應用MODIS觀測資料推估熱帶雨林淨初生產力

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：77

、訪客IP：3.147.77.119

姓名

劉振昇(Lalu Muhamad) 查詢紙本館藏

畢業系所

遙測科技碩士學位學程

論文名稱

應用MODIS觀測資料推估熱帶雨林淨初生產力
(Estimating Net Primary Productivity (NPP) on Tropical Rain Forest Using MODIS Observation Data)

相關論文

★ 區域發展對土地利用變遷之影響-以桃園縣客家地區為例	★ 應用多時期MODIS衛星影像分析於蒙古地區整合型乾旱強度指標之研究
★ WVR、GPS及氣球探空觀測可降水量之比較	★ GPS斷層掃描估算大氣濕折射係數模式
★ GPS觀測大氣閃爍之研究	★ 相關誤差神經網路之應用於輻射量測植被和土壤含水量
★ 36.5 GHz微波輻射器接收模組之研製	★ GPS 氣象中地面氣象模式之改進
★ 由GPS信號反演大氣濕折射度之數值模擬	★ 發展遙測資料反演可感熱與潛熱通量之研究
★ GPS信號估算可降水量與降雨關係之研究	★ 近即時GPS觀測可降水技術之研究
★ 利用水氣資訊改善降水估計之研究	★ GPS掩星觀測反演與反演誤差探討
★ 微波輻射計數位相關器之設計與實現	★ GPS與探空氣球資料觀測可降水量與降雨之關係

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]

本電子論文使用權限為同意立即開放。
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。
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)

應用MODIS觀測資料推估熱帶雨林淨初生產力
Estimating Net Primary Productivity (NPP) on Tropical Rain Forest using MODIS Observation Data
欲評估森林生態系中之碳循環模式，淨初生產力 (Net Primary Productivity, NPP) 被稱為一項重要的指標。然而，由於熱帶雨林面積廣大及缺乏地表實測資料的因素，要了解森林生態系如何藉由地表物質來進行循環，是一項困難的任務。因此，我們應用遙感探測資料推估熱帶雨林NPP之三個重要的參數，包括光利用效率 (Light Use Efficiency, LUE)、光合作用有效輻射比率 (Fraction of Photosynthetically Active Radiation, FPAR) 和光合作用有效輻射 (Photosynthetically Active Radiation, PAR)。 .
基本上， Montieth 方程式是用來測量熱帶雨林地上之 NPP 量。Rahman 等人(2004)提出微氣象模式，使用 MODIS 海洋波段所演算出的光化學反射率指標 (MODIS Photochemical Reflectance Index, MODPRI)，針對陸域植生進行”連續野外”的LUE推估。 FPAR 是由常態化植生指標 (Normalized Difference Vegetation Index, NDVI) 演算而得。PAR 是從45%的入射短波太陽輻射 (Rs) 取得。在這篇文研究中，我們應用 2000~2006 年的 MODIS 衛星影像資料所推出之結果，和 NASA 的 MOD17 產品進行比較。在參考資料方面，本文所採用的 NPP-APAR 資料中，APAR 是來自 Bukit Soeharto (BKS) 微氣象站的地真資料 (2001和2002年)。 .
本研究主要目的是，藉由 MODIS 遙測影像資料，在沒有使用微氣候資訊的前提下，發展一種推估 NPP 的新方法。 .
結果顯示出，以 NPP Rs、 NPP MOD17 和 NPP APAR 的方法推估婆羅洲 NPP 量，在 2001 年分別為 1076.546 gC m2/y、3028.850 gC m2/y 和 1615.509 gC m2/y， 2002 年分別為 1522.324 gC m2/y、2890.467 gC m2/y 和 1987.113 gC m2/y。其中， NPP Rs 和 NPP MOD17 之間沒有顯示出相關性；相同的情形也發生在 NPP APAR NPP Rs 和 NPP APAR 的結果顯示出，在 2001 年和 2002 年的資料趨勢上有良好的相關性。 .
從這個結果來看，我們同時可以了解到：在這研究區域中，NASA 提供的 NPP 資料有高估的情況，而 NPP Rs 和 NPP APAR 相比較時，雖然 NPP 以平均來看仍然是低估的情況，但結果比 NASA 所提供的結果好。這結果也提供我們應用遙測影像資料推估 NPP 的一項新方法。隨著先前研究運用本研究結果，將可以在推估 NPP 時上得到較準確的結果。

摘要(英)

Estimating Net Primary Productivity (NPP) on Tropical Rain Forest Using MODIS Observation Data
Net Primary Productivity (NPP) is known as an important index for evaluating the carbon cycle in forest ecosystem. However, due to the factors of large area and lack of ground measurement data in tropical forest, it is hard to understand how forest ecosystem runs by land materials. Therefore, we use remote sensing data to estimate the three important parameters of NPP in tropical rain forest such as Light Use Efficiency (LUE), Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR), and Photosynthetically Active Radiation absorbed by vegetation (PAR).
Basically, the Monteith equation is used to measure aboveground NPP in the tropical forest. Rahman et al. (2004) proposed a micrometeorological model to estimate ‘‘continuous field’’ LUE for terrestrial vegetation using MODIS Photochemical Reflectance Index (MODPRI) from the MODIS ocean bands. FPAR is calculated from Normalized Difference Vegetation Index (NDVI). PAR is from 45 % of incoming shortwave solar radiation (Rs). In this study, we use MODIS data from 2000 to 2006 and compare the results with MOD17 of NASA Product. Absorbed Photosynthetically Active Radiation (APAR) from in-situ data (2001 and 2002) of Bukit Soeharto (BKS) site of micrometeorological station is used to produce a NPP APAR. Then, it’s a reference data in this study.
The main objective of this study is to get a new method to estimate NPP from MODIS remotely sensed data without using micrometeorological information over the study areas. This method creates a challenge to monitor ecosystem productivity in global area and even in difficultly-accessed regions.
The results show estimated value for the distribution of NPP over Borneo Island by three methods : NPP Rs, NPP MOD17 and NPP APAR. These values are 1076.546 gC m-2 y-1; 3028.850 gC m-2 y-1; 1615.509 gC m-2 y-1 in 2001, and 1522.324 gC m-2 y-1; 2890.467 gC m-2 y-1; 1987.113 gC m-2 y-1 in 2002, respectively. NPP Rs and NPP MOD17 values showed that there is no correlation between them. NPP Rs and NPP APAR outcomes represent good correlation in the trend of 2001 and 2002 data.
From this result we can conclude that: the NPP data provided by NASA is overestimated in this region, NPP Rs gives better result in comparison with NPP APAR, although it is still underestimated. Improved methods for estimating NPP by using MODIS observation data is needed to be developed. With advanced study employing the methodology of this study, better accuracy in estimating NPP can be achieved.

關鍵字(中)

★ Rs
★ APAR
★ PAR
★ FPAR
★ MODPRI
★ NPP
★ MODIS

關鍵字(英)

★ Rs
★ APAR
★ PAR
★ FPAR
★ MODPRI
★ NPP
★ MODIS

論文目次

Chinese Abstract i
English Abstract iii
Acknowledgments iv
Table of Contents v
List of Figures viii
List of Tables xi
Explanation of Symbols xii
I. INTRODUCTION 1
1.1 Global Warming and Climate Change 1
1.2 Global Carbon Cycle and Ecosystem 3
1.3 Tropical Forest and Global Carbon Cycle 7
1.4 Remote Sensing and Ecosystem Productivity Modelling 9
1.5 Objectives 11
1.6 Some Facts 11
1.7 Research Approach 12
II. METHODS AND MATERIALS 14
2.1 Study Area 14
2.1.1 Geographic Location 14
2.1.2 Climate 14
2.1.3 Vegetation Cover and Topography 15
2.2 Research Methods 19
2.2.1 Normalized Difference Vegetation Index (NDVI) 20
2.2.2 Incoming Shortwave Radiation (Rs) 20
2.2.3 Estimating Photosynthetic Light Use Efficiency (LUE) 22
2.2.4 Estimating fraction Photosynthetically Active Radiation (FPAR) 23
2.2.5 Estimating Photosynthetically Active Radiation (PAR) 24
2.2.6 NPP Rs 25
2.2.6 NPP APAR (APAR from in situ data) 26
2.3 Materials 26
2.3.1 MODIS Product Overview 26
2.3.2 MODIS Level-1B Product 29
2.3.3 MODIS Geolocation Product 32
2.3.4 NPP MOD17 Product 32
2.3.5 APAR BKS Data 33
III. DATA PROCESSING 35
3.1 Geo-referencing MOD021 35
3.2 Resizing MOD021 42
3.3 Subsetting MOD021 using AOI 45
3.4 Resizing and Subsetting MOD03 50
3.5 Calculating NPP Rs Using MATLAB 53
3.6 Calculating NPP APAR Using MATLAB 56
3.7 Adjusting NPP MOD17 Using MATLAB 58
IV. RESULT AND DISCUSSION 60
4.1 MOD021 and MOD03 Data 60
4.2 BKS’ APAR Data 66
4.3 NPP APAR 68
4.4 NPP Rs 71
4.5 NPP MOD17 78
4.6 Comparing NPP Rs, NPP MOD17 and NPP APAR 85
V. CONCLUSIONS 88
Bibliographies 90
Appendices 92

參考文獻

Allen RG, Tasumi, M., and Trezza, R. . Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) Model. J. Irrig. Drain. Eng., (2007b) 133(4):380–394.
AsiaFlux. Asia Flux Network (2010) http://asiaflux.yonsei.kr (accessed April 13, 2010) (Type of Medium).
Asrar G. MRB, Choudhury B.J. . Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study. Remote Sensing of ENVIronment (1992):85-103.
Batjes NH. Total carbon and nitrogen in the soils of the world. European Journal of Soil Science (1996) 47:151-163.
Chen LF, Gao, Y., Li Li, Lio Qin Huo, Gu Xing Fa. Forest NPP estimation based on MODIS data under cloudless condition. Sci. China Ser D-Earth Sci. (2008) 51(31) 331-338.
Chen LF, Gao, Y., Liu, Q.,Yu, T.,Gu, X.,Yang, L. Tang,Y.,Zhang, Y. . The MODIS-based NPP model and its validation. IEEE International Geoscience and Remote Sensing Symposium (2005):3028-3031.
Falkowski PG, R.T. Barber, and V. Smetacek. Biogeochemical controls and feedbacks on ocean primary production. Science of The Total ENVIrontment (1998) 281:200-206.
Falloon P, Smith, P., Coleman, K. and Marshall, S. Estimating the size of the inert organic matter pool from total soil organic carbon content for use in the Rothamsted Carbon Model. Soil Biology and Biochemistry (1998) 30:1207-1211.
Fluxnet. Introduction of Fluxnet (2010) http://www.fluxnet.ornl.gov/fluxnet/introduction.cfm (accessed April 13, 2010) (Type of Medium).
Heinsch FA, Reeves, M., Votava, P., Kang, S., Milesi, C., Zhao, M., et al. . User’s guide: GPP and NPP (MOD17A2/A3) products, NASA MODIS land algorithm, version 2.0 (2003).
IPCC. Technologies, policies and measures for mitigating climate change. In: IPCC Technical Paper I--Robert T. Watson Marufu CZ, Richard H. Moss, ed. (1996) Cambridge, United Kingdom and Newyork, USA, : Intergovernmental Panel on Climate Change. 94.
IPCC. Land Use,Land-Use Change, and Forestry. In: A Special Report of the Intergovernmental Panel on Climate Change (2000a) Cambridge, United Kingdom and Newyork, USA, : Intergovernmental Panel on Climate Change. 30.
IPCC. Stabilization of Atmospheric Greenhouse Gases: Physical, Biological and Socio-economic Implications (2000b).
IPIECA. A Guide to the Intergovernmental Panel on Climate Change (2006).
Jiquan Chen KDB, Asko Noormets,Thomas R. Crow, Mary K. Bresee, James M. Le Moine, Euge Nie S. Euskirchen, Steve V. Mather, Daolan Zheng. A Working Framework for Quantifying Carbon Sequestration in Disturbed Land Mosaics. ENVIronmental Management (2003) 33:S210-S221.
Laake PEV, Sanchez-Azofeifa, G. A. Mapping PAR using MODIS atmosphere products. Remote Sensing of ENVIronment (2005):747-766.
Mario Rautner MH, Raymond J. Alfred. Borneo: Treasure Island at Risk. Status of Forest, Wildlife and related Threats on the Island of Borneo. (2005 ) Frankfurt am Main, : WWF Germany. 80.
Martin Hardiono RJA. Borneo: Treasure Island at Risk. Maps on Status of Forests, Wildlife and related Threats on the Island of Borneo. (2005 ) Frankfurt am Main, : WWF Germany. 17.
Milliman JD. Production and accumulation of calciumcarbonate in the ocean - budget of a nonsteady state. Global Biogeochemical Cycles (1993) 7:927-957.
Mounteith J. Solar Radiation and Productivity in Tropical Ecosystem. Journal of Applied Ecology (1972):747-766.
Nugroho NP. Estimating Carbon Sequestration in Trophical Rainforest Using Integrated Remote Sensing and Ecosystem Productivity Modelling. In: Geoinformation Science and Earth Observation (2006) Enchede: International Institute for Geo Information Science and Observation Netherlands. 103.
Pidwirny M. Net Radiation and the Planetary Energy Balance (2006) http://www.physicalgeography.net/fundamentals/7i.html (accessed March 16, (Type of Medium).
Prentice IC. The carbon cycle and atmospheric carbon dioxide. In: Climate Change 2001 : The Scientific Basis--G.D. Farquhar MJRF, M.L. Goulden, M. Heimann, V.J. Jaramillo, H.S. Kheshgi, C. Le Quéré,, R.J. Scholes DWRW, eds. (2001): Cambridge Univ. Press. 183–237.
Rahman AF, V. D. Cordova, J. A. Gamon, H. P. Schmid, and D. A. Sims. . Potential of MODIS ocean bands for estimating CO2 flux from terrestrial vegetation: A novel approach. Geophysical Research Letters (2004) 31.
Schimel DS, B.H. Braswell, E.A. Holland, R. McKeown, D.S. Ojima, T.H. Painter, W.J. Parton, and A.R. Townsend. Climatic, edaphic and biotic controls over carbon and turnover of carbon in soils. Global Biogeochemical Cycles (1994) 8:279-293.
Schlesinger WH. Evidence from chronosequence studies for a low carbon-storage potential of soils. Nature (1990) 348:233-234.
Schlitzer R. Applying the adjoint method for biogeochemical modeling: export of particulate organic matter in the world ocean. In: Inverse methods in global biogeochemical cycles--Kasibhatla P, M. Heimann, P. Rayner, N. Mahowald, R.G. Prinn, and D.E. Hartley, ed. (2000): Geophysical Monograph Series. 107-124.
Scholes M, and M.O. Andreae. Biogenic and pyrogenic emissions from Africa and their impact on the global atmosphere. Ambio (2000) 29:23-29.
Schowengerdt RA. Remote Sensing : Models and Methodes for Image Processing. (2006) Third Edition edn. London: Academic Press.
Thomas Hilker NCC, Michael A. Wulder, T. Andrew Black, Robert D. Guy. The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total ENVIronment (2008) 404 (2-3):411-423.
Vincent V. Salomonson WBaEJM. Introduction to MODIS and an Overview of Associated Activities. In: Earth Science Satellite Remote Sensing Volume 1: Science and Instruments--John J. Qu WG, Menas Kafatos, Robert E. Murphy, Vincent V. Salomonson ed. (2006) Berlin/Heidelberg: SPRInger/Tsinghua University Press.
Williams SN, S.J. Schaefer, M.L. Calvache, and D. Lopez. Global carbon dioxide emission to the atmosphere by volcanoes. Geochimica et Cosmochimica Acta (1992) 56:1765-1770.
Wolfgang Cramer AB, Sibyll Schaphoff, Wolfgang Lucht, Benjamin Smith and Stephen Sitch. Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation. Phil. Trans. R. Soc. Lond. (2004) 359:331–343.
Xiaoxiong Xiong AIaWB. MODIS Level-1B Products. In: Earth Science Satellite Remote Sensing Volume 1: Science and Instruments--John J. Qu WG, Menas Kafatos, Robert E. Murphy, Vincent V. Salomonson, ed. (2006) Berlin/Heidelberg: SPRInger/Tsinghua University Press.
Y. Yamagata GAA. Global Potential of Carbon Sinks under the Kyoto Protocol. In: Present and Future of Modeling Global ENVIronmental Change: Toward Integrated Modeling--Kida TMaH, ed. (2001): TERRAPUB. 421–426.
Zhangyan Jiang ARH, Jin Chen, Yunhao Chen, Jing Li, Guangjian Yan, Xiaoyu Zhang. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of ENVIronment (2006) 101:366–378.

指導教授

劉說安(Yuei An, Liou)

審核日期

2010-7-12

推文