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姓名 何炫騏(Hsuan-Chi Ho) 查詢紙本館藏 畢業系所 土木工程學系 論文名稱
(A TOA-reflectance-based Spatial-temporal Image Fusion Method for Aerosol Optical Depth Retrieval)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 遙測衛星提供週期性的地球觀測資料，許多研究將週期性的觀測影像經不同的處理程序，由資料處理成資訊應用至各領域進行分析，如土地覆蓋分析、氣象分析等。由於城市發展及工業化，空氣汙染日益嚴重，空氣品質的監測成為重要的議題。許多研究應用衛星影像進行大尺度的空氣品質監測與分析，然空氣品質具動態局部變化特性，而現有單一衛星無法提供同時具有高空間與高時間解析度之影像。有些研究者因而提出衛星影像融合的方法來達到同時具有高空間與高時間解析度的衛星影像，例如STARFM (Spatial Temporal Adaptive Reflectance Fusion Model)。過去的研究使用STARFM融合了Landsat衛星影像與MODIS衛星影像，提供同時具有高空間解析度(30公尺)與高時間解析度(1至2天)之影像。然而STARFM主要針對地表的反射率並進行融合，其融合影像僅能進行土地覆蓋之相關分析應用，對於大氣相關的應用並無法支援。
因此，本研究基於STARFM影像融合方法提出TOA-STFM (A TOA-reflectance-based Spatio-Temporal image fusion method)方法，使用大氣層頂(top-of-atmosphere, TOA)反射率進行時間空間影像融合以保留大氣訊息。由於Himawari-8氣象衛星具有極高之時間解析度(10分鐘)，且與Landsat-8及SPOT-6的光譜波段相似。因此，本研究將透過TOA-STFM進行Lansdat-8及SPOT-6之高空間解析度影像與Himawri-8高時間解析度影像融合，並應用高空間與高時間解析度的衛星影像至空氣品質監測。研究結果進行兩項驗證，第一項驗證與真實觀測影像比對，第二項驗證以融合影像反演氣膠光學厚度(Aerosol optical depth, AOD)與AERONET (AErosol RObotic NETwork)地面測站進行比較。第一項驗證發現，相較於以STARFM直接融合大氣層頂反射率，雖反射率絕對值差異不大，但TOA-STFM明顯可保留較佳之影像細節。第二項驗證顯示STARFM及TOA-STFM融合影像之AOD反演成果皆較原始Himawari-8影像之成果更為穩定，且精度較佳。第二項驗證亦顯示本研究提出之TOA-STFM在保留大氣訊息後比STARFM可得更精確之AOD反演成果。在七天Landsat-8及三天SPOT-6測試案例中，TOA-STFM有八天可得最佳成果，且此六天之AOD每日相對誤差在15%以內。
整體而言，本研究具有以下貢獻；一、所提出之TOA-STFM可針對大氣層頂反射率進行時間與空間影像融合，以高時間與空間解析度之融合影像提供大氣遙測相關應用。二、本研究測試並證明可使用Himawari-8影像進行影像融合，進而提供高時間解析之動態環境監測。三、本研究針對AOD反演應用進行案例驗證，相較STARFM與原始Himawari-8影像，TOA-STFM可得穩定且高精度之反演成果，說明此方法可有效保留大氣層頂反射率並進行動態大氣環境監測應用。
摘要(英) Satellite remote sensing images provide periodical and multispectral information about the atmosphere and ground surface, which empowers various monitoring applications. Among the applications, some of them require high spatial and temporal resolutions, such as disaster management, crop phenology monitoring, and land cover change detection. Recently, with urban development and industrialization, air pollution becomes an important issue to human health. While ground stations can monitor air quality effectively, the spatial resolution of ground stations is usually low to observe local phenomena. Therefore, some studies apply remote sensing techniques on air quality monitoring. However, while air quality changes are local and rapid, the spatial resolution and temporal resolution of a single satellite is not enough to observe such dynamic phenomenon. To address this issue, this research aims at proposing a spatial and temporal image fusion method to synthesize high spatial and temporal resolution images for air quality monitoring. To be specific, this research proposes the TOA-reflectance-based spatial-temporal image fusion method (TOA-STFM), which is a variation of the STARFM method. While the STARFM method focuses on the surface reflectance, to preserve the atmospheric properties in the fused images, the TOA-STFM first uses the band with longer wavelength to estimate the fusion weightings and includes blurring effect adjustment in the image fusion process, and then finally fuses with the green band images to preserve atmospheric effects. In addition, this study aims at examining the feasibility of fusing high spatial resolution and Himawari-8 images. By integrating these two types of satellite images, high spatial resolution and ultra-high temporal resolution images can be generated to observe rapidly-changing phenomena.
To evaluate the proposed solution, we first compared the fused images with actual observation images, and then retrieved aerosol optical depth (AOD) from the fused images and compared with in-situ observations from the AErosol RObotic NETwork (AERONET). The result shows that the fused images from the TOA-STFM can provide more detail information than the STARFM. In addition, the fused images (from both the STARFM and the TOA-STFM) could provide better AOD estimation than the original Himawari-8 images. Furthermore, the purposed TOA-STFM method is more accurate than the original STARFM method on AOD retrieval, where the TOA-STFM can achieve 2% to 14% relative error.
In summary, this research has the following contributions. The TOA-STFM can predict TOA reflectance successfully, and provide ultra-high temporal resolution and high spatial resolution fused images for air quality monitoring. In addition, based on the evaluation result, we can find that the TOA-STFM can provide more stable and accurate result than other methods, which means the TOA-STFM is effective in preserving TOA-reflectance from coarse-resolution images, and is useful for dynamic air quality monitoring.
關鍵字(中) ★ 時間與空間之影像融合
★ STARFM
★ Himawari-8
★ 氣膠光學厚度關鍵字(英) ★ Spatial-temporal image fusion
★ STARFM
★ Himawari-8
★ AOD retrieval論文目次 摘要 i
Abstract iii
Acknowledgement v
Table of Contents vi
List of Figures and Illustrations viii
List of Tables xi
1. Introduction 1
1.1 Background 1
1.2 Challenges and objectives 4
2. Related Work 6
2.1 Spatial and Temporal image fusion 7
2.2 Aerosol effect 9
3. Methodology 11
3.1. The preparation stage 12
3.1.1 Geometric registration 12
3.1.2 Land cover classification 12
3.1.3 Brightness adjustment 13
3.2 The spatial and temporal image fusion stage 14
3.2.1 The spatial and temporal adaptive reflectance fusion model (STARFM) 14
3.2.2 TOA-reflectance-based spatial-temporal fusion model (TOA-STFM) 18
3.3 AOD retrieval 32
4. Result 33
4.1 Satellite images used in this research 33
4.2 Testing dataset and image fusion result 35
4.3 Validation of fused images 38
4.3.1 Absolute difference between observation and fused images 38
4.3.2 Evaluation of AOD retrieval 50
5. Conclusions and Future work 61
6. References 63
參考文獻 1.Brakenridge, R., & Anderson, E. (2006). MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. Transboundary floods: reducing risks through flood management, 2006, 1-12.
2.Chu, D. A., Kaufman, Y. J., Zibordi, G., Chern, J. D., Mao, J., Li, C., & Holben, B. N. (2003). Global monitoring of air pollution over land from the Earth Observing System‐Terra Moderate Resolution Imaging Spectroradiometer (MODIS). Journal of Geophysical Research: Atmospheres, 108(D21).
3.Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote sensing, 44(8), 2207-2218.
4.Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y. C., & Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30), 5880-5892.
5.Hansen, M. C., DeFries, R. S., Townshend, J. R., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21(6-7), 1331-1364.
6.Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J. G., Gao, F., & White, J. C. (2009). A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8), 1613-1627.
7.Hsu, N. C., Tsay, S. C., King, M. D., & Herman, J. R. (2004). Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3), 557-569.
8.Imai, T., and Yoshida, R. (2016). Algorithm theoretical basis for Himawari-8 Cloud Mask Product. Meteorol. Satell. Center Tech. Note, 61, 1-17.
9.Jurgens, C. (1997). The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing, 18.17, 3583-3594.
10.Kopeika, N. S., Dror, I., & Sadot, D. (1998). Causes of atmospheric blur: Comment on atmospheric scattering effect on spatial resolution of imaging systems. JOSA A, 15(12), 3097-3106.
11.Levy, R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T. F. Eck. (2010). Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos. Chem. Phys., 10, 10399–10420.
12.Lin, C., Li, Y., Yuan, Z., Lau, A. K., Li, C., & Fung, J. C. (2015). Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM 2.5. Remote Sensing of Environment, 156, 117-128.
13.Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riédi, J. C., & Frey, R. A. (2003). The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 459-473.
14.Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., & Eck, T. F. (2005). The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences, 62(4), 947-973.
15.Schwartz, J., Dochery, D.W., Neas, L.M., (1996). Is daily mortality associated specifically with fine particles. Journal of Air and Waste Management Association, 46, 927-939.
16.Shettigara, V. K. (1992). A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogrammetric Engineering and Remote Sensing, 58(5), 561-567.
17.Sifakis, N., & Deschamps, P. Y. (1992). Mapping of air pollution using SPOT satellite data. Photogrammetric Engineering and Remote Sensing, 5, 4.
18.Sano, I., Mukai, M., Iguchi, N., & Mukai, S. (2010). Suspended particulate matter sampling at an urban AERONET site in Japan, part 2: relationship between column aerosol optical thickness and PM 2.5 concentration. Journal of Applied Remote Sensing, 4(1), 043504.
19.Tanré, D., Deschamps, P. Y., Devaux, C., & Herman, M. (1988). Estimation of Saharan aerosol optical thickness from blurring effects in Thematic Mapper data. Journal of Geophysical Research: Atmospheres, 93(D12), 15955-15964.
20.Vogelmann, J. E., Howard, S. M., Yang, L., Larson, C. R., Wylie, B. K., & Van Driel, N. (2001). Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing, 67(6).
21.Wu, M.; Wang, J.; Niu, Z.; Zhao, Y.; Wang, C. A model for spatial and temporal data fusion. J. Infrared Millim. Waves 2012, 31, 80–84.
22.Wang, J., Xu, X., Spurr, R., Wang, Y., & Drury, E. (2010). Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China. Remote Sensing of Environment, 114(11), 2575-2583.
23.Wang, J., & Christopher, S. A. (2003). Intercomparison between satellite‐derived aerosol optical thickness and PM2. 5 mass: implications for air quality studies. Geophysical Research Letters, 30(21).
24.Zhu, X., Chen, J., Gao, F., Chen, X., & Masek, J. G. (2010). An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11), 2610-2623.
25.Zhang, W., Li, A., Jin, H., Bian, J., Zhang, Z., Lei, G., Qin, Z., & Huang, C. (2013). An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data. Remote Sensing, 5(10), 5346-5368.
26.Zeger, S. L., Thomas, D., Dominici, F., Samet, J. M., Schwartz, J., Dockery, D., & Cohen, A. (2000). Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environmental health perspectives, 108(5), 419.
指導教授 黃智遠(Chih-Yuan Huang) 審核日期 2018-1-29 推文 plurk
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