Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/28043
標題: 利用多頻譜觀測儀觀測地表水體濁度及總固體濃度
Estimation of total solids and turbidity of surface water using Hyperspectral sensing in Taiwan
作者: 黃家慶
Huang, Chia-Ching
關鍵字: Hyperspectral;多頻譜;Multiple-regression;Neural Network;Total solids;Turbidity;複迴歸;類神經網路;總固體
出版社: 土壤環境科學系所
引用: 朱佳雯. 2004. 案例式推理與類神經網路在心電圖診斷之應用研究, 真理大學碩士論文. 余俊昌. 2004. 調適性類神經模糊推論系統之應用–建構個人化之圖文購物引擎, 大葉大學碩士論文. 吳啟南, 蕭國鑫, 彭淼祥, 李元炎, 黃金鴻, 曾富雄, and 李惠容. 1991. 遙測應用於石門水庫水質調查. 行政院環境保護署. 林涵文. 2004. 類神經模糊系統在營建知識發掘中資料缺漏問題之研究, 中華大學碩士論文. 曾忠一. 1988. 大氣衛星遙測學 渤海堂, 台北巿. 葉怡成. 2003. 類神經網路模式應用與實作. 8 ed. 儒林. 鄭文哲, 吳啟南, and 盧誌銘. 1988. 遙測應用於德基水庫水質污染調查報告. 能礦所服務報告第252號. 蕭芥陽. 2004. 應用類神經網路進行解制電業市場中可利用傳輸能力之估測, 中原大學碩士論文. 謝景棠. 2004. 類神經網路於隔震結構動力分析之應用, 中原大學. Adsavakulchai, S., and P. Panichayapichet. 2003. Water Quality Monitoring Using Remote Sensing Technique. SPIE 4886. Brando, V.E., and A.G. Dekker. 2003. Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE T GEOSCI REMOTE 41:1378-1387. Chen, L. 2003. A study of applying genetic programming to reservoir trophic state evaluation using remote sensor data. INT J REMOTE SENS 24:2265-2275. Choubey, V.K., and V. Subramanian. 1990. Nature of suspended solids and IRS-1A, LISS-I data; A case study of Tawa reservoir (Narmada basin). REMOTE SENS ENVIRON 34:207-215. Choubey, V.K., and V. Subramanian. 1992. Estimation of suspended soils using Indian Remote Sensing Satellite-1A data: a case study from Central India. INT J REMOTE SENS 13:1473-1486. Dekker, A.G., R.J. Vos, and S.W.M. Peters. 2001. Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. Science Of The Total Environment 268:197-214. Donald, C.R., L. Han, J.F. Schalles, and J.S. Peake. 1996. Remote Measurement of Algal Chlorophyll in Surface Water: The Case for the First Derivative of Reflectance Near 690 nm. PHOTOGRAMM ENG REM S 62:195-200. Edwin, T.E. 1985. Surface Water Monitoring. Ekstrand, S. 1992. Landsat TM based quantification of chlorophyll-a during algae blooms in coastal waters. INT J REMOTE SENS 13:1913-1926. Forster, B.C., X. SHA, and B. XU. 1993. Remote sensing of sea water quality parameter using Landsat-TM. INT J REMOTE SENS 14:2759-2771. Gallegos, C.L., and D.L. Correl. 1981. Modelling spectral diffuse attenuation, absorption and scattering coefficients in a turbid estuary. Limnology and Oceanography 26:671-698. Gallie, E.A., and P.A. Mutha. 1992. Specific absorption and backscattering spectra for suspended minerals and chlorophyll-a in Chilko Lake, British Columbia. REMOTE SENS ENVIRON 39:103-118. George, D.C. 1997. The airborne remote sensing of phytoplankton chlorophll in the lakes and tarns of the English District. INT J REMOTE SENS 18:1961-1975. Gin, K.Y.H., S.T. Koh, Lin, II, and E.S. Chan. 2002. Application of spectral signatures and colour ratios to estimate chlorophyll in Singapore''s coastal waters. ESTUAR COAST SHELF S 55:719-728. Ha, S.R., S.Y. Park, and D.H. Park. 2003. Estimation of urban runoff and water quality using remote sensing and artificial intelligence. WATER SCI TECHNOL 47:319-325. Hakvoort, H., J. de Haan, R. Jordans, R. Vos, S. Peters, and M. Rijkeboer. 2002. Towards airborne remote sensing of water quality in The Netherlands - validation and error analysis. ISPRS J PHOTOGRAMM 57:171-183. Harma, P., J. Vepsalainen, T. Hannonen, T. Pyhalahti, J. Kamari, K. Kallio, K. Eloheimo, and S. Koponen. 2001. Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland. SCI TOTAL ENVIRON 268:107-121. Hedger, R.D., T.J. Malthus, and A.M. Folkard. 2001. Estimation of velocity fields at the estuary - coastal interface through statistical analysis of successive airborne remotely sensed images. INT J REMOTE SENS 22:3901-3906. Hedger, R.D., N.R.B. Olsen, T.J. Malthus, and P.M. Atkinson. 2002. Coupling remote sensing with computational fluid dynamics modelling to estimate lake chlorophyll-a concentration. REMOTE SENS ENVIRON 79:116-122. Hu, C.M., Z.Q. Chen, T.D. Clayton, P. Swarzenski, J.C. Brock, and F.E. Muller-Karger. 2004. Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL. REMOTE SENS ENVIRON 93:423-441. Iwashita, K., K. Kudoh, H. Fujii, and H. Nishikawa. 2004. Satellite analysis for water flow of Lake Inbanuma, p. 284-289 Monitoring of Changes Related to Natural and Manmade Hazards Using Space Technology, Vol. 33. Jensen, J.R. 1996. Introductory digital image processing:a remote sensing perspective. keiner, L.E., and X.H. Yan. 1998. A Neural Network model for estimating sea surface chlorophyll and sediments from Thematic Mapper imagery. REMOTE SENS ENVIRON 66:153-165. Kloiber, S.N., P.L. Brezonik, L.G. Olmanson, and M.E. Bauer. 2002. A procedure for regional lake water clarity assessment using Landsat multispectral data. REMOTE SENS ENVIRON 82:38-47. Koponen, S., J. Pulliainen, K. Kallio, and M. Hallikainen. 2002. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. REMOTE SENS ENVIRON 79:51-59. Kutser, T., D.C. Pierson, K.Y. Kallio, A. Reinart, and S. Sobek. 2005. Mapping lake CDOM by satellite remote sensing. REMOTE SENS ENVIRON 94:535-540. Lahet, F., P. Forget, and S. Ouillon. 2001. Application of a colour classification method to quantify the constituents of coastal waters from in situ reflectances sampled at satellite sensor wavebands. INT J REMOTE SENS 22:909-914. Lopez garcia, M.J., and v. Caselles. 1990. A multi-temporal study of chlorophyll-a concentration in the Albufera lagoon of Valencia, Spain, using Thematic Mapper data. INT J REMOTE SENS 11:301-311. Mat Jafri, M.Z., K. Abdullah, S. Marshall, and H.S. Lim. 2003. Multi-spectral back-scattering spectrometer for total suspended solids measurement. SPIE 4897. MatJafri, M.Z., K. Abdullah, H.S. Lim, M.N. AbuBaker, Z.B. Din, and S. Marshall. 2003. Algorithm for total suspended solids mapping using digital camera images. Ocean Remote Sensing and Applications. Mayo, M., A. Gitelson, Y.Z. Yacobi, and Z. Ben-Avraham. 1995. Chlorophyll distribution in Lake Kinneret detemined from Landsat Thematic Mapper data. INT J REMOTE SENS 16:175-182. Ostlund, C., P. Flink, N. Strombeck, D. Pierson, and T. Lindell. 2001. Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper. SCI TOTAL ENVIRON 268:139-154. Pattiaratchi, C., P. Lavery, A. Wyllie, and P. Hick. 1994. Estimates of water quality in coastal waters using multi-date Landsat Thematic Mapper data. INT J REMOTE SENS 15:1571-1584. Prieur, L., and S. Sathyendranath. 1981. An optical classification of coastal and ocearnic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter and other particulate materials. LIMNOL OCEANOGR 26:671-698. Schiebe, F.R., and J. Harrington. 1992. Remote sensing of suspended sediment: the Lake Chicot, Arkansas project. INT J REMOTE SENS 13:1487-1509. Strickland, J.D.H., and T.R. Parsons. 1972. A practical handbook of seawater analysis. Stumpf, R.P., and T.R. Pennock. 1991. Remote estimation of the diffuse attenuation coefficients in a moderately turbid estuary. REMOTE SENS ENVIRON 38:183-191. Svab, E., A.N. Tyler, T. Preston, M. Presing, and K.V. Balogh. 2005. Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations. INT J REMOTE SENS 26:919-928. Thomas, M.L., W.K. Ralph, and W.C. JONATHAN. 2004. REMOTE SENSING AND IMAGE INTERPRETATION. Tolk, B.L., L. Han, and D.C. Rundquist. 2000. The impact of bottom brightness on spectral reflectance of suspended sediments. INT J REMOTE SENS 21:2259-2268. Wang, Y.P., H. Xia, J.M. Fu, and G.Y. Sheng. 2004. Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data. SCI TOTAL ENVIRON 328:195-206. Zhang, Y.Z., J.T. Pulliainen, S.S. Koponen, and M.T. Hallikainen. 2003. Water quality retrievals from combined Landsat TM data and ERS-2 SAR data, in the Gulf of Finland. IEEE T GEOSCI REMOTE 41:622-629.
摘要: 
本研究目的在利用遙測資料建立台灣地面水體水質推估模式,在水體水質之觀測上,由於水體本身對電磁波譜反射率極低,因此取決於水體反射率大小的主要是水體中的物質如葉綠素、有色可溶性有機物質(coloured dissolved organic matter) (CDOM)以及總固體(Lahet et al., 2001; Pattiaratchi et al., 1994)。因此本研究目標將以台灣河川為主要觀測對象,使用多頻譜觀測儀來分析河川水體中總固體及濁度反射波譜,以建立反射波譜與水體中濁度及總固體之推估模式。在遙測資料之處理及模式之建立,本研究將比較前人文獻結果、電磁波譜反射率曲線圖、統計複迴歸分析及類神經網路瞭解各分析方法之優缺點及適用範圍。
依據分析結果,具有觀測水體中總固體及濁度之波長範圍為400~920 nm。其中又以700~900 nm 較具代表性,使用複迴歸及類神經網路分析水體水質,濁度結果推估之R2分別為0.88及0.87,而總固體則為0.63及0.66。此外比較類神經網路與複迴歸分析,此兩種模式雖然都具有不錯之推估效果,但若改變其樣品數,則可發現在較少的樣品數(<25)時以類神經網路的印證效果為佳,而若是可獲得較多的樣品時,則以複迴歸分析較為準確。在衛星推估上,以地面高光譜資料模擬衛星各波段並以總固體及類神經網路推估,類神經網路在模擬衛星的推估上優於複迴歸分析,尤其在較低波譜解析力之衛星上如Landsat TM更為明顯。此外具較高波譜解析力之衛星如MODIS其推估效果也較低波譜解析力之衛星來的好。
若探討總固體R2較濁度低之原因,推測可能濁度為表示光入射水體後被散射的電磁波強度,其反映水體整體之光散射狀態,而總固體僅為影響水體光散射的因子之一,故以後作為後續遙測水質研究時,其土壤粒徑大小與礦物性質都需詳加探討。

This study is to establish empirical relationship between spectral reflectance and TS and turbidity from hyperspectral measurements. Several factors can affect the spectral reflectance characteristics; these include mainly suspended sediments, phytoplankton biomass (e.g., chlorophyll-a), and dissolved organic carbon. The objective of this research is to explore reliable and fast methods for water quality monitoring in terms of total solids in rivers of central Taiwan. Reflective radiance was made simultaneously with total solids and turbidity measurements of freshwater rivers in central Taiwan. The optimal wavelength is identified when correlation coefficients between the reflective radiance and TS or turbidity are maximum using both the stepwise approach, i.e., multiple-regression and neural network techniques. The aim of was to use neural network and multiple-regression methods in conjunction with field water quality monitoring using remote sensing system as a means to predict water quality changes. It was to identify conditions under which neural network or multiple-regression can be better applied to water quality monitoring .Different sample number affect the performance of both the neural network and the multiple-regression methods. We find that if sample number was less than 25, neural network is better than multiple-regression. The study was also to test the feasibility of applying satellite generated data to river water quality monitoring in Taiwan. Results showed if only the spectral resolution of the satellite was of concern, MODIS would be the best satellite sensor for water quality analysis.
The results show that water quality variables correlate well with those predicted with both the neural network and the multiple-regression approaches. The degree of correlation between the reflective radiance and turbidity is better than that with total solids. Particle size and type of minerals in the suspended solids appear to be important factors controlling the correlation coefficients.
URI: http://hdl.handle.net/11455/28043
其他識別: U0005-3008200600194200
Appears in Collections:土壤環境科學系

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.