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基于MISR数据大兴安岭地区叶面积指数反演及尺度转换验证研究

温一博 常颖 范文义

温一博, 常颖, 范文义. 基于MISR数据大兴安岭地区叶面积指数反演及尺度转换验证研究[J]. 北京林业大学学报, 2016, 38(5): 1-10. doi: 10.13332/j.1000-1522.20150204
引用本文: 温一博, 常颖, 范文义. 基于MISR数据大兴安岭地区叶面积指数反演及尺度转换验证研究[J]. 北京林业大学学报, 2016, 38(5): 1-10. doi: 10.13332/j.1000-1522.20150204
WEN Yi-bo, CHANG Ying, FAN Wen-yi. Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation[J]. Journal of Beijing Forestry University, 2016, 38(5): 1-10. doi: 10.13332/j.1000-1522.20150204
Citation: WEN Yi-bo, CHANG Ying, FAN Wen-yi. Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation[J]. Journal of Beijing Forestry University, 2016, 38(5): 1-10. doi: 10.13332/j.1000-1522.20150204

基于MISR数据大兴安岭地区叶面积指数反演及尺度转换验证研究

doi: 10.13332/j.1000-1522.20150204
基金项目: 

十二五”

国家科技支撑计划项目(2011BAD08B01)、中央高校基本科研业务费专项(2572014AA37)

详细信息
    作者简介:

    温一博,博士生。主要研究方向:林业定量遥感。Email:100004644@qq.com 地址:150040黑龙江省哈尔滨市和兴路26号东北林业大学林学院。

    责任作者:

    范文义,教授,博士生导师。主要研究方向:定量遥感。Email:fanwy@163.com 地址:同上。

Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation

  • 摘要: 叶面积指数(LAI)是气候研究和生态研究中重要的植被冠层结构参数,遥感技术为快速获取大面积叶面积指数提供了有效途径。大兴安岭地区是我国重要的生态功能区,本文以大兴安岭为研究区域,根据森林林分特征,采用基于物理过程的4-Scale几何光学模型,利用多角度MISR遥感数据反演该区域叶面积指数数据。几何光学模型特点在于参数具有物理意义,考虑地面反射的热点效应,模型反演过程不依赖于样本数据适用于大区域反演研究,MISR数据提供同一区域多角度遥感数据,有效解决了单一观测角度植被指数和叶面积指数函数关系饱和点低的问题。由于地面验证数据空间尺度无法满足MISR数据的空间分辨率,本文采用TM数据对样地实测叶面积指数数据进行尺度转换,针对不用坡向叶面积指数空间异质性进行分析,讨论不同空间分辨率验证数据的合理性,研究表明大兴安岭区域使用600m空间分辨率验证数据对MISR数据反演结果检验最优,该分辨率下叶面积指数变化随空间尺度变化趋于稳定,并较好地避免了2种遥感数据几何配准带来的误差。结果表明:4-Scale几何光学模型适用于我国大兴安岭地区森林叶面积指数反演,实验中MISR数据反演叶面积指数的平均绝对误差为25.6%、均方根误差为0.622。本研究为大兴安岭地区叶面积指数大区域快速定量反演提供了研究基础。

     

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出版历程
  • 收稿日期:  2015-06-05
  • 修回日期:  2015-06-05
  • 刊出日期:  2016-05-31

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