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遥感技术与应用  2020, Vol. 35 Issue (5): 1070-1078    DOI: 10.11873/j.issn.1004-0323.2020.5.1070
LAI专栏     
面向遥感叶面积指数产品的地形校正研究
胡月童1,2(),武爽1,2,冯险峰1,2(),刘洋1
1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
2.中国科学院大学,北京 100049
Topographic Correction of Leaf Area Index Product Derived from Remote Sensing Data
Yuetong Hu1,2(),Shuang Wu1,2,Xianfeng Feng1,2(), LiuYang1
1.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy Sciences,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

地形校正是提高复杂地形区地表参数遥感定量化反演精度的重要手段。当前广泛应用的遥感叶面积指数产品(Leaf Area Index, LAI)多具有一定的地形误差,减少地形影响、提升其产品精度有着非常重要的意义。以我国江西省千烟洲地区为研究区域,利用地面实测LAI数据、LandsatTM数据和高程数据等,基于高程标准差和GLOBMAP LAI产品值的关系,建立面向叶面积指数产品的地形校正模型,利用这一模型对GLOBMAP LAI产品进行地形校正。结果表明:校正后的LAI与地面实测数据更为接近,LAI产品与地面测量值的RMSE由2.11下降到2.04;校正后LAI产品的标准差由2.08下降至1.69,LAI产品的地形误差得到了较好的改正。该方法较好地完成了LAI产品的地形校正,进一步提高了产品精度,具有一定的实用价值。

关键词: 千烟洲地区叶面积指数地形校正高程标准差叶面积指数产品    
Abstract:

Terrain correction is an important approach to improve the accuracy of remote sensing quantification of surface parameters in complex terrain areas. The widely used remote sensing Leaf Area Index(LAI)productsalwayshave certain terrain error. It has a great importance to eliminate the influence of terrain and improve LAIproducts’ accuracy. Taking the Qianyanzhou area of Jiangxi Province as the research area, the paper aims to establish a terrain correction model which takes terrain error into account to promote the accuracy of GLOBMAP LAI product. Based on the measured LAI data, Landsat TM data, GLOBMAP LAI product and elevation data, the model achieved terrain correction by establishing the index relationship between elevation standard deviation and LAI product values. The terrain correction model of GLOBMAP LAI product was established , and then used to correct the product in the study area. The results indicated that the corrected leaf area index was closer to the ground measured data, and the RMSE between the LAI product and the ground measurement decreases from 2.11 to 2.04. The standard deviation of the corrected LAI dataset was reduced from 2.08 to 1.69, which meant the terrain error could be eliminated. The method in this paper had well completed the terrain correction of LAIproduct. The model is meaningful to improve the accuracy of LAI product.

Key words: Qianyanzhou area    Leaf Area Index (LAI)    Topographic correction    Standard deviation of elevation    LAI product
收稿日期: 2019-05-21 出版日期: 2020-11-26
ZTFLH:  TP79  
基金资助: 中国科学院战略性先导科技专项“泛第三极环境变化与绿色丝绸之路建设”(XDA20040401)
通讯作者: 冯险峰     E-mail: huyuetong15@mails.ucas.ac.cn;fengxf@lreis.ac.cn
作者简介: 胡月童(1993-),女,湖北襄阳人,硕士研究生,主要从事生态遥感研究。E?mail:huyuetong15@mails.ucas.ac.cn
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引用本文:

胡月童,武爽,冯险峰,刘洋. 面向遥感叶面积指数产品的地形校正研究[J]. 遥感技术与应用, 2020, 35(5): 1070-1078.

Yuetong Hu,Shuang Wu,Xianfeng Feng, LiuYang. Topographic Correction of Leaf Area Index Product Derived from Remote Sensing Data. Remote Sensing Technology and Application, 2020, 35(5): 1070-1078.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.5.1070        http://www.rsta.ac.cn/CN/Y2020/V35/I5/1070

图1  研究区位置
数据项数据格式年份空间尺度时间尺度来源
高程数据栅格200930 m地理空间数据云
GlobeLand30土地覆盖数据栅格201030 m国家基础地理信息中心
中国植被图矢量20011:100万中国科学院中国植被图编辑委员会
GLOBMAP LAI产品栅格2008500 m8 d中科院地理所
LAI实测数据文本2008//南京大学
LandsatTM数据栅格200830 m
表1  数据列表
图2  技术路线
图3  研究区30 m分辨率的高程和高程标准差
植被类型GlobeLand30中国植被图
1 针叶林201
2 阔叶林203
3 针阔混交林202
4 灌丛404
5 草和农作物10,307,8,11
0 非植被809,0
表2  不同植被类型分类体系的转换关系
图4  千烟洲地区的TM LAI
图5  地形因子与LAI的相关性
图6  TM LAI随高程标准差变化情况
图7  高程标准差随LAI差别值的变化情况Correlation between σand δ
植被类型p1p2p3p4R2
针叶林1.02×10-5-1.60×10-4-6.92×10-20.500.91
阔叶林-3.61×10-54.21×10-31.63×10-20.860.98
灌丛-4.00×10-8-6.11×10-4-3.32×10-30.160.98
草和农作物1.43×10-51.35×10-3-1.26×10-20.570.90
不区分类型2.09×10-51.83×10-3-6.81×10-30.440.88
表3  研究区地形校正模型参数
图8  研究区地形校正前后的LAI
图9  地形校正前后LAI与地面实测数据对比
统计参数地形校正后地形校正前
最大值10.2310.07
最小值0.000.04
均值2.102.80
标准差1.692.08
表4  校正前后研究区LAI统计参数比较
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