A New Method of MCI Eztraction with Multi-Temporal MODIS EVI Data
With Huabei Plain of China as the study area, this research proposed a new method for pixel-level MCI (multiple cropping index) extraction with multitemporal MODIS EVI data based on crop phenology and DT (decision tree). The method could be divided into two steps. First, based on the local crop phenology, several features were identified to discriminate pixel-level MCI, which includes three types: fallow, single cropped and double cropped. Jeffries-Matusita (JM) distance was employed to measure the features MCI discriminating ability and determine which feature to be used. Second, the threshold of each feature was calculated with the CART algorithm of Tree model in SPSS. Then two types of data (statistics and visual interpretation results gained from TM images) were used to validate the accuracy of the method. Furthermore, the new method was compared with the HANTS method commonly used in MCI extraction in previous researches. The results showed that the new method was superior in both data requirement and processing speed with no loss in performance.
Multiple cropping indez Multi-temporal MODIS EVI Crop phenology Decision tree Huabei Plain of China
Lijun Zuo Tingting Dong Xiao Wang Xiaoli Zhao Ling Yi Bin Liu
Institute of Remote Sensing Applications, Chinese Academy of Sciences Beijing, China Liaoning Research Institute of Water Resources and Hydropower Shenyang, China
国际会议
2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)
厦门
英文
537-543
2010-04-12（万方平台首次上网日期，不代表论文的发表时间）