| 净初级生产力遥感估算模型空间尺度转换 |
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| 引用本文： | 王莉雯,卫亚星,牛铮.净初级生产力遥感估算模型空间尺度转换[J].遥感学报,2010,14(6):1082-1096. |
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| 作者姓名： | 王莉雯 卫亚星 牛铮 |
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| 作者单位： | 1. 辽宁师范大学,城市与环境学院,辽宁,大连,116029
2. 遥感科学国家重点实验室,中国科学院遥感应用研究所,北京,100101 |
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| 基金项目： | 辽宁省教育厅科学研究一般项目(编号: L2010226); 教育部人文社会科学重点研究基地项目(编号: 08JJD790142); 辽宁省教育
厅高等学校创新团队项目(编号: 2007T095)和国家重点基础研究发展规划(973)项目(编号: 2007CB714406)。 |
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| 摘 要： | 采用基于混合像元的结构分析方法和支持向量机(SVM)算法,建立了高分辨率遥感数据(TM)向低分辨率遥感数据(MODIS)的尺度转换模型,实现了由高分辨率遥感数据获得的NPP向低分辨率遥感数据获得的NPP的空间尺度转换。对低分辨率遥感数据(MODIS)估算的NPP结果进行了尺度效应校正。结果表明:SVM回归模型模拟出的尺度效应校正因子Rj_corrected与1-F中覆盖度草地之间的相关性较高,R2达到0.81。尺度效应校正前的NPPMODIS与NPPTM的相关性较低,R2仅为0.69,RMSE为3.47;尺度效应校正后的NPPMODIS_corrected与NPPTM的相关性较高,R2达到0.84,RMSE为1.87。因此,经过尺度效应校正后的NPP无论是在相关性还是在误差方面有了很大程度的提高。
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| 关 键 词： | 净初级生产力 光能利用率模型 遥感 尺度转换 SVM |
| 收稿时间： | 2009/12/14 0:00:00 |
| 修稿时间： | 6/7/2010 12:00:00 AM |
Spatial scaling of net primary productivity model\nbased on remote sensing |
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| WANG Liwen,WEI Yaxing and NIU Zheng.Spatial scaling of net primary productivity model\nbased on remote sensing[J].Journal of Remote Sensing,2010,14(6):1082-1096. |
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| Authors: | WANG Liwen WEI Yaxing and NIU Zheng |
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| Institution: | College of Urban and Environment Science, Liaoning Normal University, Liaoning Dalian 116029, China;;College of Urban and Environment Science, Liaoning Normal University, Liaoning Dalian 116029, China;;The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences,
Beijing 100101, China |
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| Abstract: | Spatial scaling for net primary productivity (NPP) refers to the transferring process of establishing quantitative
correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the
algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main
problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine (SVM)
algorithm are used to make the scaling model from the fine resolution (TM) to the coarse resolution (MODIS). Spatial scaling
from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of
scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation
between Rj_corrected of the correction factor for scale effect and 1-Fmiddle density grassland estimated by SVM regression model is higher
(R2=0.81). Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower (R2=0.69; RMSE=3.47),
while the correlation between NPPTM and corrected NPPMODIS_corrected is higher (R2=0.84; RMSE=1.87). Therefore, NPP corrected
for scale effect has been greatly improved in both correlation and error. |
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| Keywords: | net primary productivity light use efficiency model remote sensing scaling support vector machine |
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