請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/36776
標題: 不同前處理方法對台農67號(水稻)植被反射光譜預測產量之影響
Predicting TN67 (Oryza Sativa) yield using canopy reflectance after different pretreatments
作者: 劉長利
Liu, Chang-Li
關鍵字: pretreatment
前處理
canopy reflectance
PLSR
植被反射光譜
淨最小平方迴歸
出版社: 農藝學系所
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摘要: 藉由遙感探測(或簡稱遙測)技術所獲得的水稻植被光譜資料可用於水稻產量的預測。但由於光譜資料具有共線性與干擾(noise)的問題。因此,除了建立不受共線性影響的水稻產量預測模式之外,仍需利用前處理方法在資料分析前進行校正,以減少干擾造成的影響。在本研究中先以藍光、綠光、紅光、近紅外光、植生指數:normalized difference vegetation index(NDVI)、green normalized difference vegetation index(GNDVI)、sample ratio vegetation index(SRVI)、與green ratio vegetation index(GRVI)分別建立單一解釋變數的簡單迴歸產量預測模式與多個解釋變數的複迴歸產量預測模式,並以交叉驗證法所得的決定係數與RMECV值,作為模式預測能力的判斷標準。預測模式具有最大的決定係數值與最小的RMSECV值,代表此模式具有較佳的預測能力,藉此建立一個適當的產量預測模式。再以常用於光譜資料的前處理方法,例如常態標準離差(Standard normal variate, SNV)、多重散射校正(Multiplicative signal correction, MSC)與直交訊息校正(Orthogonal signal correction, OSC),對台農67號水稻產量與光譜資料先進行校正。然後再利用多變量檢量方法迴歸(淨最小平方法迴歸partial least squares regression;PLSR)建立預測模式,並與植生指數(GNDVI)進行比較。另外,對所使用的光譜資料,進行不同變異程度的模擬試驗,並與實際資料相比較。 結果發現使用OSC前處理方法先對資料進行干擾校正後,再以PLSR所建立的預測模式具有最佳的預測能力。此外,模擬試驗結果亦顯示在不同變異程度下以PLSR建立的預測模式均具有最佳的預測能力。經本研究結果,建議先以OSC前處理方法對光譜資料進行干擾校正後,再使用PLSR來建立水稻產量預測模式,此產量預測模式具有較佳的預測能力。
The reflectance spectra from the remote sensing images can be used to predict the yield of the rice. However, the reflectance spectra data should be pre-processed to solve the problem of colinearity and noise. Some pretreatment methods, e.g. standard normal variate (SNV), multiplicative signal correction (MSC), and orthogonal signal correction (OSC) were employed to correct the noise before establishing the model. In order to keep off the colinearity between the independent variable, we may replace with the suitable independent variable or use the multivariate statistical analysis method. In the study, blue band (BLUE), green band (GRN), red band (RED), near infrared band (NIR), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), sample ratio vegetation index (SRVI), and green ratio vegetation index (GRVI) were used to build the first order linear models. In addition, the reflectance spectra data (between 350nm to 1100nm by 10nm) were employed to build the PLSR models after using SNV, MSC, and OSC pretreatment methods. Because of the small sample size, the cross validation was implemented to estimate the prediction ability. From this study, it was found that the best predicted ability of the rice yield model was the PLSR model after using the OSC pretreatment.
URI: http://hdl.handle.net/11455/36776
其他識別: U0005-0602200716500800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0602200716500800
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