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遥感技术与应用  2016, Vol. 31 Issue (1): 51-62    DOI: 10.11873/j.issn.1004-0323.2016.1.0051
山地遥感专栏     
基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究
张正健1,李爱农1,边金虎1,2,赵伟1,南希1,靳华安1,谭剑波1,2,雷光斌1,2,夏浩铭1,2,杨勇帅1,2,孙明江1,3
(1.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;
2.中国科学院大学,北京100049;
3.成都信息工程大学,四川 成都610041)
Estimating Aboveground Biomass of Grassland in Zoige by Visible Vegetation Index Derived from Unmanned Aerial Vehicle Image
Zhang Zhengjian1,Li Ainong1,Bian Jinhu1,2,Zhao Wei1,Nan Xi1,Jin Huaan1,Tan Jianbo1,2,Lei Guangbin1,2,Xia Haoming1,2,Yang Yongshuai1,2,Sun Mingjiang1,2,3
(1.Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;
3.Chengdu University of Information Technology,Chengdu 610041,China)
 全文: PDF(10480 KB)  
摘要:

地上生物量是衡量草地长势及生态系统服务功能的重要参数,对于草地生态系统碳收支、资源可持续开发等研究具有重要意义。研究基于若尔盖高原典型样带的无人机可见光影像和地面实测样本,建立生物量与多种可见光植被指数的指数回归模型,对比不同植被指数模型的生物量估算精度的差异。结果表明:可见光植被指数能够有效区分草地和其他覆盖类型,生物量与植被指数具有较好的相关关系。但基于不同波段建立的植被指数对生物量的估算精度存在差异,其中利用红、绿波段建立的植被指数NGRDI模型对生物量具有最高的模拟精度(R2=0.856)和预测精度(验证样本ABE=94 g/m2,RMSE=124 g/m2)。研究获取了高空间分辨率的草地地上生物量,相关成果可为若尔盖高原碳收支、卫星遥感产品真实性检验、生态模型、资源可持续利用等研究提供方法与数据支撑。

关键词: 无人机影像可见光植被指数生物量估算若尔盖    
Abstract:

Aboveground biomass is an essential parameter for estimating the growth trend and ecosystem services of grassland.It is important to correctly evaluate biomass for grassland ecosystem carbon budget and sustainable development of resources.In this study,the field measurements and synchronous observations from UAV were combined to estimate the aboveground biomass of grassland.Based on the “valley\|peak\|valley” morphological features of green vegetation in the visible band reflectance,multiple types of vegetation indices were constructed.Then,the regression models between the fresh weight of grassland and vegetation indices were established.Accuracy analysis showed that visible vegetation indices had the potential to distinguish grassland from others and the correlation between biomass of grassland and vegetation indices was strong.The highest estimation accurate model was provided by NGRDI which derived from red and blue band,and the RMSE and R2 were 124 g/m2 and 0.856,respectively.The prediction accuracy of the regression model with the same band combination was stable,regardless of the fitting methods Among the different regression models.The results of this study was anticipated to support the research of carbon budget and validation of remote sensing products researches in Zoige.

Key words: UAV image    Visible-light    Vegetation index;    iomass evaluation    Zoige
收稿日期: 2015-12-09 出版日期: 2016-04-05
:  TP 79  
基金资助:

国家自然科学基金项目(41271433,41571373,41401425),中国科学院战略性先导科技专项子课题(XDA05050105),中国科学院创新团队国际合作伙伴计划项目课题(KZZD-EW-TZ-06)共同资助。

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。Email:ainongli@imde.ac.cn。   
作者简介: 张正健(1986-),男,重庆璧山人,工程师,主要从事山地生态遥感及无人机应用研究。Email:zhangzj@imde.ac.cn。
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引用本文:

张正健,李爱农,边金虎,赵伟,南希,靳华安,谭剑波. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1): 51-62.

Zhang Zhengjian,Li Ainong,Bian Jinhu,Zhao Wei,Nan Xi,Jin Huaan,Tan Jianbo,Lei Guangbin,Xia Haoming. Estimating Aboveground Biomass of Grassland in Zoige by Visible Vegetation Index Derived from Unmanned Aerial Vehicle Image. Remote Sensing Technology and Application, 2016, 31(1): 51-62.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.1.0051        http://www.rsta.ac.cn/CN/Y2016/V31/I1/51

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