博碩士論文 105022603 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:30 、訪客IP:18.191.228.88
姓名 亞吉妲(Jeddah Yanti)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 印尼地區地表性質與雲特徵之探討
(Investigation of Land Surface Properties and Cloud Characteristics in Indonesia)
相關論文
★ 地球同步衛星觀測資料之雲區像素辨識★ 結合掩星折射率與高光譜紅外線觀測之大氣溫溼度垂直剖面反演
★ 結合衛星反演資料與WRF模式探討梅雨鋒面水氣傳送關聯性之個案研究★ Optimal Use of Satellite Sounding Products for Numerical Weather Prediction
★ The spatial correlation of satellite-estimated PM2.5 and epidemiological diseases in Taiwan★ Assessment of the NWP Model Physical Fields from Radiative Quantity
★ 海表面風場與通量於熱帶氣旋發展影響之探討★ 使用衛星資料評析全球預報模式之 雲參數特性
★ 衛星輻射強度與反演產品之資料同化研究--尼伯特颱風(2016)個案分析★ 日本氣象同步衛星 Himawari-8 向日葵八號 之雲微物理參數反演驗證與評估
★ 掩星資料於颱風快速增強機制之模擬研究-梅姬颱風(2010)★ 利用多頻道衛星觀測評估WRF數值模式於不同微物理方案之雲特性:以梅雨鋒面降水系統個案為例
★ 應用多時期向日葵8號衛星影像進行雲像素的偵測與追蹤★ 使用CloudSat及ECMWF再分析資料探討南海及海洋大陸地區深對流之環境因子
★ 使用 CloudSat 分析南海與海洋大陸地區之深對 流與動力環境特徵★ 台灣及南海地區雲的時空特徵: 向日葵8號於夏季觀測之前導研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 摘要
雲在水循環中扮演相當重要的角色,在過往的研究中,發現雲可能和大氣條件以及地表種類有著相當大的關係。地表的植被改變和蒸發散的特性息息相關,蒸發散的量會影響到大氣中的水氣通量,而水氣通量又和大氣中的層狀和對流雲有著密切之聯繫。本研究主要是從雲微物理的參數出發,將地表的特性和雲微物理特徵做連結。研究的時間是從2003年到2016年,一共14年的時間,研究的範圍是在印尼地區,使用的是Moderate-Resolution Imaging Spectroradiometer (MODIS) level-3的資料,其中包括地表特性及雲產品。而雲微物理參數的分析主要包括雲量、雲頂氣壓、雲光學厚度、雲的有效半徑,至於地表特性的變化則是使用常態化差異植被指數(NDVI)來做為參考指標。本研究發現在研究試區植被比較少的陸地上有較大的雲分量外,雲的有效粒徑較小且較低的光學厚度;反之在植被指數較高的之處,易出現較大的雲滴粒徑、雲頂高度較高、且雲分量減少的高雲光學厚度對流性雲形特徵,且在溼季的以上關連性更為顯著。推估其機制為當地表有較多的植被狀況時,就會有較多的水氣蒸散發至大氣中,而引發上升氣流促進雲生成並加強雲的垂直發展。最後比較印尼各島嶼的統計分析顯示,在加里曼丹和蘇門答臘的空間相關性尤為顯著,且資料顯示此二區為印尼諸島中去森林化面積最大之處。
摘要(英) ABSTRACT

Cloud has the sensitivities and may lead to the response to atmospheric conditions along with surface properties due to its role in the hydrological cycle. The change of surface vegetation also links to the evapotranspiration pattern so that the moisture flux might be affected by the atmospheric stratiform or convective clouds. The aim of this study to analyze the complex phenomenon and links of cloud response towards land surface change that ensued from cloud microphysical components. Fourteen years from 2003 to 2016 over Indonesia was applied that issued by Moderate-Resolution Imaging Spectroradiometer (MODIS) level-3 (L3) provides both cloud and land surface products. Cloud microphysical features consist of cloud fraction, cloud top pressure, cloud optical thickness, and cloud effective radius, whereas Normalized Difference Vegetation Index (NDVI) was applied to identify the land surface change. The analysis of annual and seasonal climatology is used as the method to determine each cloud microphysical components response to land surface change. This study shows wet season is crucial season to observe the phenomenon between clouds and vegetation event. Because of the obviously characteristic of clouds over less vegetated land areas are more diffuse clouds (cloud cover), small particle size, and thin clouds. Meanwhile, increasing vegetation index encourages the formation of convective clouds which are characterized by large particle sizes, high-altitude, and more compact cloud shapes, as known convective clouds. More vegetation more water evaporates from the Earth′s surface and rises upon warm updrafts into the atmosphere where it condenses into clouds, with clouds are encouraged to develop vertically. Most significant of spatial correlation shows over Kalimantan and Sumatra.
關鍵字(中) ★ 雲參數
★ 正規化地表植生指數
★ 印尼
關鍵字(英) ★ Cloud properties
★ NDVI
★ MODIS
★ Indonesia
論文目次 摘要 ................................................................................................................................... i
ABSTRACT .......................................................................................................................... ii
ACKNOWLEDGEMENTS................................................................................................... iii
Table of Contents................................................................................................................... iv
List of Figures and Illustrations............................................................................................. vi
List of Table........................................................................................................................... x
Appendix ............................................................................................................................... xi
Chapter 1: Introduction..................................................................................................... 1
1.1. Land Surface properties and its changes .................................................................. 1
1.2. Clouds Microphysical and its characteristics ........................................................... 3
1.3. Relationship between Land Surface Properties and Cloud
Characteristics ........................................................................................................6
1.4. Objectives.................................................................................................................. 8
Chapter 2: Data .................................................................................................................. 9
2.1. Remote Sensing Imagery........................................................................................... 9
2.2. Remote Sensing for Cloud Product ........................................................................... 10
2.3. Remote Sensing for Vegetation Product ................................................................... 10
2.4. Algorithm Theoretical Basis of MODIS ................................................................... 12
Chapter 3: Methodology .................................................................................................... 16
3.1. Collect the dataset...................................................................................................... 16
3.2. Geographic Correction .............................................................................................. 16
3.3. Regridding ................................................................................................................. 17
3.4. Extract Parameters..................................................................................................... 19
3.5. Mask ......................................................................................................................... 23
v
3.6. Annual and Seasonal Analysis .................................................................................. 24
3.7. Statistical Analysis .................................................................................................... 25
Chapter 4: Study Area ....................................................................................................... 28
4.1. Location .................................................................................................................... 28
4.2. Environmental Profile................................................................................................ 29
4.3. Climate Profile .......................................................................................................... 31
Chapter 5: Result and Discussion ..................................................................................... 34
5.1. General Findings........................................................................................................ 34
5.2. Relationship between NDVI and Cloud Characteristics........................................... 37
5.3. Relationship between time derivation of NDVI and Cloud Characteristics.............. 41
5.4. Spatial Correlation between anomaly of NDVI and anomaly of cloud properties.... 44
Chapter 6: Conclusion........................................................................................................ 53
References ............................................................................................................................. 56
參考文獻 REFERENCES
1. Worldometers, 2019: Largest Countries in the World (by land area) Latest update: 22 January 2019, (Accessed [13/01/2019]). http://www.worldometers.info/geography/largest-countries-in-the-world/
2. Trenberth, K.E., J.T. Fasullo and J. Kiehl, 2009: Earth’s Global Energy Budget, Bulletin of the American Meteorological Society. doi: 10.1175/2008BAMS2634.1.
3. Akbari, J., M. Pomerants, and H. Taha, 2001: Cool Surfaces and Shade Trees to Reduce Energy Use and Improve Air Quality in Urban Areas. Elseiver-Solar Energy, 70,3, pp: 295-310.
4. Margono, B.A., P.V. Potapov, S. Turubanova, F. Stolle and M.C. Hansen, 2014: Primary forest cover loss in Indonesia over 2000–2012, Nature Climate Change, 4, pp. 730–735, doi: 10.1038/nclimate2277.
5. Global Forest Watch, 2019: Indonesia, Latest update: 8 January 2019, (Accessed [13/01/2019]). https://www.globalforestwatch.org/dashboards/country/IDN
6. Wicke, B., R. Sikkema, V. Dornburg, and A. Faaij, 2010: Exploring land use changes and the role of palm oil production in Indonesia and Malaysia, Land Use Policy, 28, pp: 193-206, doi: 10.1016/j.landusepol.2010.06.001.
7. Abood, S.A., J.S.H. Lee, Z. Burivalova, J.G. Ulloa, and L.P. Koh, 2015: Relative Contributions of the Logging, Fiber, Oil Palm, and Mining Industries to Forest Loss in Indonesia, Conservation Letter, 5, 1, pp: 58-67, doi: 10.1111/conl.12103.
8. Lawrence, D., and K. Vandecar, 2015: Effects of tropical deforestation on climate and agriculture, Nature Climate Change, 5, pp: 27-36, doi: 10.1038/NCLIMATE2430.
9. Higginbottom, T.P. and E. Symeonakis, 2014: Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions, Remote Sens, 6, 9552-9575, doi:10.3390/rs6109552.
10. Bannari, A., D. Morin, F. Bonn and A.R. Huete, 1995: A Review of Vegetation Indices, Remote Sensing Reviews, 13, pp. 95 – 120, doi: 10.1080/02757259509532298.
11. Weier, J. and D. Herring, 2000: Measuring Vegetation, August 2000, September 2018, Article, NASA Official.
12. SOS NOAA, 2018: Solar System Script, (Accessed [13/09/2018). sos.noaa.gov/_media/cms/docs/solar_system.doc
13. Wielicki, B.A., R.D. Cess, M.D. King, D.A Randall and E.F. Harrison, 1995: Mission to Planet Earth: Role of Clouds and Radiation in Climate, Bulletiri of the American Meteorological Society, doi: 10.1175/1520-0477.
14. Duveiller, G., J. Hooker and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance, Nature Communication, 9, 679, doi: 10.1038/s41467-017-02810-8.
15. Lindsey, R., 2009: Climate and Earth’s Energy Budget, January 14, Article, NASA Earth Observation.
16. King, M.D., S. Platnick, W.P. Menzel, S.A. Ackerman, and P.A. Hubanks, 2013: Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites, IEEE Transactions on Geoscience and Remote Sensing, 51, 7, pp. 3826-3852, doi: 10.1109/TGRS.2012.2227333
17. Hidayat, T., P. Mahasena, B. Dermawan, T. W. Hadi, P. W. Premadi, and D. Herdiwijaya, 2012: Clear sky fraction above Indonesia: an analysis for astronomical site selection, Monthly Notices, 427, pp: 1903-1917, doi: 10.1111/j.1365-2966.2012.22000.x.
18. Eastman, R., and S.G. Warren, 2011: Variations in Cloud Cover and Cloud Types over the Ocean from Surface Observations, 1954–2008, Journal of Climate, 24, pp: 5914-5934, doi: 10.1175/2011JCLI3972.1.
19. Gianotti, R.L., and E.A.B. Eltahir, 2014: Regional Climate Modeling over the Maritime Continent. Part I: New Parameterization for Convective Cloud Fraction, Journal of Climate, 27, pp: 1488-1503, doi: 10.1175/JCLI-D-13-00127.1.
20. Gianotti, R.L., and E.A.B. Eltahir, 2014: Regional Climate Modeling over the Maritime Continent. Part II: New Parameterization for Autoconversion of Convective Rainfall, Journal of Climate, 27, pp: 1504-1523, doi: 10.1175/JCLI-D-13-00171.1
21. OK-FIRST Project, 1997, Precipitation, Oklahoma Climatological Survey, September 2018, http://okfirst.mesonet.org/train/meteorology/Precipitation.html
22. Wang, J. et al., 2016: Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall, Nature, 539, pp: 416-419, doi: 10.1038/nature19819.
23. Machado, L.A.T., 2016: Amazon rainstorms transport atmospheric particles for cloud formation, Nature, DOE/Brookhaven National Laboratory. (Accessed [13/09/2018). https://www.sciencedaily.com/releases/2016/10/161024133844.htm
24. Wu, P., N. Christidis and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle, Nature Climate Change, 3, pp. 807–810, doi: 10.1038/nclimate1932.
25. Riipinen, I., T.Y.-Juuti, J.R. Pierce, T. Petäjä, D.R. Worsnop, M. K ulmala and N.M. Donahue, 2012: The contribution of organics to atmospheric nanoparticle growth, Nature Geoscience, 5, doi: 10.1038/NGEO1499.
26. Xue, Y. And J. Shukla, 1996: The Influence of Land Surface Properties on Sahel Climate, Part II: Affirestation, Journal of Climate, 9, pp. 3260 – 3275, doi: 10.1175/1520-0442(1996)009<3260:TIOLSP>2.0.CO;2.
27. Shukla, J. and Y. Minz, 1982: Influence of Land-Surface Evapotranspiration on the Earth′s Climate, Reports, Science, 215, 4539, pp. 1498 – 1501, doi: 10.1126/science.215.4539.1498.
28. Fisher, B.J., F. Melton, E. Middleton, C. Hain, M. Anderson, R. Allen, M.F. McCabe, S. Hook, D. Baldocchi, P.A. Townsend, A. Kilic, K. Tu, D.D. Miralles, J. Perret, J.‐P. Lagouarde, D. Waliser, A.J. Purdy, A. French, D. Schimel, J.S. Famiglietti, G. Stephens, E.F. Wood, 2017: The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, doi: 10.1002/2016WR020175.
29. Igel, A.L., S.C. Van den Heever and J.S. Johnson, 2018: Meteorological and Land Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a Dry Environment, 123, pp. 22-37, doi: 10.1002/2017JD027339.
30. Hofer, S., A.J. Tedstone, X. Fettweis, and J.L. Bamber, 2017: Decreasing cloud cover drives the recent mass loss on the Greenland Ice Sheet, Science Advances, 3, 6, pp: 1-8, doi: 10.1126/sciadv.1700584.
31. Teuling, A. J., C.M. Taylor, J.F. Meirink, L.A. Melsen, D.G. Miralles, C.C. van Heerwaarden, R. Vautard, A.I. Stegehuis, G-J. Nabuurs, and J.V-G. de Arellano, 2017: Observational evidence for cloud cover enhancement over western European forests, Nature Communications, 8, 14065, pp: 1-7, doi: 10.1038/ncomms14065.
32. Platnick, S., K.G. Meyer, M.D. King, G. Wind, N. Amarasinghe, B. Marchant, G.T. Arnold, Z. Zhang, P.A. Hubanks, R.E. Holz, P. Yang, W.L. Ridgway, J. Riedi, 2017: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE Transactions on Geoscience and Remote Sensing, 55, 1, pp. 502-525, doi: 10.1109/TGRS.2016.2610522.
33. Campbell, J.B. and Wynne, R.H., 2011: Introduction to Remote Sensing Fifth Edition, Guilford Press, ISBN: 1572300426, 9781572300422.
34. Xiong, X., M.D. King, V.V. Salomonson, W.L. Barnes, B.N. Wenny, A. Angal, A. Wu, S. Madhavan and D.O. Link, 2016: Moderate Resolution Imaging Spectroradiometer on Terra and Aqua Missions. In: Optical Payloads for Space Missions, [S.E. Qian (eds.)]. John Wiley & Sons, pp. 53-89, doi: 10.1002/9781118945179.ch3.
35. Baum, B.A and S. Platnick, 2006: Introduction to MODIS cloud products. In: Earth science satellite remote sensing, pp 74–91.
36. Hengl, T., G.B.M. Heuvelink, M.P. Tadic and E.J. Pebesma, 2012: Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images, Theor Appl Climatol, 107, pp. 265 – 277, doi: 10.1007/s00704-011-0464-2.
37. Ahmad, S.P., V.V. Salomonson, W.L. Barnes, X. Xiong, G.G. Leptoukh, and G.N. Serafino, 2014: Modis Radiances and Reflectances for Earth System Science Studies and Environmental Applications, William Barnes, P1.6.
38. Hubanks, P.A., M.D. King, S.A. Platnick and R.A. Pincus, 2008: MODIS Atmosphere L3 Gridded Product Algorithm Theoretical Basis Document, https://modis.gsfc.nasa.gov/data/atbd/atbd_mod30.pdf
39. Hubanks, P.A, 2018: MOD08 V6 Atmosphere Monthly Global Product Bands, (Accessed [13/09/2018), https://developers.google.com/earth-engine/MOD08_bands
40. Didan, K., 2015: MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set], NASA EOSDIS LP DAAC, doi: 10.5067/MODIS/MOD13Q1.006.
41. Heute, A., C. Justice and W.V. Leeuwen, 1999, MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document (ATBD).
42. King, M.D., 1997; Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase,
43. Hill, L.L, 2006: Georeferencing: The Geographic Associations of Information, MIT Press, ISBN-10: 0-262-08354-X.
44. Janky, M.J, M.V. McCusker, H.L. Longaker, and P.G. France, 2015: Image-Based Georeferencing, United States Patent, Patent No.: US 8,942.483 B2.
45. Hankin, S., J. Callahan, A. Manke, K. O’Brien and J. Li, 2007: Ferret User’s Guide Version 6.02, NOAA, https://ferret.pmel.noaa.gov/Ferret/sites/default/files/atoms/files/ferret_users_guide_v602.pdf
46. Anonym, 2018: CCD Binning or Pixel Binning, (Accessed [13/09/2018), http://www.spotimaging.com/resources/glossary/binning/
47. Hansen, J. E., and L. D. Travis, 1974: Light scattering in planetary atmospheres, Space Sci. Rev., 16, pp. 527-610, doi:10.1007/BF00168069.
48. Bodhaine, B.A., N.B. Wood, E.G. Dutton and J.R. Slusser, 1999: On Rayleigh Optical Depth Calculations, Journal of Atmospheric and Oceanic Technology, 16, pp. 1854 – 1861, doi: 10.1175/1520-0426(1999)016<1854:ORODC>2.0.CO;2.
49. Anonym, 2018: Shapefiles, (Accessed [13/09/2018), https://www.ncl.ucar.edu/Applications/shapefiles.shtml
50. Hover, S., et al., 2017: Decreasing cloud cover drives the recent mass loss on the Greenland Ice Sheet, Sci. Adv. 2017; 3: e1700584
51. Hall, G., 2015: Pearson′s correlation coefficient http://www.hep.ph.ic.ac.uk/~hallg/UG_2015/Pearsons.pdf
52. Sedgwick, P., 2012: Pearson′s correlation coefficient, BMJ, 345, e4483, doi: 10.1136/bmj.e4483.
53. Anonym, 2018: Probability Distribution Function, (Accessed [13/09/2018). https://www.ncl.ucar.edu/Applications/pdf.shtml
54. Library of Congress – Federal Research Division, 2004, https://www.loc.gov/rr/frd/cs/profiles/Indonesia-new.pdf
55. Asian Development Bank, 2016: Indonesia: Country Water Assessment, Mandaluyong City, Philippines, ISBN 978-92-9257-360-7 (Print), 978-92-9257-361-4 (e-ISBN), https://www.adb.org/sites/default/files/institutional.../ino-water-assessment.pdf
56. Geospatial Information Agency of Indonesia, 2017: Identification of Islands and Standardization of Their Names, 11th United Nations Conference on the Standardization of Geographical Names, New York, 8 -17 August 2017.
57. Anonym, 2018: Indonesia, (Accessed [13/12/2018). https://www.worldatlas.com/webimage/countrys/asia/id.htm
58. Coren, M.J., C. Streck and E.M. Madeira, 2011: Estimated supply of RED credits 2011–2035, Climate Policy, 11, 6, pp. 1272-1288, doi: 10.1080/14693062.2011.579318.
59. FAO, 2015, Global Forest Resources Assessment 2015 - Food and Agriculture Organization United Nation. www.fao.org/3/a-i4808e.pdf
60. Wong, K.M., 2011: A Biogeographic History of Southeast Asian Rainforests. In: Managing the Future of Southeast Asia′s Valuable Tropical Rainforests [Wickneswari R., Cannon C. (eds)]. Advances in Asian Human-Environmental Research, Springer, Dordrecht, 2, doi: 10.1007/978-94-007-2175-3_2.
61. Teuscher, M., 2015: Ecological impacts of biodiversity enrichment in oil palm plantations, dissertation, Centre of Biodiversity and Sustainable Land Use Section: Biodiversity, Ecology, and Nature Conservation, Georg August Universitat.
62. Appanah, S., 1999: Introduction. In: A Review of Dipterocarps: Taxonomy, ecology and silviculture [Appanah, S. and J.M. Turnbull (eds)]. Center for International Forestry Research, ISBN: 979-8764-20-X.
63. Deb, J.C., S. Phinn, N. Butt and C.A. McAlpine, 2017: The impact of climate change on the distribution of two threatened Dipterocarp trees, Ecol Evol, 7, 7, pp. 2238 – 2248, doi: 10.1002/ece3.2846.
64. Pope, I.C., 2014: Deforestation of Cloud Forest in The Central Highlands of Guatemala: Soil Erosion and Sustainability Implications for Q′eqchi′ Maya Communities, Theses, Purdue University, pp: 1 – 234.
65. Myers, N., 1988: Tropical Deforestation and Remote Sensing, Forest Ecology and Management, 23, pp. 215-225, doi: 10.1016/0378-1127(88)90083-7.
66. Skole, D. And C. Tucker, 1993: Tropical Deforestation and Habitat Fragmentation in the Amazon: Satellite Data from 1978 to 1988, Article, Science,
67. Achard, F., H.D. Eva1, H-J. Stibig, P. Mayaux, J. Gallego, T. Richards and J-P. Malingreau, 2002: Determination of Deforestation Rates of the World’s Humid Tropical Forests, Report, Sciece, 297, 5583, pp. 999-1002, doi: 10.1126/science.1070656.
68. Lambin, E.F., H.J. Geist and E. Lepers, 2003: Dynamics of Land-Use and Land-Cover Change in Tropical Regions, Annual Review Environmental Resource, 28, pp. 205 – 41, doi: 10.1146/annurev.energy.28.050302.105459.
69. Siswanto, S.Y. and F. Francés, 2017: The Impact of Land Use Changes on Soil Erosion and Sediment Cycle Using Distributed Modeling in A Tropical Watershed in Indonesia, Proceedings, 23-28 April, 2017, Vienna, Austria., pp.10018.
70. World Bank, 2018: Average Monthly Temperature and Rainfall for Indonesia from 1901-2015, in: Country Historical Climate – Indonesia. Access: September, 2018, http://sdwebx.worldbank.org/climateportal/index.cfm?page=country_historical_climate&ThisCCode=IDN.
71. Kobayashi, T. And K. Masuda, 2009: Changes in Cloud Optical Thickness and Cloud Drop Size Associated with Precipitation Measured with TRMM Satellite, Journal of the Meteorological Society of Japan, 87, 4, pp. 593-600, doi: 10.2151/jmsj.87.593.
72. Anonym, 2018: Indonesia Mountain Weather Map, (Accessed [13/12/2018). https://www.mountain-forecast.com/weather_maps/Indonesia?over=arrows&symbols=none&type=cloud
73. Lambin, E.F., H.J. Geist and E. Lepers, 2003: Dynamics of Land-Use and Land-Cover Change in Tropical Regions, Annual Review Environmental Resource, 28, pp. 205 – 41, doi: 10.1146/annurev.energy.28.050302.105459.
74. Gastellu-Etchegorry, J.P, 1988: Cloud cover distribution in Indonesia, International Journal of Remote Sensing, 9, 7, pp: 1267-1276, doi: 10.1080/01431168808954934.
75. Page, S.E., F. Siegert, J.O. Rieley, H-D.V. Boehm, A. Jayak, and S. Limink, 2002: The amount of carbon released from peat and forest fires in Indonesia during 1997, Nature, 420, pp: 61-65.
76. Fargione, J., J. Hill, D. Tilman, S. Polasky, and P. Hawthorne, 2008: Land Clearing and the Biofuel Carbon Debt, Science, 319, pp: 1235-1238, doi: 10.1126/science.1152747.
77. Marzuki, M., T. Kozu, T. Shimomai, W. L. Randeu, H. Hashiguchi, and Y. Shibagaki, 2009: Diurnal Variation of Rain Attenuation Obtained From Measurement of Raindrop Size Distribution in Equatorial Indonesia, IEEE Transactions on Antennas and Propagation, 57, 4, pp: 1191-1196, doi: 10.1109/TAP.2009.2015812
78. Moron, V., A.W. Robertson, and J-H. Qian, 2010: Local versus regional-scale characteristics of monsoon onset and post-onset rainfall over Indonesia, Climate Dynamic, 34, pp: 281-199.
79. Chang, C.-P., Z. Wang, J. McBride, and C.-H. Liu, 2005: Annual Cycle of Southeast Asia—Maritime Continent Rainfall and the Asymmetric Monsoon Transition, Journal of Climate, 18, pp: 287-301.
80. Haylock, M., and J. McBride, 2001: Notes and Correspondence: Spatial Coherence and Predictability of Indonesian Wet Season Rainfall, Journal of Climate, 14, pp: 3882-3887.
81. Kogan, F.N., 1990: Remote sensing of weather impacts on vegetation in non-homogeneous areas, International Journal of Remote Sensing, 11, 8, pp: 1405-1419, doi: 10.1080/01431169008955102.
82. Lazaro, R., F.S. Rodrigo, L. GutieHrrez, F. Domingo, and J. Puigdefabregas, 2001: Analysis of a 30-year rainfall record (1967-1997) in semi- arid SE Spain for implications on vegetation, Journal of Arid Environments, 48, pp: 373-395, doi:10.1006/jare.2000.0755.
83. Wheeler, M.C., and J.L. McBride, 2005: Australian-Indonesian monsoon. In: Intraseasonal Variability in the Atmosphere-Ocean Climate System, Springer Praxis Books (Environmental Sciences), doi: 10.1007/3-540-27250-X_5.
84. Jiang, J.H., N.J. Livesey, H. Su, L. Neary, J.C. McConnell, and N.A.D. Richards, 2007: Connecting surface emissions, convective uplifting, and long-range transport of carbon monoxide in the upper troposphere: New observations from the Aura Microwave Limb Sounder, Geophysical Research Letters, 34, pp: 1-6, doi:10.1029/2007GL030638.
85. Chruchill, D.D., abd R.A. Houze, Jr., 1984: Development and Structure of Winter Monsoon Cloud Clusters on 10 December 1978, Journal of Atmospheric Sciences, 41, 6, pp: 933-960.
86. Heymsfield, A.J. et al., 2002: Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns, Journal of Atmospheric Sciences, 59, pp: 3457-3491.
87. Ray, D.K, 2013: Tropical Montane Cloud Forests, Climate Vulnerability, 5, pp: 79-84, doi: 10.1016/B978-0-12-384703-4.00519-0.
88. Hannah, L., 2015: Chapter 5: Ecosystem Change, Climate Change Biology, pp: 103-133, doi: 10.1016/B978-0-12-420218-4.00005-6.
89. Hays, J., 2008: Weather and Climate in Indonesia, Last updated June 2015, (Accessed [13/09/2018]), http://factsanddetails.com/indonesia/Nature_Science_Animals/sub6_8a/entry-4079.html
90. Dufrene, E., 1989: Photosynthese, consommation en eau et modelisation de la production chez le palmier a` huile (Elaeis guineensis Jacq.), PhD Thesis, Universite de Paris XI, Orsay, Paris, pp. 156.
91. Bonnell, M., 2005: Runoff generation in tropical forests. In “Forest-Water-People in the Humid Tropics: Pas, Present and Future Hydrological Research for Integrated Land and Water Management” (M. Bonnell and L. A. Bruinjnzeel, Eds.), pp. 314–406. Cambridge University Press, Cambridge.
92. Comte, I., F. Colin, J.K. Whalen, O. Grunderger, and J.P. Caliman, 2012: Agricultural Practices in Oil Palm Plantations and Their Impact on Hydrological Changes, Nutrient Fluxes and Water Quality in Indonesia: A Review. Advances in Agronomy, Volume 116, ISSN 0065-2113, doi: 10.1016/B978-0-12-394277-7.00003-8.
93. Parkhomenko, S., 2004: International competitiveness of soybean, rapeseed and palm oil production in major producing regions, Braunschweig, Federal Agricultural Research Centre (FAL).
94. Sabajo, C.R., G.le. Maire, T. June, A. Meijide, O. Roupsard, and A. Knohl, 2017: Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia, Biogeosciences, 14, pp: 4619–4635, doi: 10.5194/bg-14-4619-2017.
95. Peterson, R.R.M., and N. Lima, 2018: Climate change affecting oil palm agronomy, and oil palm cultivation increasing climate change, require amelioration, Ecology and Evolution, 8, pp: 452–461, doi: 10.1002/ece3.3610.
96. Shean, M., 2009: Indonesia: Palm Oil Production Growth to Continue, Last updated 19 March 2009, (Accessed [13/09/2018]), https://ipad.fas.usda.gov/highlights/2009/03/Indonesia/
97. Minnemeyer, S., E. D. Goldman and N. Harris, 2017: New Deforestation Hot Spots in the World’s Largest Tropical Forests, Last updated 7 February 2017, (Accessed [13/09/2018]), https://www.wri.org/blog/2017/02/new-deforestation-hotspots-worlds-largest-tropical-forests
98. Giorgio, N., 2017: Relation between Cloud Cover and Relative Humidity, Bachelor Thesis, Institute for Marine and Atmospheric Research Utrecht.
指導教授 劉千義(Chian-Yi Liu) 審核日期 2019-1-30
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明