| 运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测 |
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| 引用本文： | 江波.运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测[J].遥感学报,2010,14(1):23-37. |
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| 作者姓名： | 江波 |
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| 作者单位： | 1. 北京师范大学,地理学与遥感科学学院,遥感科学国家重点实验室,北京,100101;北京师范大学,环境遥感与数字城市北京市重点实验室,北京,100875
2. 美国马里兰大学地理系 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)项目(编号: 2007CB714407)和国家自然科学基金项目(编号: 40640420073, 40871163)。 |
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| 摘 要： | 运用动态谐波回归模型(Dynamic Harmonic Regression,DHR)对MODIS的长时间序列的LAI产品进行分析,可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分,通过建立的模型实现LAI年间变化的短时预测。本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取,对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产品的时间序列分析与预测的效果良好。
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| 关 键 词： | 叶面积指数 时间序列 MODIS DHR模型 |
| 收稿时间： | 2008/11/6 0:00:00 |
| 修稿时间： | 2009/2/10 0:00:00 |
Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model |
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| JIANG Bo.Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model[J].Journal of Remote Sensing,2010,14(1):23-37. |
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| Authors: | JIANG Bo |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China; 2. Research Center for Remote Sensing and GIS, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China;3. Department of Geography, University of Maryland, College Park, MD 20742, USA;1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China; 2. Research Center for Remote Sensing and GIS, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China;1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China; 2. Research Center for Remote Sensing and GIS, School of Geography, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China |
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| Abstract: | Leaf Area Index (LAI) is one of the most important parameters in describing the dynamics of vegetation on land surfaces. LAI products have been produced from data of many remote sensing satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we used the Dynamic Harmonic Regression (DHR) model to analyze the LAI time series products. The model can decompose the trend, seasonal and residuals components from the original time series, and predict the short-time LAI values. We use the DHR model to extract the time change information from the MODIS LAI time series products. The results show this method to be very effective in predicting the short-term LAI on the pixel basis. |
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| Keywords: | leaf area index (LAI) time series MODIS DHR |
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