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植被盖度遥感反演模型在稀疏高寒草原的对比研究
Alternative TitleComparisons on Sparse Alpine Grassland Based on Vegetation Coverage Inversion Models of Remote Sensing
夏颖1,2; 范建容1; 李磊磊3; 李炫1,2
Corresponding Author范建容
2017-03-30
Source Publication四川农业大学学报
ISSN1000-2650
Volume35Issue:1Pages:37-44+59
Abstract

【Objective】Based on Landsat8 Multispectral images. Vegetation coverage of sparse Alpine Grassland in Shigatse city was estimated.【Method】The dimidiate pixel model、regression model based on NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) 、modified three-band gradient difference model were used to inversion vegetation coverage of sparse Alpine Grassland. The measured vegetation coverage calculated by photographic method, which was used to validate the estimation results. 【Results】The dimidiate pixel model had a higher precision than the other two models, its precision is 82.02%. The estimation accuracy of the other two models were less than 80%. 【Conclusion】The dimidiate pixel model is more suitable for the inversion of sparse alpine grassland vegetation coverage. 

Other Abstract

目的基于Landsat8遥感影像数据,反演西藏日喀则地区高寒草原植被盖度。【方法】采用比较常用的3种植被盖度反演模型,即像元二分模型、基于归一化植被指数(NDVI)与土壤调整植被指数(SAVI)的回归模型、改进的三波段梯度差模型,对稀疏高寒草原植被盖度进行反演,并采用照相法实测植被盖度进行反演精度分析。【结果】3种模型对高寒草原植被盖度的反演精度以像元二分模型最高,反演精度为82.02%,其他两种模型均小于80%。【结论】像元二分模型相对于回归模型和改进的三波段梯度差模型更适用于稀疏高寒草原植被盖度的反演。

Keyword植被盖度 稀疏高寒草原 像元二分模型 改进的三波段梯度差模型 回归模型
Subject AreaS812
DOI10.16036/j.issn.1000-2650.2017.01.006
Indexed ByCSCD ; 北大中文核心
Language中文
CSCD IDCSCD:5955703
Funding Organization中国科学院战略性先导科技专项(A类)(XDA05050506)
Citation statistics
Cited Times:3[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18642
Collection数字山地与遥感应用中心
Affiliation1.中国科学院水利部成都山地灾害与环境研究所
2.中国科学院大学
3.易智瑞(中国)信息技术有限公司
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
夏颖,范建容,李磊磊,等. 植被盖度遥感反演模型在稀疏高寒草原的对比研究[J]. 四川农业大学学报,2017,35(1):37-44+59.
APA 夏颖,范建容,李磊磊,&李炫.(2017).植被盖度遥感反演模型在稀疏高寒草原的对比研究.四川农业大学学报,35(1),37-44+59.
MLA 夏颖,et al."植被盖度遥感反演模型在稀疏高寒草原的对比研究".四川农业大学学报 35.1(2017):37-44+59.
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