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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65863

    Authors: 雷蒂楓;Le,Thi-phuong
    Contributors: 遙測科技碩士學位學程
    Keywords: English
    Date: 2014-07-16
    Issue Date: 2014-10-15 17:16:02 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 越南的稻米生產不僅在亞,洲也是世界上的主要生產國之一。大部分的水稻產於被視為越南米倉的湄公河三角洲,該區域每年生產國家近一半的米糧,並且提供超過80%的稻米出口量。稻作產量估測的顯著性對區域與國家的農業經營與政策發展扮演十分重要的角色。本研究意欲使用2007與2008年的MODIS與ENVISAT合成孔徑雷達(ASAR)影像發展湄公河三角洲區域稻米生產模式的估算方法。資料處理流程有:(1)建立時序MODIS影像的 NDVI與EVI資料;(2)以小波轉換進行時序NDVI與EVI資料的去噪;(3)MODIS與ENVI ASAR影像融合;(4)建立稻作產量預測模型建立稻作產量預測模型;(5)以均方根誤差(RMSE)成果評估、平均絕對差(MAE)與平均偏誤差(MBE)進行成果評估。
    在本研究中,MODIS NDVI/EVI 時序資料與 ASAR影像分別進行稻米產量的預測,最後融合彼此進行另一個估測的比較;同時,研究中亦測試了NDVI-LST(地表溫度), EVI-LST 與 EVI-ASAR,檢測是否可以增強預測的結果。稻作產量統計與不同參數亦以線性、多變數與二次方程進行回歸分析。
    由回歸分析的結果可以發現統計模式的稻米產量預測模型有很好的成果,同時使用二次方程回歸模式較線性回歸模式為佳。整合兩個光學與雷達影像的模式較其他個別使用的影像模式有更高精準的預測成果,2007年一期稻與二期稻的相關係數分別為0.83 與 0.77;2008年一期稻與二期稻的相關係數分別為0.77 與 0.75。
    建立模式的穩健性以2007年與2008年20個抽樣區的預測產量與現地統計產量進行比較分析。比較結果揭示了在這兩年由MODIS EVI與ASAR後向散射係數結合的二次項預測模式都有令人滿意的結果。實際產量與預測產量的百分率差異都在可以接受的限制內(約10%,p-value <0.05)。在2007年冬春季稻產量預測RMSE、MAE與MBE分別為10.85%、 9.39% 與 -3.39%;對於當年夏秋季稻作則各為12.01%、9.99% 與 9.31 %。在2008年冬春季稻產量預測RMSE、MAE與MBE各為8.39%、6.6% 與0.54%,當年夏秋季稻作則各為 8.96%、7.29%與0.45%。這些結果明確地說明了預測的產量與統計產量的高相關性,並且也顯示了建立的模式可以用以預測研究區的稻作產量。
    事實上,許多的因素如蟲害、稻作病害與雨季期氣候變化的狀況都會降低稻作的產量估測的精度。本研究探索在湄公河三角洲在收穫季節前,使用MODIS NDVI /EVI 時序資料與ASAR影像對稻米產量估測的潛力與有效性。本研究方法亦可移植至其他區域的研究上。;Vietnam is one of the most important countries in producing rice in Asia as well as in the world. The majority of rice is produced in the Mekong Delta (MD) which was known as the rice bowl of Vietnam. Annually, it produces approximately a half of the country′s rice and account for more than 80% amount of rice export. The significance of rice crop yield estimation plays a critical role in agricultural management and policy development at regional and national scale. This study aims to develop an approach for rice crop yield prediction in the Mekong Delta, Vietnam using MODIS and ENVISAT ASAR data for rice crop seasons in 2007 and 2008. The data were processed through five main steps: (1) constructing time-series MODIS NDVI/EVI data, (2) noise filtering of the time-series NDVI/EVI data using the wavelet transform, (3) Fusion of MODIS and ENVISAT ASAR images, (4) developing a rice-crop yield prediction models, and (5) result verification using the root mean square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE).
    In this study, an attempt has been made to study the potential of MODIS NDVI/EVI time-series data and ASAR images individually for the purpose of rice yield forecasting. Then, fusion data was also used as another case to estimate rice crop yield. At the same time, the combinations between NDVI-LST, EVI-LST and EVI-ASAR were also implemented to test if there is an improvement in the correlation and prediction results. The regression analysis between rice crop yield statistics and different parameters was implemented using linear and quadratic models.
    From the regression analysis results, it was found that the statistical model-based can be successfully used for the purpose of rice yield estimation in the study area. The rice crop yield in MD could be better modeled using quadratic models compared to linear models. The quadratic model using combination of 2two variables (MODIS EVI and Backscattering coefficients) is the best one and gave more accurate prediction results than others, with correlation coefficients of 0.83 and 0.77 for the first and second crop in 2007 and R2 were 0.77 and 0.75 for crops in 2008, respectively.
    The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistic for 20 sampling districts in 2007 and 2008. The comparisons revealed satisfactory results obtained from the quadratic model using combination of MODIS EVI and Backscattering coefficients (derived from ASAR data) in both years. The percentage difference of the predicted from the actual yield is within acceptable limit (around 10%) and p-value < 0.05. The root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the prediction results. In 2007, The RMSE, MAE and MAE were 10.85%, 9.39% and -3.39% for winter-spring crop respectively. And for the summer-autumn crop, those parameters were 12.01%, 9.99% and 9.31 %. In 2008, for the first crop, the RMSE, MAE and MBE were 8.39%, 6.6%, and -0.54%. For the second crop, the RMSE, MAE and MBE were 8.96%, 7.29% and 0.45%. Those results were clear that there was a good correlation between the predicted yield and the rice yield statistics and the established model can be used to estimate rice crop yield in the study area.
    In fact, there are many factors like pest, rice diseases, and the variations of climate conditions in the rainy season could lower the accuracy in rice crop yield prediction results. This study explored the potential of MODIS NDVI/EVI time-series and ASAR data for rice crop yield estimation in Mekong Delta before the harvesting period. The methods used in this study could be transferable to the other regions.
    Appears in Collections:[遙測科技碩士學位學程] 博碩士論文

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