博碩士論文 100022006 詳細資訊




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姓名 李品儀(Pin-yi Lee)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 地球同步衛星觀測資料之雲區像素辨識
(Discrimination of Cloud Pixels for Geostationary Satellite from MODIS Product)
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摘要(中) 雲是指停留大氣層上的水滴或冰晶以及其他空氣分子的集合體,遠看是不透明的,近看則發現它並不是不透明,只是能見度有限,這樣不連續的特性,導致雲的邊界模糊難以判定,然而眼見不為憑,肉眼所能看見僅限於可見光波段、能辨認的顏色僅約60種,因此我們需要更多的資訊來彌補眼睛的缺失,正因為不同波段的特性都不盡相同,因此在波段的選取上也是門學問。
雲量的多寡是影響地球系統能量平衡的重要因素之一,因為雲會反射大量太陽光,對地球系統來說,吸收的能量相對減少,但雲會吸收地面輻射的紅外線,並將吸收能量再以輻射方式發射回地面,有助於地球增溫,因此雲的存在對於全球暖化的議題中也佔有重要的成分。而雲的存在會遮蔽地表,造成地表資訊的削弱或流失,導致人們無法獲得精確的地表資訊,因此在觀測地表之前,應最先確認的是有雲的區域,將有雲的資訊濾除或校正,然而該如何判斷有雲或無雲呢?正如上述所說,雲是變化無常、模糊而不確定的,因此雲的判斷是相當具有挑戰性的,也是本篇論文所探究的主題。
雲的變化將反映當時天氣狀況,提供了大氣資訊、狀態與穩定程度,因此觀測雲的變化也是天氣預測的重要指標,人們藉由多年知識與經驗的累積,掌握了一些跡象來預測未來天氣的狀況並加以分析,盼望能夠為未來天氣演變形式及天氣預報提供一定的幫助。
MODIS ( Moderate-resolution Imaging Spectroradiometer) 是搭載在繞極衛星Terra與Aqua上的重要儀器,其資料已廣泛應用於氣象、環境、林業、漁業、自然災害監測等領域,乃氣象觀測之重要儀器,具有全球性觀測、高空間解析及多頻譜之優點; MTSAT (Multifunctional Transport Satellites)為地球同步衛星,具有高時間解析及視野寬廣的優點,然而目前MTSAT仍未有雲分類的相關產品,因此本文使用MODIS雲區遮罩產品 MOD35與 MTSAT資料搭配相近的時間與地點嘗試反演出MTSAT雲區遮罩產品,並將結果與MOD35較驗。本篇論文著重於晴空區域的辨識,使用kappa值進行一致性的評估並嘗試尋找有意義的邏輯判斷式及有根據的門檻值來完成整個雲區遮罩的架構,其中使用了波段特徵上的辨別與數學式子上的討論,其結果幾乎完全與MODIS雲區遮罩產品吻合。
摘要(英) Clouds are a visible mass of liquid droplets or frozen crystals made of water or various chemicals suspended in the atmosphere. Seeing from a distance it is opaque, but closely it is discontinuous and can be seen through with certain visibility. When cloud existing, it will cover the earth’s surface and weaken the information surface and lead to receive imprecise information. Therefore, before further analysis of the data, we should determine the cloud region first so that it can be filtered or corrected. However as mentioned, cloud is capricious, fuzzy and uncertain. This characteristic made the border of cloud uncertain and uneasy to determine whether the cloud existing or not. Therefore the identification is quite challenging. MODIS (Moderate-resolution Imaging Spectroradiometer) and MTSAT (Multifunctional Transport Satellites) are two major sources for cloud monitoring. MODIS is an important imager onboard the sun-synchronous satellites of Terra and Aqua. The advantages of MODIS are global observations, high spatial resolution and multi spectral. On the other hand, MTSAT is a geostationary satellite covered the Asia area which having a superiority of high temporal resolution. Therefore in order to get better temporal resolution to observe same location sustained, we combine MTSAT data collocates with MODIS cloud mask product-MOD35 to retrieve MTSAT cloud mask. This study focuses on the detection of clear pixels. The performance of the algorithm shows an almost perfect result by using kappa as standard compared to MODIS cloud mask.
關鍵字(中) ★ 雲區遮罩
★ 像素
★ 反演
關鍵字(英) ★ cloud mask
★ pixel
★ retrieval
論文目次 ABSTRACT IN CHINESE---------------------------------------------------ⅰ
ABSTRACT IN ENGLISH---------------------------------------------------ⅲ
CONTENTS---------------------------------------------------------------------ⅳ
CONTENTS OF FIGURES--------------------------------------------------ⅵ
CONTENTS OF TABLES---------------------------------------------------ⅷ
CHAPTERS----------------------------------------------------------------------1
Introduction------------------------------------------------------------------1
1.1 Background---------------------------------------------------------------1
1.2 Motivation----------------------------------------------------------------3
Methodology-----------------------------------------------------------------5
2.1 Algorithm description---------------------------------------------------5
2.2 Data -----------------------------------------------------------------------6
2.1.1 MODIS cloud product (MOD35) -----------------------------8
2.1.2 MTSAT data-----------------------------------------------------10
2.3 Collocation---------------------------------------------------------------12
2.4 The channel characteristics of Cloud and Clear pixels------------17
The Detection of Clear Pixel----------------------------------------------24
3.1 The combination use of MTSAT 3 channels observation---------24
3.2 The evaluation of detection algorithm--------------------------------26
3.2.1 Contingency table description--------------------------------28
3.2.2 The Calculation of Kappa-------------------------------------28
3.3 Accuracy assessment of selected case--------------------------------30
Sensitive Test on Channel Characteristics----------------------------31
4.1 11-μm (〖IR〗_1) -------------------------------------------------------------31
4.2 12-μm (〖IR〗_2) -------------------------------------------------------------34
4.3 6.7-μm (〖IR〗_3) ------------------------------------------------------------35
4.4 The optimal combination of finer predicate-------------------------36
5. Mathematical Threshold Tests-------------------------------------------38
6. Conclusion and Future work----------------------------------------------41
References----------------------------------------------------------------44
參考文獻 [1] Ackerman, S., Strabala, K., Menzel, P., Frey, R., Moeller, C. and Gumley, L., “Discriminating clear sky from clouds with MODIS”, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 103, NO. D24, PAGES 32,141-32,157, DECEMBER 27, 1998.
[2] Li, Z., Li, J., Menzel, P., Schmit, T. and Ackerman, S., “Comparison between current and future environmental satellite imagers on cloud classification using MODIS”, Remote Sensing of Environment, Vol. 108, 311–326, 2007.
[3] Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R., & Wang, X., “Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5”, Journal of Atmospheric and Oceanic Technology, 25(7), 1057-1072, 2008.
[4] S. Platnick, M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey., “ The MODIS Cloud Products: Algorithms and Examples From Terra”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 2, FEBRUARY, p.459–473, 2003.
[5] Sedano, F., Kempeneers, P., Strobl, P., Kucera, J., Vogt, P., Seebach, L., & San-Miguel-Ayanz, J., “A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors”, ISPRS Journal of Photogrammetry and Remote Sensing, 66.5 (2011): 588-596.
[6] Strabala, K. I., “MODIS Cloud Mask User’s Guide”, University of Wisconsin--Madison, 2005.
[7] Ishida, H., Nakajima, T.Y., “Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager”, JOURNAL OF GEOPHYSICAL SEARCH, VOL. 114, D07206, 2009.
[8] 何全軍, et al.基於多光譜綜合的MODIS數據云檢測研究. 國土資源遙感, 2006, 3: 19-22.
[9] 李微, et al. 基於光譜分析的 MODIS 雲檢測算法研究. 武漢大學學報 (信息科學版), 2005, 30.5: 435-438.
[10] Choi, Y. S. and Ho, C. H., “Validation of cloud property retrievals from MTSAT-1R imagery using MODIS observations”, International Journal of Remote Sensing, Vol. 30, No. 22, 20 November 2009, 5935–5958.
[11] 鞠未了, 韓雷. 基於MTSAT衛星數據的雲掩膜閥值算法.電子設計工程, 2012, 第24期, 第20卷, P.21-23.
[12] Lutz, H. J., “Cloud processing for METEOSAT second generation”,
EUMETSAT Tech. Department (1999).
[13] Cohen’s kappa from Wikipedia: [http://en.wikipedia.org/wiki/Cohen’s_kappa]
[14] A., SHIMIZU, “The Basis of RGB image composites”, VL resources of Visual Laboratory from Meteorological Satellite Center. [http://mscweb.kishou.go.jp/VRL/resource/rgb_composites/RGB-all.pdf]
[15] 交通部民用航空局,氣象產品手冊 (2012):
[http://aoaws.caa.gov.tw/data/tamc/DownloadFiles/product.pdf]
指導教授 任玄、劉千義
(Hsuan Ren、Chian-Yi Liu)
審核日期 2013-7-29
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