利用多時期之衛星影像改進孟加拉地區之地表水量化

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施帛宏(Po-Hung Shih) 查詢紙本館藏

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土木工程學系

論文名稱

利用多時期之衛星影像改進孟加拉地區之地表水量化
(Using Historical Satellite Imagery to Improve Surface Water Quantification in Bangladesh)

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摘要(中)

在過去，人們通常利用水文站以及地下水井監測水資源，然而這些方法不但昂貴、費時且需要許多人力資源進行記錄、維護。因此利用衛星資料來建立水資源監測網路是一個合適的替代方案。孟加拉擁有世界上人口密度第10高的國家，其人口密度是台灣的2倍高。在如此高人口密度的影響下，如何獲取水資源變成孟加拉相當重要的議題。雖然每年雨季時，來自印度洋的季風為孟加拉帶來大量的降雨甚至造成及為嚴重的水災，但因為其平坦的地形以及快速的逕流導致孟加拉無法將這些雨水儲存為可用水資源。因此孟加拉需要一個可以提供大尺度且連續水資源資訊的監測網絡。本研究使用多時期的衛星資料對錫爾赫特平原以及布拉馬普特拉河(雅魯藏布江下游)進行觀測，利用長期的觀測資料量化地表水的體積並結合重力衛星資料預估地下水的變遷。使用的資料包含兩個光學衛星Terra/MODIS 以及Landsat-5/-7/-8 的影像、SRTM之數值高程模型以及重力衛星GRACE的重力異常量資料。為達成量化的目的，首先必須填補光學影像中被雲所遮蔽的部分，我們針對光學衛星影像計算兩個研究區域的改良常態差異化水指數，從中萃取出水面積資訊，並將所有結果相加建立每個星期的淹水機率模型，利用此模形我們可以填補影像中雲的遮蔽。此外由於SRTM之數值高程模型在孟加拉地區的誤差可達數公尺，因此在本研究中我們利用建立出來的淹水機率模型改進SRTM的資料。利用填補後的水面積以及改進後的數值高程模型，我們便可以量化孟加拉的地表水體積。另一方面，在量化地表水體積後，將地表水資訊從GRACE的重力異常量中減去，我們便可以推估地下水的變遷趨勢。本研究方法在兩個研究區所量化的地表水體積曲線中皆可以觀測到於2004及2007年時發生的嚴重洪災之訊號，在地表水體積曲線與GRACE資料的比較中，兩研究區的相關係數皆大於0.9，而方均根誤差約為10公分。而由推估出的地下水變化量中可得知錫爾赫特平原的地下水有下降趨勢(約為0.5公分/年)，而布拉馬普特拉河則呈現下降趨勢(約為1.7公分/年)。此現象符合過去研究所觀測到的水位變化趨勢。

摘要(英)

Conventional measures to monitor terrestrial water resources are the deploy of water gauges and in situ well. However, these methods are not only expensive and time-consuming, but also require lots of manpower and infrastructure setups. Therefore, using satellite observations to build a water resource monitoring network becomes an attracting alternative. Ranking as the 10th highest population density in the world, Bangladesh is suffering from multiple freshwater issues. Although the monsoon heading from the Indian Ocean brought lots of rainfall that even induce serious floods every year, it is not practicable for Bangladesh to store surface water due to its flat terrain. Meanwhile, the over-pumping of groundwater has induced extensive land subsidence in many administrative divisions. Therefore, Bangladesh needs a monitoring network that can provide large-scale and continuous data to manage their water resources. This research proposes a method to quantify surface water volume and further estimate the sub-surface water (include soil moisture and groundwater) trend. The study case focuses on Sylhet Plain which has the highest annual precipitation in Bangladesh. We first use the modified normalized difference water index to extract water area from Terra/MODIS MOD09A1 product and Landsat-5/-7/-8 Thematic Mapper (TM)/Enhanced TM plus (ETM+)/Operational Land Imager (OLI) imageries. Then we accumulate a sequence of images to create flood chance model for the recovery of cloud-covered surface. This approach extends the time series of WA with an overall accuracy of 70–80% in rainy season and 40–50% in dry season. This model can be further used to refine Shuttle Radar Topography Mission (SRTM), which has few meters uncertainty. Next, we simulate the flood extent using the modified SRTM and obtain and the overall accuracy of flood extent increases 19% compared to original data. By combining recovered WA and reconstructed DEM, the surface water volume (WV) is quantified and the signals of two extreme flood events in 2004 and 2007 are well observed in the estimated WV curve. The shifting days between estimated WV and GRACE equivalent water heights (EWHs) are 4 days in Sylhet Plain and 15 days in Brahmaputra River. The correlation coefficient and RMS of the EWH difference are 91.7% / 0.10 m in Sylhet plain and 95.48% / 0.12 m in Brahmaputra River. Finally, we subtract surface water from GRACE EWH and the result shows a decreasing trend of sub-surface water at 0.5 cm yr-1 in Sylhet Plain and decreasing trend at 1.7 cm cm yr-1 in Brahmaputra River, which agree with previous studies.

關鍵字(中)

★ 地下水
★ 孟加拉
★ 光學衛星
★ 重力衛星

關鍵字(英)

論文目次

摘要 i
Using Historical Satellite Imageries to Improve Surface Water Quantification in Bangladesh. ii
Abstract ii
致謝 iv
Table of Contents v
List of Figures and Illustrations vii
List of Tables x
1. Introduction 1
1.1 Background 1
1.2 Objective 5
1.3 Architecture 5
2. Related Work in Satellite Hydrology 7
2.1. Remote sensing in hydrology research 7
2.2. Apply satellite image to surface WA observation 8
2.3. Estimate WL by satellite altimetry and DEM 11
2.4. Combine milti-satellite data to estimate groundwater trend 13
3. Study area 14
3.1. Geography of Bangladesh 14
3.2. Surface water in Bangladesh 18
3.3. Ground water in Bangladesh 20
4. Data and Methodology 22
4.1. Optical Satellite Imageries 23
4.2. Cloud Removal and Water Area Restoration 30
4.3. Model Validation by Sentinel-1A Image 35
4.4. Estimation of Surface Water Volume 39
4.5. Sub-surface water trend estimation 41
4.6. Estimation of surface water 44
5. Results 46
5.1. Validation of recover result 46
5.2. Reconstructed model analysis 54
5.3. Estimation of surface water volume and sub-surface water trend 56
6. Discussions and Future Work 66
7. Conclusions 68
8. Q&A 71
References 74

參考文獻

1. St Jacques, J. M., & Sauchyn, D. J. (2009). Increasing winter baseflow and mean annual streamflow from possible permafrost thawing in the Northwest Territories, Canada. Geophysical Research Letters, 36(1).
2. Andersen, O., Berry, P., Freeman, J., Lemoine, F. G., Lutsckhe, S., Jakobsen, F., & Butts, M. (2008). Satellite altimetry and GRACE gravimetry for Studies of Annual Water Storage Variations in Bangladesh. Terrestrial, Atmospheric, and Oceanic Sciences, 19, 47.
3. Mohammed S.I. 114,000 hectare Boro crops damaged in 4 Sylhet districts. DHAKA TRIBUNE 2016.
4. Milliman, J. D., Broadus, J. M., & Gable, F. (1989). Environmental and economic implications of rising sea level and subsiding deltas: the Nile and Bengal examples. Ambio, 340-345.
5. Hoque, M. A., Hoque, M. M., & Ahmed, K. M. (2007). Declining groundwater level and aquifer dewatering in Dhaka metropolitan area, Bangladesh: causes and quantification. Hydrogeology Journal, 15(8), 1523-1534.
6. Hasin J. Arsenic in Bangladesh: how to protect 20 million from the world′s largest poisoning. The Guardian 2016
7. Alam, M. G. M., Allinson, G., Stagnitti, F., Tanaka, A., & Westbrooke, M. (2002). Arsenic contamination in Bangladesh groundwater: a major environmental and social disaster. International Journal of Environmental Health Research, 12(3), 235-253.
8. 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.
9. McFeeters, S. K. (2013). Using the Normalized Difference Water Index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: a practical approach. Remote Sensing, 5(7), 3544-3561.
10. Han-Qiu, X. U. (2005). A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)[J]. Journal of Remote Sensing, 5, 589-595.
11. Hostache, R., Matgen, P., Schumann, G., Puech, C., Hoffmann, L., & Pfister, L. (2009). Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. IEEE Transactions on Geoscience and Remote Sensing, 47(2), 431-441.
12. Henry, J. B., Chastanet, P., Fellah, K., & Desnos, Y. L. (2006). Envisat multi?polarized ASAR data for flood mapping. International Journal of Remote Sensing, 27(10), 1921-1929.
13. Silveira, M., & Heleno, S. (2009). Separation between water and land in SAR images using region-based level sets. IEEE Geoscience and Remote Sensing Letters, 6(3), 471-475.
14. Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., ... & Traver, I. N. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9-24.
15. de Oliveira Campos, I., Mercier, F., Maheu, C., Cochonneau, G., Kosuth, P., Blitzkow, D., & Cazenave, A. (2001). Temporal variations of river basin waters from Topex/Poseidon satellite altimetry. Application to the Amazon basin. Comptes Rendus de l′Academie des Sciences-Series IIA-Earth and Planetary Science, 333(10), 633-643.
16. Maheu, C., Cazenave, A., & Mechoso, C. R. (2003). Water level fluctuations in the Plata basin (South America) from Topex/Poseidon satellite altimetry. Geophysical research letters, 30(3).
17. Fu, L. L., & Smith, R. D. (1996). Global ocean circulation from satellite altimetry and high-resolution computer simulation. Bulletin of the American Meteorological Society, 77(11), 2625-2636.
18. Laxon, S., Peacock, N., & Smith, D. (2003). High interannual variability of sea ice thickness in the Arctic region. Nature, 425(6961), 947-950.
19. Moholdt, G., Nuth, C., Hagen, J. O., & Kohler, J. (2010). Recent elevation changes of Svalbard glaciers derived from ICESat laser altimetry. Remote Sensing of Environment, 114(11), 2756-2767.
20. Berry, P. A. M., Garlick, J. D., Freeman, J. A., & Mathers, E. L. (2005). Global inland water monitoring from multi?mission altimetry. Geophysical Research Letters, 32(16).
21. Frappart, F., Calmant, S., Cauhope, M., Seyler, F., & Cazenave, A. (2006). Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote sensing of Environment, 100(2), 252-264.
22. Deng, X., & Featherstone, W. E. (2006). A coastal retracking system for satellite radar altimeter waveforms: Application to ERS?2 around Australia. Journal of Geophysical Research: Oceans, 111(C6).
23. Legresy, B., Papa, F., Remy, F., Vinay, G., van den Bosch, M., & Zanife, O. Z. (2005). ENVISAT radar altimeter measurements over continental surfaces and ice caps using the ICE-2 retracking algorithm. Remote Sensing of Environment, 95(2), 150-163.
24. Kuo, C. Y., & Kao, H. C. (2011). Retracked Jason-2 altimetry over small water bodies: Case study of Bajhang River, Taiwan. Marine Geodesy, 34(3-4), 382-392.
25. Schumann, G., Matgen, P., Cutler, M. E. J., Black, A., Hoffmann, L., & Pfister, L. (2008). Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), 283-296.
26. Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., & Watkins, M. M. (2004). GRACE measurements of mass variability in the Earth system. Science, 305(5683), 503-505.
27. Rodell, M., Chen, J., Kato, H., Famiglietti, J. S., Nigro, J., & Wilson, C. R. (2007). Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeology Journal, 15(1), 159-166.
28. Feng, W., Zhong, M., Lemoine, J. M., Biancale, R., Hsu, H. T., & Xia, J. (2013). Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground?based measurements. Water Resources Research, 49(4), 2110-2118.
29. Bellerby, T., Todd, M., Kniveton, D., & Kidd, C. (2000). Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. Journal of applied Meteorology, 39(12), 2115-2128.
30. Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., & Dozier, J. (2009). Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113(4), 868-879.
31. Tang, Q., Gao, H., Lu, H., & Lettenmaier, D. P. (2009). Remote sensing: hydrology. Progress in Physical Geography, 33(4), 490-509.
32. Peltier, W. R. (2004). Global glacial isostasy and the surface of the ice-age Earth: the ICE-5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci., 32, 111-149.
33. Jacob, T., Wahr, J., Pfeffer, W. T., & Swenson, S. (2012). Recent contributions of glaciers and ice caps to sea level rise. Nature, 482(7386), 514-518.
34. Voss, K. A., Famiglietti, J. S., Lo, M., Linage, C., Rodell, M., & Swenson, S. C. (2013). Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris?Euphrates?Western Iran region. Water resources research, 49(2), 904-914.
35. Shum, C. K., Guo, J. Y., Hossain, F., Duan, J., Alsdorf, D. E., Duan, X. J., ... & Wang, L. (2011). Inter-annual water storage changes in Asia from GRACE data. Climate Change and Food Security in South Asia, 69-83.
36. ESA Science Education through Earth Observation for High Schools (SEOS) Project. http://www.seos-project.eu/modules/classification/classification-c00-p05.html
37. Bangladesh map with province borders and names, provincial capitals and main rivers. http://asiapacific.anu.edu.au/mapsonline/base-maps/bangladesh-base
38. Bangladesh elevation map. http://www.floodmap.net/Elevation/CountryElevationMap/?ct=BD
39. Agrawala, S., Ota, T., Ahmed, A. U., Smith, J., & Van Aalst, M. (2003). Development and climate change in Bangladesh: focus on coastal flooding and the Sundarbans (pp. 1-49). Paris: OECD.
40. Brammer, H. (1996). geography of the soils of Bangladesh. University Press.
41. Rahman, M. M., Hossain, M. A., & Bhattacharya, A. K. (2007). Flood management in the flood plain of bangladesh. In International Conference on Civil Engineering in the New Millennium: Opportunities and Challenges (CENM).
42. Brown, S., & Nicholls, R. J. (2015). Subsidence and human influences in mega deltas: the case of the Ganges–Brahmaputra–Meghna. Science of the Total Environment, 527, 362-374.
43. Arsenic contamination map in Bangladesh. http://www.banglapedia.org/
44. Tanre, D., Kaufman, Y. J., Herman, M., & Mattoo, S. (1997). Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research: Atmospheres, 102(D14), 16971-16988.
45. Huete, A., 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.
46. Koren, I., Kaufman, Y. J., Remer, L. A., & Martins, J. V. (2004). Measurement of the effect of Amazon smoke on inhibition of cloud formation. Science, 303(5662), 1342-1345.
47. Lohmann, U., & Feichter, J. (2005). Global indirect aerosol effects: a review. Atmospheric Chemistry and Physics, 5(3), 715-737.
48. LP DAAC datapool. https://lpdaac.usgs.gov/
49. LP DAAC MRT web iterface. https://lpdaac.usgs.gov/about/news_archive/modis_reprojection_tool_web_interface_20_release.
50. USGS EarthExplorer https://earthexplorer.usgs.gov/
51. Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?. Remote sensing of Environment, 75(2), 230-244.
52. Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113(5), 893-903.
53. Uing the USGS Landsat 8Product. http://landsat.usgs.gov/Landsat8_Using_Product.php.
54. Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23-35.
55. Alaska Satellite Facility. https://vertex.daac.asf.alaska.edu/
56. Sentinel-1 toolbox. https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1
57. Lee, J. S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE transactions on pattern analysis and machine intelligence, (2), 165-168.
58. Lehner, B., Verdin, K., & Jarvis, A. (2008). New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union, 89(10), 93-94.
59. Kiel, B., Alsdorf, D., & LeFavour, G. (2006). Capability of SRTM C-and X-band DEM data to measure water elevations in Ohio and the Amazon. Photogrammetric Engineering & Remote Sensing, 72(3), 313-320.
60. Rodriguez, E., Morris, C. S., Belz, J. E., Chapin, E. C., Martin, J. M., Daffer, W., & Hensley, S. (2005). An assessment of the SRTM topographic products (p. 143). Technical Report JPL D-31639, Jet Propulsion Laboratory, Pasadena, California.
61. Heiskanen, W. A., & Moritz, H. (1967). Physical geodesy. Bulletin Geodesique (1946-1975), 86(1), 491-492.
62. Kusche, J. (2007). Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models. Journal of Geodesy, 81(11), 733-749.
63. Kusche, J., Schmidt, R., Petrovic, S., & Rietbroek, R. (2009). Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model. Journal of geodesy, 83(10), 903-913.
64. The GRACE Plotter. http://www.thegraceplotter.com/
65. Knudsen, P., & Andersen, O. (2002). Correcting GRACE gravity fields for ocean tide effects. Geophysical Research Letters, 29(8).
66. Khaki, M., Forootan, E., & Sharifi, M. A. (2014). Satellite radar altimetry waveform retracking over the Caspian Sea. International journal of remote sensing, 35(17), 6329-6356.
67. Guo, J., Chang, X., Gao, Y., Sun, J., & Hwang, C. (2009). Lake level variations monitored with satellite altimetry waveform retracking. IEEE journal of selected topics in applied earth observations and remote sensing, 2(2), 80-86.
68. Legresy, B., Papa, F., Remy, F., Vinay, G., van den Bosch, M., & Zanife, O. Z. (2005). ENVISAT radar altimeter measurements over continental surfaces and ice caps using the ICE-2 retracking algorithm. Remote Sensing of Environment, 95(2), 150-163.
69. Bamber, J. L. (1994). Ice sheet altimeter processing scheme. International Journal of Remote Sensing, 15(4), 925-938.
70. Bangladesh Water Development Board (BWDB). http://www.ffwc.gov.bd/index.php/hydrograph/monsoon
71. Dartmouth Flood Observatory. http://www.dartmouth.edu/~floods/index.html
72. Bangladesh Agriculture Development Corporation. (2006) Ground Water Monitoring Data Book.
73. MacDonald, A. M., Bonsor, H. C., Ahmed, K. M., Burgess, W. G., Basharat, M., Calow, R. C., ... & Lark, R. M. (2016). Groundwater quality and depletion in the Indo-Gangetic Basin mapped from in situ observations. Nature Geoscience, 9(10), 762-766.
74. Global Land Data Assimilation System. https://disc.sci.gsfc.nasa.gov/services/grads-gds/gldas/

指導教授

曾國欣

審核日期

2017-8-24

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