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遥感技术与应用  2016, Vol. 31 Issue (1): 42-50    DOI: 10.11873/j.issn.1004-0323.2016.1.0042
(中国科学院水利部成都山地灾害与环境研究所,四川 成都610041)
Leaf Area Index (LAI) Estimationfrom Remotely Sensed Observations in Different Topographic Gradients over Southwestern China
Jin Huaan,Li Ainong,Bian Jinhu,Zhao Wei,Zhang Zhengjian,Nan Xi
(Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China)
 全文: PDF(2778 KB)  

叶面积指数(LAI)遥感估算是植被定量遥感研究的热点之一,监测植被LAI时空变化对于研究陆地生态系统碳循环及全球变化等具有非常重要的意义。在我国西南山区设置10个50 km×50 km的观测样区作为研究区,其中包括5个森林生态系统样区、3个农田生态系统样区和2个草地生态系统样区。分别获取不同优势植被类型LAI地面实测数据,结合同期获取的遥感数据,考虑地形因素影响,基于偏最小二乘原理分别构建各样区LAI遥感估算模型,并采用交叉验证的方式对模型精度进行评价。结果表明:考虑了海拔、坡度和坡向等地形因子的森林LAI遥感反演模型与未考虑地形变量的模型相比,其验证精度有所提高,R2由0.30~0.75提高至0.50~0.80,RMSE由0.52~0.93 m2/m2降低至0.48~0.89 m2/m2;所有样区优势植被类型LAI反演模型验证R2在0.40~0.80之间,RMSE在0.22~0.89 m2/m2之间。发展的LAI遥感估算方法有助于认知山地植被LAI反演的地形效应问题,可为进一步的山地植被长势监测提供科学依据。

关键词: 山地遥感叶面积指数(LAI)反演    

The leaf area index (LAI) estimation from remotely sensed data is one of hotspots in quantitative remote sensing of vegetation.Monitoring the spatial and temporal changes of LAI is very significant for carbon cycle of terrestrial ecosystem,global changes and other related studies.The paper selected ten 50 km×50 kmsampling regions as our study area,including five forest regions,three crop regions and two grassland regions.The several parameters,such as leaf area index (LAI),canopy density,biomass,were measured in these regions.Taking leaf area index as a case,this study applied the partial least\|squares regression method to build the estimation model of LAI combining remote sensing with in situ data and considering topographic effects for different vegetation types.Then,the cross\|validation approach was used to test model accuracy.The results indicated that the forest LAI inversion models taking topographic effects (altitude,aspect and slope) into accout is superior to those that topographic effects were not considered (R2 increased from 0.30~0.75 to 0.50~0.80;RMSE decreased from 0.52~0.93 to 0.48~0.89 m2/m2).For all vegetation types,the model validation R2 and RMSE changed between 0.40~0.80,0.22~0.89 m2/m2,respectively.The method regarding LAI estimation from remotely sensed observations developed in this paper can help to understand topographic effects on LAI retrieval,and further provide scientific proof for monitoring vegetation growth status over mountain areas.

Key words: Mountainous area    Remote sensing    Leaf Area Index(LAI)    Retrieval
收稿日期: 2015-12-10 出版日期: 2016-04-05
:  TP 79  


通讯作者: 李爱农(1974-),男,安徽庐江人,研究员,中国科学院“百人计划”、四川省“千人计划”入选者,主要从事山地定量遥感及其应用研究。。    
作者简介: 靳华安(1984-),男,山东东平人,助理研究员,主要从事山地地表参数遥感反演研究。E\|。
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靳华安,李爱农,边金虎,赵伟,张正健,南希. 西南地区不同山地环境梯度叶面积指数遥感反演[J]. 遥感技术与应用, 2016, 31(1): 42-50.

Jin Huaan,Li Ainong,Bian Jinhu,Zhao Wei,Zhang Zhengjian,Nan Xi. Leaf Area Index (LAI) Estimationfrom Remotely Sensed Observations in Different Topographic Gradients over Southwestern China. Remote Sensing Technology and Application, 2016, 31(1): 42-50.


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