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• Main Authors: Hong-Yi Fang, 方鋐亦
• Other Authors: Chi-Farn Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/39661414619890541922

碩士 === 國立中央大學 === 遙測科技碩士學位學程 === 103 === Rice is the main food crop in Taiwan, mostly grown in western plains and eastern Tai-wan. In recent years, satellite data are used for crop monitoring. High temporal resolution data can provide information of crop phenology. However, only high temporal resolution data are not sufficient for rice mapping in Taiwan, because rice fields here are generally small and fragmented. In this case, it is necessary to combine different satellite image, get-ting a high spatial resolution and high temporal resolution fusion image for rice mapping. The study aims to identify rice fields in areas in Taiwan using time-series MODIS (8-day)-Landsat (30m) fusion data in 2012 and 2013. The study consists of five steps: (1) Correct geometric and radiometric errors of Landsat data in data pre-processing; (2) Use the spatial temporal adaptive reflectance fusion model (STARFM) to blend the fusion data; (3) Construct smoothed normalized difference vegetation index (NDVI) time-series data by wavelet transform function; (4) Use support vector machine (SVM) to classify data; (5) Es-timate major rice crop area and assess mapping accuracies. The results indicate a high correlation between the mapping results and ground truth data. The overall accuracies are upper than 80%, and Kappa values are upper than 0.65. Compared with the government rice statics, the R2 are upper than 0.85, and the root mean square error (RMSE) are up to 3% of total rice statics. Most of the mapping results of town-ship level are commission. It means using time series fusion data to mapping rice is useful, it’s about 80% accuracy.