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基于蒙特卡洛方法的植被参量测量与误差分析(英文)

Measurement of vegetation parameters and error analysis based on Monte Carlo method
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摘要 In this paper we bring up a Monte Carlo theory based method to measure the ground vegetation parameters, and make quantitative description of the error. The leaf area index is used as the example in the study. Its mean and variance stability at different scales or in different time is verified using both the computer simulation and the statistics of remotely sensed images. And the error of Monte Carlo sampling method is analyzed based on the normal distribution theory and the central-limit theorem. The results show that the variance of leaf area index in the same area is stable at certain scales or in the same time of different years. The difference between experimental results and theoretical ones is small. The sig- nificance of this study is to establish a measurement procedure of ground vegetation pa- rameters with an error control system. In this paper we bring up a Monte Carlo theory based method to measure the ground vegetation parameters, and make quantitative description of the error. The leaf area index is used as the example in the study. Its mean and variance stability at different scales or in different time is verified using both the computer simulation and the statistics of remotely sensed images. And the error of Monte Carlo sampling method is analyzed based on the normal distribution theory and the central-limit theorem. The results show that the variance of leaf area index in the same area is stable at certain scales or in the same time of different years. The difference between experimental results and theoretical ones is small. The sig- nificance of this study is to establish a measurement procedure of ground vegetation pa- rameters with an error control system.
作者 梁博毅 刘素红 LIANG Boyi1, LIU Suhong2(1. College of Urban and Environment Sciences, Peking University, Beijing 100871, China 2. Faculty of Geography, Beijing Normal University, Beijing 100875, Chin)
出处 《Journal of Geographical Sciences》 SCIE CSCD 2018年第6期819-832,共14页 地理学报(英文版)
基金 National Natural Science Foundation of China,No.41171262
关键词 remote sensing vegetation parameter error analysis GLASS LAIoduction remote sensing vegetation parameter error analysis GLASS LAIoduction
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