing. 40. 287–297. Birth G. S. and McVey G. R. (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal. 60. 640-643. Breiman L., Friedman J.H., Olsen R.A., Stone C.J. (1984) Classification and regression trees. Wadsworth and Brooks, New York, USA. Brown C.D., Davis H.T. (2006) Receiver operating characteristics curves and related decision measures: A tutorial. Chemometrics and Intelligent Laboratory Systems. 80(1). 24-38. Canadian National Wetlands Working Group (1988) Wetlands of Canada. Ecological Land Classification Series, No. 24. Canadian National Wetlands Working Group. (1997) The canadian wetland classification system. Ceccato P., Flasse S., Tarantola S., Jacquemoud S., Grégoire J.M. (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22-33. Chapman, V. J. (1977) Wet coastal ecosystems. Ecosystems of the world, 1, 1-30. Chen T.Q., Guestrin C. (2016) XGBOOST: A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA. 785-794. Cibula W. G., Zetka E. F., Rickman D. L. (1992) Response of thematic bands to plant water stress. Int. J. Remote Sensing. 13. 1869-1880. Classification of wetlands and deepwater habitats of the united states. (1979) U.S. Department of the Interior Fish and Wildlife Service Office of Biological Services. Convention on Biological Diversity (2003) Inland Water Ecosystems: Review, Further Elaboration and Refinement of the Programme of Work. Corcoran J.M., Knight J.F., Gallant A.L. (2013) Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota. Remote Sensing. 5(7). 3212-3238. Costanza R., Dŕge R., Groot R. et al. (1997) The value of the world's ecosystem services and natural capital. Nature, 387, 253–260. Cowardin, L. M., V., Carter, F. C., Golet and E. T., LaRoe (1979) Classification of wetlands and deepwater habitats of the United States. Davranche A., Lefebvre G., Poulin B. (2010) Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sensing Environment. 114(3). 552-562. DeFries R.S., Hansen M., Townshend J. (1995) Global discrimination of land cover types from metrics derived from AVHRR pathfinder date. Remote Sensing Environment. 54. 9-22. DeFries R.S., Foley J.A., Asner G.P. (2004) Land-use choices: Balancing human needs and ecosystem functions Frontiers in Ecology and the Environment, 2, pp. 249-257. Dixon, R. K., and O. N. Krankina, Can the terrestrial biosphere be managed to conserve and sequester carbon? in Carbon Sequestration in the Biosphere: Processes and Products, NATO ASI Ser., Ser. I, Global Environ. Change, edited by M. A. Beran, vol. 33, pp. 153–179, Springer‐Verlag, New York, 1995. Eid A. N. M., Olatubara C. O., Ewemoje T. A., El-Hennawy M. T., and Farouk H. (2020). Inland wetland time-series digital change detection based on SAVI and NDWI indecies: Wadi El-Rayan lakes, Egypt. Remote Sensing Applications: Society and Environment. 19. 100347. Elvidge, C. D. (1990) Visible and near infraRed reflectance characteristics of dry plant materials. Int. J. Remote Sensing. 11. 1775-1795. European Space Agency (2016) Level 2A Input Output Data Definition. European Space Agency (2018) Sentinel-2 Products Specification Document. Findlay S.C. Houlahan J. (1997) Anthropogenic correlates of species richness in southeastern Ontario wetlands. Conservation Biology, 11, 1000–1009. Foley J.A., DeFries R., Asner G.P., Barford C., Bonan G., Carpenter S.R. (2005) Global consequences of land use Science, 309 (5734), 570-574. Gagne S.A., Fahrig L. (2007) Effect of landscape context on anuran communities in breeding ponds in the National Capital Region, 22, Landscape Ecology, Canada, pp. 205-215. Gao B.C. (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environment. 58. 257-266. Georganos S., Grippa T., Vanhuysse S., Lennert M., Shimoni M., Wolff E. (2018) Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting. IEEE Geoscience and Remote Sensing Letters. 15(4). 607–611. Gislason P.O., Benediktsson J.A., Sveinsson J.R. (2006) Random Forests for land cover classification. Pattern Recognition Letters. 27. 294-300. Gitelson A.A., Merzlyakc M.N. (1994) Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology. 143(3). 286 – 292. Gitelson A.A., Gritz Y, Merzlyakc M.N. (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology. 160(3). 271 – 282. Gitelson A.A. (2004) Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. Journal of Plant Physiology. 161(2). 165-173. Gutman, G., Byrnes, R., Masek, I., Covington, S., Justice, C., Franks, S., Headley, R. (2008) Towords monitoring land-cover and land-use change at a global scale: the global land use survey 2005. Photogrammetric Engineering and Remote Sensing. 74, p.6-10. Ham J., Chen Y., Crawford M., Ghosh J. (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing. 43. 492-501. Hayes D.J., Sader S.A. (2001) Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photo Eng in Remote Sensing. 67(9). 67-75. Hess L.L., Melack J.M., Affonso A.G., Barbosa C., Gastil-Buhl M., Novo E.M.L.M. (2015) Wetlands of the lowland amazon basin: extent vegetative cover, and dual-season inundated area as mapped with JERS-1 synthetic aperture radar. Wetlands. 35. 745-756. Hirayama H., Sharma R. C., Tomita M., Hara. K. (2019) Evaluating Multiple Classifier System for the Reduction of Salt-and-pepper Noise in the Classification of Very-high-resolution Satellite Images. International Journal of Remote Sensing. 40(7). 2542–2557. Huang C.D., Song S.H., Zhu h.q. (2011) An analysis on some biophysical parameters of Hangzhou Xixi Wetland based on remote sensing. 2011 International Conference on Electric Technology and Civil Engineering (ICETCE), Lushan. 2894-2897. Huete A. R. (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing Environment. 25. 295-309. Huete A. R., Didan K., Miura T., Rodriguez E. P., Gao X., Ferreira L. G. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83(1). 195-213. Hunt E.R. Jr., Rock B.N., Nobel P.S. (1987) Measurement of leaf relative water content by infraRed reflectance. Remote Sensing Environment. 22. 429-435. Hunt E.R. Jr., Rock B.N. (1989) Detection of changes in leaf water content using near- and middle-infraRed reflectances. Remote Sensing of Environment, 30, 43-54. Imanishi J., Sugimoto K., Morimoto Y. (2004) Detecting drought status and LAI of two Quercus species canopies using derivative spectra. Computers and Electronics in Agriculture, 43, 109-129. IPCC (2013) Supplement to the 2006 Guidelines for National Greenhouse Gas Inventories: Wetlands (Wetlands Supplement). Jackson R. D., Slater P. N., Pinter P. J. Jr. (1983) Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres Remote Sensing Environment. 13. 187-208. Jakubauskas M.E., Peterson D.L., Kastens J.H., Legates S.R. (2002) Time series remote sensing of landscape-vegetation interactions in the southern great plains. Photogram Eng Remote Sensing. 68(10), 21-30. Kaplan G. and Avdan U. (2017) MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY. ISPRS Annals of Photogrammetry, Remote Sensing Spatial Information Sciences. 4. 271-277. Kearns M. (1988) Thoughts on hypothesis Boosting. ML class project. Lane C.R., Liu H.X., Autrey B.C., Anenkhonov O.A., Chepinoga V.V., Wu Q.S. (2014) Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach. Remote Sensing. 6. 12187-12216. Langbein, W. B. and K. T., Iseri (1960) General introduction and hydrologic definitions manual of hydrology. Lawrence R.L., Ripple W.J. (1998) Comparisons among vegetation indices and band wise regression in a highly disturbed, heterogeneous landscape: Mount St.Helens, Washington. Remote Sensing Environment. 64. 91-102. Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wybron, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, w., Ten, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, b., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P., Wang, L.W. (2017) The Australian Geoscience Data Cube—Foundations and lessons learned. Remote Sensing of Environment. 202, p.276-292. Li, F., Jupp, D.L., Reddy, S., Lymburner, L., Mueller, N., Lewis, A., Held, A. (2012) A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment. 124, p.756-770. Li W., Hughes M. (2020) Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques. Remote Sensing. 12(10). 1683. Lin J.T. (1987) An ecological investigation of Kuantu wetland vegetation. 1987 Ecological Study no. 018. Taipei: Council of Agriculture. Lillesand T.M., Kiefer R.W., Chipman J.W. (2004). Remote sensing and image interpretation . John Wiley Sons. 763. Lillesand T., Kiefer R.W., Chipman J. (2014) Remote Sensing and Image Interpretation. John Wiley Sons. Lyon J.G., Yuan D., Lunetta R.S., Elvidge C.D. (1998) A change detection experiment using vegetation indices. Photo Engin Remote Sens. 64(2):143-50. Mahdianpari M., Salehi B., Mohammadimanesh F., Motagh M. (2017) Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 130. 13-31. Man C. D., Nguyen T. T., Bui H. Q., Lasko K. K., Nguyen T. N. T. (2018) Improvement of Land-cover Classification over Frequently Cloud-coveRed Areas Using Landsat 8 Time-series Composites and an Ensemble of Supervised Classifiers. International Journal of Remote Sensing. 39(4). 1243–1255. McCarthy J., Gumbricht T., McCarthy T.S., Frost P.E., Wessels K., Seidel F. (2004) Flooding patterns in the Okavango wetland in Botswana, between 1972 and 2000. Ambio. 7. 453-457. 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. Mutanga O., Adama E., Cho M.A. (2012) High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation. 18. 399-406. Muttiah R.S. (2002) From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems. Springer, Dordrecht, Netherlands. Na X.D., Zhang S.Q., Li X.F., Yu H., Liu C.Y. (2010) Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data. Photogrammetric Engineering Remote Sensing. 76(7). 833-840. Nayaket, S. R. and B., Sahai (1985) Coastal morphology: a case study of the Gulf of Khambhat (Cambay). International Journal of Remote Sensing, 6, 559–567. Ozesmi, S. L. and M. E., Bauer (2002) Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10, 381-402. Paludan U., Alexeyev F.E., Drews H. et al. (2002) Wetland management to Reduce baltic sea eutrophication. Water Science and Technology, 45, 87–94. Penuelas J., Baret F., Filella I. (1995) Semi-empirical indices to assess carotenoids /chlorophyll a ratio from leaf spectral reflectance. Photosynthetica. 31(2). 221 – 230. Ramsar Convention (1971) Convention of Wetlands of International Importance Especially as Waterfowl Habitats. Ramsey R.D., Falconer A., Jensen J.R. (1995) The relationship between NOAA-AVHRR NDVI and ecoregions in Utah. Remote Sensing Environment. 53. 88-98. Rapinel, S., J.B. Bouzillé, J. Oszwald, A. Bonis (2015) Use of bi-seasonal Landsat-8 imagery for mapping marshland plant community combinations at the regional scale. Wetlands. 35. 1–12. Rollin E.M., Milton E.J. (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sensing of Environment, 65, 86-92. Rouse J.J., Haas R.H., Schell J.A., Deering D.W. (1974) Monitoring Vegetation Systems in the Great Plains with Erts. NASA Special Publication, Washington, USA. 309. Sahagian, D., and J. Melack, Global wetland distribution and functional characterization: Trace gases and the hydrologic cycle, IGBP Rep. 46 , Intl. Geosphere Biosphere Programme Secretariat, Stockholm, 1988. Sculthorpe, C. D. (1967) The biology of aquatic vascular plants. Shan B., Yin C. Li G. (2002) Transport and retention of phosphorus pollutants in the landscape with a traditional, multipond system. Water Air and Soil Pollution, 139, 15–34. Shapley, L.S. (1951) Notes on the n-Person Game -- II: The Value of an n-Person Game. Santa Monica, Calif.: RAND Corporation. Silva T.S.F., Costa M.P.F., Melack J.M., Novo E.M.L.M. (2008) Remote sensing of aquatic vegetation: theory and applications. Environmental Monitoring and Assessment. 140. 131-145. Sims D.A., Gamon J.A. (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing Environment. 84(4). 526-537. Soil Survey Staff (1999) Soil taxonomy: a basic system of soil classification for makingand interpreting soil surveys. Stehman, S. V., J. D., Wickham, J. H., Smith and L., Yang (2003) Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results. Remote Sensing of Environment, 86, 500−516. Swets, J.A. (1996) Signal detection theory and ROC analysis in psychology and diagnostics : collected papers. Lawrence Erlbaum Associates, Mahwah, NJ. Talukdar S. and Pal S. (2019) Effects of damming on the hydrological regime of Punarbhaba river basin wetlands. Ecological Engineering. 135. 61-74. Tucker C. J. (1979) Red and photographic infraRed linear combinations for monitoring vegetation. Remote Sensing of Environment. 8. 127-150. Tucker C. J. (1980) Remote sensing of leaf water content in the near-infraRed. Remote Sensing Environment. 10. 23-32. Wang, L., J. L. Silváncárdenas, W.P. Sousa (2008) Neural network classification of mangrove species from multi-seasonal IKONOS imagery. Photogrammetric engineering and remote sensing. 74. 921–927. Watson, R. T., I. R. Noble, B. Bolin, N. H. Ravindranath, D. J. Verardo, and D. J. Dokken, Land use, land‐use change, and forestry, in Intergovernmental Panel on Climate Change Special Report, Cambridge Univ. Press, New York, 2000. Ward D.P., Hamilton S.K., Jardine T.D., Pettit N.E., Tews E.K., Olley J.M., Bunn S.E. (2013) Assessing the seasonal dynamics of inundation, turbidity, and aquatic vegetation in the Australian wet-dry tropics using optical remote sensing. Ecohydrology. 6. 312-323. Welch, P. S. (1952) Limnology, 2nd edition. Wester L. (1988) Vegetation change in Guandu marsh, Taiwan 1978-1985. In: Detailed Planning of Guandu Nature Park, Taipei. Taipei: Soc. of Wildlife and Nature ROC. 415-426. Wester L., Lee C.T. (1992) Mangroves in Taiwan: distribution, management and values. Geoforum. 23(4). 507-519. Wickham, J. D., S. V., Stehman, J. H., Smith and L., Yang (2004) Thematic accuracy of the 1992 National Land-Cover Data for the western United States. Remote Sensing of Environment, 91, 452−468. Wolski P., Murray-Hudson M., Thito K., Cassidy L. (2017) Keeping it simple: Monitoring flood extent in large data-poor wetlands using MODIS SWIR data. International Journal of Applied Earth Observation and Geoinformation. 57. 224-234. Wright, C. and A., Gallant (2007) Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107, 582-605. Yu G.R., Miwa T., Nakayama K., Matsuoka N., Kon H. (2000) A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties. Plant and Soil, 227, 47-58. Zedler J.B. (2000) Progress in wetland restoration ecology. Trends in Ecology and Evolution, 15, 402–407. Zha Y., Gao Y. and Ni S. (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing. 24. 583–594. Zhadin, V. I. and S. V., Gerd (1963) Fauna and flora of the rivers, lakes and reservoirs of the U.S.S.R. Oldbourne Press, London. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81613 | - |
| dc.description.abstract | 目前台灣官方對於溼地的分類主要停留在《溼地保育法》的功能分區，並沒有以水文、植被等生態指標作為依據的分類系統，然而美國、加拿大等國家已有完整的國家濕地分類系統，並且已經應用到許多濕地分類的研究之中。因此本研究欲參考美國濕地與水域棲地分類系統(Classification of wetlands and deepwater habitats)，以內政部營建署所列出的國家重要濕地91處作為分類的目標，以Open Data Cube平台蒐集之衛星影像訓練之XGBoost模型，建置一套評估濕地狀況的方法，並評估其分類效果，最後以該模型估算國家重要濕地的面積及分類比例。本研究進行的流程為濕地分類系統的建置、建立訓練樣本資料庫、資料品質檢驗、建立雙連埤及關渡濕地影像庫、分類效果檢驗以及濕地分類面積估算。本研究依據地景的差異，將美國濕地與水域棲地分類系統的海洋系統及河口系統歸類為第一類群，河流系統、湖泊系統及沼澤系統歸類為第二類群；資料品質檢驗的結果第一類群及第二類群的整體準確度分別為99.67%及99.17%，訓練資料能夠有效的對濕地類別進行區分；分類能力檢驗以關渡濕地(第一類群)及雙連埤(第二類群)作為樣區，整體準確率為64.92%及84.52%，關渡濕地水體及泥灘地的分類效果不佳，將兩者整併後，準確率提升至90.90%；國家重要濕地面積估算的結果顯示，海洋系統、河口系統、河流系統、湖泊系統及沼澤系統的面積分別為4329.24、26221.75、8930.37、367.36以及1275.08公頃，最高面積的分類依序分別為泥灘地、泥灘地、草生地、水域及水域。 | zh_TW |
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| dc.description.tableofcontents | 謝誌 I 摘要 II ABSTRACT III 目錄 V 圖目錄 VII 表目錄 IX 壹、 前言 1 貳、 文獻回顧 5 2.1 濕地定義與分類 5 2.1.1 濕地的定義 5 2.1.2 各國的濕地分類 7 2.2 遙測於濕地分類的應用 15 2.3 XGBOOST 22 2.3.1 演算法簡介 22 2.3.2 分類回歸樹(CART) 22 2.3.3 Boosting 23 2.3.4 XGBoost 23 2.4 變數權重 26 2.5 準確性評估 28 2.5.1 混淆矩陣 28 2.5.2 ROC曲線 29 參、 研究材料 32 3.1 研究區域 32 3.1.1 關渡濕地 32 3.1.2 雙連埤 33 3.2 SENTINEL衛星影像 35 3.3 航照影像 42 肆、 研究方法 44 4.1 濕地分類系統建置 47 4.2 建立訓練樣本資料庫 61 4.2.1 變數設定 61 4.3 XGBOOST分類與結果評估 65 4.3.1 變數設定 65 4.3.2 分類流程及後續分析 67 4.3.3 XGBoost參數 69 伍、 結果與討論 71 5.1 訓練資料檢驗 71 5.1.1 準確度評估 71 5.2 訓練資料檢驗 74 5.2.1 分類結果 74 5.2.2 變數權重分析 78 5.2.3 準確度評估 81 5.2.4 訓練資料及演算法之準確度比較 86 5.3 濕地分類面積估算 88 陸、 結論 89 柒、 參考文獻 92 附件一、拉薩姆公約訂定簡易分類系統之詳細分類 105 附件二、訓練樣本資料庫之訓練樣區衛星影像 106 附件三、國家重要濕地各分類像元數 114 附件四、國家重要濕地各分類百分比 118 | |
| dc.language.iso | zh-TW | |
| dc.subject | XGBoost | zh_TW |
| dc.subject | Open Data Cube | zh_TW |
| dc.subject | 濕地面積估算 | zh_TW |
| dc.subject | 遙感探測 | zh_TW |
| dc.subject | Sentinel-2 | zh_TW |
| dc.subject | 濕地分類 | zh_TW |
| dc.subject | remote sensing | en |
| dc.subject | XGBoost | en |
| dc.subject | Open Data Cube | en |
| dc.subject | wetland area estimation | en |
| dc.subject | Sentinel-2 | en |
| dc.subject | wetland classification | en |
| dc.title | Sentinel-2衛星影像於濕地自動化分類之探討與應用 | zh_TW |
| dc.title | Research and Apply in Automatic Wetland Classification By Sentinel-2 Satellite Image | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鍾智昕(Su-Feng Roan),王素芬(Meei-Ju Yang) | |
| dc.subject.keyword | 濕地分類,遙感探測,XGBoost,Open Data Cube,濕地面積估算,Sentinel-2, | zh_TW |
| dc.subject.keyword | wetland classification,remote sensing,XGBoost,Open Data Cube,wetland area estimation,Sentinel-2, | en |
| dc.relation.page | 123 | |
| dc.identifier.doi | 10.6342/NTU202200384 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-02-13 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 森林環境暨資源學研究所 | zh_TW |
| 顯示於系所單位： | 森林環境暨資源學系 | |
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